ClickAI Radio

In this episode, we take a look at some simple steps to protect the privacy of the data for your AI.

Welcome everybody to another episode of click AI radio. Well, certainly data privacy has been on the minds of a lot of people and organizations and governments and governments and institutions, and so forth. No surprise there. One of the things though about AI is that, in general, it's not spent as much time if you will, putting a focus on that area. And it's been a bit of a problem and will become more of a problem if we don't do something about it. As we work with our application and use of AI itself. Now I was looking at several different groups and what they talked about and what they felt about it. At the end of this episode, I'm going to throw out two steps that I have seen that help us mitigate the challenges around this right to help to prevent some of the challenges of slippage, if you will, of getting the privacy of people's information out there that that shouldn't be now to frame that up. I want to introduce a framework for it. One of the one of the blogs, I looked at those, it was called beware the privacy violations and artificial intelligence applications it came from comes from is a see if I can say that is aca.org. There you go. A blog from there. Anyway, here, I'm gonna read an interesting quote here, right said, Look, artificial intelligence has been no different when seen through a privacy by lens design lens, as privacy has not been top of mind in the development of AI technologies. Yeah. All right. So end quote there.

I agree with that that has been true. In fact, a lot of our efforts have been, can we just prove the viability of this technology in terms of helping people, individuals, businesses, certainly, there's been success with AI. And there's been some challenges. Now, they what what's introduced here is three interesting pieces to consider. When we're looking at privacy, one of those has to do with what's called data persistence. All right, so put your nerd hat on gonna be nerdy here for a moment, data persistence, that means the data existing today will last longer than the human subjects that created it. All right. And that's of course, driven by things like low data storage costs, and all the technologies that are available to allow our data to live a lot longer than us as people. So that creates a potential privacy challenge. There's another data privacy challenge. It's called Data repurposing, and that is, data that was originally created then gets used in ways that is beyond what it was originally intended. And AI is data hungry, we'll use that up, suck that up. Alright. And the third sort of area around privacy includes what's called a data spillover. And here's where, and this happens a bit in it actually drove a lot of the GDPR stuff, right, which is data collected on people who were not the target of data collection. And so of course, driving out of it was things like, you know, GDPR, out of Europe, certainly CCPA out of California, all of those things drive to or point to the need for having some regulation around it.

Now, it's one thing to have regulation that's entirely different, to enforce it. And some of that comes upon us as business owners, it means that there are few things that we can and must do in order to protect the privacy of people's data and their information while still delivering value from AI. And that's that's certainly the balance that we're going for. One of the primary concerns, of course, with AI is its ability to replicate or reinforce or even amplify harmful biases, right? And this is a challenge because those biases can proliferate and then end up driving insights and and recommendations and predictions that of course, take you know, are wrong, right have have this human bias in it, there's there's another challenge to that we have with AI, let's say that we're going to try to fix that or solve for that. One of the problems, though, is that a lot of our auditing methods used today are based on the fact that something has already occurred, meaning in this case, with AI, that makes it even more difficult, right? Because it means that I've created an AI model, and I'm starting to then employ recommendations, insights and decisions, ways in which I work with people or deliver solutions, all with the incorporated bad behavior already. So it's kind of late, that doesn't mean that we shouldn't do the audits on the data. But they're post deployment by nature, meaning I've, I've already deployed it. So what we have to figure out is to find a balance, right, with privacy, as well as AI progress, it's finding the ability to say I'm gonna, I'm going to grow my my data usage in a way that that protects the privacy of the individuals involved. But I also need to allow AI to move forward and that right, there is, of course, a challenge that we're looking to pursue it some groups use consent today, right? It's a get someone to consent that they you know, you can be happy to share your information with them.

That has some challenges with that those consents, not always as powerful as a tool as we might believe. And there's been examples where consent has been still misappropriated, right, what we thought originally what people thought originally was consent for certain use, then there was spill over into other areas, meaning people, you know, people didn't know their data was being used by AI for other purposes. So again, even though consent might be there, and organizations or people are well, intending, controlling, controlling the the, you know, the the boundaries of the consent, and enforcing that still is a real challenge, and relies on a lot of people to manually handle that, which means that there's more opportunities for us to mess up. I was looking at a report from the Brookings Institute, they were talking about AI Governance Initiative. And most interesting, there's this is some legislation that was pursuing this balance of how to pass privacy legislation, while while still allowing for AI to do the kind of work that it needs to bring about some of the benefits to humanity that we feel that we can do with this with this awesome technology. One of the techniques that that is mentioned here in this Brookings Institute report was, and you've heard it before, is all around what's called algorithm clarity, right? It's having clarity on how the algorithms using your information, right, that seems to be a useful piece to help dealing with it. But one of the problems with that is, you know, as an SMB owner, is that it ends up giving though, the burden typically on the backs of the of the SMB owner to say two things, one, I have to I have to make things transparent, so that my customers are aware that some aspects of their business information is leveraged by AI, right. And so that's, that's sort of the first incumbency is to say, I'm going to tell you, here's, here's what, here's what we're going to do with your data, it's going to be, you know, used in AI, and to draw the line on what information is not used. Okay? So that's what kind of comes out of this, right? It's, it's a, it's an activity or, you know, being forthright with, with our customers, on on what the intended use of the information that will or will not be used right in to draw that line. That's the first sort of consideration in algorithmic clarity. The second consideration, though, is explainability. Right. So that's, again, where you let your customers know what kind of algorithms are being used.

Right. And this may include, you know, access to a human to provide that clarity, you know, okay. The thing I struggle with this, and of course, I that's all well and good. The thing I sometimes get challenged with here is what the heck does it mean to have one of the people in your team explain? Oh, yeah, you know, we use linear regression, or we use a particular classification model or, you know, a Bayes model, right, etc. We used all these different machine learning models and algorithms to that's what good is that right? 99% of people are gonna be like, What are you talking about? How did that help me understand any better? So this area of explainability, right, so the transparency Hey, we're going to use this kind of information or Not this kind of information in terms of the AI. So being clear with your customers on that. And then number two, coming up with a well Set Description for the kinds of algorithms being used, the, here's us maybe a simple way to think about it, you can break it into two buckets, right? When you're trying to explain AI to your customer say, we're going to be using this set of data for our AI algorithms. But we want to let you know that there's sort of two major areas, right. And what I'm gonna say here doesn't apply to all of AI, but for sort of vague, you know, AI for analytics, then then this applies. And it can be simply this. For those kinds of AI problems, where we're trying to determine yes or no answers, then we use what's called classification models, right? So it will be good for us to sell you this product or that product, yes or no? All right, classification kinds of problems. Then there's the other kind of problem, which is we'll use some AI algorithms to help us know, what might be the right price range, right? Now, of course, those are called more regressions, style algorithms. But you don't need to say that. It's simply we're going to use AI to help us understand yes, or no kinds of answers or questions to, you know, answer the questions. And the others, we're going to use it to understand, you know, proper pricing, perhaps right? Things that are not necessarily yes or no, but but degrees of difference, right? Those are two major buckets. And we can work on developing pretty simple language to explain this stuff. Otherwise, what good is it right if people can't, can't get it? Alright, I want to just point out one other thing here. So let me just summarize. So there's two things I think that an SMB can do to apply AI.

