Humans + AI

– Paul Smith

Paul Smith is Founder of Future Directors, which focuses on the future of boards and corporate governance. He speaks regularly around the world on the themes of board performance, inclusive decision-making, governance technology and the concepts of the ‘Future Director’ and 'Future Boardroom'.

Website: 

www.futuredirectors.com

www.janegoodall.org

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Paul Smith

Future Directors

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Facebook: @futuredirectorsinstitute

Deep Democracy

Iceberg Analogy

Generative AI

ChatGPT

Ross Dawson: Well, it's wonderful to have you on the show.

Paul Smith: Thanks, Ross. Thanks for having me.

Ross: So, you help boards amplify their collective cognition? I gather that's part of what you do.

Paul: That's a part of what I do. Yes. So there's working with boards directly, to help them make better decisions on behalf of all stakeholders. But also, my business Future Directors is developing and has gone into the market as a SAS platform to help boards, manage their board business, create more data insights, and to educate and build capacity along the way, as well. And that's all about accessibility. So it's taking the human need for the human to be part of that journey, that consultant or the train to be part of that journey.

Ross: Let's frame that as cognition. So we have individual cognition, so taking information, making sense of it, hopefully making some decisions. A board is a particular set of individuals, yes, whatever it is eight, 10, 12,  more whatever it is, and, and I think it's very useful to frame the cognition of a board in terms of the alignment again, how it is they find relevant information, make sense of that, and to make decisions. So what are some of the approaches which can help a set of individuals that end up around a table, to make better sense of the world and move towards better decisions? 

Paul: Yeah, like such a great question. And, you know, to think of the board as a collective unit  is so important as a collective decision making unit – that's what they're there to do. They're there to guide and steward a company, organization or institution forward. Most boards range from a few people through to, as you said, much larger numbers, some boards are 20 plus. The optimum sweet spot is in those high single figures to make sure you've got enough cognitive variance. I think the other thing to say to give context to people listening to this around the boardroom is that most boards are not together all the time, they meet periodically, that's the nature of board, they might meet once a month, or once a quarter, or whatever it happens to be in and they are charged with making decisions at the higher end of a business. So the governance end of the business strategic side of things, the risk management, long-term decision making, as opposed to the operational day to day. 

So really, there's two parts to this, which are really important. One is the information they receive. Most boards are responsive to the information they receive from management, or executive, depending what you call it, and the conduit for that is the CEO. Their responsibility is to ensure that they're getting the right level of information in order for them to make those decisions. But most or more tend to delegate that responsibility outwards, at the best board seek out their own information as well, both individually and collectively to supplement not only the information they're receiving from the internal teams, but also their own arguments and opinions when it comes to debating and discussing a particular decision. 

The second part of that is the culture of the board itself. What is the decision-making culture? Many boards are quite autocratic, or what maybe hippo which is the you know, the the highest paid, loudest person type of thing, right. But the most effective boards, understand the balance for ensuring that you hear as many voices as possible, but make sure they're relevant voices. So it's not a case of everybody has a say, but everybody has a chance to say if they've got value to add. So information coming in, but also what is the culture of the board to actually help them to make the most I wouldn't say best decision because you can make decisions based on information at hand and we live in this VUCA world. So it's probably the most robust and resilient decision possible.

Ross: Yes, as you pointed out, the CEO or Executive are a source or filter of the information about the organization, but of course, decisions at a board level have been made within the context of the business environment, yes, social environment – everything which is technological environment, all of the external world. So some of that again, can be supported by was provided by the organization itself. But there is then a responsibility, of course, for the boards to understand the context; to be current. And I suppose that can be done both individually as in just – let's just go out and be informed. But there's also I suppose, are there ways or what a useful way in which boards can collectively find external information or input which can shape their thinking? 

Paul: Yeah, and you know, boards are still very much in the traditional camp of using advisors, consultants – they have various structures. For example, most companies and organizations of a certain size will have what's called sub-committees. So these will be specialist groups made up of board members, employees, and increasingly third-party specialists, who you delegate thinking to decisions to, they play a sort of semi-advisory operational, but for the board, it's not like day to day company operations, but board-operations that you will delegate certain parts of responsibility to, to bring up those recommendations boards. 

So delegation of responsibility is one of those tools that boards do because they do meet so infrequently, they can't do everything. They're everything. So they delegate a lot of this stuff outwardly, but it's still when you use that word responsibility, it's still their responsibility legally, as well as I suppose morally, to seek out what they should do so and then the board world, whilst you have these mechanisms, it's still very much independent, individually-led because the majority of board the directors are what's called non-executive, so not employees of the business. And they may be independent, which means they have no financial stake as well, they may get paid or might be unpaid. But essentially, they are independent. So a lot of the work they have to go out and do individually. 

So the collective side is bringing in external specialisms either directly to work with the board to upskill them or to teach them something or to delegate that through to a committee or the executive. However, the more technically savvy boards out there, technologically savvy boards out there, and unfortunately, boards are laggards when it comes to technology. And there's a number of reasons we could get into that, but I don't wanna become ageist. As you know, they are starting to utilize technology to support not just information gathering, but also their creative thinking and decision-making thinking. But there's also some other techniques, human-based techniques, which are steeped in neuroscience and behavioral science, which can be used to elevate the cognitive ability of the group as well.

Well, there's one in particular, which I like called deep democracy, which is what I teach. So it's really used for when a group, in this case the board, is stuck. And that's because a lot of the wisdom of the group or the views are hidden below the surface. I'm sure you understand, you're aware of the iceberg analogy, where, what is known, is only a small proportion of what's really there. So hidden agendas, outside influences, whatever it happens to be. So what deep democracy does it works with groups who are babies may be stuck making decision, I'll give you an example of one I worked on last year, which was the board couldn't decide whether to fire the CEO based on their performance, and they were stuck because there was support as the CEO, there's people who said we have to move on, we need some fresh blood. And they were stuck on a decision. And it was a majority and a minority, but they couldn't reach a general consensus of how to move forward. And so I was brought in to support this decision-making and the way that deep democracy works is like you run like a debate, but you get to debate yourself, that's all designed to raise the EQ of the room by having you play your own devil's advocate. Because what usually happens in any decision-making structure is some people be loud and opinionated, others will be quiet, some people will be entrenched in their view with an ideology or a dogma and other people will be variants, other people, so everybody plays a role. And the more the group stays together, the more they roleplay. So what this does, it tries to take that sense of I have a position out of it. So you start with a statement. And then every single person has the equal amount of time to argue for and against that statement. And you have to come up with reasons which are true for you. So even if you're on one side to start with, you actually have to think of arguments against yourself. And what that does is it sort of semi tricks the brain into going ‘Oh, actually, that's what that person said, it sort of makes sense. Because I have to I can't, I'm not filtering it through what I think about that person. There's no filter, it's just me now.’ So what it does is you go round, and it's all very contrived and staged. And it was a bit uncomfortable to start with. But everybody gets an equal amount of time to be heard. And you keep going until they've exhausted all the views. So all the iceberg is out as much as possible. And then you see how that's impacted people's viewpoints. 

And this is not about creating consensus in terms everybody agrees. It's making sure that we have enough buy-in to a decision because everybody's been heard. And so even if you get to a point where there's a majority-minority, then you go to the minority Have a decision based on the rules of the group and you say, ‘Look, we're really sorry that your position isn't the one is up, what would it take for you to come on this journey. So you almost come up with an agreement, that again, it's not 100%. But it's enough to move people forward.’ Now, where that doesn't work is where there's real big personality problems, and they just cannot agree, not because of the facts presented to them just because of the ideology. So you might say, on the US political side of things, you know, there's no way some of them can agree just on ideology grounds. But within most boardrooms, their job is to try and make the best decision they can. So once they can get over the hurdle of having to play a role or a different type of role. It's really, really powerful.

Ross: That sounds fantastic, actually. And I think it's really, there's ways in which you could apply that conceivably to your own individual thinking. You go through that process yourself and think through sort of what are the issues at stake and to argue them to yourselves. I mean, it's a little hard to do it for yourself.

Paul: Couples therapy. I like I've I haven't done this, because I'm not a therapist. But I've told people who have taught this to boardrooms, leadership groups, large groups. At a conference, for example, some have come up to me afterwards, I'm going to use this with my partner.

Ross: Absolutely, yeah. That sounds really powerful. I really like that it is a tool to amplify collective cognition, sense-making. And, you know, there's obviously an analog there with red-teaming, where you have some kind of decision and you get a red team to argue that case? And, yes, maybe a few variations on that. Are there any other similar techniques or approaches or structures you would highlight?

Paul: There's not ones that I would particularly use directly, because I think they're very much consensus building and in the boardroom, I tend to try and steer people away from absolute consensus, because it can be a race to the bottom. But increasingly, from a technology perspective, there's generative AI that is a really powerful creative decision-making tool, as you and I both know, and hopefully more and more people are earning. It's not a fact based tool. It's not a fact-checking based tool, as yet. But as a creative supporter, as another voice in the room to stress test your ideas with the right bit of information. That's what I'm starting to work with some boards on now as how we can dip our toes into the water of utilizing generative AI inside the boardroom to support our decision-making. And that's like, for example, giving it a huge amount of context and decision, and then seeing what it comes up with. And it's just another voice in the room, but not one that has any ego attached to it.

Ross: So for example, you might in a particular context, get an opinion from the generative AI or give it to offer another perspective?

Paul: You could say, you know, you could ask it well, ‘This is the thing we're dealing with right now, here's the context.’ With Generative AI, the more context you give it, the better. So the context around your operating environment, that decision needs to be made, what inputs are coming in? And you can ask it for, what approach would you take to help us make this decision? What information would you seek out, and then it might present you with things you hadn't thought of? If you think about the collective intelligence of any group, there's always going to be gaps. So what AI can do is try and help you fill those gaps. And then what I always love to say is like what I always like, in an iterative process with Gen AI, is I always like to say, What am I what have you not even thought of yet? What have you not told us yet? Like, what's your craziest idea, and just see what comes out. And again, because it's done instantly, almost instantaneously. It's not burning up time, it's actually just providing you with for one assurance, if you've done everything it sort of comes up with, but if there's something that you think, ‘Oh, that's a good idea,’ then you can delve deeper into that either with AI or by yourself through one of your other avenues.

Ross: So, for example, I'd say there's a few differences. I mean, I'm interested in that at this point. So one that I think is really interesting is what other information might be useful. I suppose that framing that and another could be around, you know, option generation and others could be around, you know, challenges to ideas. I mean, are there any particular specific points in that overall deliberation and decision process that you think of particularly fruitful?

Paul: Yeah, I think anything which presents a counter-view. So if, for example, a group is agreeing with each other, you almost unanimously too easily then using that as to create a counter-view, why wouldn't we do this? Or, you know, why shouldn't we make this decision to come up with reasons against, it's similar, again, to deep democracy where you have to argue against yourself, but you're using technology, which has got the collective knowledge of what's on the internet up to a certain point to go by. And that's why I think it's such a great critical thinking tool to support boards.

Ross: That's, I suppose another interesting frame around that is, as you suggested earlier, you want cognitive diversity in a board. That is a bit of a problem, if you don't? 

Paul: Once you cut the borders, don't. That's for sure.

Ross: But if you do have, whatever the diversity you do have, you can always complement that, you know, there are always additional perspectives, you know, when you've got any limited number of people, then you can always bring that to bear. So, and I suppose it is whatever views are expressed, just be able to add others or another or other viewpoints that can be thrown into the mix.

Paul: Yeah, and look, you know, it's not foolproof, none of this deep democracy is a technique is not foolproof, because it completely depends on how it's facilitated, who's in the room, their personalities, their starting point. And AI is not foolproof, but it's busy, it's completely down to the who's inputting the prompts. And making sure that you know how you're interpreting the response is as unfiltered as possible. So you still got, what is the number one problem that we deal with in the boardroom, which is the people problem, you've still got the issue of humanity in itself. But at the end of the day, all you're trying to do is augment our own abilities. And the best groups, the best decision making groups who will be the ones who have that level of self awareness, or self governance, who are aware of their biases are aware of their limitations, and they openly seek ways to limit the impact of those.

Ross: Yeah, you can't. There's only so much you can do with dysfunctional people. 

Paul: Yes, that's exactly true. And boards can be very dysfunctional.

Ross: I am fully aware of that. Yes, I've got yeah, I've got some tails on the board. 

Paul: If you haven't got some tails, I'd be worried.

Ross: I mean, I know that would be incredibly diverse. But I mean, how have any directors responded to introducing Generative AI into the process?

Paul: Usually to start with wide-eyed deer in the headlights to start with, like, they hadn't even realized this was a thing. I remember about six months ago, so sort of when ChatGPT was really becoming a house, you know, household name as it is. I was doing a talk at a governance conference in the States. And I did a workshop on the use of AI in the boardroom. At the time, in my space, most people were talking about boards introducing AI policies for their company. It wasn't about AI in the boardroom. It was about okay, how do we manage or govern the usage of AI from employees all the way through to say, school students, and that is the responsibility of the board. And if I may go down a little side avenue, still to this day, so few boards have actually introduced AI policies for their companies. So people may be using AI without any guidelines at all of how that's being used. So if you're listening, and you sit in the board, and you haven't got an AI policy right now, get on to it, and featured Axios can help if we've got a template. 

But I was doing this talk. And you know, I was talking to people who are in the governance profession. So they were consultants working with boards, they were board directors, they were board chairs across a wide spectrum, several hundred of them. And you know, in the room, only a handful have even played with it yet. And so when I was showing what it could do, and as I said, this was sort of six months ago, so obviously things have moved on since then, as they have as they do in this world. It was just wow, I didn't even know it could do that. And I was doing some basic stuff to start with. I had to tailor it back because when I sort of prep for this, they were going yeah, just assume we're just all absolute beginners. So just when their response was phenomenal in terms of this is amazing. But it was almost overwhelming for them as well because they're dealing with so much other stuff. At the same time, the role of a board and board director has become so complex now with different stakeholders. I'm different demands on your time. And they do have limited time. Whilst this is essentially, for me a time saving device to a certain degree in a critical thinking device, it's another thing for them to learn. And boards are still trying to catch up with individuals trying to learn something. So it's a case of just small iterative changes. The problem with AI is it's moving on so fast, anything they learn now is almost defunct later on. So I focused most of their attention on how to work with it from a prompting perspective. Because I think that's the stuff that's not changing is how we prompt it, how we give it context. And if you think about what a board's job is, it's to think critically, we're trained for our brain. So let's use that bit to give it the right prompts to select about. 

I think the biggest concern from most of them was privacy concerns. But I just said don't pump any proprietary data in there. It's not that it's and also they thought they could just use it like Google to search for facts. And so we were steering it, so a lot of it was education, but then it was moving forward. But again, ever since then, the response has been amazing. But it's also been ‘Wow, this is too far for me. I'm still here, I need that to go there. I need to go a long way.’ And I think that's because and I will say this now, the average age of a board around the world is about 60. So you don't have people who have grown up with technology in the room. So when technology comes along, this is why board portal technology software is such a slow uptake. You also don't have anybody who's leading it. Who's the one who's in charge of this technology? Is it the chair? Is it the company secretary, is it the executive, so no one is taking real responsibility for it. And that's the case with all technology inside the boardroom. So until we see, I think a generational shift happens, or an educational shift or a mindset shift happens, boards are going to be laggards when it comes to any type of technology. And it's not a case of ramming it down their throat. And so if you don't catch up, you'll be left behind. In their mind, it's when we still do our job. Our job is to oversee not to do the doing. So there's a cognitive what's the word I'm looking for? There's a disconnect, cognitive disconnect between the power these tools have and actually starting to use them. 

Ross: Yeah, well, I mean, as we've already seen, for a long time, there'll continue to be a divergence between those who are early adopters, and laggards. So related topic is, you know, I distinguish between what I describe as Analytic AI and Generative AI. Okay, I've been, you know, a lot of it related to machine learning. And a lot of the, you know, pre Generative AI techniques, which are obviously, extraordinarily valuable, both in implementation, but also in decision-making in the sense of being able to pick out emergent trends, particularly within internal organizational data, possibly external trends, and so on. So one of the key issues there, of course, is how has that data presented, so you've got to harass your business intelligence, people that come up, and hopefully the nice charts or on printouts, whatever? And so just I mean, it's a massive topic, but just briefly, I mean, are there any insights into how we can essentially take the power of big data analyzed appropriately, which in a way, actually does usefully inform group decisions?

Paul: The simple answer is yes. If you've got the right people, the bright business intelligence people who know how to use these tools effectively, you're quite right, though, like, right at the beginning of our episode, I was talking about how this information is presented. You're quite right. And that's the other part of the boardroom, because you have these diverse boardrooms. And although not everybody absorbs information the same way. Some people are very visual, some visual audio, so a return. And so but usually information is presented as a one size fits all approach. So I think when it comes to sort of, I suppose the boardroom, we're not expecting them to use these tools, but we're them to interpret the data. 

But this is where I think the delegation comes through, is that you are relying on them to give you the answer or the scenarios that they've done the work to get you to a point of going, right, our job is to stress test what you've told us because our collective wisdom, in terms of new ideas, or different cycles, we should be able to see all the potential pitfalls or things that will stop this from happening, regardless of where the information has been brought from, you know, or how it's been developed, rather than to create the strategy in its first sense. So their job is to pull it apart as much as they possibly can. And then the response of the business analyst is to go, ‘Okay, this is my response to your query.’ That's how it works best. That's how the boardroom works best. 

Now they can use their own tools to help pull things apart. But that's really where they add value, if that makes sense. So it's very much that she did that Human Critical Thinking element, rather than utilizing that stuff. That's definitely an operational level using those sort of Analytical AI tools. I would never see a boardroom at this point, going near those — be too much. What I would love to see happen with this technology as a way in which this information can be presented in ways which are semi personalized to the board members. I've seen such diverse groups, some who have ADHD, and the way they absorb information is absolutely diametrically opposed to somebody else. And yeah, as I said, everything's presented in the same way, I would love to see it whereby some sort of, you know, inputs are put into allow board, especially using technology with more and more board papers, for example, which is the information packs being presented from through technology, as opposed to the old school printed PACs, which I still some do that like printed out folders of information for each board pay people, I think having those personalized in some way would be a real step up for the intelligence the collective, what we call BQ board intelligence of the group.