Alright, so and to help solve for this problem of data privacy, after looking at and applying AI in multiple situations, or excuse me, over many years, come down to these two, two steps. They're a bit overly simplified, but I still think that if you print these out, put it on your wall, it could save you some real pain. Alright, so here's the first one. The first one is where I've seen a fair amount of pain. And this will sound really overly simple. First one is this. Start your AI journey with vetted questions and data oversight. Like what? Alright, I'll I'll come back to that. Number two, apply AI using smart steps. What? Alright, I'll come back to that. Awesome. Alright, so let me talk about number one here for a moment. So uh, number one here starting your AI journey with vetted questions and data oversight. It seems like an obvious first step, but when you examine the case studies of AI failures empirically, it looks like you know, this step has either been skipped or was not given the proper waiting look, a key technique here is to first vet what while you're doing this is to first bet by leveraging an independent party or going under NDA with someone, but what you want to challenge is the intended question you're trying to address with AI? Right, some of the biggest missteps with AI has been? No, that was the wrong question to be asking, this is the wrong use case to be pursuing right? There was inherently something that was either, you know, prone to lots of bias or was an unethical use of AI. Right. So in this step, the anticipated questions for AI. I know that sounds so simple, but it should be written down and evaluated in the context of the impact to your customers, and to other interested parties and to humanity for crying out loud, right? Just stop and do that simple stuff. I know, it sounds so doggone obvious. But alright, now, what does it mean to vet your AI questions? Well evaluate the AI implications to your customers, as well as look at some of those three elements that I introduced earlier. In other words, hey, will there be data persistence? What does it mean for the data that I'll be collecting to be in existence longer than the humans that created it? Right? What does that do in terms of in terms of privacy impacts or data repurposing? Wait a minute, are we going to be using the data beyond its originally you know, imagined purpose? And if so, you know, what obligation do we have to the people of all and number three data spillovers, right? Ask the question. Wait, are we collecting data on people that were not targeted those with whom we you know, initially intended? So stop and ask do I have the right question and then And then also, what's the impact of these two these three AI privacy areas of data persistence, repurposing, and data spillovers? All right, that's step one, stop and do a little vetted questioning, right and data oversight before you get too far. All right, number two is this. This in this one I call applying AI using smart steps. What this means is to iterate on the AI model and continue to refine and refactor and rebuild it as more as learned. What that does is it allows us to even adjust our AI models that might have some bias that we discover, right? So what it means is to build your vetted model, learn from experience, and then evaluate the impact to your business, your customers and then iterate.

Right. So there's a book that came out not too long ago, it's by Bernard Marr. It's called "Artificial Intelligence In Practice". And what he's got in there is he's got 50 company use cases where AI was applied. Now I just want to pull something from that book, that's interesting. He goes, in that book, there's this one use case about Alibaba in China, right, who ultimately built a virtual platform that mimicked customer behaviors. And one of the reasons they did it was because it would take too long and too much effort to continually refactor their system. And so this virtual platform is used to allow the AI to continue to be refined and rebuilt and refactor. Now, you know, to do you know, to do lots of model rebuilding, in some situations, right, that's really heavy effort to do. So you either gonna have to put in the extra effort upfront to really ensure that you got the data privacy problem solved for or it well do that. And if you're able to then do the smart steps, which I found really helpful, which is, take the model Build it, try to apply it look for where a bias might be exposed, or privacy might be exposed when you didn't expect it. The lesson is this. Adjust your mindset as a business owner to refine your AI model over time, right take into consideration the changes in context, the changes in the economy, as well as lessons learned. And put in your mind that part of doing AI means that that will you know, continue to refine and improve and rebuild this AI model. Now when you combine these two steps, right, this first step is the vetted privacy aware questioning, right and looking at your data, as well as the mindset of smart steps where you simply refactor and approve you and model over time. What I found is that if you do those two things, you're in a much better position. For better privacy AI data privacy considerations. It puts you on a great path for both near term as well as long term viable business impact, you know, to your organization. Alright, everybody, thanks for joining and until next step. Until next time, use the two steps to ensure privacy for your AI that brings incremental business growth.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com now.

In this episode, we take a look at how AI turns your sharing into business growth.

Grant
Okay, welcome, everybody to another episode of ClickAI radio. So I'm very excited to have here with me today ShareThis business development leader. I think it got that right. Michael Gorman, business development leader. But before I go any further, Michael, would you introduce yourself?

Michael
You bet. Grant. Yeah, it's great to be here. Like you said, I oversee business development, but also product and marketing at ShareThis. I've been I've been with ShareThis for a couple of years. In that role. I have a background in data, really, data and analytics has been my passion. Also media and marketing sort of themes. I've worked for big data companies like Axiom, I've worked for an email marketing leader, digital impact, they got bought by Axiom. That's how I got there. And I've also worked for big consulting firms. And for ESPN back in the earlier days of my career.

Grant
Oh, wow. Could you maybe give us a play by play? I bet you could write ESPN. Interesting. Wow.

Michaels
It was a fun period. I was like years eight through 11 of the history of of ESPN, which, so is a fun time to be there.

Grant
How fun. All right. And he did some some consulting roles as well. So data and analytics, huh? Yeah. Right. All throughout all throughout the career. So what led you into this work was ShareThis what was it was the journey there?

Michael
Well, one thing is that, that I've worked with our CO CEO on the past, at axiom, so we knew each other, but ShareThis is a really, really special data asset. In a lot of ways, and within the world of the of the advertising that I've worked in for quite a few years. It's it was well known. So when I had an opportunity to do a little consulting for them, I jumped into it. And that led to the to the role. It's a Yeah, sure this is, you know, it's Well, shall I tell you a bit about the company? Or is that?

Grant
Yeah, yeah. I mean, yeah, tell me a little bit about how it got started. And its purpose and sort of the vision of it.

Michael
You know, well, like a lot of companies, it started with one purpose and, and things evolved a little bit over time, it, it started off in the early days of social networks, when Facebook was still a new idea and mind MySpace was, was beginning to slow down, it was with the idea of making it easy for any website to make to make it easy for their users to share content to all the social networks that they might have an interest in. And so a developer with a simple, taking, you know, taking a piece of code and pasting it on their website that they could then have sharing. We and so it was one of two or three tools that really started in those early days and became a leader in the space. We actually have a how to still maintain a trademark on that little little V on the site there. Yeah, I mean, that's what you're known for. Yeah. So it's a sign if that's there, it's a sign that sharing is you know, sharing tools are present. It's essentially the balance value for the for the publisher for the owner of the site who doesn't have to does no work to have sharing available will get some analytics as a result, sharing is valuable because it makes it attracts more people to the site new users more more content. And, and so it's it's grown up naturally. And we're, you know, so really well established. But a number of business models were tried over the years, but but about five years ago, we started focusing, moving towards being 100% about our data, is that really as a special asset, we have around 3 million publishers using us sort of our live arm 3 million now, that's been pretty stable, you know, half to three quarters and in the rest of the world, a quarter in the United States, a little biased towards English language, but we have every language in the world represented among the users on the sites. And, and so that data and and we'll talk more about this when we get into things like, you know, the the technology in the AI. Yeah, but we're really just, you know, it's like a window into what, what people are what's on people's minds? What are they looking for? What are they searching about online, and we can, you know, discern trends and also, you know, make sure that advertising is more relevant for for users.

Grant
So I have a question for you on that. So you've, of course, are familiar with the terminology of neuromarketing, right. And, you know, as a way of sort of tracking, how are people interacting with a site, right, and where do they go? And where do they point and click and, you know, there's organizations that look at, you know, extracting what the user is doing on the site, this feels like this starts to come into that world right that day. I mean, I don't know that it's tracking every single movement, but it's tracking, obviously, the event of I want to share something. Any thoughts on that?

Michael
Yeah, that's really interesting. I mean, there's a lot of different ways to make inferences about about people, we tend to focus a bit more on the on the broad, the broader picture, that the thing that's that, I mean, there's, like you say, so many choices. But the thing about online content is, it's very rich. So when a person visits a site, there's a lot of things there, there's a lot of things on the page they're looking at. And so what we've really focused on is using the page as a source of clues about what a person is interested in, we also might look at the link in and out of the page, and get a clue from, say, a search term as well, that's a that's useful, and clearly when someone shares, you know, content that's that that sort of zoned in on exactly what they care about on the page. But we've opted more for the broad picture of focusing, you know, taking all that richness and attributing some probability of interest that for you, for user to the things that are on the page. And that way we can we have just such a broad, you know, broad palette to work with. And I think also from the point of view of, of, you know, user consent and user experience, it means that what we're actually collecting is is relatively light, it's just that this user was on this page at this time. And any inference we make is not based on what he or she did, or how are their eye movement, there's no no personal collection, we just have the that event, and we get all the all the power.

Grant
So it's when they were there. Is it anything about how they got there? Or where are they left?

Michael
Yeah, exactly. We do. We do use the inbound links and outbound links when we can get them. And that sometimes, as I said, yields a search term, those can that was sort of part of the of the link the part of the information that what came with the user, you know, the referring search term or so that so there's some some useful data there as well.

Grant
Yeah. So so when you collect this, and then that's got to be a massive repository, I think I saw somewhere else and I'm looking at, was it three terabytes of raw data and 100 million keywords in 200 languages a day? Is that right?

Michael
It sounds roughly right. I haven't counted it lately. But, yeah, you're right. But But yeah, we we see about half a billion, you know, unique, what we call events, something, you know, something happened at a point in time, visits a share per day.

Grant
This is a grounds for, you know, a playing field for AI, right, just you have so much data. So tell me what it is you learn from it with the AI, right? What kinds of problems are you looking to solve? As you and I know, when we pursue AI, we, it would tend to be better served if we're going after a particular question or thought in mind. Now, obviously, we get surprised with AHA insights from Ai. But going intentionally after something makes a lot of sense. Can you give a scenario the kinds of things that you're looking for?

Michael
Well, the I would say that the theme that has worked for us so far, is to try to do is to focus on being the able to represent and reflect human interest, what are people interested in? And yeah, and so. So we, we use, and I guess where the AI comes in is that we use the latest techniques of language analysis and language modeling. So we capture all of the linguistic content on the page and then we represent it in a number of ways. What are all the prominent keywords? What are the what are the entities that are you know more that are Unusual, you know, a brand name, a celebrity name, a business name? What are the what is this page about the concept? Or what are? What are some of the concepts that accurately describe what this page is about. And then we have some standard categorization techniques are basically a taxonomy of topic interest topics that we we screen for, you know, and and it's not, it's not a yes, one of the nice things about this is it's not a, a, it's not a, it, we don't have to decide one thing, you know, we were able to say, all of the prominent keywords, and all of the interesting entities and several concepts and all the categories that this page is about. So it could be a page, it's about, you know, mountain climbing and and what shall we say? And Utah, and the, or the American West and, and road vehicles? And, you know, and beverages, you know, skiing or whatever? Right, right. Exactly.

Grant
Yeah, so some form of an ontology there, right, that allows you to sort of connect these together?

Michael
Yeah, we used a number of techniques that you said, One is, we built a custom ontology, using relative and you know, we're, we're not a huge company. So we, we try to wherever we can do something open source or free as the entry point we do that. And so we, we use some Wikipedia, it's slash DBPedia is a source for us. And, as is some Google free offerings that help us sort of the provide the raw material for building our customer ontology. We've also take great advantage of some of the latest open source language modeling tools. One is when it goes by the name of the Google released one, I forget what the what the acronym stands for, but one that's called Bert, and then more recently, one that's called Muse. Yeah, we use muse. Okay, that, that allows us to represent anything, either a word or a sentence, or the whole page as a as a set as a vector of 500 numbers. And if two pages have the same values for those 500 vectors, then they are about the same thing. Yeah, you got you have some affinity there right now, even though in practice, they might be in different languages use totally different, you know, different sets of words, but they're still about the same thing. That's, that's, that's really, for us that technology has been a real breakthrough. Because it's we've been sometimes keywords and can be very, you know, they can be false positives or No, yeah, negative.

Grant
I mean, there, yeah, there's nothing that governs some, you know, webpage designer to, you know, say, hey, are they using the actual right keywords? Right?

Michael
Yes, or even? Or even? How do you a lot of words have multiple meanings? How do you disambiguate to get the right one? Yeah. So this this, embedding technology, this Muse model helps us do that. And then Facebook is given we use a tool, they think it's called Facebook. Ai similarity search. Yeah. And both of these are open source tools, y'all you have to put in the effort and have the knowledgeable people to master their use. And that allows us because great, it's great that you've now got all these numbers you can compare, but that's a lot of numbers. That's you half a billion a day, you know, and we have we see 600 million unique pages every month. So so how do I great, I want to rank the 600 million pages to see which ones are most about skiing in Utah. Yeah, that's, you know, how do I do that quickly, and then and affordably? So fate, the Facebook tool helps us a lot with that.

Grant
So let me ask you a question that So so far, you've been talking about leveraging AI technologies to help you get your arms around that sheer volume of data on a daily basis and to try to extract some meaning and semantics and understanding from it. That's a good point that's on the side of ShareThis and the benefits to ShareThis. What about it from pivoted to the other side? What does it mean to it is, you know, I talk a lot with small medium organizations, how does that benefit them? What takeaways or values come over to help them through something like that?

Michael
Well, what the I mean, the industry that we started with, is was is advertising and programmatic online advertising as a place where we make our solution available. And so we were at this point, probably the leading source of the ability to target ads based on interest. So if if A small business were doing online display advertising and they went to Google's, if they use Google's platform or trade desk, or any of the major platforms, and they searched on, I want to find people interested in skiing in Utah, our data would be one of their choices to find that. And so it's designed to provide a broad set of individuals who in the last 30 days have shown some interest in that topic. And it could be, you know, it might be at the level of skiing, and they might, then they might, but but the nice thing about it is that we we've, I mean, it's hard, this is harder for the stats, that's what's available for the smaller business. That's, it's, it's right off the shelf, you can, you can use $1 worth or $10 worth or $100 worth if it works for you. But then on the big company side, we use some of those tools I talked about for is, well, what if, what if we don't actually have ski in Utah, we just have skiing, right? Well, we well, for an advertiser can can say, well, I need to skiing in Utah. In fact, I need to, you know, skiing in snow. But what is the alter? You know, we can create a segment using keywords and, and topics that is just about that is exactly what they need.

Grant
So if I were to look at maybe an advertising opportunity, leveraging, you know, this great insight that you have, does it allow me to target specific demographics, specific locations or locales? So like, you know, you're able to?

Michael
Absolutely, it's pretty much anything you could, I mean, because every kind of website needs sharing, we have our, our customer base, our base of publishers use our tool is pretty representative of the internet as a whole. And so if your interest is travel, we've got sites that are about, you know, traveling Las Vegas, traveling to Europe traveling to do outdoor activities, if you're interested in financial products, we can we can find things, you know, content that relates to whatever be at a mortgage or or FinTech to know. And we we represent those in about 1500 standard audiences that we distribute every day. And every day, the nice thing about our data, compared to a lot of datasets is we refresh it every day. Yeah,

Michael
I mean, it's every second, right? I mean, yeah, it could be, you know, people talk about real time, and we were always looking for people who've got a real time use case. But yeah, at this point, the the most frequently we refresh for a client, the customer is up by a by his hourly.

Grant
Oh, it's hourly, okay, that's, that's still really up to date. Yeah. I mean, if you had hourly insights on what the what's in the mind of people are the consumers that's really fresh data?

Michael
Yeah, yes. Yeah. Yeah, one of the areas that we that we are moving towards is trying to go beyond advertising and inform other activities like demand forecasting, you know, how much should we order for a store in a given location? Well, our data about how much interest is being shown on the products of that store, and in that store, in that area, we can sort that way, and provide that as an input.

Grant
That makes that makes a lot of sense. You know, there's, there's some retail organizations I've worked with with AI. And obviously, it always comes back to or not always, but most of it comes back to the supply chain, right, getting further and further left in terms of their their demand forecasting. And if they were able to understand you know, where that interest lies, it does almost gets to, oh, I know, this is a stretch in terms of language, but it's kind of a sentiment analysis, a play on that. Right. It's the ability Yeah, the ability to say I understand what the sentiment is in terms of where their interests are. And if I understood what that was, in terms of particular set of products or other things I'm offering, and I could get that further into my, into my supply chain, that would be really valuable to Yeah,

Michael
I mean, it's nice that you mentioned that we do we do actually score the sentiment of the content on the page. So we're sentiment is useful, either to only talk to the people who are in favor or opposed or the middle, we can we can build an audience that or provide that as a data element as well.