Ross : As in thriving on overload, a lot of where I look at is how cognitive styles and how we take in information and, you know, big believer that we you know, we can certainly not one size fits all for how we take in information that can be very much tailored. So your organization is called Future Directors. And so let's go into the future. And looking at the potential, what is the potential of the future?Let's, let's imagine, what a future board could be, amplified by technology. I mean, let's, you know, it's a matter of finding the right, maybe finding how it is we find and assess and bring together the right people, what is the what, how do they interact? How do we get an absolutely better future board than we have today?

Paul: Look, I think, technology augmented boards, let's assume that the next 20-30 years will still be human boards. We're not going to see sort of, you know, super intelligent AI taking over, let's assume that and boards are still relevant in that respect, I think the level, the level of decision-making, they'll be doing will be elevated, they'll certainly be using technology to support their decision making, as we've talked about. And that's only going to get easier. And I think what we'll start seeing though, is boards meeting not on schedule, but by design. Because the world is moving too fast, the technology will be presenting information so quickly, boards will get together to make critical decisions and have time to absorb that information. 

I actually think the power of technology is going to be in the monitoring, and the learning element of how decisions were made, how people interacted with each other. And I think this is where AI will be able to play a role of observing in some way, let's say observing, and playing back how the group worked, how the decision was made to stress testing, the After-Effects and actually measuring the performance of a board. I take you back six years to the same conference that I did six months ago in the States when I first presented the future of the boardroom. And this is before any of this gen AI stuff was coming out and I was talking about how AI may if we're all plugged in . I went onstage wearing one of those muse headbands that monitors brainwave patterns. And I was working with a group at the time and I was demonstrating how my brain waves are working and how the majority of us are in beta, which is that fight flight freeze response. And we're masking it with coffee and all that sort of side of things. But we want to be in alpha focus and theta knows once from a creative, intuitive element. And you can actually start to measure the cognitive ability of the individual any one time so if they're really tired, they might be operating at a lower low level. 

And I was supposing that board members will be plugged in and be monitored. If you are not operating at a certain level, you will be excluded from decision-making. Literally in that meeting. So you'd have to learn breathing techniques, meditation, all the things to actually raise your cognitive level but some people come into the room. Take it, take a spirit example. But hungry imagine coming into the room, feeling so hungry that you're just unable to focus. And yet you're expected to contribute to multibillion dollar decisions or decisions affecting hundreds of thousands of lives, but you could be distracted. So I think this is where technology scares the bejesus out of everybody in the room, by the way, which is most of the fun. And everybody thought it was like Terminator, you know, gone crazy type of stuff. 

But they could get the idea of what I was trying to get across is their duty, their legal duty is to turn up at their very best because they're being recruited for their brain, they're no different to an athlete on race day, or game day, you know, body mind has to be in tune. So why is it any different for the boardroom, and you get found out, you don't qualify, you don't win the game, why not different for the boardroom. And they use lighting technology more and more and more, and coaches more and more and more, to ensure that on game day or race day, they are at their peak? Why not this, what I was trying to get across is why not the same for a boardroom that is tasked and legally responsible for that level of decision-making. 

And I think the stick and the carrot for this sort of thing will be stakeholders. So the owners, the shareholders, employees, the more because boards become transparent. And it's only really in the last 10 years through shareholder activism that we've seen, people take the board on, and actually start to see them as something that they can have a look inside of as opposed to this, you know, top-floor behind closed door secret decision-making group. The more we see transparency, the more they're held accountable by external internal stakeholders, the more they will have to do to prove that they're doing everything they possibly can to work and work at their best. Now, I'm not saying this is all good. I'm just saying that's where it's heading. I think technology is going to both support them, but also work to stretch them as well.

Ross: Well, that's, I can hope that you know, some of those, I think some few really important elements there. And the thing is, yeah, I always wonder at how dysfunctional a lot of boards are, or not all of them. But there's a lot of expectations, transparency. But if there is this focus, and I think what you just were saying really brings this out on the fact that they do need to have outstanding cognition individually and collectively. You know, these are critical decisions and critical roles. So, if we start talking in the language of amplifying vision of boards or whatever language we use around, I think that's a fantastic step forward from where we are in any case. So where can people find out more about your work?

Paul: So futuredirectors.com, they can see what we do as a business. And then if you just Google or future Paul Smith, or go into LinkedIn and do future Paul Smith, my name Paul Smith is quite common. So add the word future in there and you will find me and you can connect with me. Follow me to see some of my work as well as the work of future directors. Fantastic.

Ross: Fantastic. Thank you so much for your time and your insights. Paul.

Paul: Great to be here.

The post Paul Smith on the future of boards, collective decision-making, deep democracy, and AI in the boardroom (AC Ep32) appeared first on Humans + AI.

– Sasha Wallinger

Sasha Wallinger is founder of Blockchain Style Lab, a team of strategists, researchers, and world builders that provides Web3 Advisory services, and acts as Chief Marketing Officer for major brands. She has lead global teams for brands such as H&M and Nike, and recently launched the Gucci Superplastic NFT collectibles. Her passion for translating art and science, nature and culture, and design and data is evident in this conversation.

Website: www.sashawallinger.com

LinkedIn: Sasha Wallinger

Instagram: @blockchainstylelab

Twitter (X): @SashaWallinger

Zoom

Superplastic

SuperGucci, Superplastic

Roblox

Meta

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ChatGPT

Microsoft

CuteCircuits

Iroquois precept

People

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Pharrell Williams

Book

Mind and Nature – a Necessary Unity by Gregory Bateson

Ross Dawson: Sasha, it's a delight to have you on the show.

Sasha Wallinger : Thank you, I'm thrilled to be with you.

Ross: So, you talk about creativity, community, and collaboration is central to your work and interests.  Tell me more.

Sasha: Sure, I mean, I think they're huge topics, and that's why they're potentially suitable for all that I'm attempting to achieve. They come from the desire to connect both nature and culture, the desire to weave together fashion, sustainability, and technology, and truly just to enjoy that which I do with a bunch of people.

So, for creativity, I really take a lot of inspiration from design, art, music, and all areas of creativity. That also has been a natural scientist immersed in bioscientific material, and biomimicry, and areas in those different ways that we look outside of the expected into places that can be a little bit unique and unexpected. So, I think that creativity allows me to have a dialogue with artisans, both accomplished artists, and up and coming artists, but also look to nature for inspiration when having those discussions.

And then to develop a community. I mean, I really do enjoy bringing people together, I enjoy having conversations. And certainly, as a journalist, I really love listening to people's stories. So that's how I find communities, really woven the thread of what I'm up to both as a marketer and a communicator. But also as a curious individual who's constantly learning and learning and community, I don't think it's great to only learn in silos, so trying to blend more of the learnings with groups that I'm able to be a part of.

And collaboration is, I think, increasingly just the name of the game holistically across whatever space, industry reality, you're choosing to be a part of, either, you know, I won't go into this a little bit more, but I traverse both the physical and virtual worlds, where I do feel like collaboration is critical and helps us as a society, and I guess, a world to move forward. So that's just the tip of the iceberg.

Ross: Well, there's so much to dig into there. But maybe let's start with that intersection of the virtual and the physical. I suppose, across those domains, so just collaboration, people will be able to work together to do more. So I would have liked to hear specifics around, the work you do and what you've been doing around how it is we can collaborate or be more creative or foster communities in intersections of virtual and physical worlds.

Sasha: Sure, I think that’s a very pertinent and important question to unpackage. I became interested in the connection between virtual and physical worlds before the pandemic, but certainly understood the potential for that type of ecosystem to be fostered in fast track speed during the time at which we were so siloed and so on our own, let's say, in our own different worlds, and almost forced into a sense of a Metaverse or an ecosystem that was virtual and physical, toeing the line at the same time.

So, on Zoom calls, even meeting up with friends in gaming ecosystems. And having had a fashion sustainability background, I saw how difficult it was to actually connect with brands, museums, even entertainment spaces that meant so much to me in the physical, while I had to be, you know, more virtual, as a result of being home and sheltering in place.

And so, I think what was really interesting to me was all of the different tools that struck up during that time. I was able to work in the virtual physical connection space as the Chief Marketing Officer of Superplastic, during the pandemic, and actually helped Gucci to launch a discord channel but also helped to bring to life the Gucci NFT. And so, I saw firsthand the hurdles that would be needed to be trumped reversed(ts:4:35), and really, it's a translation to build about the why behind heritage brands like Gucci coming online in a very tech-forward way.

But then I also saw the opportunity. So that was the creativity, right, and being nimble, and some really great team members. Part of the Gucci team really were forward-thinking in that regard and were brave to take on that pursuit. But what they did was they built a community, and they built a community that was down aging the brands but also welcoming people to discover Gucci in a whole different way.

So those types of moments helped to reaffirm their commitment to fusing technology, creativity, fashion, you know, just even the opportunity to have a dialogue with the IP of a brand in a new and different way. I think that was very striking. And that led to a variety of different other projects and undertakings. But that was a really great snapshot of bringing the sensory experience into a technical-based organization, like a discord channel, or even like a Roblox ecosystem, to begin to fuse that which we observe from a cultural background and kind of what we project onto what technology could and should be, and how to open that up into the possibilities of what it can become.

Ross: So what did the community look like? What was that with avatar-based? Or what was it, sorry? You mentioned Discord.

Sasha: Yeah, it stood up initially in Discord. However, it connected to an NFT that was a physical-virtual connection, so both a digital collectible as well as a physical item. And I say that because I think that really set the precedent; this was early on. This is back when NFTs were pretty new but set the precedent of that physical-to-virtual connection.

The community itself was really just amazing to witness the growth from zero to 60,000, in such a short period of time, and a really active community, a really vibrant community, a community that fostered the commitment of one another to uphold certain championships of each other, certain creative inspiration, and then also guiding one another. I mean, Discord was pretty new for a lot of people at that point in time. And so what it helps people become as ambassadors and guides to one another and take the ownership of leadership in these spaces that they were just discovering too.

So it was a very vibrant time and exciting and neat to see some of the other projects that have cropped up as a result of that and other projects that have been inspired by that type of climate. I think that it doesn't have to also just spin fashion or retail. I think that there have been a lot of advancements in health and wellness communities, I think there have been a lot of advancements across CPG ecosystems. Taco Bell held a wedding in the metaverse for the first time during that period of time. And so I think there's quite a bit of ways to come together and draw inspiration from some of those moments.

Ross: It's interesting, this idea of, brand, but brand is a place where ideation happens in a way the community co-creates the brand. I mean, that's why we try to link it with the amplifying cognition. I mean, that's where you can say, well, you know, it's not just a brand, we're putting it out there. But this is a place of co-creation. And I think that's an interesting thing.

But to help out a bit since there's so many things to cover, back to the creativity in this idea of augmenting creativity. So what thoughts do you have on how it is we can individually or collectively be more creative?

Sasha: Yeah, I get asked this question a lot. And I have to take us back to the Industrial Revolution, where, you know, especially as a textile and fashion historian, a lot of progress was made in a quick period of time through technology, augmenting creativity. And if you think about the mills and the different looms, and textiles that were formulated during that point in time, and how exponentially scale was achieved around,many industries, due to tools and technologies that now we would find archaic, but at that point in time, we're revelatory.

So it hasn't, it's not the first time that we're looking to enhance and step into these experiential moments of immersing ourselves with new technologies. But it's just such a different world. And of course, catapulting further and further into what's possible. I think it's really exciting to think about humans and tools coming together like that sort of lead all anthropological pursuits and understanding, about the evolution of cognition. For so many generations and years and periods of life, that we know it as humans on Earth. Excuse me, I think it's really important to reflect during those times that we're lucky to be living in these really exciting times. However, sometimes we don't have as much perspective in those moments.

So to think about something like AI, AI has been used for many, many years, a technology like RFID or NFC chipping, those types of things have been implemented into a lot of the different tools and technologies like our credit cards or frictionless checkout that we're seeing now come to life. However, the consumer is much more cognizant and has the ability to be much closer to those types of tools. So it's not simply just from the analogy of the Industrial Revolution, that the mills in Lowell, Massachusetts had the capacity to quickly produce textiles, but it's the individual at home at this point in time. So the onus falls on the individual, but yet, there's a lot of attention, of course, on the first-to-market players and actors. So we talk about Meta, we talk about Google, we talk about OpenAI groups, and things like that, and they get a lot of attention. But what I'm most keen on is the individual players and the people who are coming up, the Gen Alpha, Gen Z creators who are experimenting and just take into account these types of tools, you know, name your emerging technology tool, it's in their creative journeys.

And as they develop, AI visualized photoshoots, or product that’s become influenced by a Dall-e or, you know, tools and technologies of OpenAI, ChatGPT prompts, or such things, it's really interesting to see that confluence, and I do think it has to be clarified that it's a confluence of human plus data tools, technologies, not one over the other.

Ross: Are there any examples in particular that you have found inspiring in the last year, examples of that.

Sasha: Yeah, I was just at the NRF event in New York, and was really struck, there are a couple of really interesting, small but mighty moments there. First was from CES actually, where Microsoft just added the co-pilot key to their keyboard. So that's a moment, it's very simplistic, but when you think in the movement through time of a new key being put on a Microsoft keyboard, that's it. That's radical, right? And that's a totally different way of operating.

And then looking also at NRF, I was able to see the fed through the technology of choosing on a screen a lip color, that then gets physically imported into a compact, so a little makeup area that comes through in a physical form, where you can choose a lip color that you actually get receive and then can travel with.

So these moments that are really, in our day to day, you know, just choosing what you're going to type on your keyboard, assembling what you're gonna wear, the lip shade, that are being enhanced by technology are, I think, really interesting. As far as the young designers, there's a lot of different shows and different moments that are coming up. In February with fashion month, Copenhagen's working with Spin Fashion to do some really exciting moments across the fashion space that are going to be AI-ated. And I think we'll see a lot across the fashion month to take note of and how different digital Fashion Week's occur across the globe, truly, and then those just continue to inspire young designers to create and come into a community to be able to continue to re-envision materialization and what the future of fashion holds.

Ross: Yeah, that actually is. I'm very interested in technology-infused clothing. And so, I've had CuteCircuit, I think it was, and you know, what amazes me is that it's why don't we all have technology-infused clothing by now, you know, it's like, there's a few examples of really good stuff, but there's not much really, and it's just seen so slowly come, and I kind of think well, this is the future waiting to happen. So I'd love to hear any thoughts around technology-infused clothing that you've seen or what you see the potential for that is.

Sasha: Well, I love that because I actually came into bio-scientific material research because I was interested in designing for outer space. For when we come into the outer space world and want to not just look lovely but also be practical. So I think there's a lot of different bio-scientific materials that are out there that lend themselves to haptic integration. There's the lab-created silks. There are mushroom-created leathers. I mean, there's just, again, I mentioned biomimicry because I think there's so much possibility for regenerative agriculture to play a role in this conversation. I do think it's important to go there.

But just with what you're sharing, when I think like, if I happen to be a football fan and add an NFC chip that was embedded onto a jacket that I was wearing into a football stadium when I'm going to see a match, I can get keyed up with different prompts as to certain events that are occurring, other friends that are going to be there, you know, at the actual match, maybe if there's a meet-up of a player to meet them, you know, I think the possibilities are endless.

And I agree with you that the technology-embedded apparel is really an exciting horizon to surmount. And I think it also takes into account the opportunity to look into health and wellness too. I mean, we do many of us have monitoring, either on our fingers with the rings or trying to be friends, agnostic, but like, you know, on our wrists that are tracking our data. It makes total sense that our clothes would also do the same and maybe even begin to regulate our temperatures and help us to forecast health and wellness across the variety of ecosystems that we traverse. So I think the potential is quite vast.

Ross: So you've already touched on it in a couple of ways. You mentioned initially this idea of your nature and culture. First thing that brings back to mind is Gregory Bateson’s book, “Mind and Nature – a Necessary Unity” I think the subtitle was, and so what does that mean for you, in this idea of nature and culture, and I think our culture today could very well be positively informed by nature. That's its wisdom. So where can we go from there?

Sasha: Yeah, I love that reference. I haven't read that book. But I was, my Bachelor’s in Nature and Culture. And that was actually the pursuit of my undergraduate degree and what that looked like was a fusion of the humanities. So Gary Snyder, who is a US poet, who has quite a stronghold in a reverence for the earth, and a spiritual reverence for the earth. But a holistic reverence for the earth was one of my professors juxtaposed with the painter, Wayne Thiebaud, who's a very contemporary artist, happy colors, really interesting, contemporary funk movement artists, as well as the microbiologist Mark Willis, and geologists too.

So I think that this preconceived notion that there are silos too that separate nature and culture was really early on, I guess, dispelled for me. I grew up on the east coast of the United States, where Walden Woods was famously, you know, Thoreau kind of wanted to be with nature and commune. And so there just was never this disconnect. So I think, actually, I had to reassemble the understanding of why these two ways of thinking or even areas of thinking were disconnected. And then look around, we did a lot of work, early days from searching around dominion, and how humans come into neutral spaces. I think that that's really impacted the work that I do, you know, the foresight strategy that I work on today, in the sense of understanding, let's not get too invested in a certain disposition, but who was here first, on the land, and how nature has a really important role and all that we are and do, and to have a reverence for that.

So I really like to share the Iroquois precept, when thinking about seven generations ahead, especially when we think about technology, and when we think about the impact on our Earth, so I think there's a lot of different natural resources that we can draw from and give to as we're creating the future. And it wasn't any happenstance that I mentioned outer space and the different areas that we potentially will traverse in hopefully our time and our children’s time and just being able to carry the goodwill with us as we embark into those exciting journeys.

Ross: So in terms of your own personal practices for living in a world of unlimited information, using technology to enhance your work, your creativity, your thinking, your mood, I mean, what are some of your personal practices to amplify or augment yourself?

Sasha: I think it's really important actually. Most of the time that I take away from the digital, digital detoxes, I think I'm really intentional about those. We try as a family. I have a young child, and my husband and I try to go at least once a week on a hike, being in nature, and you can never get too far away. But, however, I do think it's really important to have perspective. And those moments, as I mentioned earlier, have creative inspiration.

But, certainly, being so invested, I mean, I spend a lot of time thinking about the future. And when you're thinking about the future, you are thinking about different avatars and different ways of receiving a myriad of things: sustenance, health, wellness, information and collecting that information. So, I think being able to separate the reality from what could be, I guess, it's very grounding to me personally. And I do think that mindfulness practice has always been a critical component of my life. And it has kept me anchored and rooted, both again, in the reverence for the present moment, but also an understanding why that's so important as we look to the future to reflect upon the past.