Grant
Yes. See, that's that's powerful to understand the the sentiment of the page itself, even how people are talking about it, or what they're doing, have you ever ran into the ability to use it in terms of IP tracking, right. So in other words, if there is an organization that had a certain set of IP and, and and really, yeah, they felt like oh, my IP, I've lost control my intellectual property, it's showing up in other places.

Michael
Oh, that's interesting. You know, I was thinking of I was thinking of the I the the IP address the Internet Protocol address. Yeah. Should have been more clear. Yeah, I'd love to answer that question. But that wasn't what you were asking.

Well, yeah, answer. Oh, we'll start with intellectual property. Yeah. One sec. Regarding intellectual property? You know, we have it. Let me think about that. Let me give you the scenario. I had, one of the things I've thought about that we haven't taken on it, you know, is that is, is using using intellectual property as a data set? Yeah. If if we were to, to read to do the same kind of analysis I talked about earlier on trademarks. Yeah, it could mean be the means for discovering which, what sites were about branded products by seeing the correspondence between the trademark and the, because that's always you run into difficult How do you tell something's a brand? When is Jaguar a brand? You know? Exactly.

Grant
Yeah. Yeah, it's a fascinating problem. I had a company reach out to me and say, Hey, can you develop something in this area, and we did some work on that. I called it smart catch, but they were looking to protect their IP, their intellectual property, which was, we've got this corpus of information. And, and we've got others that are, you know, getting access to it and are promoting it, you know, elsewhere out into the, you know, online universe there, or metaverse. And, and I want to be able to discover, you know, when it's opportunistic, and you can use, you know, SERP and other technologies to try to find some of that stuff and do lots of scraping. But that's got its own challenges in terms of a solution. And where you've got this opportunity to listen. Right, right, to observe what people are sharing and to the to compare that against a corpus of protected material, right?

Michael
Kind of an intro you're giving, you're giving me a product idea. Seriously, one of the things that we've done this year, is to create what we what we call, you know, similarity scoring. So similarity, and that's gonna cause Yeah, you can literally give someone who was curious about the dispersing dispersion of intellectual property, give us a domain. Yep. And, or a, you know, the piece of content that describe their, their stuff, and we would rank our sites for which ones had it most. Right. And, you know, whatever the top 100, you know, and you know.

Grant
What I found interesting on that, when I built the initial piece on that was that I found that, in some of the discovery, in some cases, what I found was a foe. And in other cases, it was a friend. Exactly right. That, you know, okay, just because I found it doesn't mean it's an enemy. But, but it might be, and so you want to then notify them? Is this? Is this someone that's an ally or not? Anyway, interesting thought?

Michael
Because I think I think that sometimes there is a, you know, I don't know, there's a presumption that fraud detection or a bad actor detection is, is, you know, worth more, etc. But I do find that in a lot of cases, the pro cases are actually, you know, sometimes you just by suppressing something, you do more yourself more harm than good. Yeah. Yeah. Right. Right. That's another I wanted to touch on the other meaning of it. Yeah.

Yeah. Now IP address. Yeah, yeah. So So an IP address is one of the four or five things that we capture for each case. And there's a lot that you can tell from an IP address, like, it can be translated into a location of origin, we approximate we resolve that to within half a mile. So that it's still relatively privacy compliant, and you know, not too revealing, but it certainly helps understand, you organize the data by where it's coming from example. And so the one use that is, has been an important one for us is business to business. So we, we have a number of the major companies that are in the business to business world license our data as one source where they're able to see people from a from an intellect Internet Protocol address that is owned by or been associated with a particular company. Oh, and then see what sites that that IP address is showing interest in? Oh, it just can be. Yeah, so it can be a signal that oh, it seems like you know, Chevron is interested in a new CRM system because they're you know, there's there's a big spike in that kind of traffic Awesome. Yeah, that's awesome. Yeah. Talk about so almost like a lead management. Yeah, solution for sure. That's, that's powerful. Yeah, to do that. that. Oh, there. Yeah. And that's yeah. And IP in general, I think the location implications are a really well, it's how I can, how we can do that demand forecasting I mentioned earlier, it's about looking at the origin of the data.

Grant
So some of the AI solutions that I've built take into consider location. So So in other words, okay, but in what I've been doing is more around, oh, some transaction occurred? Where was that transaction initiated? From? Oh, this, you know, here's the IP address. Okay, I know that where they are on the planet. Now, tell me what the context of what's taking place in you know, at that location? What is what's the weather like, right, what are other events that are taking place in that location? And then then use an AI to help draw inferences on, you know, to what degree are those factors affecting it? It sounds like you might be doing some similar things with that

Michael
I well, I think we could be a great contributor to any solution that was along those lines. I was adding that dimension of what are people looking at? What are people interacting? What topics? Are people in this location more engaged by then people in general, fascinating those comparisons?

Grant
Yeah, it's fascinating is okay. Very good. All right. So let me ask you on. Okay, so we've gone from the the big corpus of what you're collecting on a daily basis, or hourly, actually, hour by hour. And then we talked about the impact to, you know, maybe businesses organizations, when when is there a particular case or outcome that you feel like you could talk about some specific example where some organization used the advertising from that? What you did, and it had this sort of impact or effect on them? Do you have any sort of case study like that? Well, it's,

Michael
I guess that some of the ones that are coming to mind, I think, I mean, there's some of it's very straightforward. Yeah. An advertiser, like Western Union, is looking for people who want to make payments, you know, at a distance, I mean, wire wire transfers and payments, and we offer people showing interest in wire transfer, so that the simple act of being able to get your message in front of people who have recently shown interest in it is the is the, you know, it just doesn't need no explanation. We've taken that though, one of the things we did this year that I'm proud of is we were inspired by some of the events of last summer, to get more try to take a more active role and figure out what our data was good for. Beyond commercially, and, and we ended up creating a data for good part new part of our taxonomy, we call data for good. And so people interested in social justice loving people entered interested in veterans issues people wanted in. And so and those those segments, you know, have gotten are getting a growing amount of usage by advertisers who either, you know, wanting to demonstrate their commitment to the court to a cause, like, or to find or teachers or to, you know, communicate, right people who have concerns of that kind. So that's been one. Yeah. Another kind of it's, it's not in the mainstream of what we do. But we've, I think this data could be really great as a as a resource for educational institutions. So we've actually a major business school has has is testing I've taken a take taken a subsidiary six months of our data, and they're looking at using it in a project that they have to investigate unemployment. So fascinating. How could you How could you see earlier unemployment trends in a in a location or region that could help the for the process of forecasting the unemployment rate, and it sort of feed into it? Because I've, what I've, I think that lots of people govern organizations included, are somewhat frustrated by the fact that, you know, traditional means of forecasting that were invented before there were personal computers or barely work computers. Take a long time, you get to find out that 40 days after the month, what happened in the month, I love both data can be used to generate that much more quickly.

Grant
Yeah, Michael, that's I love how you're bringing that up. It seems like it has both the opportunities for not only the capitalistic aspects, but the altruistic aspects of this, the values and benefits that can help society and be pulled out of that. I think that's awesome. So all right. I've thrown a lot of questions at you. So let me ask you this, if you will. To direct direct my listeners to where to go to learn more, where would you send them?

Michael
Well, I would, I would love them to visit our site, because and in particular to, you know, to ShareThis.com, look, look at our news and our, our blogs, we we basically we publish both as you know, as a demonstration of our the value of our data. And and it's just a general service, we publish a lot of educational and informative information about trends in the economy, and, and public interest generally about how to do marketing well about trends in data. So so we we, we try to be a resource for people and I love I'd love people to visit that content, sometimes. Some of the best stuff is is not on on the nightly news. It's like putting some of it out. I could also you know, I can give you some examples. It would be fun. I go right ahead. Knowing that knowing this audience I we are getting a sense of who maybe was listening is interested in the show, I asked our team to identify some current trends. Yeah, I guess as we come to the end of 2021. Yeah. And so so we put these together. So what one is that, that, that while the world isn't, we're seeing the trend of the gradual resumption of events in person events, even though COVID continues to cycle up and down against the backdrop of COVID. So as of August, for example, 77% of advertised events were in person events, there was a period where, you know, year and a half ago, there was there, they basically no almost having anything, it was just shut down. It was virtual or nothing. That's interesting. So as we adapt, we are adapting. And so as you as you think about should I make plans for a virtual vet, should I invest in advertise? Should I invest in participating in virtual event? Yep, don't count them out. Even if you're nervous, you know, they, they're coming back steadily. Another thing, pattern we observed in finance, that again, you know, COVID is inevitably one of the backdrops to what any of us are thinking about, but people are continuing to be engaged with saving money. So, it so as you think about what, oh, you know, what is what's going on in the in the economy? As the, as virus uptake increases, as one of the things to extract is, is increased saving? And so if that's a, again, depending on your business, how that factors in if savings is your business? Yeah. When your could be good, good to you. If if, and then let's see, what's another one? Let's see. You know, we've heard a lot about supply chain issues. And you know, what, but what, if your retailer what a consumers most worried about? When and so the top concern is shortages and out of stock, and 51% a second costs, inflation and rising prices at 28%. And then staffing issues like worker shortages and strikes, 14, and last last of all shipping delays. So it's thinking about communication strategies, what's on people's minds that might make them not come to the store? That sort of thing? So I'm not surprised. Yeah, yeah. So and we're, we're putting out new new stuff of this kind every, every month in the blog. And and I firstly, look, I think we did we have Superbowl trends out, as of yesterday, I think.

Grant
So it's already started to build right. That's right. That's, that's amazing. So So you gather it on an hourly basis, and then you do the AI on it

Michael
Truthfully, truthfully, Grant, it's being gathered continuously. Okay, that's, that's what I thought, yeah, I thought we built we build it as it happens, okay. We literally, you know, record a record for each thing. That's, that's, that's filled out all the way with all the data that will that will need eventually. And then once an hour, we some or as we frequently as our we'll sum it up into a distribution and push it to someone but the most people get their get their data delivered overnight. Amazing. It's picking it up on their AWS bucket. Like Well, this is

Grant
Fascinating. Any final comments as we wrap up here?

Michael
Well, you know, I guess that I hope I've given you a sense of the I mean, AI is critical to our business. We are you know, we When we started on this track, we were about a 50 person company, we're approaching 100 person company. And so you don't have to be, you know, IBM to use AI AI to build a great business. So it's a combination of finding the right tools and a core of of talent, the right kind of talented people, and you can and and then, frankly, sustained effort over a period of years and you can build a business that is really hard to replicate, without without it, so very hard. Right. That's, that's my thought. That's, that's

Grant
Wonderful. Well, Michael, thank you so much for taking your time today. I appreciate you sharing your insights and guidance with us today, everyone. Thanks for joining another episode of ClickAI Radio and until next time, go get some ShareThis.com.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com now.

Welcome to ClickAI Radio. In this episode I have a conversation with some AI experts on how AI ethics affect your business.

Grant
Okay, welcome, everybody to another episode of clique AI radio. Well in the house today I have got a return visitor very excited to have him and a brand new person. I'm excited to introduce you to Carlos Anchia. I've been practicing that I get that right, Carlos.

Carlos
Sounds great. Good to see you again.

Grant
Even old dogs can learn new tricks. There we go. All right, and Elizabeth Spears. Now I got that one easily. Right, Elizabeth?

Elizabeth
You did it? Yeah. I'm really happy to be here.

Grant
This is exciting for me to have them here with me today. They are co founders of plain sight AI. And a few episodes ago, I had an opportunity to speak with Carlos and he laid the foundation around the origin story of AI for vision, some of the techniques and the problems they're solving. And I started to nerd out on all of the benefits. In fact, you know what, Carlos, I need to tell you, since our last conversation, I actually circled back to your team and had a demo of what you guys are doing. And yeah, I think it was very impressive, very impressive, you know, a guy like me, where I've coded this stuff.

And I was like, Oh, wow, you just took a lot of a lot of pain out of the process. You know, one of the pains that I saw come out of the process was the reduction in time, right that how long it would take for me to cycle another model through it. That was incredible, right? I can't remember the actual quantification of time, but it was at least a 50% of not even 80% reduction of cycle time is what I saw come through, there's even model versioning sort of techniques, there's just, you know, there's another really cool technique in there that I saw. And it had to do with this ability to augment or, or approximate test data, right, this ability to say, but but without creating more test data, it could approximate and create that for you. So now, your whole testing side just got a lot easier without building up, you know, those massive test cases and test basis for for doing the stuff So, alright, very impressive product set. And let's see, Elizabeth can you explain that?

Elizabeth
That's right, Chief Product Officer. So basically, kind of the strategy around what we're building and how we build it. And the order in which we build it is is kind of under my purview.

Grant
Okay, very good. Awesome. Well, it's so great to have both of you here today. So after I spoke with Carlos, last time, after we finished the recording, I said, You know what, I want to talk to you about ethics about AI ethics. And so as you heard in my previous podcast, I sort of laid the foundation for this conversation. And it's not the only areas of ethics around AI, but it's a place to start. And so we want to build on this. And we're gonna talk about these sort of four or five different areas just to begin the conversation. And I think this could translate certainly into other conversations as well. But to do that, could could one or both of you spend a little time giving the foundation of what is AI ethics as a relates to computer vision itself? What are some of the challenges or problems or misunderstandings that you see in this specific area of AI?

Carlos
Sure, I can take that one. So I think really, when we're talking around ethics, we're bowling any sort of technology, we're talking around how that technology is implemented, and the use of that, right and what's acceptable. So in the case of this technology, we're talking around computer vision and artificial intelligence, and how those things go into society. And it's really through its intended use on how we evaluate the technology. And I think really, computer vision continues to provide value to allow us to get through this digital transformation piece. As a technology, right? And, you know, once we start with, yes, this is a valuable technology, the conversation really now shifts to how do we use that technology for good, some cases bad, right? Where this is where that conversation arises around, you know, having the space to share what we believe is good or bad, or the right uses or the wrong usage just right. And it's a very, very gray area, when we look to address technology and advancement in technology against a black and white good or bad kind of a situation, we get into a lot of issues where, you know, there's a lot of controversy around some of these things, right, which is really, you know, as we started discussing it after the last podcast, it was really, you know, man, I really need to have a good podcast around this, because there's a lot to it. And you clearly said there was a previous one.

And now there's this one, you know, I hope that there's a series of these so we can continue to express value and just have a free conversation around ethics in artificial intelligence. But really, what I'm trying to do is set the context, right. So like technology works great from the just the science of the application of that technology. And if you think of something like super controversial facial recognition, now, absolutely. I don't want people to look at my face when I'm standing on a corner. But if there's, you know, child abduction cases, yes, please use all the facial recognition you can I want that to succeed really well. And we've learned that technology works. So it's not the solution itself. It's how we're applying that solution. Right. And there's there's a lot of new ones to that. And, you know, Elizabeth can help shed a little bit of light here, because this is something that we evaluate on a constant basis and have really free discussions around.

Grant
Yeah, I would imagine you have to even as you take your platform into your into your customer base, even understanding what their use cases are imagined, at times, you might have to give a little guidance on the best way to apply our uses. What have you seen with this, Elizabeth?