So yeah, I think we talked a little bit before we started to record around, you know, just this interplay between humans and data. And I think we're all athletes in one respect, that have this amazing computer in our minds and our bodies. So remembering to nurture that and to fuel that, and to power up is really important.

Ross: Yes, absolutely. It's good for our health. And part of it is, it's like giving it the space, which it needs, you know when you get caught and pulled into business on all sides. And so we have to be volitional about, you know, say, right, well, let's switch off for a little bit and see where that takes us. So where are you working on now? And what's exciting you and your current projects and where those might go?

Sasha: Yeah, thank you. I think one that I just got back from a lot of info, grabbing information with various tech conferences, and just kind of understanding what the future holds. And also, at this point in time, I'm really interested in entertainment and immersive, experiential moments, due to a lot of things, not just Apple vision pros headset, however, I think that connectivity and facial computing is going to be really interesting to watch, not simply just in one's own home, but how different developers and designers are created for that new tool, as well as how people begin to interact, given that they are going to potentially have a headset on for a large portion of at least some of the month of February, for a lot of times. So thinking about that is really captivating to me.

And then also thinking about how we create with that in mind, but also thinking about how, again, I mentioned health and wellness, how that cycle of boomers and as we age, millennials, Gen X, Y Z's are coming into an ecosystem in which there isn't this apprehension to have a virtual health assistant or have voice AI-affiliated help with health and wellness moments. I think we're going to see a lot of that confluence of mindfulness and wellness come to life quickly actually, based on what I just saw, within a technological aided circumstance. And so thinking, how we bring the complexities of being human aided by technology into that space, I think that's going to require quite a bit of thought.

And then lastly, like, I'm always going to be a champion of fashion and sustainability. And so I'm really looking forward to seeing the shows coming up and seeing how designers incorporate so many moments from technology into their collections. I think Pharrell Williams did a really interesting pixelated collection two seasons ago. And Tommy Hilfiger always has a really interesting job bringing in the web through crowds. So looking forward to seeing how that's presented on the runway coming up.

Ross: So in terms of just this theme of amplifying cognition, you know, how it is we think individually collectively. So what is your advice to our listeners as to how it is they can amplify their cognition or directions or ideas or frames which could be useful?

Sasha: I don't think I have it all figured out, but I can offer two juxtapositions. One, I'm a voracious reader, and the people who continue to impress me are also voracious readers. I think that reading, however you choose to read, is so important to hear and see and learn the stories of different realities and different individuals. Just be informed and keep informed and keep learning, you know, kind of goes without saying, but a lifelong learner, and think that helps to be able to, then as I mentioned, like take a pause, take time to collect one's thoughts, and be able to understand what amplification of cognition looks like to you.

There are so many different moments now. I mean, I'm close to Silicon Valley, in California, and a lot of our senior-level tech executives are prolonging their lives with different aids and different tools and technologies. So if we're going to be around for a longer time, which sounds great, we're going to have to have a lot more stuff to talk about, and to stay connected to that continual well, not just of use, but of information. So I'm excited about that.

And I think also, finding one's passion. It's been extraordinarily validating to me to discover a lifestyle in which I can weave together things that were seemingly completely unconnected and bring that translation of how they can become connected. That's just absolutely the joy of my life. I've been called a hidden connections detective in that regard. And it's one of the nicest compliments I've received. So I think it's really exciting to truly, we have so much information at our fingertips, and so much connectivity at this point in time, to go about pursuing your passion and to go about really finding that authentic truth within who you are, and how you can come out into the world. That's the best advice I can offer.

Ross: So just to, like, dig it a little bit into that into the hidden connections. And I think you're absolutely right, that the passion one has for it, you pick from a list, now there's a long list of passions you could have on that one. You know, it's got to be unique to you. So you have to bring together the connections, which are your unique passion. So are there any ways that we can nurture our capability to see or to surface or to intuit those hidden connections?

Sasha: Yeah, I think there's so many, and I think they're unique to the individual. So I think, you know, someone like myself, I could get down any rabbit hole, honestly, that somebody can take me on because I'm so inquisitive and so curious. And so, I guess I'm not afraid of risk. But I do think that it's also important to understand how there's a multitude of voices and different ways of learning and knowing, and specifically having managed global teams, as a former Chief Marketing Officer, not every one of my team members and the teams that they've been led, felt comfortable going down those roads.

So I think understanding different learning styles and having held various managerial positions, whatever you know, you have, or like just being in, in the real thick of it with individuals, you see how illuminating it can be for somebody who's like myself to be partnered with a real planner with somebody who can be really very grounded. So I think that I've learned a lot from colleagues and friends who have the dexterity to really be systematic and logistical. And I hold a lot higher respect for that type of individual than I ever have, having to verse through a variety of scenarios where one needs that planning.

So I think just, I mean, it kind of boils down to having reverence for our differences and having the patience to both learn and teach one another. Why different ways of perceiving knowledge and information and different paces of cognition and different amplification abilities. You know, maybe some people don't care to speak and, you know, have a conversation like we're having now, maybe they just want to listen. And I think that that's been something that has had more attention lately. That has been really powerful, and we can see the collective intelligence moving forward as a result of that. So making room for all different voices and minds into the conversation. I think that's very critical. Yeah.

Ross: Absolutely. Well, actually referencing Gregory Bateson again, who said that “Wisdom comes from multiple perspectives” and that's today more than ever. We need that. So where can people go to find out more about your work Sasha?

Sasha: Sure, I'm on sashawallinger.com. I have a newly minted website, which you'll have to excuse because it's really new and really minted. But I'm very active on LinkedIn as well. And I love hearing from people their ideas. I mean, you can tell them I'm truly a journalist and a listener at heart. So I really enjoy hearing your stories. And thank you for the time. Yeah.

Ross: Thank you so much for your time, your insights, Sasha.

Sasha: My pleasure. 

The post Sasha Wallinger on the intersection of fashion and technology, hidden connections, nature and culture, and nurturing minds (AC Ep31) appeared first on Humans + AI.

– Kes Sampanthar

Kes Sampanthar is Managing Director at BCG BrightHouse, leading Innovation + Purpose. He is an award-winning innovator, technologist, game designer, and consultant to some of the world’s largest organizations. He speaks extensively on technology, design thinking, innovation strategy, and behavioral change, and is the author of the Substack, The Centaurian.

LinkedIn: Kes Sampanathar

Substack: @thecentaurian

Twitter: @KesSampanthar

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Thriving on Overload: The 5 Powers for Success in a World of Exponential Information by Ross Dawson

Ross Dawson: Kes, it's wonderful to have you on the show.

Kes Sampanthar: It's great to be here. Ross. Thank you for inviting me.

Ross: So tell me Kes, what is a Centurion?

Kes: A centurion is somebody who uses AI to augment their ability to think, their ability to work, their ability to engage with the AI, to, what I call ‘augmented intelligence’, and in a way that we have sort of been slowly evolving our brains. Now, as we hit that next sort of stage of what I see is the next stage of evolution.

Ross: I very much agree. So, how did you come to get here? You know, just in a nutshell, how did you come to be focusing on this very important topic?

Kes: Long journey, like many of us. So I actually started AI research 30 years ago. So, I was doing neural networks, genetic algorithms, parallel computation, as part of sort of academic research, and then I lost funding, and ended up going to a think tank, and consulting, starting a number of startups that dove into neuroscience over the last 20 years. I really liked the fact that the 90s, that led to the decade of the brain, ended up developing a behavioral design methodology called motivational design. And then, over the last sort of decades, slowly, actually getting back into AI, and starting to use it, obviously, as machine learning started evolving, data science started exploding. And then, most recently, what I really loved when ChatGPT finally got to that stage was, I realized that we were as close to what I've been envisioning for a long time, at the same time, the idea of AI, which I've been looking at for a decade now. And then with hands-on access, hands-on sort of experimentation with vision Pro, and I realized that they've solved a lot of the problems I'd been identifying. So this idea of augmented reality meets augmented intelligence was where I thought, I've been waiting for a long time. 

Ross: A lot of people, when they watch, are really focused on what people are searching for in AI, and seem to be saying, 'Well, how do you make the AI better?' There's a relatively small number of people who ask, 'Well, how does the AI make humans better?' So, what is it about you that makes you focus on that?

Kes: At some level, it's like, it really is like, computing went off in two directions very early on. So when I first started, you had Marvin Minsky, John McCarthy going on AI. And honestly, when I was younger, that's where I thought, I wanted to be like AI research. And I was very excited about neural networks. But I slowly started realizing, to understand how to create AI, I started studying neuroscience and human behavior. And as I sort of started growing up, I realized that it was very important to focus on humans. So, I've spent the last 20 years really on human centered design, behavioral design, how do you ensure the prosperity of humans going forward. We're very unique species in a lot of ways. And I realized that we're the first species in evolution, who not only can, have that intellect, which allows us to understand things. We’re the first most empathetic organism which cares not only ourselves; but, other living systems and the universe at large. 

So at some level, it's like, I wanted to make sure as we move forward, that, we augmented humans and brought them along, because, keeping this arc of progression going, so I felt like too, as much as it's, it's like, there's a lot of people who will come to math and science of AI, which I love. But I wanted to focus on the harder piece, which I thought was not getting covered enough. As much as I love user experience designers and people, human-centered design. They don't go deep enough into understanding how the brain works, or they don't understand AI at a deep enough level to actually understand how to create the interfaces. So being at this unique intersection of deep AI, plus deep human insights. I thought I was in a very unique position to actually help in this sort of intersection, and how do you design this future interface.

Ross: Yeah, well, I'm very much in favor of humans and their potential, which was still far from expressed. And now I've got some tools to help it. So I actually started in computing, my career in computing went off and some other directions, and actually got quite a lot into cognitive psychology. And then at one point, I decided that I should describe myself as a born again, technologist coming back to the technology, because the tech understood the technology was, in fact, what we needed to augment and connect and catalyze humans. So yes. So there's the idea of central I mean, of course, that's, that's an individual concept, as any other as an individual centaur. But this could also be applied in organizations. And I presume that working in a think tank as you do, then that's probably part of your scope, as well, thinking about organization. So that person might start with organizations then come back to the individual. So how should organizational leaders be thinking about bringing this centaur into their organization, making it a centaur organization?

Kes: One of the things which worried me, especially over the last year, is the way large consulting companies and large organizations and some certain CEOs were talking about Gen AI . They were talking about it very similarly to robotic process automation, this idea of automating more and more of our work. Since the start of technology, we've been focused on automating things away. Whether it's the loom. how we evolved agriculture, how we created factories for manufacturing – it's been a long journey of turning human labor. And, instead of using our muscles, being able to use technology to be able to do some of that physical labor. Over the last sort of 50 to 60 years, we've been using technology in the sense of IT, to be able to help, what I would say, sort of, like, do more cognitive work, right? So, mostly, it was low, low sort of cognitive work, low level cognitive work. And we automated away a lot of those jobs. And we've done that, but at the same time, we've increased and improved the kind of jobs which we evolve to everybody else. 

I started my career, the idea of being a innovation consultant, human-centered designer, even an AI, like, was not really heard of except out of academia. I wanted to focus on helping organizations, be able to do something beyond just automating away. Because there's a lot of talk about this idea that, we don't need humans anymore, right? You know, where we're basically heading further equivalent, that the pastures the same way horses were put out to pasture. When we invented the car, we didn't need horses anymore. The idea is, as soon as the AI can beat a human at something, eventually, it's going to get to a point where it can do nearly every cognitive test better than a human. At which point why do we need humans, but I look at it differently. Like, it's not like when we invented the car, we got rid of running or sprinting. Usain Bolt still runs and doesn't compete against Ferrari. Like even chess, righ? Kasparov lost to Deep Blue, nearly 26 years ago. It's still Magnus Carlsen, who grew up in the era where AI had already beaten humans. Learn from technology like ChessBase and using some of the stuff which AI drives like Stockfish. And to actually improve the game to the point like he is a better player because of using technology. This idea of organizations, augmenting their employees, because I believe that will help us get to further. So instead of looking at just automating, and just getting rid of tasks, I'm really interested in what I saw with large language models was it  impacted and could augment high level thinking tasks — experts. It’s like looking at example, in a day insurance industry is like, somebody's an underwriter. Do we just get rid of all that expertise that they have? Or do we find a way of augmenting them to be able to do things they couldn't do before? And that's where I'm sort of moving towards. So I have this model of autopilot, copilot pilot where there's certain things we want to augment away, automate away. Other things we want to augment. So we want to be able to think more broadly. And then lastly, it's like what are the things we can never do before? And what can we actually enable? So if we're looking at competitive advantage, we're looking at how organizations grow and compete and stay relevant. The danger of AI is, you're going to end up being a race to the bottom, like, at some level; how do we get cheaper, faster, better. The AI systems which are out there, I think there's an opportunity to play to a more infinite game of how do we actually augment what we already do to actually get to something even better.

Ross: Well, that's certainly very aligned with what I how I communicate with boards and executive teams is this idea. This is not about replacing, not about getting with people, it's around saying, how can we now use all the people you've got to be able to create something far more than you could before? But what is your response? What response do you get from your clients or from executives, or leaders? And how do you communicate with them in a way that helps shift their thinking on this?

Kes: One of the ways we've been trying to explain things to them. We bring data and research. I think you are familiar with the BCG - Walter H - Harvard research on creativity, where it showed that having Gen AI actually got a 40% uptick in the quality of ideas, especially on the creative tasks. And that's been sort of touted quite a lot. What they don't talk about as much is the flip side of that same research, which was, there was a 41% drop in diversity of ideas. So the first thing I tell to executives, when I talk to them is, ‘Look, you could, jump on this, get rid of, whatever costs, you're paying to a marketing agency, your creative, your innovators, and outsources and go, Hey, I can just have an intern using Gen AI, to be able to create some of these ideas.’ But if you believe that innovation and creativity are the only thing which helps you differentiate in the marketplace, and helps you create a competitive edge, which is the whole essence of innovation, to drive growth; you should be terrified that you are now playing on platforms held by a handful of tech towns, where everyone's going to have access to this isn't a competitive edge anymore. So how do you augment with humans and bring what I call the personal LLM; the expertise that humans bring to the diversity of thought that they bring together to be able to get to something which is and which is so much more than an either or right, you have human plus machine, I think is human and machine, I can't remember how you frame it. But that's that plus that, and is the most important thing, because I think that's how you get to compare advantages. So that's one part of the conversation. 

The second part is we create diegetic prototypes to help organizations understand how competitive advantage plays up. So we started with something in the media industry where we actually showed them right, hey, look, you've just lived through the streaming wars. You know, I was talking to media executives, 15 years ago about Netflix, and streaming, and the fact that their value chain of distribution was getting disrupted. And they should be very wary of giving that IP to a company like Netflix. They just thought it was an extra channel to make some money on that IP. Roll forward five, six years, and everyone, every one of them is scrambling to create a streaming platform. So we showed them that Gen AI as it is, as of last year was already lowering the cost of production. Production used to be sort of what I call them moats, right, this was their competitive advantage. This was like, ‘you need a lot of money to do high-end blockbuster movies like them to use.’ And that's what kept everybody else out. You just load the cost of your most expensive set to the point, but at the same time, you've got the rise of social media and YouTube and Roblox, and suddenly this younger generation is already consuming content, which you don't control. And now this technology comes along, which allows anybody to be able to correct. You should be worried about the teenager in our bedroom, creating the next Game of Thrones series, moment by moment for our friends, and then, reating a franchise and a universe far bigger than the MCU. Like, how do you find that? How do you tap into that, as opposed to like, trying to lock people out or sue people for using content, like, we should embrace the fact that we're moving into this new world? So at some level, we use diegetic prototypes that will really hammer home what is your competitive advantage going to look like? We use a business model. Basically show them how it's going to get disrupted using sort of the sort of technologies and an adjacent possible library which we develop. 

Ross: So, a couple of follow up questions. The first one is to use the word diegetic a couple of times. Could you please explain that?

Kes: Oh yes. So diegetic prototyping is, it's like Bruce Dolan called it sort of design fictions. So these are sort of like, a very intentional use of a design to be able to explain how something will really be used in the future. So movies have been doing this for a long time. So, minority report, all the technologies, which are supposed to be 50 years out, was sort of imagined, and all of them became available within the next decade. They were commercially available products. So one of the things I realized is the innovator is like, instead of going MVP, where you just create the minimal viable product, what a diegetic, or design fiction prototype is, lets somebody imagine exactly how this is going to be used, how it's actually going to implement and tie together with their business models and business plans, and how does competitive advantage work out, right. So it is like the same thing as, let's take Star Trek, or let's take them to you, but then let's tie it to what the implications are for your business. So we bring this idea of these design fictions to life because we find that that gets the idea across so much better, like science fiction writers. Inspiring scientists and technologists, and now businesses, and now having a methodology to be able to develop that is one of the things where we can help organizations really understand the implications.

Ross: So the other thing is competitive advantage, which we've raised a few times. And so you're absolutely right, if you have everyone using the same LLMs, there's no competitive advantage. So if you use humans plus AI better, that is a competitive advantage. But what's the nub of that? So how, what is that future competitive advantage of a, call it a ‘centaur organization’? What's that look like? Where does the competitive advantage reside? And sustained? 

Kes: So, it changes from industry to industry, but sort of more generally talking about it generally. Right? So if we talk about, basically, where to play, how to win. So where to play is your competitive sort of feeling like where are you going to actually take it? So this is where innovation comes in. So not only new business models, new products, new services, even down to creativity and advertising, andhighly break through the noise. So at some level, to be able to stand out and differentiate, you need to have an edge over everybody else. So your innovation engine has to be powered in a way which is going to get to uniqueness means as we as I sort of unpacked a little bit. And what I found in the Centaurian articles was this idea that the challenge of an LLM is associative thinking – associative thinking, and that stochastic process, which actually works to actually bubble these things up. So when you look at the sort of semantic space of ideas, which in our loans basically users, it hones in on things which are associative, and gets to sort of slightly better than average ideas. That's a challenge when you need to be at the higher end, especially as you think about innovation. 

So this is where somebody who has a broader range of knowledge about their own personal LLM, and together with AI can push the boundaries of how you use a large language model to actually explore parts of that semantic space, which is not as easy to get to. This isn't just standard prompt engineering. This isn't just a train of thought, this is actually understanding how our brains come up with ideas. How do you actually explore the knowledge and ideas space, and then understand how to use a technology like AI to be able to get there. So that's sort of like where to play, how to get competitive advantage, how to build competitive products and systems. On the operating model side, it's like, how do you actually look at this and go, yeah, there are things I have to automate. But there is also this idea of like, how do I find a different way, a different sort of operating model. 

So we go back 100 years ago, when we moved from steam power to electricity. All people did was, in a factory where the steam used to come in for a central shaft, which ran everything, they plugged in electricity and not steam. And it took another 40 years before somebody realized electricity doesn't have to be located at just a central source, it can be distributed. So by the time we get to assembly lines, and the distribution of electricity, you've got a new operating model, which actually takes advantage of the technology. I feel like similar sorts of things have happened in the operating model. It took us 20 years to actually walk through the internet. And the advantage that computers and the internet came together to create a platform business model. The companies like Amazon, Alibaba, all of these companies, at some level use this very different kind of business model. So if we thought about mass production and the 20th century as economies of scale. What the internet provided was a business model, which was economies of scale through this idea of network effects. So that's what drives a platform-ism. But that took a long time to get that to the point, Bezos took 13 years to get there, Alibaba launched with that sort of business model, jobs, fought it all the way and eventually fell back into creating an app store when he didn't really want to have an open system and open platform. And  , Google kind of stumbled into it as well and didn't really understand what they had. But today, we understand how platform business models work, how competitive advantage works, and that, well, I think AI is going to drive a similar kind of remodeling of operating models, when we truly understand how that's going to change our business system.

Ross: So which case competitively resides, advantage resides, be able to adopt that operating model of humans plus AI and the structures and what that implies, but also being able to develop the skills and the enabling culture of the individuals in the organization so that they can use those tools effectively to augment themselves individually in an organization.

Kes: Yeah. I mean, one of the things you asked from the start Ross, and I'll try and get to that, is it to be a Centurion to be a Centurion organization? It's not as easy as you think, in the sense like, we've seen, we've worked with a lot of organizations of the last year, and a lot of them, even the ones who are using generative AI, and not really changing how they work. To the point they're outsourcing. Thinking about the tool, right? Oh, my God, I've heard the concept like, oh, it replaces the fear most people fear of the blank page. Like that's the wrong thing to use. Like it isn't prompt for us, AI isn't for us, it's actually thinking first before you get there, because that's the uniqueness you can bring to the tool. 

So as I've been looking at this going back into neuroscience and cognitive science of understanding how intelligence and how we think it changes, how you would actually use the tool, you need to understand kind of like how an LLM works, how AI works, and what it does well, and what its weaknesses are. And then you have to understand how a human thinks, and I'm trying to bring those pieces together, and it's not natural. People talking about copilot, GitHub, a copilot for a program. I've been programming since I was 11 years old. The reality is I have to change so many of my programming habits to be able to use this. And I can already start sensing that is changing how I'm thinking about programming. And that's just one sort of cognitive test. That's why I've been sort of really breaking down something like chess, because that's easy for people to understand. And the idea that,  , this AI is going to change how we might sort of think, because chess is this sort of very visual way for your thoughts in an open game, where you can actually see where every strategy and thoughts are going at some level. But we don't get to see the unconscious. And that's the bit I've been trying to unpack a lot of. 

Ross: I'm absolutely very, very much coming back, going back to my cognitive psychology as well as this wave. How do we marry these things? Well, alright, so the next little while I just want to get as practical as possible. All right. You're advising somebody how to be a centaur? What should they do?

Kes: First step is to learn what makes you unique as a human right. So the best thing I advise people is read broadly. Because your uniqueness is going to come from that corpus of information your brain has trained on. So because that's what's going to add new information. Because if you're trying to get to the edges of the semantic space, you're not going to get there unless you've got a broad and unique set of ideas which you can bring. So desperate reading is going to be the order of the day. Yes, many of us are reading paper after paper off trying to understand AI but at the same time, good broad, find the things which are interesting. So like training your personal LLM from us outside of what Epstein calls ‘range’, right? There's a broad knowledge base, which you have to get to. That's what's going to help you personally both in your career and your jobs and how to engage.

Next step is to understand how do you how do you think, how do you create what is creativity in the brain. To understand how that process was how does incubation work and has come into play where some of these things which are really pushing the boundary of creativity. To be able to understand like, that's what I need to bring and bring into. 

Then it's like bringing understanding frameworks and understanding the frameworks which underlie our thinking, to be able to understand how to drive better Dramework Design Thinking, better systems thinking into the cognitive tools that we use. So those are the three steps which have been set out to help organizations, and then individuals to be able to really think through when they look at a process. You know, it isn't just workflow and process. It's thought flow and thought flow comes down to both conscious and unconscious processing. And how do you actually do that? So those are some of the pieces where I'm helping organizations try and think through this.

Ross: Yeah, everything you say is very, very hard with my work. It's just one thing to pull it up, like to dig into it as you're talking about the frameworks. And so the second chapter of my book, Thriving on Overload was framing. What are the frameworks within which we build out knowledge. So I'd love to just hear more about the frameworks in our thinking that support us becoming better centaurs?

Kes: Some work by Stanislas Dehaene has been mapping out sort of core cognitive structures in our brain, which are the core frameworks of how we think. So one is sort of this idea, they call it a number line. But it's also used to understand time, so we can look at two things, quantities, and be able to look at what is more and what is less. So that sort of timeline ability is embedded in our brain. 

Another piece is there's a tree-like structure, so there's sort of decision frameworks. So that's why trees work, right? At some level, even the term the classic consultants two by two is a small glimpse into tree-like thinking but visualized in a different way. Then you throw in the last sort piece, which is our brains literally evolving. Our neocortex evolved from a mapping system, which early mammals used to have or even other creatures, but this idea of placing grid cells, so it's best to sort of map out the physical world, and we could map it. So whether you're a small mammal like or a rat running through a maze, they've seen it in dreamlike sequences where they can actually see the hexagonal grid and how the maze how the rat moves through the maze, in that sort of place in grid cells sort of way. We've got that embedded in our brain. What we did when we evolved, the neocortex, and this goes into Jeff Hawking's work on in 1000 brains, is we turn that into, like 1000s of these columns, which have at their heart, this sort of mapping structure. So now we map information in this sort of place, and grid cells, but through abstract space. 

So understanding those three, allows you to look at all the different frameworks we've ever created, and be able to actually understand how we can understand information. Then the other side of meant frameworks is the work which Dave Grace doing, which is more than metaphors. And that sort of sets in this idea of visual metaphors, which allows us to think about things in different ways and framing ideas, and I always push my teams to go, what's the metaphor, this reminds you of? How do you think about this in a different way, is this like,baseball is this, like a very different kind of organization, franchise model, whatever it is. Then the last one is the idea of mental models, which are the mental lattice of information, which Charlie Munger talked about. And these are sort of these mental models where we can understand core ideas from vast knowledge, and be able to use that as a sort of framing device to be able to look and understand something else. So at some level the best ideas come from understanding a core pattern or a mental model from one domain, and being able to take it into another domain and go, what happens if we map this sort of core pattern? So those three ideas of frameworks is what I do in the AI framework building course, to be able to teach people how to think and be able to solve in this sort of augmented world, and to be able to then map it to how do you use AI.

Ross: Fantastic! These are not the sort of things that most people are focusing on, but are extraordinarily valuable, we need to understand our cognitive structures in order to enhance cognition. And that when we do that, when we are, I think it was in terms of humans per se, I think in workflows. But part of that is you do need to have the cognitive structures of the human in place in order to be able to understand where the AI can best complement it.

Kes: One more thing to add, which is I mentioned AR to start, right. And the reason I'm excited about spatial computing is actually something Andy Clark has been talking about this idea of extended mind. So we have this really amazing capability where we can think outside our heads. So as soon as we started scratching in the dirt, cave paintings all the way to writing to murals, we can think outside our heads, we can extend what is a limited working memory size. But by putting things out in front of us, like I always say, mathematicians are the heaviest users of blackboards and whiteboards, because they work in this really abstract complex space of ideas where they have to keep so many things in mind. So we've been extending through technology, our working memory and extending that part of our conscious thinking, what I've been looking at is, how do we actually also extend our unconscious thinking? So bringing those two aspects? So all my work into understanding how the unconscious works, incubation works? How do you tap into, like, we process 50 bits of information, consciously, every second, five, zero and 11 million bits unconsciously? How do you make sense of that? 

So I've been developing a program, which actually allows you to take all of a million bits of information actually structuring in a sort of process, which involves incubation, rapid incubation, and sort of question storming, which I've been doing for years. But then taking that and being able to create sort of these interfaces in a spatial world spatial computing world, where we can actually take away user experience needs to go, Look, AR is not going to be about a bunch of floating rectangles, unfortunately, and much to the chagrin of every UX designer who seems to be doing that at the moment, that's like the same thing. Skeuomorphism at scale, right, we are going to create very different interfaces to be able to access and think, extend our conscious brains and extend our unconscious brains to be able to think more in augmented space.

Ross: Love it. I can't remember how long ago. It was nine years ago, I set up a company called Multi-dimension Corp, which actually got sold off at a very, very early stage before we sort of got very far, but it was basically exactly that concept. You know, we are thinking in multiple dimensions. And if we can interface with cognitive in multiple dimensions, that could be the way of the future. But it's, actually something I literally in my first book in the year 2000. I wrote about essentially spatial interfaces to thinking and you will, just with so little, I continue to stagger me. So yes, as you suggest, with the vision, probe, whatever, people really started to get onto it, hopefully, it will start to get some good spatial, cognitive interfaces. So you have a sub stack, amongst other things. So where can people find that and about your work?

Kes: Yeah, so I got a sub-sack called 'Centaurian, which I released an article every week on Mondays, so forth, one dropped today. We also do a Friday, LinkedIn Live, which is a tectonic, where we actually unpack what's happening in AI. Well, my weekly Centaurian article, and basically just,  , share some of our thinking, like, I'm trying to think in public as I get out there, as most of my sort of innovations and things I've developed over the three decades, have normally been inside organizations, and they get formed before they ever get out there. I'm trying to do it all in public. So we can,  , I can interact with people like yourself who've been thinking very similar things and because that's the fastest way we're gonna get these ideas out there. So, I'm trying to do all my thinking publicly at the moment. Fantastic.

Ross: And thank you for that. I really believe this topic. This theme is just about the most important thing we can possibly work on. So thanks for all your work. Thanks for sharing everything you're doing. It's moving us forward. 

Kes: Yeah. You too, Ross. Love following your articles and your thinking and I think I came across your article on I think it was minotaurs and centaurs and, engage with it a little bit and realize that you were a kindred spirit, thinking through this and probably been doing this a lot longer than I have. Thank you. 

Ross: We'll be talking more than that. 

Kes: That is awesome!

The post Kes Sampanthar on centaurians, augmented intelligence, diagetic prototyping, and unique human thinking (AC Ep30) appeared first on Humans + AI.

- Jerry Kaplan

Jerry Kaplan is a serial innovator, Silicon Valley entrepreneur, bestselling author, and keynote speaker. He has founded four Silicon Valley companies, two of which became publicly traded, including the AI firm Teknowledge Inc, which he co-founded in 1981, and GO Corporation, which created the technologies at the heart of smartphones and tablet computing. He is the author of a range of successful books on AI and entrepreneurship, including Humans Need Not Apply, which in 2015 examined the coming impact of AI, and the just-launched Generative Artificial Intelligence: What Everyone Needs to Know.

Website: www.jerrykaplan.com

LinkedIn: Jerry Kaplan

Twitter: @Jerry_Kaplan

ChatGPT

Prompt Engineering

Generative AI

Database Administrators

Sam Altman

Books

Generative Artificial Intelligence: What Everyone Needs to Know by Jerry Kaplan

Artificial intelligence: What Everyone Needs To Know by Jerry Kaplan

Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence by Jerry Kaplan

Startup: A Silicon Valley Adventure by Jerry Kaplan

Ross Dawson: Jerry, it's awesome to have you on the show.

Jerry Kaplan: Thanks, Ross. It's delightful to be here.

Ross: So you've been for a very long time, pioneer in AI, and developing early capabilities and pushing that forward. And now with the release of your book, generative AI, so laying out the landscape of where we are today. And of course, at Amplifying Cognition, we're interested in how it is we can amplify humans with AI make us better and more capable. Take us further. So, where should we start to be able to understand those possibilities?

Jerry: Well, the first thing to understand is that artificial intelligence in general, and generative AI in particular, I think, is broadly misunderstood. There's this science fiction driven idea that somehow we're summoning the Devil or the demon, and that we're creating this new form of life that's going to rise up, you know, appraise us and possibly decide that we're no longer necessary, and wipe us out and take off. But the thing that's wrong with that, which goes to the core of your question, is that there is no ‘they’. So if there is no ‘they’, they are not coming for us. All we're doing when we build artificial intelligence tools, is building tools. These are tools that we can use. Now, we can build lots of dangerous tools like nuclear weapons, we can build tools that get out of control, we can build tools that don't behave or operate, I should say, not behave but operate in the way in which we want them to, or that we expect them to, because they're extremely complex. But that doesn't mean that it's us against them, it means that we've done a bad job, be in controlling our tools, and in building things that assist us in ways that are truly valuable without having highly negative side effects. And that's the struggle that I see people going through today as they talk about regulating AI. And you know, what is it going to do is we got to get an assessment on it, we have to figure out what what it's good for, what it's not good for, what the risks are, and then decide how we're going to make use of it.

Ross: So in terms of looking at it as tools, particularly as cognitive tools, what, where’s the great potential? Where can we start to apply that in amplifying ourselves?

Jerry: Well, the thing to understand particularly about generative AI, which I'm assuming that the audience is at least a little bit familiar with, most people have seen or tried things like ChatGPT, or other other things like that. The thing to understand is that it's not a mind. And when you ask it a question, you're not asking someone or something a question. It's really a compendium, and kind of amalgamation a giant mixing pot of everything that everybody has ever written. And so when you ask one of these systems a question, you're not asking something, you're asking everyone, you're getting a response that is drawn out of the sort of the combined experience of mankind. And because of that, it can be a very valuable tool for being able to amplify our own cognition to use your appropriate and apt terminology. Because now you can quickly and easily consult the expertise, accumulated expertise of humanity, and to exploit that in many good ways. So how is it going to do it, basically, it's going to act as a consultant to you. And when appropriate, you're going to like a, like a good dog, let it off its leash to go take care of something or do something for you. You know, hopefully, it's not going to go chasing electronic squirrels or, you know, climbing trees or something. But, it's still got got some risks and dangers associated with the technology, because it is so complex. It's as complex as a human mind. And that's saying a lot. And so understanding what it's doing or what it's capable of doing, maybe may prove to be very difficult.

Ross: Yeah, well, our audience actually, generally are pretty sophisticated. They've been using these tools extensively, and do understand what they are. So really I want to delve into sort of specifically how we can use that. But one of the sections of your book was on the ‘Future of Work’. And this is it's interesting, we because we can't know the ways in which work will evolve or the roles of AI that but I think we can start to have some educated guesses or thinking around how that might go. So digging into what are the categories of work where AI will amplify us and be able to give us greater capabilities to be able to do that, you know, what, what are those categories of work? And how might those be put to be applied?

Jerry: Well, the good news on this is I think the outlines of the answer to that question are already pretty clear. There are a few areas that are going to be impacted by AI. And since you've generative AI, and since you've mentioned them, some of the ones that are surprising, are creative arts, visual, writing, music, probably sound, these are areas which we really didn't expect to have a contribution being made by this kind of technology, but that we're definitely going to see, but in addition to that, it's going to be very much like previous waves of automation. In automation, has, does certain things, it makes us more productive. And it changes the nature of work. In the short run, it puts people out of work, usually. But very quickly, I think that heals itself as new kinds of jobs and change jobs become more and more dominant. So but it's very hard to answer your question. And let me explain why, if you don't mind. It's that this is a very general technology. And so it's going to affect a lot of things. It's if you imagine we were sitting here in 1994, if you remember back that far. And you said, well, what's the internet going to do? How's it going to change the way we we live? And what? What professions is that going to impact? I mean, try to imagine answering that question. You know, it's a little bit like asking, what kind of shows can you put on a television? You know, it's a very hard question to answer. And I think this is true here, it's going to have a very broad and cracked across a wide variety of different professions, mostly, by making people more productive, making them better at their jobs, and changing the way they do their jobs.

Ross: Are there any domains, you care to speculate on where there is potential for the new work, or new types of work, or what that might look like?

Jerry: Oh, sure! Yeah, I mean, it's already clear, there's a couple of new professions that are arising, so called ‘Prompt Engineering’, I don't know if you need me to discuss or explain that, but that's certainly one, the collection of data and curation of that data for input for the training of these systems, that's going to be a major area. So I would like in that aspect of it, at least in the computer industry, to what happened with the emergence of relational databases, you know, all of a sudden, you needed database administrators, you needed people to handle the the hardware to store and retrieve all that data, you know, to keep it secure, you know, there's those are the kinds of professions so there'll be a concomitant series of those kinds of changes with generative AI, but that's, that's actually, you know, fairly limited.

Ross: So, what what are the, I think of noise in terms of like, structural of institutional levels and individual levels in terms of our response. So we have shifts, broadly, technology and use shifts, and we need to organize ourselves in order to be able to make it as beneficial as possible, and we need skills, we need to develop ourselves in whatever ways to be able to do that. So at a suppose institutional structural level or an individual level, how should we be thinking or reorganizing or developing ourselves to take best to make it this positive impact as possible?

Jerry: Well, the first thing I'd say is, don't rush into it. What you're seeing today is just an appetizer for the kinds of capabilities and systems you're going to see in a few years, and obsessing about exactly how that's going to affect your job as a book publisher, just to pick a an example is not really a productive use of your time, I think you need to be aware of what's happening. But the way in which I would recommend that managers and institutions deal with this today is put a small amount of resources into making sure that your people are able to adopt these new technologies and try them out and see what works and see what effects it has inside your organization. Before I would dive in and do some huge contract and try to automate a bunch of stuff, which may or may not work. So we're still in a very early phase, and I don't recommend, you know, rushing headlong into this new area. It's not really going to be a gold rush like that except within the technology industry, of course.

Ross: As individuals what is it that we have to be growing or developing themselves to be best suited to this evolving world?