Elizabeth
Yeah, you know, there's, it's interesting what Carlos is saying there's a lot of the same themes for evaluating the ethics and technology in general are similar ones that come up with when AI is applied. So things like fraud or bias is actually more can be more uniquely AI. But that absolutely exists in other technologies. And then inaccuracy and how that how that comes up in AI, and then things like consent and privacy. So a lot of the themes and we can we can talk about how AI applies to these, but a lot of the themes that come up, are really similar. And so one of the things that we try to do for our customers is, especially kind of your listener base, that's that small, medium businesses is take a lot of that complexity out of the, like, Hey, I just want to apply, you know, I just want to solve this one problem with AI, what are all of these concerns that I, you know, I may or may not know about. So, we try to do things like build, build things into the platform that make it so something like bias. So, for example, is usually comes down to data balance. So if we provide tools that really clearly show your data balance, then it helps people make unbiased models, right, and be confident that they're going to be using AI ethically,

Grant
So that I'm sure you're aware of this Harrisburg University in Pennsylvania case where they ended up using AI to predict criminality using image processing, right. And, of course, that it failed, right? Because it you know, looking at a an image of someone and saying, Oh, that person is a criminal, or that person's not a criminal. That's using some powerful technology, but in ways that, of course, has some strong problems or challenges around that. How do you help prevent something like this? Or how do you guide people to use this, this kind of tool or technology and in ways that are beneficial?

Elizabeth
Yeah. What's interesting about this one is that the same technology that causes the problem can also help solve the problem. So so when you're looking at your corpus of data, you can use AI to find places where you have data in balance and and just to kind of re explain the what happened in that case, right. So they had a data imbalance where it was Miss identifying Different races that they had less data for. So, you know, a less controversial example is if we're talking about fruit, right, so if we have a dataset that has 20, oranges, two bananas and 20, apples, then it's just going to be worse than identifying bananas, right? So one of the things that that can be done is you apply AI to automatically look at your data balance and say, and surface those issues where it can say, hey, you have less of this thing, you probably want to label more of that thing.

Grant
So I'll try to manage the data set better in terms of proper representation. And try finding, finding bias is a real challenge for organizations. And I think one of the things that your platform would allow or unable to do is if you can take off the pain of all of the machinery of just getting through this and and free up organizations time to be more strict in terms of evaluating and finally, oh, taking the time to do those kinds of things, I think you might have an opportunity to improve in that area meeting customers might be able to improve in there, would that be a fair takeaway?

Elizabeth
Yeah, it's something that that we're really passionate about trying to provide tools around. And, and we're kind of prioritizing these these tools. The other one is, is that has to do with your data is as well is finding inaccuracies in, in your models. So the one example is X ray machines. So they did. They've basically they had an inaccuracy in a model that was finding a correlation, I think it was for it was for the disease detection. So it was finding a correlation just when the X ray was mobile, versus when they went into kind of a hospital to get the X ray. And so, you know, these models are in many cases, really just very strong pattern detectors, right. And so one of the things that can really help to, you know, to prevent something like that is to make it easy to slice and dice your data in as many ways as possible, and then run models that way. And make sure that you aren't finding the same correlation, or the same sort of accuracy with a different data set, or a different running of the model in a different data set. So said, in other words, you would be able to say, I'm going to run all of the portable X-ray machines versus all of the hospital ones, and see if I'm getting the same correlation as I am with, you know, cancer versus not cancer, or whatever they were looking for.

Grant
A quick question for you on this. So in my experience with AI, I have found sort of two things to consider. One is the questions that I'm trying to get answered guides me in terms of, you know, how I prepare the model, right? I'm gonna first lean towards certain things, obviously, if I want to know that this is a banana, right, or an apple or what have you. So the kind of question that when I answer leads me to how I prepare the model, which means it leads me to the data that I select. And the question is, is do I do I? Should I spend the time really putting together the strong set of questions? And rather, rather than do that, just gather my data? And then and then execute that data, the build a model? And then Ben, try to answer some questions out of that, you see what I'm saying that way, maybe I'm not going to introduce any bias into it.

Elizabeth
So we we encourage a very clear sort of understanding of the questions that you want to answer, right? Because that helps you do a few things, it helps you craft a model that's really going to answer that question, as opposed to accidentally answering some other questions, right. But it also helps you right size the technology so far, for example, if you're doing if you're trying to answer the question of how many people are entering this building, because you want to understand, you know, limits of how many people can be in the building or, you know, COVID restrictions or whatever it is, that that solution doesn't need to have facial recognition, right. So to answer that question, you don't need you know, lots of other technologies included in there. And so yeah, So, so defining those questions ahead of time can really help in sort of a more ethical use of the technology.

Grant
So one of the first jobs we would then have a small medium business do would be get clarity around those questions that actually can help us take some of the bias out. Is that a fair takeaway from what you share?

Elizabeth
Exactly like the questions you're trying to answer. And the questions you aren't not trying to answer can also be helpful.

Grant
Oh, very good. Okay. All right. So all right, the opposite of that as well. All right. So while we could keep talking about bias, let's switch to something that is that I think comes right out of the movie iRobot, right. It's robot rights. Is this, is this a fluke? Or what, you know, is this for real? I mean, what do you think? Is there really an ethical thing to worry about here? Or what? What are your thoughts?

Elizabeth
You know, in most of the cases that I've seen, it's really more like, it comes down to just property, like treating property correctly, you know, like don't kick the robots because it's private property. So not really around sort of the robot rights but you know, some already established rules be in for the most part, I see this as kind of a Hollywood problem, more than a practical problem.

Grant
Maybe it makes good Will Smith movies. But other than that, yeah, fighting for rights, right. Now that seems like it's way out there in terms of terms of connection reality. Okay, so we can tell our listeners, don't worry about that for right now. Did you add something back there?

Carlos
Just an interesting point on the robot rights, right. While while it's far in the future, I think for robot rights, we are seeing a little bit of that now today. Right? When like Tesla AI day, when they came out, they decided that the robot shouldn't run too fast that the robot shouldn't be too strong. I think it's a bit. It's a bit interesting that, you know, we're also protecting the human race from for us building, you know, AI for bad and robots for bad in this case. So I think it's, it's, it's on both sides of that coin. And those are, those are product decisions that were made around. Let's make sure we can run that thing later. So I think I think as we continue to explore robots AI, the the use of that together, this topic will be very important, but I think it's far far away.

Grant
I'm wondering is that also blends into the next sort of ethical subtopic we talked about, which is threat to human dignity. And it might even crossed into that a little bit, right, which is, are we developing AI in a way that's going to help? protect the dignity of human certainly in health care situations? That certainly becomes important, right? You probably heard on the previous podcasts that I did, I played a little snippet from Google's duplex technology that was three year old technology, and those people had no idea. They're talking and interacting with AI. And so there's that aspect of this. So where's the line on this? When? When is it that someone needs to know that what you're interacting with is actually not human? And then does this actually mean there's a deeper problem that we're trying to solve in the industry, which is one of identity, we've got to actually create a way to know what it is that we're interacting with. And we have strong identity? Can you speak to that? Yeah,

Elizabeth
I think I think the there's two things that kind of come into play here. And the first is transparency, and the second is consent. So in this case, it really comes down to transparency, like it would be very simple in that example, for that bot to say, Hey, I'm a bot on behalf of, you know, Grant Larsen, and I'm trying to schedule a hair appointment, right, and then going from there. And that makes it a much more transparent and easy interaction. So I think in a lot of cases, really paying attention to transparency and consent can go a long way.

Grant
Yeah, absolutely. All right, that that that makes a lot of sense. It seems like we can get around some of these pieces fairly, fairly simply. All right, Carlos, any other thoughts on that one?

Carlos
The only thing there and then touches on the stuff you guys were talking about on the bias piece, right? We're really talking about visibility and introspection into the process. Right. And with bias, you have that in place, right? We can detect when you know there's a misrepresentation of classes within the the model. In some cases, there's human bias that you can get that right but it's it's having that visibility in the same case with the threat to human data. With that visibility comes the introspection where you can make those decisions. You see more about the problem.

Grant
Mm hmm. Yeah, yeah. So if we were to to be able to determine we have a bad actor, if there's not transparency, that would be a way that we could help protect the dignity of humans through this. Alright. That's reasonable. All right. So let's move on to again, sounds Hollywood ish, but I'm not sure it is weaponization of AI. Right? What are the ethics around this? I'll just throw that one on the table. Where do you what do you want to take that? Carlos, you wanna start with that one?