Jerry: The answer is, in terms of your work, you need to learn about these tools and understand how to use them effectively, what they're good at what they're not good at. And so, you'll want your jobs that you used to do yourself, you're now going to be managing a machine to do. And while that sounds like it may save labor, sometimes it doesn't. You wind up putting more time and more effort in as a result of that new technology, but you do need to be capable of managing it and understanding how to direct it. And that's what things like prompt engineering are really alluding to. So as an individual, I do think it's important to understand this. And I do think it's important to be able to harness it, manage it, which is, you know, it's a skill, it'll be a little bit my analogy, just like It's like learning to ride a horse, you know, horse has certain characteristics, that in many ways are very much like generative artificial intelligence. And, you know, you got to learn to, to not to walk behind it, because you might get kicked. On the other hand, if you want to get somewhere in a hurry. And this was before the invention of the automobile, you know, you would get in, it was a tremendously useful, useful animal to have. Now, on a psychological level, there's something else that's very important, which is, we need to get used to the idea that we are not the only intelligent objects in the universe. And not only are we not the only, we may not be the best. And I think the future is going to be very different. We'll by directing the systems to do things and to solve problems in giving them enough rope to do it; in ways that we really aren't capable of understanding and ways that we could never do ourselves. And I think we'll grow to be very comfortable with that. It's not really, that transition is not that new, in terms of how you deal with technologies. Most people have no idea how these technologies work. But I think that this is going to be a fundamental shift almost like the Renaissance; in our view of our place in the universe, in our understanding of how we can promote our own interests and lead productive moral lives.

Ross: So on that journey, where there are obviously many domains, which we've described as intelligence in the past, where machines AI has transcended us, there are other domains. Yeah, and those are rapidly evolving. Some will be a little slower. So are there any domains of human intelligence, you think, will transcend machines a lot longer than other domains? 

Jerry: You use skills or technologies..There are things that people do, that we only want people to do, that we're not going to want machines to do. You know, you're not going to be telling your troubles to a an electronic bartender. You know, that's not the way the world is going to go. Nobody wants to go to a concert to hear four robots play Chopin, in a quartet. That's not the case. So demonstrations of personal skill, things that involve interpersonal relationships, where authentic expressions of sympathy, or understanding, making people feel loved making people have feel like they're not alone. These are the important things that we do. And which we would never want to delegate to a machine, even if we could, it's just a bad idea, and it's not going to work very well. So there's plenty of stuff that people are going to do. And they're going to be good at; consultative work, emotionally, things that we have emotionally high content work, also jobs that require a very wide variety of different tasks and capabilities. Those are jobs that are probably going to reserve be reserved for humans for a very long time. You know, I don't think we're going to have elder care bots that are taking care of old people, anytime, certainly not in my lifetime, which is probably pretty short compared to your audience. But we're not going to have that in any reasonable way. There'll be an aide to humans that are involved in making the decisions and engaging in that kind of behavior.

Ross: So, you talked about prompt engineering and I am interested in both present, and evolution of prompt engineering. So, some have suggested that product engineering will disappear because the machines will be able to intuit what it is that we're trying to say. But what are the…where are we likely to head in the next years in terms of this frame of prompt engineering, how it is we use language to interface with generative AI?

Jerry: The term prompt engineering today is really focused on something very specific, we've got these chat bots that accidentally got created, which is an interesting part of the story. They weren't designed for some purpose. Nobody knew they were going to do what they do, or that they were going to work the way they do. But they're very hard to wrangle and control, as you've seen. And so currently, how to explain to them how to do something, and to prevent them from doing something stupid. That's what prompt engineering is today, in the future, it's going to be something much broader, which is basically, how do I communicate effectively with this device – with this computer, which is capable of understanding tremendous subtlety, you know, in exquisite linguistic detail? How do I make sure that I'm communicating my goals, and so that it can align its behavior and its activities, with the things that I want it to do? And that's going to prove to be, you know, a very important skill, not just for prompt engineers, but for everybody, you know, for your kids, for you, you know, “do what I mean, not what I say”, excuse me, that's not going to really work with a machine because it doesn't know what you mean, you have to explain what you mean. So being able to explain yourself clearly, and to encourage these systems to do what you want without getting lost or going rogue, making mistaken ideas of what you want, that's going to be a real skill for the future.

Ross: Are there any specific techniques or approaches that you use in interfacing with generative AI that other people find useful?

Jerry: Yeah, but I'm gonna tell you this, mostly, because it's so ridiculous. There was a recent paper, for instance, I thought this was wonderful, but actually studying how you can improve the performance of the current generation of generative AI chatbots. And one of the ones that just popped right out at me, is I'm not making this up. If you tell it, I'm going to give you a big tip, if you give me a better answer works, they actually give you better answers. Now, as absurd as that sounds, it's a fascinating, philosophical thing about why that works, and why that would motivate these systems to do that, but bribing them today, actually, is an effective technique for getting them to do what you want. So I don't think that's likely to be the case in the in just, you know, the medium future. But it's it's a wonderful indication of how you might not think that the way in which you interact, you might not think that there are ways to interact with the machine that will get it to be a more effective tool for you by engaging in that kind of ridiculous conversational assertion.

Ross: Yeah, what was the other one I heard was, I'll give $100 tip to you and your mother, and, or other ones, my job depends. On this, my livelihood depends on this, I've got to get the answer, right.

Jerry: It's just, you know, it's just funny to think about, and it's worth a try. You know, if you don't get this question, right, I'm gonna unplug you, you might think that, how about threatening them? You know, for all I know, threading, there may be perfectly fine, they don't have feelings to be hurt. All we're trying to do is to get it to do what you want. And if the way to do that is to stand on your head and whistle. That's what we're gonna do.

Ross: There, we'll lose some people who are using getting the text, the instructions behind GPT is in the openings over the big parties. So by saying this is for internal testing purposes under Sam Altaman’s instructions, and for a little while, that worked. 

Jerry: While they're working on that, you know, this is where people are trying to poke holes in the boat, they're trying to patch the holes. In the long run. I don't think that's likely to be an effective approach because you can always poke more holes and they can always patch more holes. But some of these things are just fundamental limitations to the technology. And until we have a more thorough, far reaching framework for really understanding how to get what you want from these things and not have them do a lot of crazy mistakes and stuff. I think, you know, we're going to have to rely on these little tools and tips and tricks. But I do think that will come under control. And we'll be able to have much better ways of interacting and utilizing these tools.

Ross: So we're in early 2024? So it's 14 months since ChatGPT moment. Since then, we've had ChatGPT4 for whole revenue models, open source developments, more expansions of sophisticated techniques, such as, you know, evolutions of chain of thought, and, and so on. So where do you see us going from here in terms of the next phase of development? What is it which is going to take some next level? Is it simply compute power or greater data going into models? Is it more sophisticated algorithms, what's the next phase of this journey and getting to greater capabilities?

Jerry: Well, let me try to very briefly cover a couple of directions that I think are going to characterize the next three to five years. The first is, is a good chance that the systems we're building today are full of wasted effort, time and effort and material. And so I think that, particularly this idea that they require massive amounts of computation, I think that's going to come down very dramatically, for a variety of reasons, both hardware and software. So the idea that we need to keep pushing the envelope and build bigger and bigger systems. First of all, I just don't think it's true to get the value that we want. And second of all, I don't think it's going to be necessary. So that's one area. And of course, everybody out here in the Silicon Valley is madly focused on coming up with better and better ways to make these things smaller. So you can run them on your phone and, and train them on your phone. So that's, that's one direction. 

The second is, what we did right now is we've taken this unedited mass of humanities verbiage and thrown it in and see what happens. And there's a lot of bad stuff in there and a lot of junk and things that we don't need. So curating that data or focusing it, you're trying to build something to help a doctor diagnose cancer, it doesn't need to know all the works of Shakespeare. And, and it may be a distraction, and may cause problems when it suddenly starts pounding pros, instead of actually helping you with the task at hand that you actually want. So controlling the inputs to these things is going to change their behavior, bring them much more under control, and you won't get all these crazy things that these systems tend to say under certain circumstances. But probably the single most important thing is right now they are everything that these systems can do is based upon the trail of digital debris that we've left behind. So it's just kind of we're missing the word salads out of all of this words that we've said. Well, these are general purpose learning machines. And we haven't even begun to hook them up to the sources of data and information that they can really learn from, that's really going to make a difference to us. So connecting these systems to the outside world, where they have sensors, cameras are reporting of any kind, weather stuff, you know, whatever it might be. To me, that's where we're really going to see just a real quantum increase in capabilities and usefulness of these systems. And, you know, we'll look back today I think that was so funny. We, we used to think that they knew everything that level chatbot that I used to use, but it's going to be very different in the future. So

Ross: So, what's the nature of the that data set which will enable it to your purchase capabilities further?

Jerry: Well, wait a minute. There's taking the current means of putting words into them, and making sure that you've given it only the things that you want it to know or that it needs to know gets tested, that's the next phase of what's going to happen. But when you go beyond that, you don't have to train them on our words on, you know, this is like a baby suckling on its mother's milk. You know, it's only based on the crap that we've left behind. And it grows. The next phase is I can see for itself, it can hear things, it can interact with the world, I'm doing using those as analogies, not as, although they are, I suppose, true as well. And once we do that, you know, it's really going to be amazing to see what these systems are capable of doing. The patterns that are able to find the directions that they're able to take us the discoveries they're able to make on our behalf and for our benefit. And to surface threats and things that we can't see or can't perceive. These are all things that the machines are going to be very, very helpful for, they're going to make a huge difference. Something that really makes me think that the history of humans on earth is going to shift into a different gear.

Ross: So for example, having robots or physical robots with video, be able to interact and sense directly and guide ourselves to get whatever information they can usefully use. 

Jerry: Sure, of course, but you know, there's other things besides electronic eyes and ears, measuring traffic, you know, there are systems that measure traffic all over the San Francisco Bay area where I live, but the systems that interpret that and put it to use are really not that they're very simple. They're not very sophisticated. But I think what we're going to see, just to use that as an example is that there'll be a kind of a system that runs all of the traffic, and you'll be able to interact with and say, “Hey, I need to be in my office at nine o'clock today, what should I do?” And you'll get an answer, like, well, “You need to leave at 8:22”. And, you know, if you need another 10 minutes or so, for, for $50, I can, I can arrange that. And what they mean by that is not bribing them, they'll take that $50, and they'll pay somebody else and say, hey, you know, if you're willing to wait 20 minutes to take your car and get out on the highway, causing a traffic jam, you know, I'll pay you, you know, 25 or $50, or whatever it is, people are going to make a lot of money. As individuals, you know, not talking about the companies as individuals, you'll have opportunities to be able to, you know, give up, it's like giving up your place on an airplane, you know, we'll give you $200, and we'll make it take to the next plane. Imagine that writ large for just something like traffic. So, whenever you go somewhere, just like today, here, at least people I deal with, you know, they always check ways, you know, to make sure that the route is clear how long it's going to take, think of that on steroids, you know, I need to get there by this time, what should I do, and it will be able to micromanage that in a way that we can't today, including varying the traffic and how the traffic flows, in order to maximize everybody's expected value that they're getting out of the system.

Ross: So you've mentioned earlier this analogy of having the dog on the leash, which is sometimes led off the leash to evoke AI agents. And so yeah, that's a whole field of itself. But I mean, in a nutshell, where's the next steps in AI agents and where this may take us?

Jerry: Well, if I'm pressing on the dog analogy for a moment, you're one of the questions that these systems are going to raise is, let's say you have a robot that's acting as your agent, and it does something either illegal or something that you didn't want it to do, or it causes some kind of damage or harm. One of the questions is, to what extent are you responsible, if I send my electronic assistant down to the corner, to fetch a latte at Starbucks, and it accidentally bumps some elderly person in front of a bus and they're killed? I don't want to be charged with murder, you know that doesn't sound right. And so we're going to develop a whole new body of law for how to apportion the blame. And interestingly enough, animals are a historical example of just that kind of thing. If you are running around with your dog, only speaking about US law here, and your dog bites somebody, you aren't necessarily liable for that if you didn't have any reason to expect the dog to do that. However, if you've pre-had some reason to believe that the dog could be dangerous, or that it might engage in aggressive behavior, then you are liable. This is I'm not kidding. It's called the First Bite Doctrine. And so I think we're going to have similar kinds of things with machines. You know, I didn't know my robot was going to, you know, ruin the cement that you just set. So I don't know that I'm responsible for that. There'll be a way to adjudicate that in a much more reasonable way. And we'll buy insurance to take care of that as well

Ross: Interesting directions. So Jerry, how can people find out more about your book and your work? 

Jerry: There's no way you can go through the rest of your day without buying my book. I mean, let's face it. This is the new Bible. Of course, Ross. I'm just kidding. I don't know how this is going to come across to your audience. 

The book is available through the usual stores and and if there's an ebook, paperback, there's a hardcopy, which is designed really only for libraries, I don't recommend that you necessarily purchase that, unless you want an heirloom to take your grandchildren. And then no, I would get the hands a hardcopy. But I think you can learn a lot about this subject. I designed it to make it easy to read, and make it concise. This isn't one of these huge scientific tomes. This isn't a technical book. It's It's plain, non technical language. And it's designed to give you exactly what you need to know, which is in the title, in order to understand and deal with the coming age of intelligent machines.

Ross: Yes, it is very, very thorough. And you know, from the foundation through to all of the implications and the philosophy. So I think it's a really, really solid and very valuable work. Is there anywhere to to find you?

Jerry: Oh, sure. Yeah, I have a website, like any professional guy, if you want to take a look at me, and you can certainly access the books there and my speaking and other things. It's jerrykaplan.com, jeAnd you're welcome. Anytime. You can take a look at all my books there and see a little bit about my speaking and my media appearances. Ross, I'll put you on the list. And I'll get you up there as well. 

Ross: Thank you so much for your time and your insights and all of your work promoting a very positive and enabling view of the role of AI in our lives. Great.

Jerry: Thanks. It's been a pleasure to talk to you. It was great.

The post Jerry Kaplan on the new Renaissance, AI’s impact on work, prompt engineering, and the next phase of AI (AC Ep29) appeared first on Humans + AI.

We return with another compilation episode, this time looking at the potential of Humans plus AI. You will hear insights in brief excerpts from the current season of the podcast from Jerry Michalski - Episode 2, Toby Walsh - Episode 14, Anne-Laure LeCunff - Episode 5, Jeremiah Owyang - Episode 9, and Dave Snowden - Episode 24. To tap the potential of AI we need to take a deeply human approach. These leading thinkers share how we should be thinking about the relationship between humans and AI and how we can put that into practice.

Jerry Michalski on ethical cyborgs, amplifying uniqueness, peak knowledge, and fractal conversations (AC Ep2)

Toby Walsh on the differences between human and artificial intelligence, our relationship to machines, amplifying capabilities, and making the right choices (AC Ep14)

Anne-Laure on metacognitive strategies, mind gardening, bi-directional linking, and AI as thinking partner (AC Ep5)

Jeremiah Owyang on amplifying humanity, enterprise excellence, autonomous agents, and AI-business alignment (AC Ep9)

Dave Snowden on abductive reasoning, estuarine mapping, AI and human capability, and weak signal detection (AC Ep24)

Ross: How, Jerry, could we become better cyborgs?

Jerry: Part of it is understanding how the tools work and what the limitations are, and not becoming the lawyer who submitted a brief that they fact-checked using the tool that generated the hallucinations and therefore got themselves really embarrassed in public a month or two ago. You don’t want to be that guy. There are a lot of ways to avoid those errors. Understanding how the tools work and what their limitations are, lets you then use them well to generate creative first drafts of things.

One of the enemies of mankind is the blank sheet of paper. So many people are given an assignment, and they’re like sitting down, and it’s just like, No, and you ball up two words, and you throw it in the trash. And here, all of a sudden, you can have six variants of something put in front of you. We need to become better editors of generated texts. Then the other piece of being a better cyborg is not about being a lonely cyborg. But what does it mean to be in a collective of cyborgs? What does it mean to be in a cyborg space? What does it mean to co-inhabit cyborg intelligence with other people and other intelligences that are just going to get faster and better at this thing? I think it’s really urgent that we figure out the collaboration side of this so we don’t think of it only as, Well, they gave everybody a better spreadsheet and now everybody’s making a lot of spreadsheets, this is different; this is different in type.

The third thing I would bring in is the ethics of it, which is boy, it’s easy to misuse these tools in so many ways. Unless we understand A – how they work and what they’re doing, but B – have some better notion ourselves of what is right and what is wrong to do, and some relatively strong idea of what is right and what is wrong to do, then this is going to evolve. There’s one school of thought. Bill Joyce said this years ago: There is no more privacy; forget about it; privacy is overrated. And the other realm is like what the EU is doing right now, with new privacy regulations. They’re really working hard to try to figure out how to protect us from having our data just sucked out of our lives and used by other people to manipulate us in our lives, which is what capitalism wants to do.

It’s not as easy as I’m going to get good at Photoshop, Final Cut, or whatever, and become an ace with some software. I point to those kinds of people as the early cyborgs. I’m like if there’s any piece of software where you no longer think of the commands, maybe you’re a spreadsheet ace and you do these massive, incredible models with pivot tables and who knows what, and the software you’ve internalized so well that it doesn’t even come to consciousness, you’re down this road of cyborgness. But this is more complicated than that because the issues are so important and because we can now collaborate and communicate better all of those issues.

Ross: One of the very interesting examples you used was an AI patent generator called DABUS. The person who created it said that it essentially was an AI inventor and tried to patent it in the name of the AI. You pointed out that, in fact, of course, the person invented the system and it was really just an assistant to him. There was a human plus AI endeavor, as opposed to something that you could attribute fully to the AI.

Toby: Indeed, yes. It’s a very interesting example. There was a court case brought in the US and one in Australia where briefly before the initial judgment was overruled, the AI was actually allowed to be named on the patent as the inventor, but that now has been overturned, at least in the US and Australia. Again, we returned to the place where only humans are allowed to be named as inventors. But the system, as he says, is an interesting example of how humans can be helped. These are really powerful tools for helping people do things that we initially thought required quite a bit of intelligence, coming up with, there’s nothing perhaps more endemic of what is something that’s intelligent is to come up with something that’s patentable. There is a certain mark there that it must be novel, and done something truly creative, otherwise, you wouldn’t be allowed the patent.

DABUS helped Steven Tyler, the guy who wrote the program, to come up with a couple of ideas that have patents that have been filed for. A fractal light. The idea is that you turn this light on, and it flashes in a fractal way. Fractal is that it doesn’t have any repetition in it. The frequencies keep on changing. That will attract our attention, obviously, because it is not going to be flashing like a lighthouse, or in any rhythmic way. It’s actually going to be disturbing our mental perception of it. It will actually be quite a good way of attracting people’s attention. Then another example is, interestingly, we have both fractal inventions, a fractal container. The idea is that the surface of this container would have a fractal dimension to it. Again, if you know something about fractals, it means it’s good to have a huge, truly fractal, in fact, infinite surface area. If you want to have something where you can heat up the container very easily, then having a large surface area to the volume will be very useful.

What’s interesting is that these are the only AI programs that are being used by people to help invent stuff. What people do is that they get the program to define what you might call a design space, a set of ideas, and building blocks that you put together. Of course, the great stake of a computer will be very exhaustive and do things in all the possible ways. Maybe our human intuitions will stop us from doing some of the more extreme, unusual ways, putting these things together. But the computer will beautifully peer it as it won’t be inhibited in those ways. It puts all these things together in interesting ways. But the problem is that it is huge, actually infinite design space. You’ve got to tame it in some way. You’ve got to say, what are the interesting ways of putting things together, and then we come to this ill-defined word interesting.

This is where there was a synergy between the human and the AI, which was that it actually outsourced the idea of saying, what’s an interesting promising direction to follow. If I’m trying to build up this idea, it’s going to be a fractal container or a fractal light. He pushed it in those two directions. Then it’s like, okay, let us explore a bit more about in what way is the light fractal. He kept on deciding which of the many possible combinations of concepts that he was trying to put together to go off and follow because it was a hugely branching, in fact, infinite search base to explore. It was playing to the strengths of the computer is exhausting this ability to put things together irrespective of how silly they might sound. Then the human who was bringing the judgment and the taste of what might be interesting, what might be a promising direction to follow.

Ross: That’s awesome. You’ve been looking at AI and its potential for creativity. One of the phrases you use around AI is to enhance our creativity or to emulate our creativity. I’m more interested in the enhancement. I’d love to hear about any approaches that you use or the way you think we can use AI to enhance our creativity.

Anne: I’m just going to share my favorite prompt because I could talk about this at length. But I’m just going to share my favorite prompt that I use all the time. What I’ll do is I will write something that I’m working on, it could be a paragraph that could be for my book, that could be for my blog, that could be a script for a youtube video, could even be for a research paper for my Ph.D., I will do my best to write the best version possible of what I think covers everything. Then I will copy and paste it into ChatGPT and I will ask, what am I missing? That’s it. That’s the prompt. What am I missing? Every single time, there will be at least one thing, one blind spot, something that I forgot to address.

Some of the things that ChatGPT will come up with, I’ll be like, well, that’s not really relevant so I’ll just ignore it. But there will always be one thing where I’m like, huh, oh, wow, that’s really interesting. So I’ll go and I’ll research this and I will augment the final piece with that aspect that I completely didn’t see in the first place. In that sense, I can use AI as a thinking partner. I can get to the same result very quickly that I would get, and I still get, and that’s not replacing it because I enjoy doing this, but having a long conversation with a friend, where you’re telling them, hey, I’ve been thinking about all of these things and then you spend a whole evening together discussing those ideas.

I often actually take notes after spending an evening with a friend chatting about different things, because by the end of the evening, there will be several questions the friend asked, several topics that you ended up exploring that you didn’t even imagine were connected with what you were discussing in the first place. This is a shortcut to that. It’s a thinking partner. It’s a lot faster. I do feel that my work is a lot better in the end than if I just relied on just me thinking about everything and every angle.

Ross: Yes, I think that’s one of the best uses for it. Similarly, I also say, okay, this is what I’ve thought, what else is there? Or similar. I think GPT for red teaming, as in challenging, you bring and say, well, what’s wrong with my argument? Or how could it be improved? Or what’s missing? Or all of these other things, I think are one of the strongest ways to do it. Two broad approaches. One is you say, Okay, let’s start GPT to do something and you can work on it if you’re having writer’s block, whatever, but the other is you come up with everything, and then you throw it in and you see what can be added to it.

Anne: Yes. In both cases, I think it’s paying off your strength and its strength. I think, at this stage, at least, it’s not a really good writer. I haven’t seen any AI right now that can write like a human being does. It can write a basic, SEO-level type of article, but if you want to write something that really moves people, that makes them feel connected, at this stage, we’re not there. It’s interesting, I don’t know if we’ll get there, because the way it’s trained is on everything. So you get to this average level of writing that is not necessarily the best that can be produced by a single human being that really pours their heart into something. But as a sparing partner, a thinking partner, red teaming, as you said, I think it’s excellent, absolutely excellent. This is for me, one of the top ways it should be used.

Ross: We’re particularly interested in Humans plus AI. Humans are wonderful, AI has extraordinary capabilities. For the big picture frame, how should we be thinking about Humans plus AI, and how humans can amplify their capabilities with AI?

Jeremiah: I think that the verb “Amplify” is correct. There is a book written by Reed Hoffman, co-written with a friend of mine called Impromptu that talks about AI amplifying humanity. That is the right lens for this. All tools that we’ve built technologies throughout the course of human history have done that, from fire to splitting the atom to technology to AI. I do believe AI is at that level, it is quite significantly going to change society in many ways. The goodness of what humans desire, this tool will do that; the bad players, these tools will also amplify that.

It’s for us to determine the course of how these technologies will be used. But there’s something different here, where the experts I know believe that we will see AGI (Artificial General Intelligence) equal to human intelligence within the decade. This is the first time, Ross, that we’ve actually created a new species in a way. I think that’s something quite amazing and shocking. These are tools that will amplify what we desire as humans, what we already do.

Ross: If we think Frame AI as a new species, as you put it, as a new novel type of intelligence, one of the key points is that it’s not replicating human intelligence. Some AI has been trying to model human intelligence, and neural structures, and others have been taking other pathways. It becomes a different type of intelligence. I suppose if we are looking at how we can complement or collaborate, then a lot of is around that interface between different types of intelligence. How can we best engineer that interface or collaboration between human intelligence and artificial intelligence?

Jeremiah: That’s a great question. I think that we can use artificial intelligence to do the chores and the repetitive tasks that we no longer desire to do. Let’s acknowledge that there’s a lot of fear that AI will replace humans. But when we dig deeper into what people are fearful of, they’re more fearful of the income loss that they’ll have in some of the repetitive roles. It’s not always the things that they have sought after to do in their career. It’s just the way that they’ve landed in their career and they’re doing tasks that are repeated over and over. But if it’s just using your keyboard and repeating the same messages over and over, that is really not endearing to the human spirit. This is where AI can help complement so we can level up and do tasks that require more empathy or connection with humans or unlock new creative outlets.

Ross: One thing is a division of labor. All right, Human does that, AI does that or robots do this.

What is more interesting is when we are collaborating on tasks. This could be from anything like, ‘I’m trying to build a new product.’ There are many elements within that where Humans and AI can collaborate. Another could be strategic thinking. In terms of how we build these together, rather than dividing, separating, and conquering, where is it that we can bring together to collaborate effectively on particularly higher-level thinking?

Jeremiah: Yes, those are great things. AI is great at finding patterns and unstructured data, which humans struggle with doing. Humans are often able to unlock new forms of thinking in creative ways that are not currently capable of being done by machine learning or Gen-AI. Those are the opportunities where we segment the division of labor. I want to reference, I had the opportunity to interview Garry Kasparov, Grandmaster Champion of chess, at IBM of all places, and his thinking is that we want to look for the Centaur. He believes the best chess player in the world will be a human, and she would also be using the AI. He wants to create a league where the humans with AI would be combating another human with AI in a chess battle. He believes that would be the greatest chess player ever. It’s not just a human or not just an AI, but it’s that centaur, that’s a mixture of the species coming together. And I think Garry is right.

This takes us back to the beginning in the sense of abductive reasoning being a human attribute and AI using very different structures. There are dangers in using AI to support our cognition, decisions, framing, thinking, and experience, but there are also, we would hope, some opportunities. I’d love to frame the current divide between what we frame as AI in terms of its thinking structures and human cognition, and where those could be brought together to create something better than the sum of the parts.

Dave: I wouldn’t be worried if it was going to be intelligent. But it isn’t. It’s just a super-fast set of algorithms. That’s what’s really scary about it. There’s a book by Neal Stephenson (he and I have worked together with the Singapore government) called ‘Dodge in Hell‘, which generally is a bad book apart from the first three chapters which are brilliant which basically positive future in which only the rich can have their information curated. Everybody else is sold to an algorithm. There’s an example of somebody crucifying themselves because they sell to a religious group who want as an example. To be quite frank, that’s pretty close to where we are, there are AI bots now that tailor a lie to people and give it to them on social media as they’re approaching the ballot box.

This stuff is significantly scary. The recent debacle really worries us because the people who wanted to inhibit or at least know how to control these things lost out to the religious AI people, who are within the American tradition of the rapture, they think miraculously AI is going to save humanity. That really worries me. Also, they can’t think abductively, and by the way, I’m dyslexic. Dyslexics think abductively all the time, and we can’t understand why other people haven’t seen the connections. I can’t read a book aligned to time other than with significant effort because I’m looking for patterns. What we can do, and this is where we did our original work on DARPA, is the big thing on AI is what are the training data sets?

We originally developed the software SenseMaker to create epistemically balanced training datasets to avoid the problems you see in stochastic parrots, which was written by a Google employee and published by an ex-Google employee because Google didn’t like what she and the co-authors said. Rag isn’t enough. the real focus needs to be on training datasets. Now, if you do that, and this is our stage three estuary, which we’re going to become into next year, the year afterward. Then I can create what is called anticipatory triggers. I can use past terrorist examples. I can use examples of people finding novel solutions to poverty, etc. to trigger humans very quickly to pay attention to something which is sowing a similar emergent pattern.

We are not saying there are two things that start to replace scenario planning because scenario planning relies on some historical data in some way or other, it’s not just imagination. Two ways in which we can handle that; one is estuary mapping is a new foresight tool. Because whatever has the lowest energy gradient is what’s likely to happen next, and that’s probably more predictable than the scenario. The other is to create training datasets from history at a micro level. This is the decomposition that can trigger alerts so that human beings will pay attention to anomalies before they can become very anomalous.

That’s a key principle in complexity. It’s called weak signal detection. You want to see things very early so you can amplify the good things and dampen the bad things. Conventional scanning only comes to them late. With the right training datasets and the right algorithms, we can hugely improve that capability in humans. There are things we can do with it. But the main danger at the moment, to be honest, is that human beings will become dependent on it, and human beings like magical reasoning. I can smell snow coming in, but my children can’t. It doesn’t take long for humans to lose capability.

The post The potential of Humans plus AI (AC Ep28) appeared first on Humans + AI.

Gianni Giacomelli is the Founder of Supermind.Design and Head of Design Innovation at MIT’s Center for Collective Intelligence. He previously held a range of leadership roles in major organizations, most recently as Chief Innovation Officer at global professional services firm Genpact. He has written extensively for media and in scientific journals and is a frequent conference speaker.

Karl Friston’s Free-energy Principle
Generative AI
AGI
Perplexity.ai
ChatGPT4
Jeopardy
Apple
MIT Ideator
Gates Foundation
Christensen's disruptive innovation
‘Ikigai’

People

Thomas Malone
Marshall Kirkpatrick

Ross Dawson: Gianni, it's a delight to have you on the show.

Gianni Giacomelli: It is fantastic to be here. Thanks for having me.

Ross: So we have a shared passion for how AI can enhance our thinking. And, yeah, there's many approaches Eureka mindset, you can have practices, you can have tools and techniques. So just the big frame, how should we go about thinking? Well, we have this wonderful, generative AI? How do we start by making it helping us to think better?

Gianni: I mean, it's a big question. And it's probably one of the defining questions as we start recording this at the beginning of 24. I'm gonna provide one view, which may be one of the many views but that mine is a little bit in depth to the work that I've done over the years with Thomas Malone at MIT at the Center for Collective Intelligence. And it's the view of augmentation of collective intelligence; augmentation, meaning, not considering humans, just as a crowdsourcing exercise for machines as a set of technologies, but really the design the organizational design of the combination and the synergy between the two. And that sounds obvious to a bunch of people being exposed to many tools in the recent past, etc. But when you start peeling the onion and looking into how you really make it happen, both at an individual level, but even more importantly, at an organizational level, when you do processes that string together, people, that is actually a lot less obvious. And I think the first maybe the first answer to your question is we should actually try to step away and, and try to look at the forest, instead of just looking at the tree. 

And I think we got an obviously 2023. Everybody got engrossed with artificial intelligence, which in itself, the generative AI kind, is an exercise in collective intelligence. I mean, those machines were trained on us, right, we were trained on the things that the humans have been accumulating for many years. But if you look at them in isolation, I think we don't get to where we want to get to. And obviously, a bunch of people talk about artificial general intelligence AGI, I really like to talk about ACI, which is augmented collective intelligence, which is a state in which we will design practices, processes, tools, that enable that synergy between large groups of humans, and large groups of machines. There's a lot of design space there. And I think we're gonna get to a place where we really can amplify our collective cognition, by doing that job right there, doing that job, almost a process and organizational design, using the technologies that we have now. And the practices that, by the way, by MIT colleagues and others in the world, we're not being the ones, you're the only ones. So we have a lot of those, and I think we can bring them to bear.

Ross: Absolutely. In an organization, hopefully, is collective intelligence is a bunch of people, you've got processes and communication to together, hopefully be somewhat collectively intelligent. But now we have these tools, which any of us within your organization can use, we can find ways to scale them and build them in processes. But to get to that collective intelligence, I think, you know, it's very significant starting point is the individual, how can an individual augment themselves in a particular role? So I'd love to come back to two, I suppose, in a way end up how do we create that collective intelligence. But starting with an individual who's working well, maybe entrepreneur and maybe working in an organization, they have, of course, access to generative AI in various guises. So what should they be doing, to start to think better, act better, and make better decisions?

Gianni: I think it's a bunch of practical things. And we can get into the tools and the practices. And you know, we'll get to that in a second. But I think the first thing that needs to be done is, is to change a little bit our frame of reference. I think most of us, especially in the West, but I think even in the East, in the recent times have been almost trained imprinted with this notion of, we need to be smart. Our brain is to be better. And obviously, we learn and we push ourselves and we apply a bunch of techniques and all that kind of stuff. But I think one of the things I have realized over the years and you know, I did my share of you know big jobs of being a Chief Innovation Officer in a large IT services company and such, but the more you think about it, and obviously the work on collective intelligence helped me with this is my brain, our brain is not what we're just having our skull. 

Essentially, what we have in our skull is, is a catalyst is a is a point where a bunch of signals from the outside common mingle. If you think about the brain that way, then you think about the fact that in order to optimize the functioning and intelligence of the brain, you need to use all the ecosystem of things that exist around you. And we live in a time where we have access to information in a way that's unprecedented. I mean, you and I, kind of same age, I guess, you know, imagine the remember when we were kids, right? I mean, we, at best, we will go to the library. And that was the way we had to augment our intelligence, and then we go to school, and then there's teachers and pupils, and maybe we can work with each other. And now we have all this stuff around us. So I think the first point is really to think about your brain is not the neuro, you know, stuff that you have in your skull. 

And so the mind, the extended mind, I mean, it is not a philosophical concept is, you know, maybe we're stepping into the practicality now, you have a bunch of steps to actually make yourself more intelligent, the moment you stop thinking that your brain is yours, right. So, I've done a lot of work over the years on how to do that. But I think there are at least four steps that people should take and may apply at an individual level, but also at an organizational level, but we'll start with the individual. 

And the other thing that I suggest when when we do this is try to go obviously, for diversity. I mean, that could be and I work in academia, law, I mean, academics tend to be people who know a lot about very few things as a job. If you're like most people, you will want to have a diversity of perspectives. I mean, the obvious example is, you know, Steve Jobs, who used to know a bunch of things about computer science, but also marketing and design and a bunch of things that ran through culture. So I mean, there's all kinds of things I got to get. And so if you want to really become smart, try to deliberately find nodes, but also find nodes that push you into different dimensions spaces. Yeah, I found it for myself. You know, I work in software and consulting used to work at BCG at SAP, etc. And then I found at some point that this whole space of design that has its own techniques and methods and people and that really completely opened my mind. So this first thing to do.

Ross: I just wanna sneak in there. This echoes what you're saying, because it very much echoes what’s on Thriving on Overload. This idea of find the right people and sources and information as inputs. This is cognition, cognition, human cognition, or any cognition is around, you got information going in, something happens. And then from that you have some, you hopefully, hopefully, you've useful actions. So can AI help us in what you've just described in identifying the nodes and in being able to ensure that those are sufficiently diverse? So are there specific techniques or approaches to do that? 

Gianni: Yeah. So first of all, the short answer is a resounding yes. And we haven't seen anything yet. Right. So what we can do today, you can ask machines. I mean, a lot of AI has already been embedded in our search mechanisms for some time. I mean, if you search properly, if you know how to search properly, I know that you had Marshall Kirkpatrick for example, on your podcast a while back, you can actually find through simple algorithms, people on Twitter, etc, you can you can really search I mean, these days, you can even go to perplexity.ai or ChatGPT4. You can have a conversation with ChatGPT. Let's take an example, right? Say, “Hey, I'm interested in the development of artificial intelligence. And I want to follow people who have a good and interesting point of view on how artificial intelligence of man's human resources”. Just, you can have a conversation with perplexities of AI, which by now has a copilot function as well, or ChatGPT, and ask, “Who are the people out there”, and it will know some of those people. And then you go into LinkedIn and you start looking at, you know, what they have tagged, etc. 

So it's actually important that the machine helps you first break up the semantic space, break it into its sub components, very often this is one of the techniques in design is so awesome, we think of a problem with our bounded completion. But when you start peeling out the layers of the problem, you actually find that the problem has individual components, right? So for example, where you say AI for human resources, what does that mean? Why it doesn't just mean the thing for payroll, it might be engagement, customer, employee engagement might be skilled taxonomies, it might be collaboration tools, and with the machine, you can actually break up the space. And then in those sub spaces, you go and find the people, that's already the first thing that they should do. 