Carlos
Sure. I mean, so weaponization and and I think when we talk about AI and, and the advancements of it, you quickly go to weaponization. But really, weaponization has two different pieces to it, right? It's obviously it depends on which side of that fence you're on, on whether you view that technology is beneficial or detrimental. But in some cases, that AI that same technology that is helping a pilot navigate, it also helps for a guided missile system or something like that. So we really have to balance and it goes back to use cases, and how we apply that technology as a people. But you know, weaponization, the rise against the machines, these kind of questions. While they're kind of out there. They're affecting society today. And we have to be able to have productive conversation around what we believe is good and bad around this while still allowing technology to succeed. So there's a lot of advancements in the weaponization and AI in that space, but it's really, I think we have to take it on a case by case basis, and not like a blanket statement, we can't use technology in these ways.

Grant
Interesting thoughts? What are your thoughts there? Elizabeth?

Elizabeth
Yeah, you know, I it makes me think of sort of turning it on its head is, is when is it? You know, when is it unethical not to use AI, right. And so, some of those questions come up when we are talking about weaponization, you can also be talking about saving human lives and making it safer for them to do some of these operations. And and that same question can come up in some of like, the medical use cases, right? So here in the US, we have a lot of challenges around being able to use AI in medical use cases, and there's, and there's some where you can have really good human oversight of the cases, you can have sort of reproducibility of those models, they can be as explainable as possible. But it's still really, really difficult to get FDA approval there. So I, again, I think there's two sides to that coin.

Grant
And, yeah, it's it's an interesting conversation have stuff wrong, because like, in that medical case, you talked about, you could see the value of using the same kind of technology that would be used to identify a human target, and then attack it, you could take that same capability, and instead use it in a search and rescue sort of scenario, right? Where you're flying something overhead, and you're trying to find, you know, pictures or images of people that might be lost out there. Same kind of thing, right, so, so where how, go ahead, you're gonna say something was, but I can see.

Elizabeth
And there's even simpler cases in medical, where it's like, you know, there's a shortage of radiologists right now in the US and, and you can use, you can use AI to be able to triage some of that imaging. So, because right now, people are having to, in some cases, wait a really long time to get their sort of imaging reviewed. And so can can, can, and should AI help there. There's also another one along those same lines, where, with things like CT scans, you can use what's called super resolution or de noising the image. And basically, you can use much less radiation in the first place to take the imaging and then use AI on top of it to be able to essentially enhance the image. So again, you know, ultimately exposing the patient to less less radiation. So yeah, there's it's pretty interesting when when we can and can't use it. Mm hmm.

Carlos
Yeah. And I think just to add a little bit to the one we can and can't right, so, advancements through drug discovery have largely been driven through AI in the same fashion weaponization of various all drugs or other types of drugs have also benefited from Ai. So, I mean, from a society's perspective, you know, you really have to Evaluate not only greater good, but that that ultimate use case like, where where do you want to make a stance around that technology piece. And understanding both sides really provides that discussion space that's needed, you have to be able to ask really honest questions to problems that are, you know, what you can see in the future.

Grant
So is the safeguard through all of this topic around ethics? Is the safeguard, basically, the moral compass that's found in the humans themselves? Or do we need to have less, you know, legislative or policy bodies? Right, that puts us together? Or is it a blending? What do you what's your take?

Elizabeth
Um, it's interesting, the UK just came out with a national AI strategy. And they are basically trying to build an entire AI assurance industry. And, and their approach is, so they want to make sure that they're make, they're keeping it so that you can be innovative in the space, right? They don't want to make it so regulatory, that you can't innovate. But they also want to make sure that there's consumer trust in in AI. So they're putting together from a, you know, a national perspective, a guidelines and tests and, and ways to give consumers confidence in whether a model is you know, reproducible, accurate, etc, etc, while at the same time not stifling innovation, because they know, you know, how important that AI is for a essentially a country's way to compete and and the opportunities for GDP that it provides as well.

Grant
Hmm, absolutely. Yeah, I can go ahead, Carlos.

Carlos
No, I think it's his it's your question. Left alone, should we got kind of govern ourselves? I think, I think we've proven that we can't do that as a people, right. So we need to have some sort of regulatory, and committee around the review of these things. But it has to be in the light of, you know, wanting to provide a better experience higher quality, deliver value, right. And I think I think when you start with, how do we get the technology adopted and in place and deployed in a fashion where society can benefit, you start making your decisions around, you know, what the good pieces are, and you'll start your start really starting to see the outliers around Hey, wait a second, that doesn't kind of conform to the guidelines that we wanted to get this implemented with?

Elizabeth
And I think also to your question, I think it's happening at all a lot of levels. Right. So there's, you know, state regulation around privacy and use of AI and facial recognition. And, and there's, you know, some the FDA is putting together some regulation, and then also individual companies, right, so people like Microsoft, etc, have have big groups around, you know, ethics and how AI should be used for, you know, them as a company. So I think it's happening at all levels.

Grant
Yeah, like we said, that is a people we need to have some level of governing bodies around this to, and of course, that's never the end all protection, for sure. But it is, it is a step in the right direction to to help monitoring and governance. Okay, so last question, right? This is gonna sound a little bit tangential, if I could use that word tangential. It's given the state of AI where it is today. Is it artificial intelligence? Or is it augmented intelligence?

Carlos
I can go with that. So I think it's a little bit of both. So I think the result is, is to augment our intelligence, right? We're really trying to make better decisions. Some of those are automated, some of those are not we're really trying to inform a higher quality decision. And yes, it's being applied in an artificial intelligence manner, because that's the technology that we're applying, but it's really to augment our lives. Right. And, and we're using it in a variety of use cases. We've talked about a lot of them here. But there's 1000s of use cases in AI that we don't even see today that are very easy. Something as simple as searching on the internet. That's helping a lot from you know, misspelling things and, you know, not not identifying exactly what you want and recommendation engines come and say, you know, I think I'm looking for this instead. It's like, Absolutely, thanks for saving me the frustration. We're really augmenting Life in that point.

Grant
The reason why I asked that as part of this ethics piece is one of the things I noticed. And as I work with the organizations, there's a misunderstanding of how far and what AI can do at times. And and there's this misunderstanding of therefore, what's my responsibility in this. And my argument is, it's augmented intelligence in terms of its outcome, and therefore, we can't absolve the outcomes and pass that off to AI and say, Oh, well, it told me to do this, just in the same breath, we can't absolve and say, We're not responsible for the use cases either. And the way in which we use it, so we own as a human human race, we own the responsibility to pick an apply the right use cases, to even be able to challenge the AI, insights and outcomes from that, and then to take the ownership of that in what the impacts are. Agree, disagree.

Carlos
Yeah, I would really agree with that. And if you if you think about how it's implemented, in many cases, right now, the best use of AI is with human oversight, right. So it's sort of, you know, AI is maybe making initial decision, and then the human is reviewing that, or, you know, making a judgment call based on that input. So it's, it's sort of helping human decisioning instead of replacing human decisioning. And I think that's a pretty important kind of guiding principle where, where, wherever that may be necessary, we should do it. There's one, you know, the Zillow case that that happened recently, where they were using machine learning to automatically buy houses, and it was not. There was not enough human oversight in that, and I think they ended up losing something like $500 million in the case, right. So it's not really an ethics thing, but but it's just an example where in a lot of these cases, the best scenario is to have aI paired with human oversight. Yeah, yeah. Great, I think.

Grant
Yeah, no, go right ahead. Yeah.

Elizabeth
You mentioned you mentioned being able to challenge the AI, right, and that that piece is really important, in most of the cases, especially in the one that he just mentioned, around that. That Zillow case, right, without the challenging piece, you don't have a path to improvement, you just kind of assume the role, and you get into deep trouble, like you saw there. But that challenging piece is really where innovation starts, you need to be able to get back and question kind of, you know, is this exactly what I want? And if it's not, how do I change it? Right. And that's how we drive kind of innovation in the space?