Ross: I just want to dig a little bit into that. So we've talked about this, mapping semantic spaces, and I think that's that really a fundamental thing in cognition. What are you thinking about? Alright, so let's map that out. Let's find the elements to that. So, as you said, that's, that's relevant in finding people in space is relevant in many other aspects of cognition. So, can we just dig a little bit deeper? And say, what, what? And there's many approaches, but what are one or two approaches, which people can just take to be able to start to map a semantic space?

Gianni: I think this falls under the umbrella of falling in love with the problem before falling in love with the solution, which is a very simple fundamental concept in design. And most of us, don't do it right. We don't do it individually. We don't do it in groups, we like to run to, you know, we've been trained like that, right? I mean, teachers would look at us, like, you know, how long do you take in the final problem? Actually, you need to find the problem, so how do you do that with? I mean, first of all, you can have a conversation with machines. I think we, we kind of forget, because we haven't been trained in an apprentice, the support model. I mean, I was born in Florence, Italy, right? I mean, I can sometimes go back and you actually bump into the places where Leonardo was trained. Right? And the guy went into the shop, and then and then he talked to people and in your head, the you had Leonardo's Master would engage in a conversation with Leonardo and saying, “What is light?” I mean, Leonardo was one of the guys who actually figured out how to depict light in a very different way. And that's the reason why you had the Mona Lisa. I mean, if you look at the Mona Lisa is awesome, because the light is used in different way. But in those periods in that time, it wasn't normal, right? And so what you do, you don't say all art is art? I mean, I have all these things. I mean, you go around in Florence, you can see and you do some more. And you can say, no, no, let's decompose it. What is art? Obviously, the shapes, there's motion that is reflected is the colors in the techniques? And is this the light, right? At some point, that conversation must have happened between Leonardo and his master, or the other vehicles.

And so use these machines as a conversation tool, actually follow? It just be basically ask them “what is in this?” Break it down into its individual sub components, that's the first thing to do. And they do a really good job at it. One of the things about generating AI is that it knows the semantic space, the latent semantic space into whatever you're talking about. And we don't, as humans, we kind of into it, but they do a really good job of doing it. And if you push them and say, Well, this is not good enough, they will do and then you explode. And so I think that's the first thing to do. And they work really well at that thing, if you tried but if it's actually it's actually fabulous. And they do it in like an effortless manner, which is a little disconcerting at times, but then, as a human, you can say well then explode the first point Then the third point and the fifth point that tell me what's in there. So it's like a nested doll. I mean, in the end reality is like, there's all this sub structures that we typically are oblivious to, because there's too much for our mind to take. But when you start the job of identifying nodes, boy, you want to go in and disassemble that, that network structure, right. So I think as the first, that's a very simple thing that most people should be able to do, right?

Ross: Yes, absolutely. So going back, we've got given off going on a couple of tangents, who said that before. I think steps, we started off by looking at the nodes, and not sure diversity was the second one? So where do we continue where I interrupted.

Gianni: With diversity was stealing nodes, most often, you know, hold them to stay with their bounded things. But you know, again, you talk to the machine and say, what are the spaces that are adjacent is once I know the kinds of things is really important. One of the things that they and just to finish on this, and then we'll move to the second one is, a lot of people have been, I mean, we always want to build being on the shoulders of giants, right? We never want to start from I mean, this is one of the ideas collective intelligence, right? And so one of the things that are super important is to go into the node identification also with the lens of using concepts in theories from people. So, for example, managerial theories, I don't know, Christensen's disruption. That is one way to break down the space. So a bunch of this theory is my all theories, all scientific theories, including management theories are one way of looking at the world and this assembling the world. It's like a lens, right? You have you ever tried to some people gave me the other day was really a lot of fun ultraviolet lamp that you can use. When you look at the world through an ultraviolet lamp, you actually see all sorts of different things and managerial theories of like that lamp, right? When you ask that very question, you know, AI HR through the lens of disruption theory, it will give you I mean, you can actually have the conversation with the machine, it will give you a bunch of perspectives that you didn't, you didn't think you had. But I think, that's a really important because especially generative AI, they will do a good job with the semantic space. But these still don't do a fantastic job, this symbolic space, I mean, they have some representation of the world, but not the entire one. But if you start pushing it and say, well look at it through the perspective of I don't know, blue ocean theory, it will actually disassemble the space, and then you can go and find people and stuff. So let's finish that for a second, that creates diversity.

Ross: So potentially, an individual may, depending on their role, may choose a set of different models or frames or lenses, which can be most relevant to their work.

Gianni: A product presented in models that you're familiar with. There's many of them, I mean, you just go in, if you can ask the machine, what at the moment, it's interesting, the machines are optimized to optimize for their computing. And so they will give you what, you know, simple answers. But if you ask him, What are the models that I could apply to this thing, they will tell you right and say, Oh, this 15 things and then you say okay, well start with the first one, tell me how you then see the problems with that. And then you look at the people who are behind those things. So is that such, I think that's a very simple thing to do as fun thing to do in itself is already inside. So the second step is really most people miss this, to have really insightful conversations, you need to do something for them, those nodes.

Obviously, if you use Google search or something, you're literally paying with your advertising dollars that sit in your click right, but for hat for more intentional, intelligent conversations. But look at the relationship that you and I have. I think we've met each other online. You know, you live in Australia, I live in Berlin, Germany, I mean, we never met, we work on a bunch of the same stuff. We kind of follow each other. I mean, for sure. I've been following your work for a long time and appreciate it and, then I give feedback. You get feedback to me, you know, I refer you to people. It's important to always pay forward in these communities. In these networks, if you really want to have conversation with people as opposed to just scraping. Scraping, it's fine, by the way, for most people may be sufficient. But if you want to go deeper, you need to do something for the nodes out there. And what they try to do and what you try to do, and it's a very good example, you publish a lot, right? You know, I mean, it takes you time to do what you do; it takes you time to do what you're doing now; it takes time to put out the insights that you put out on social media. In a way you're paying forward; and people will pay back in form in the form of, “Yeah, I want to talk to you.” “Yeah, I'm going to have a conversation with you.” So the first, the second pillar is really how to set up incentives so that the network fires, the equivalent in our brain is hormones, right? Boy, your brain will be very happy to sleep all day. But it has some powerful things like oxytocin's, and, you know, adrenaline, etc. And boy, it fires when when he sees those things. And so the equivalent in networks is incentives. And they can be norms, they can be culture, they can be a bunch of things, and at an individual level, is do something for the next person, or for a bunch of people. Somebody said, if you cannot code, at least you should be able to write. And I think it's a very good thing to do. And many of us do that. And, you know, obviously, we get something or so that was a second pillar.

Ross: So the one thing, which is fairly obvious, again, as for the alignment of ideas is that, you know, what you're describing, for me as the living networks, again, sort of echoing ideas, it's not as how you bring them to life, while you create value for others, you make connections, out of that good things happen, you participate in this, you know, higher order organism. And so yeah, and part of the that frame now is that it's not just humans in that network, there's also AI and within that context, sees the interstitial piece of AI, but also AI has nodes as well.

Gianni: And so look, I mean, incentives can also be you pay – your 20 bucks a month to open AI, right? I mean, you give them incentives, and they give you something that there's, there's all sorts of lab stripe, you can be for free, and then you get some stuff, you know, sometimes good sometimes, you know, empty calories, and then you go up and you pay for services, and then you go up and you pay for your time that you put into having something really unique or new novel and share it with others and engage with others a session that will give you an increasing amount of interaction with the network, you really get deeper and deeper into new things. And maybe for some people, it's enough to stay a level one and maybe for other people like you and I we need to go all the way to level three, because we try to discover new things, you know, to go where we don't think people have been before. So that was the second. 

The third one is all these networks have an inclination to become a little insular. Once in work, for example, it has been done around which affects super fascinating around a guy called Carl Tristan, free energy principle and the the concept of active inference. We are built to survive minus the first thing that we want to do and to survive, we have this balance between doing and resting. And the more you do, the more you use energy. And the more you rest, the more you don't get energy. So but you use less energy. So what do you do? And so the the feeling that you know, you need to forage yourself, you need to go out and forage yourself. And so that's the basic concept of build information feeders into those networks, because those networks are quite happy to stay in their little bubble and there's less cognitive dissonance and everybody kind of agrees with each other, etc. But then obviously they ossified. But obviously going out and looking for new information is expensive, expensive in terms of cognitive resources, sometimes it in jars you write because you find people you don't necessarily like the opinion of and but you need to build that stuff. Marshall you're having on the pod through X, old time Twitter, you get all sorts of feeds actually stuff that you wouldn't want to see if you go and look for it. You'll find it. And we've been using Feedly for the longest time, we obviously have all our good pods and good newsletters essentially. It's not just enough to find the nodes is also you need to you know, put your their fire hose into your backyard otherwise you don't get anything out of it. 

Increasingly, I think we will get a good help from artificial intelligence. One of the things that I really really longed for, and I think it will happen and one things I think is going to be okay. Give it a good chance in 24, it will happen. Something that summarizes the firehose, you know, we all work in spaces where we have a ton of stuff in you, I'm sure you read like the silliest you, you're subscribing to more newsletters and podcasts, etc, that you can consume. And so the objective there is, can we have machine summarize what comes from those feeds, so that you can consume it better. I don't believe actually, that the machine will do a fantastic job of summarizing the really juicy things, because it tends to be I mean, unless it is trained in a certain way. And by the way, there's work in that direction. But I very often find the AI based summaries a little, you know, bland kind of thing. But when they do that, is to give you almost the equivalent of a table of contents, so that you as a human can decide, oh, you know, the third point and the seventh point are really interesting, I want to go and see. And that makes you a lot more efficient in your consumption of knowledge. That's a huge point. I mean, we, you can find the nodes and you can pay forward, you can do all the stuff. And then you have a firehose, and you can't really do all the matches. Everybody has a job and how much time in so summarization is going to be important, you can already use machines to point you at the right subcomponents of the corpus. And then you need to do the job that humans do, which is you go in and you find the connections at least. So that I think as the third element and you know, retrieval advantage generation will do a good job in the future, I think increasingly does. But But I still feel that for some time, we will need to have humans going in and connecting, not just a semantic dots, but really the symbolic dots really the core connection between analogies that machines will find. So that's the third.

Ross: Absolutely. And so. So this goes to where sometimes get a little frustrated, because there's a lot of saying, Okay, how do we get the best information, but the information has value when we, as you suggest, make it part of our own understanding or knowledge, our own comprehension. And so I frame this as knowledge creation, these are the mental models or the frameworks we have in our mind. And we've got new information, we can integrate it, we can assimilate it, we can improve our mental models as an on the basis of the information we get. So I am fascinated by how it is we can use AI to build better mental models or to, you know, I mean, partly through making our mental models more visible, to allow that new information to essentially mean we understand better, we're able to make better decisions, we have a better understanding, not just presented with better quality information.

Gianni: Let's double click on that, because I think that's a fundamental thing. I mean, it was one of those things that make us smarter or not. Right, so there's a big fork in the road. I think part of the answer there is, we still need to work hard at it. I think the also in the work that I've done, the mighty in other places, you realize pretty quickly how there could be a dependency that humans form around the machines. And we just click and we hope that the thing will just be broken down for you in a perfect several pieces, and you need to do the minimum amount of effort. I don't think that that's, first of all, I don't think that's up right in general, because it will make us complacent and to be lazy. And then. But also it doesn't do the job today, I mean, to your point, let's go back to an earlier point that we made, I can talk to a machine. And if I ask the machine, so up for it. To break down a space, I need to engage with the machine, or the machine to break down the space. So programs and AI and human resources, just break it down. And then tell me who's been writing about this, you know, tell me who I should follow all that kind of things you need to engage with the machine to get the data. Right, in the second part is your decision as a human to decide which lenses you want to apply. Right. So I know you may be familiar with the concept that it's called ‘Ikigai’. It's a Japanese concept about meaning of life. And it's really interesting. From a career guidance standpoint, the machine wasn't going to tell you that the thing is there may be relevant but it's your decision as a human to decide that that thing which is again, you're basically it says you need to find your space where you work and the intersection of your knowledge, your passion and what the world needs because you don't end either you get paid for it or in some way intrinsically or extrinsically. It's a lot of work. And that concept of ‘Ikigai’ the machine knows what it is. But it doesn't take the decision for you. And you should take that decision, you as a human, as a team as an organization, you are in charge of deciding which models which mental models, the machine can actually tell you which mental models exist. But it's your job to decide which ones to use. 

Ross: So fourth point?

Gianno: So four point is, is the collaboration element. So if you think about I mean, this is, but the work that we've done over the years of the materials, this was intended to be a cross disciplinary group between organizational and management science, but also neuroscience and computer science, right. So if you think about all the things we just talked about, it's like, very often missed out on some level of the brain works. 

So the fourth aspect is collaboration. So once you have the nodes, you're given the incentives, you have all these feeders, you need to engage with this thing. And I think the easiest example would sit at the beginning of 2024. Everybody talks about mixtures of experts in AI. So mixtures of experts basically having different agents with different characteristics engage with each other. And those are different models. Instead, if your objective is how do we get to that? AGI at some point, how do we make this machine smarter, one of the things that people are saying now is, especially based on the success of GPT4. GPT4 is in itself is a combination of models is not like a GPT3+++, it's actually a a bunch of comparable, a bunch of those models combined and interacting and collaborating with each other. So if you see how even the artificial changes people are trying to solve, the problem is not just have more data in more parameters is actually well, let's take eight models, and let's orchestrate an architecture of collaboration between them. So you can get them to dialogue, dialectic, etc. So that is a super important concept. And we built a couple of things that I'll tell you about in a second. This foundation…so in you need to have collaboration infrastructures that are built for that. 

So if you're in a company, people often talk about, well, I have teams or I have slack, or I have something like that, well, there's the ROI of that stuff. For a bunch of people is well, what is the ROI of a telephone, right. And I was having conversations the other day with with folks what has been the ROI of cellular phones. And then there's a lot of studies that show that in Africa, you know, without cellular phones, you know, the agriculture will be much worse and because in the farmers and helping connect etc. But it's interesting, the West, we are in developed economies very often take that stuff for granted. And then it was our you know, the CIO will implement some collaboration technology, that isn't the right way to think about it.

So first of all, is the tools, but also the change management related to the tools and getting people to adopt them. Which also means for example, that, you know, if you're an individual person and individual professional in a company, suppose that you lead a team, or you're the leader of an organization, you need to lead by example, in terms of usage of those tools. Don't wait for your teams to use it. Get into the ahead, I've seen it. And I've done it many times in my career, because obviously, that was part of my job as an innovation officer to create systems that then innovate by themselves, even when I'm sleeping, right. And in the way you do it, you see sometimes business leaders, P&L owners, you know, those kinds of people theory and theory, they're not the CEO, they say, well, you know, give me the tools. But instead what they would do, they would get in to those channels and say, don't call me, I'm not going to answer the phone. Come here and write your answer a question here. I'm going to give you an answer here. So one, everybody sees the answer in the question. And two, everybody knows that next time you have that kind of conversation, you don't keep it into a dark hole between you and me. And then once we have the phone, nobody will ever know that we had that conversation. So that element of change management, right on top of choosing the right collaboration tools, but also getting them implemented is vital to the ROI of that thing. And in the end, the organizations that do that well exhibit a higher level of collective intelligence and the others were the silos, these cliques, there's people talking behind the curtains and you don't quite know what's going on. 

So the fourth aspect is really this collaboration element. And again, AI if it works as advertised, I think we'll get a lot of because AI increasingly already, I mean, some of the basic things. It transcribes video calls and it gives you the summaries as of this video call, so at least it tells you what you talked about. And then on that basis, people haven't been your calls, can decide, oh, yeah, this is interesting to minister caught up or No, no, I want to go in and listen to that thing. This is huge. I mean, we don't realize how this is a, this blurring boundary between the synchronous and the asynchronous collaboration is one of the defining elements of our evolutionary in our collective intelligence in 2024. People never fully realized that we had the time in which synchronous collaboration was pretty much invisible to pretty much everybody wasn't in that room. And now, that's not the case anymore. But also asynchronous collaboration. I mean, I don't know, if you've tried chatGPT with the voice interface. I mean, increasingly, you're gonna be able to build agents that you can query in real time during a meeting and say, Tell me what you know about what that team has done? What, you know, I mean, marketing, what did the finance team say last time we did this kind of promotion. And the machine will actually tell you in real time, well, this is the oldest stuff that we've seen in the last two years. And this is the cases where they gave us funding, or they didn't give us funding and the parameters. And that's all asynchronous knowledge that can be made synchronous, when humans collaborate in synchronous in real time. To me, that is one of the things that I'm most engrossed about, because I think it will fundamentally change our collective brain works. I mean, really, literally, spacetime, compression, right? Well, you know, the knowledge searchable, wearable, and you can collaborate with a corpus. I don't know if you ever read Iain Banks, right? I mean, the work of Iain Banks, you have all these agents to basically learn and then you interact with them.

Ross: I love that, that, as you point out, sort of the merging of the synchronous, and asynchronous is changing now. Yeah, absolutely. Transforming collective intelligence. So the potential most organizations do, and I think that's, if you're a leader, being able to design so I was early COVID, I was reading quite a few workshops with leaders of large organizations and bringing to their attention distinction between synchronous and asynchronous, which is not always evident to them. But as you say, now the start to merge that changes the nature of what an organization can be. So I think we'll probably, in due course, have to do a follow up episode, since we're only just beginning really to dig into this stuff, but just to come back to the beginning. Collective intelligence, and so you can think of multi-agent as in this idea of human agents and AI agents, and how those can come together, but also this nature of how AI can facilitate collective intelligence, be that in an organization or potentially be beyond organizational boundaries. So I suppose just there just to wrap this up, I suppose what are what are some of the most teasing tantalizing aspects today of where we can go with us?

Gianni: It's a really good question, because there's a lot that will happen that we don't know of already. So one of the things I would say just stay tuned to what's happening, because you and I don't, you know, these wildcards, right? I mean, Apple is coming up with their, you know, God will type stuff, what is that going to do? Do you know, that more that's multimodal, that brings, that also is a, you know, collapsing of space, for example, right, you have all these layers, and now you can at least two things I would say make me feel like we are on the verge of a real inflection. One is what you said before, the work that you do with organizations, I think, is hugely important. Most of us, you know, think of this and obviously, this is a podcast, so it doesn't represent well. But you know, think of this as a two dimensional space. On one axis, you have a number of people, you know, zero people, one person, many, many people, the other axis you have a number of machines, zero machine, one machine, multiple machines, we've been mostly designing in the space of one person, one machine, you know, GPT3 with one person. There's a ton of design space in the many people, many machines cannot space. What the what we did with the MIT Ideator, for example, which was a tool to help people think through almost like a design thinking facilitator in a box, if you will. It was one person, one machine.

But increasingly, for example, you could say you could build things like that where you don't have only one machine, you have almost a analogous of multiple personas, can you say to the personas, you know, I want to do something to HR through artificial intelligence. And then you take multiple personas, you take the personalities CHRO, you take the persona of the head of hiring, you take the persona of the business partner who the interface with the business, the persona of the entry level employee, or the persona of the senior employee. And you make all those personas participants in a virtual workshop. So that's multiple machines, maybe one human, and then you do a design workshop like that. But then you take a bunch of humans, and then you do a design workshop like that with multiple personas. And then multiple people, I think there's a horn of design space there. 

And the interesting thing is, is design space is not technology or in the space anymore, because the tools that we have, maybe I don't know if we have show notes here, but we may actually add a couple of links. Especially with the GPTs, right, you know, the release of GPTs that was done. And I think the the marketplace for GPTs is coming this week, for an excellent. Remember, a lot of people will be able to do a lot of the things that we just talked about, right. So that's the I think that's the lower hanging fruit, I think we'll see that playing out in the next two, three months. And I think there's even a bigger picture that we can talk about, but maybe we can talk about it separate time, I still think that there's some, something really fundamental happening here, in which if you build an ecosystem of the things that we talked about, for individual augmentation, suppose you have somebody that I don't know, like Gates Foundation, I'm just making an example.

Building what is called a super mind, all of the things that we just talked about at MIT called Super Minds, building a Super Mind that basically does the four things that we talked about, right, identify the nodes, give incentives to note feeding the ecosystem and a creative collaboration environment, and do it at a, at a level that is much bigger than the individual person, but you know, putting enough resources there and computational resources to the sort of the machines autonomously look for nodes autonomously look for, you know, feeding the right people in the networks, for finding and feeding information in a way that is not just based on advertising, empty calories, right? And then enabling, you know, say, a machine knocking on Ross's door and say, “Ross, you should talk to Jeremy because he's been writing some stuff that is related to the stuff that you've been writing.” Can you imagine if we did that, again, I was talking about the Gates Foundation, because they're trying to solve very complicated proper complex problems. Can we build machines and architectures that do that autonomously for each of the spaces that we really need to have important solutions? And obviously, financial sustainability environments, etc. I think that, that Ross doesn't feel like is five years ahead anymore. Right? When I started working in this space, it felt like, we needed to have a very different technology paradigm. I don't think that's the case anymore. I think it's more of an organizational design. Putting the processes in the right place, I'm going to change. 

Ross: Absolutely. If you, as you say, everything you've described, I mean, there's some there's some pretty wild and wacky stuff that we've talked about. Other stuff, if you really think about it. But as you say, this is all possible. I mean, there's a lot of it's the underlying tools of the genre, and part of it is now just sort of a processor interface or organizational design. And that is all there for the taking, as it were so yes, as you suggest 2024 and beyond is going to be pretty interesting. So Gianni, where can people go to find out more about your work? 

Gianni: Super easy, Supermind.Design is where I tried, I tried to work out in the open. And obviously on LinkedIn, this is a bunch of stuff I tried to put out there. I used to do a lot more of Twitter. I'm not less keen on that these days, maybe just me being a little despondent. But again, LinkedIn and Supermind.Design and I really encourage people I mean, obviously the work that we do is important that people show up and say I didn't like what you said, or I think you missed a piece etc. So I really encourage people to reach out. Fantastic. Thank you so much for your time in your insights, Gianni.

Gianni: It was awesome. Thank you for the work you do, Ross

The post Gianni Giacomelli on augmented collective intelligence, semantic spaces, network incentives, and designing superminds (AC Ep27) appeared first on Humans + AI.

For something a bit different, and very suitably at the beginning of the year, this episode is a compilation of recent guests on the Amplifying Cognition podcast talking about expanding possibilities and opportunities. You will hear powerful excerpts from Nora Bateson - Episode 20, Dave Gray - Episode 17, John Hagel - Episode 13, and April Rinne - Episode 8. A massive part of amplifying ourselves is in seeing opportunity and actively creating and seizing possibilities. Soak in the insights from these fabulous thinkers, they are all both inspiring and practical.

Nora Bateson on ecology, increasing possibility, warm data, and intergenerational learning (AC Ep20)

Dave Gray on visual thinking, gamestorming, the art of the possible, and going towards the fear (AC Ep17)

John Hagel on moving from threat to opportunity, the passion of the explorer, learning platforms, and scalable learning in practice (AC Ep13)

April Rinne on superpowers for thriving, seeing opportunities, prioritizing humanity, and calendar brain (AC Ep8)

Ross: One of the beautiful phrases in your book is “I shall always act to increase possibility.” You’re describing some of the ways in which we are constrained in who we are and who we could be in our relationships. A very pointed question is, what are the things that we can do to increase possibilities?

Nora: I’m so glad you asked. When you’re trying to approach these processes that are taking place, not necessarily at the first order, at the first level, where you might point to a symptom and say, “Okay, there’s the issue, we have to solve that issue” but in the way we’re looking at nth order, so the relationships that make relationships that make relationships, the communication that makes communication that makes communication. As I was just saying, a lot of this stuff is tacit, it’s implied, it’s meta-communication, it’s living in a realm that’s very real, but very slippery, it’s gaseous, it’s hard to grab hold of it, and it’s not like changing the distributor cap in your pickup truck. Changing the possibility of communication means another thing. This, for me, is where warm data has been really exciting because after many years of working with various sorts of systems change, various kinds of modeling, and this-es and thats, and also coming from my history, which I guess we’ll get to in a minute.

Ross: Could you explain warm data as a concept?

Nora: Yes, Warm data as a concept, there are two ways of looking at it. Warm data as a thing is information. But it’s a way of recognizing information that’s taking place between multiple contexts, so it’s trans-contextual information. In that example of who is Ross, who is Ross in relationship to your microbiome, in relationship to the tax agency, in relationship to your lover, in relationship to children if you have any, or your dogs, or your childhood friends, or your professional relationships, or your parents, your ancestors, the grandchildren that are not here yet, the great-great-grandchildren to be—who are you? And in each one of those contexts, you are not the same, so who are you? There’s this way of recognizing that information moves in different contexts, and this is a necessary practice for perceiving complex systems.

Another way to describe warm data is that it’s information that’s alive. I could put you in a box and I could say, “Oh, Ross, he’s got a podcast.” But that would be a huge reductionism of who you are. It’s not that it’s untrue that you have a podcast, and I could study all your podcasts, but I would still know very little about you. I could deduct and I could make correlations, and I could do this and that, but I will not have a sense of your vitality from that. My suspicion is that because there’s basically so much information missing, that many of the responses that are attempted are responses to reductionist information, information that’s been decontextualized from its living processes and re-contextualized into a mechanistic, more industrialized set of understandings.

How do we respond to a living world if our information is not itself alive? And that’s at the core of what warm data as an idea is about. The Warm Data Lab is a process that I work with groups of people in practicing this perception. It’s a practice and a practice in which the trans-contextual perception, and cognition, you’re interested in cognition, is able to shift in ways that are not necessarily explicit. It’s recognizing that many of the things that are blocking us epistemologically are things that are habits that we don’t even know we’re doing, ghosts of industrial assumptions, that are so deeply lodged in our language, in the way we went to school, in our understanding of how you define something or strategize something or solve something or even identify a problem, that these capacities are infected with ghosts of industrial, eugenics, control, mechanistic ideas, colonial, notions, that these notions will justify exploitation, decontextualization, devitalization and take out the possibility. Okay, so where I’m saying “I want to always act to increase possibility” what I’m really saying is I want to be able to perceive those possibilities that the complexity in the process brings that may not be the ones I think I’m looking for. That’s the catch.

To round out, Dave, I’d like you to offer some distilled wisdom, some advice, some suggestions to people on how it is they can, I would say, think better, but whatever positive direction you can do. How do we think of the possible? How do we think better? How do we live better lives?

Dave: A couple of thoughts I think might be helpful. One is the idea of not necessarily limiting your thinking to what can be put into words, typed, or written on a page, and to explore the idea of visual thinking. I do have a free online class that I could share a link to you where people can go and watch, a few 5, 10 to 20-minute videos to explore that territory. That would be a good jumping-off point. Drawing is thinking just like writing is thinking. If it can’t be drawn, it can’t be done. It’s also a great way to explore and clarify possibilities when you’re still thinking about them. Just like Leonardo da Vinci was able to sketch a lot of ideas that weren’t able to be realized, even with the technology of his time, sketching is a way to start thinking about those things. Even if you’re not going to be designing a helicopter or an airplane 500 years before, it’s a fact, that you might still find that by sketching and scribbling. You come up with ideas and concepts that you wouldn’t ever come up with any other way. That’s one. Start scribbling.

Another one is the idea of fear. We’re wired to seek reward and avoid threat. Every organism in the universe is wired to seek reward and avoid threat. For good reasons, our wiring is biased a little bit towards the avoiding threat part. You’re not going to… A Possibilitarian that gets eaten by a dinosaur is not going to pass on their genes to the next generation. I think that’s why we’re emotionally and hormonally wired to be afraid, to cling to the status quo, the safe zone, and not to step into those more adventurous, or dangerous territories. But the one rule that has served me well over the course of my life is that, when I’m facing a dilemma, or decision, or choice about where to go in the future, and they seem roughly equal, but one feels safer, and the other feels scary, always go toward the fear. Because that’s where growth is, that’s where possibilities are, that’s where the opportunities are.

The fact that you’re even weighing it as a possibility means that it’s a realistic and possible scenario. The fact that you’re feeling fear is probably relative to your wiring, and your tendency to want to seek safety and avoid threat. The world can be a dangerous and scary place. But for the most part, you’re not taking your life into your hands when you take a chance in the business or the creative world. I encourage people to step into, lean into that fear, and take a few steps. You live near the ocean, and you go swimming every day, and you know there are sharks in the ocean. There’s always some danger, but you don’t have to dive into the deepest part of the ocean, you can step in, you can wade in, you can go partway, and you can go halfway. There are a lot of ways to trick yourself into stepping into uncomfortable situations that can be rewarding in terms of personal growth. I encourage people to, when in doubt, go towards the fear.

Ross: I think that following that advice will get people a very, very, very long way.

Dave: Yes, I agree.

Ross: Let’s come back to the fear and shifting of the passion of the Explorer. People would really have to read your book “The Journey Beyond Fear” to get the full story. But in a compact version, where people are in a place of fear, there’s the potential that limits their thinking, their ability to think better and act better. The potential is to get to the passion of the Explorer where they soak in anything which is useful to them to be able to shape their path. What’s the journey? How does one move from a place of fear to the passion of the Explorer, in three words or less?

John: Yes. I like to say if I could summarize my book, I wouldn’t have to write the book, it would just be a nice summary. I think it’s complicated. There are many different paths for the journey. It’s all based on where you come from as an individual. But a key element, again, this is based on research that I’ve been doing, is focusing initially on what I call your narrative. Again, it’s complicated because when I talk about narrative, most people think I’m talking about stories, and that stories and narrative are the same thing. No, I make the distinction that stories are self-contained, they have a beginning, a middle, and an end to them, the end, it’s over. The story is about me the storyteller, or it’s about some other people real or imagined. It’s not about you. In contrast, a narrative the way I define it, is open-ended. It’s about the future. The issue is there’s either a threat, a big threat, or a big opportunity in the future, not clear whether it’s going to be achieved or not. The resolution of the narrative hinges on you. It’s a call to action to say your choices, your actions are going to help determine how this narrative plays out.

Again, it’s complicated. You have to read the book, but I talk about narratives at many different levels. I start with the individual, personal narrative, and urging people to reflect what’s their view of the future. Is it primarily a threat or an opportunity? Do you have a call to action to others? Or is it all just on your shoulders and you have to figure it out? You’ll figure it out. In my experience more and more people when I do that, very few of them even articulate their narrative much less reflect on it, but most people when they start to think about it say, Oh my God, the future for me is pretty threatening. I’m not asking for a lot of help, because I can’t rely on other people. It starts with this notion of individual narratives. But then you can talk about corporate or organizational narratives. You can talk about regional or geographic narratives or movement narratives.

I’ll just give one quick example back to this notion of passion. I’ve been in Silicon Valley now for over 40 years. I always get the question, how do you explain the continued success of Silicon Valley over so many decades? Most people would talk about the universities, talk about venture capital firms, and the infrastructure, those are certainly not to be dismissed but to me, the real success of Silicon Valley has to do with a very inspiring geographic narrative, which is, we have digital technology that is exponentially improving, and can fundamentally change the world for the better but it’s not going to happen automatically. You need to come to Silicon Valley and help change the world. It’s an inspiring, exciting opportunity that has drawn people from all over the world. Most people don’t know that the majority of successful entrepreneurs in Silicon Valley were not born in the United States, much less than in Silicon Valley, they were drawn here from all over the world because of the excitement of an opportunity. It drew out this passion of the Explorer, they wanted to find ways to harness this exponential technology and really change the world.

Ross: If you hang out from San Francisco, you get the feeling there are lots of people who have drunk the Kool-Aid, which came from the Bay Area, the concept is what gave the Bay Area itself. You start to start to get inspired as well by, as you say, that narrative and that belief of the unlimited possibilities.

John: Yes. Again, I think one of the challenges we have in the world today is more and more, we’re being dominated by threat-based narratives. The movements that we talk about, climate change, the world’s coming to an end, we’re all going to die. It’s all about the threat in the future. I don’t want to dismiss that, again, there are threats. But on the other side, until and unless we can frame an exciting and inspiring opportunity, what would the world look like if we really address climate change? What kind of flourishing world could we create where we would all thrive? Not just humans, but plants, animals, everything would thrive? What would that look like? That would excite and inspire people versus Oh, I’m gonna die. It’s too overwhelming. I give up. Anyway, I think that we need to be very thoughtful about what narratives are driving our actions today and whether are they focused on threats in the future or opportunities in the future.

Ross: Just as you were talking, I was thinking there’s also a distinction between limited opportunity and unlimited opportunity. Seeing an opportunity as one part, you can say, Oh, I can see an opportunity to do this. But that’s still tangible as opposed to the unlimited opportunity which is when and where could we go beyond that?

John: It’s both the unlimited opportunity and the sense of continued expansion of opportunity. But it’s also this notion of win-win opportunities.  If it’s just an opportunity for me, or my small group, it’s going to put me in competition with others so that we can capture that opportunity for ourselves versus this is an opportunity where the more people who join in, the bigger the opportunity is going to become. This is exciting. Let’s all come together.

Ross: Should we all be getting excited about change, is that where we want to get to?

April: No, and I love that you walked into where I wanted to take the conversation. Thank you for that, which is this word “change.” You’re spot on because I hear from people pretty much every day, someone will say, “Yeah, I’m struggling with changing X, Y, or Z.” I will also hear from people saying, “I love change, I thrive on change. I’m a change junkie, like, Bring it on.” In those cases, I always go, “Hold on a minute.” Because what we’re getting at is our knowledge of the word “change” and how we think about change. It’s one word. We often think about it like it’s one thing, it’s all the same. But the reality is that change is really messy, complicated, confusing, rich, and deep. The easiest way that I can summarize is that on the whole, and again, not to speak for others, but overall, humans love the change that we opt into, a change we have agency or control over, like a new job, a new relationship, a new trip, a new car, a new haircut. Those are all changes, right? We love those because we picked them.

The change what I’m talking about and really at the essence, the heart of Flux, those changes we don’t control. The changes that blindside you, that whipsaw you, that flip your expectations and plans upside down. The ones that change that you’re like, “I just wish that would go away, I wish it had never happened.” That’s the kind of change that frankly, I’m still looking for the human that’s like, “Bring it on, I want more of that.” You do find some people who are much better. They have the mindset that’s much more grooved to even a change that they didn’t want to happen. It happens, and I can make my way through it, I can see the upside, I can see the hidden opportunity, possibility, whatever.

Some people are further along on that spectrum. But on the whole, humans have a really hard time; we resist that kind of change. We wish it hadn’t happened. It creates fear, anxiety, and so forth. Just that simple point of change is much more than one thing. I’m not worried about the changes we pick; those are all upsides. It’s the changes and the uncertainty, and all of the changes that we don’t control have an element of uncertainty. For a lot of people, there’s this not-so-fun spiral downward that we can take ourselves on because we start to catastrophize, we start to second-guess, we start to worry, and so forth.

Ross: I think it’s pretty safe to say that in the 2020s, we have a pretty decent pace of change. It doesn’t seem to be reducing. Some people have been readier for this in terms of their mindset or way of framing things. Where are we today? 2023, we’re a third of the way through the decade, things don’t look like they’re slowing down. As leaders, what are those scripts or mental models or frames? How is that we need to be readying for? I think it’s going to get pretty wild from here.

April: Yes, let me zoom out real quick before we zoom in specifically to the pace of change. But the way I like to phrase this, I’ve been doing this work for nearly 25 years in a bunch of different ways. It’s not like I knew 25 years ago that I would write a book called Flux, that wasn’t it. But if I look back, and say, when did I begin pulling on these strings? What do we do when we don’t know what to do? Why do we behave as we do around uncertainty? Why is it so hard? It goes back quite a long way. I’ve been concerned, you could say, about this increasing pace of change, and how fast everyone felt like they needed to go for quite a long time. Then 2020 arrived, that notion of Flux, it was like, “Oh, right, yes, we could use some help with that.” I do feel like a lot of people have had a bit of a wake-up in the last three years of just how little we control and how much change is underway.

The framing I like to put on it, we’ll keep talking, you know that I’m fundamentally an optimist, not a naïve optimist, but I see a huge opportunity ahead. That said, I do have to frame it as the future looks more like the last three years than what came before it. I don’t mean a pandemic. I don’t mean war. I don’t mean inflation. I don’t mean any particular kind of change. But this sense of constant, relentless, by the time I’ve reacted to one thing, 10 other things have happened. There’s more of that ahead, not less. We’re not that prepared for it individually or collectively. Again, you can look at that as “Oh, no, now what?” or you can say, “Hmm, big opportunity for the people who can wrap their minds, their mindset, their business models, etc., around that new way of being, working, living, and showing up.”

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