Grant
Well, and I would say that that comes full circle to the platform, I saw that your organization's developing, which is to reduce the time and effort it takes to be able to cycle on that right to build the model, get the outcome, evaluate, oh, challenge, make adjustments, but don't make the effort to recast and rebuild that model such that it becomes unaffordable or too much time, I need to be able to iterate on that quickly. And I think as a platform you developed and others that I've seen, you know, continue to reduce that I think it makes it easier for us to do that in it from a from a financially responsible and beneficial perspective. 100%

Elizabeth
Yeah, one of the one of the features that you mentioned was the versioning. And that really ties into a guiding principle of ethical use as well, which is reproducibility. So if you are, if you want to use a model, you need to be able to reduce, reproduce it reliably. And so you're getting the same kind of outputs. And so that's one of the features that we've put in there to help people that versioning feature to help people, you know, comply with that type of a regulation.

Grant
I've built enough AI models to know it's tough to go back to a particular version of an AI model and have reproducibility accountability. I mean, there's a whole bunch of LEDs on that. That's exceedingly valuable. That's right. Yeah. Okay, any any final comments for me there? Yeah.

Carlos
I think for my side, I'm really interested to see where we go as people with ethics in AI. I think we've touched on the transparency and visibility required to have these conversations around ethics and our ethical use of AI. But really, in this case, we're gonna start seeing more and more use cases and solutions in their lives or we're gonna butt up against these ethical questions, and being able to have an open forum where we can discuss this. That's really up to us. To provide we have to provide the space to have these conversations, and in some cases, arguments around the use of the technology. And I'm really looking forward to, you know, what comes out of that, you know, how long does it take for us to get to that space where, you know, we're advancing in technology and addressing issues while we advanced the technology.

Grant
Excellent. Thanks, Carlos. Elizabeth.

Elizabeth
Yeah, so for me as a as a product person in particular, I'm really interested in these the the societal conversation that we're having, and the regulations that are starting to be put together and kind of the guidelines from larger companies and companies like ours that are, you know, contributing to this thought leadership. And so what's really interesting for me is being able to take those, that larger conversation and that larger knowledge base and distill it down into simple tools for people like small and medium businesses that can then feel confident using AI and these things are just built in sort of protecting them from making some mistakes. So I'm really interested to see sort of how that evolves and how we can productize it to make it simple for people.

Grant
Yeah, yeah. Bingo. Exactly. Okay, everyone. I'd like to thank Carlos Elizabeth, for joining me here today. Wonderful conversation that I enjoyed that a lot. Thanks, everyone for listening. And until next time, get some AI with your ethics.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com now.

In this episode, we take a look at the question where is the line for artificial intelligence ethics?

Alright, everybody, welcome to another episode of click AI radio. So ethics, what are they? Well, you know, if we look at Google definition here, right moral principles that govern a person's behavior, or conduct or con or the contacting of an activity. Alright, so I'll give you an example here, right? Without going into some again, some of my business law classes that I took in college. Here's, here's an example. I was in college and my brother came into town to visit and said, Hey, let's go skiing. Oh, sorry. I said, my brother, I've got to work. He says, Hey, no, come on. I'm in town. Let's go skiing. So what did I do? I lied, I said to my boss, I don't feel well. And I went skiing. Well, to make matters worse, I'm up skiing, and I get hurt.

To make things even more embarrassing. As I come down off the hill being pulled by the ski patrol. They open up the sled there, the local TV news station was there interviewing and well, I showed up on the evening news. While my boss is watching the news. He sees me there. All right, what a way to learn a life lesson. So what I did was unethical. I don't know that I have to go deeper than that. That was unethical. That was 35 years ago. I sure hope I learned my lessons since then. That was a painful life lesson. He called me that night. And he said that grant, I hope you're feeling better now. I said, Oh, yeah, yeah, I was in pain. Alright, pain both physically as well as internally.

Alright, so now let's talk about since apparently some of us carbon based beings have struggled or do struggle with ethics? What does it mean to do this for artificial intelligence? Well, to do that, I borrowed four categories from Wikipedia, talking about this area of artificial intelligence, ethics, number one, category number one, bias in AI, Category Two, robot rights, category three threat to human dignity, and category four weaponization of AI. Alright, so let's take a look at each of these briefly. So as it relates to bias in AI, right, this is where, as us humans, as we build these models, these AI models, then our own biases can either intentionally or unintentionally get incorporated. And what that does is that drives downstream decision making that's bias in AI, we'll look a little bit further that in a moment, robot rights so this is of course, the idea that humans have while we've got you know, moral obligations, we have them or we'll have them to our machines as well. Might be somewhat akin to animal rights. So alright, that's robot rights. Number three, again, was threat to human dignity. And of course, this is in the areas where respect and care and compassion and another human attributes are needed, right, AI should not be used, certainly to replace replace people. So how do we help protect that? And then the fourth area weaponization of AI, this is of course, using AI in military combat scenarios or, or other kinds of scenarios like that.

Alright, so what I'm not going to talk about here, though, is and I think this is a separate episode, there's the concept of singularity, right? It's the notion that that we that some self improving AI becomes so powerful that humans can't stop it kind of like the movie eagle eye or iRobot with with Will Smith. Alright, so we're gonna park that one, put that over to the side. There is a nonprofit organization, however called Ready big breath, the partners the partnership of AI to benefit people and society is formed by Amazon, Google, Facebook, IBM, Microsoft, Apple, all the biggies. They're obviously looking at to develop the best practices in In AI ethics, all right, so you want, you might want to look them up, take, take a deeper view on them. Let's go a little bit deeper on each of these categories here for just a moment. So first of all bias in AI, as you've probably heard of this came out of toward data science.com. They, they pointed out an experience in 2019, where researchers found that an algorithm that had been used on over 200 million people in the US around hospitals, it was being used to predict the likelihood that someone needed extra medical care. And without going further into it, it ended up breaking it along the lines of race, black versus white, now comes to find out that what they discovered is that the data itself, the way in which was prepared, started to expose that sort of bias in the way it was being interpreted, and it translated into, you know, discriminate behavior.

So that was quite quite a painful lesson. The good news is, is that they looked into it and caught that if they hadn't have looked into it or caught that then that would have translated into obviously, even worse, long term behavior. Here's another example. This comes from Compass see Oh, MPa, s, this was the correctional Offender Management profiling for alternative sanctions, long sentence, there are long title. There's an algorithm there that was being used to predict the likelihood that a defendant would become a repeat offender in the in the correctional systems there. And of course, coming out of there it translated incorrectly that a certain class or race of people was going to translate into twice as many false positives for repeat offenders as others that weren't of that race. So again, I yeah, not good, right. Ai bias is what that's about. So the questions are, what are the best practices to preserve the fair balanced use of AI? And how do you vet your original assumptions to avoid these kinds of mistakes? I find oftentimes, as organizations get focused on their question, or the problem they want to solve, get so so dedicated to solving that, that often don't stop to step back and look back at those questions, challenging the original assumptions on that. Alright, so bias in AI. Definitely a challenge and very relevant to small to medium business owners today.

Alright, let's, let's take a look at robot rights. Alright, so this apparently came out around 2017 After an EU Parliament report, proposed reasonable approach to dealing with this right now. Here's the there's an article by an attorney that specialized in this from some of the research that I saw out of different reports some of it from did Jin No, comma. I said that wrong, as well as a CNBC report, both pointed out that there's this attorney that is specializing in business law and IP that argued for extending workplace protections to robots pointing out that people are kicking the robots, right that, you know, they're pushing them around, right? So for example, some are some of the stated look. And here's what's driving that argument by 2025. Some predict that robots and machines driven by AI are gonna are gonna perform half of all productive functions in the workplace. Holy smokes, I don't know, it's 2021, almost 2022? I don't know I've done enough AI right now to know, I don't know if that's really the case. That's half of all productive functions AI. Anyway, whether or not you believe that, when it's not clear, what is not clear is whether the robots will have any worker rights.

And again, they pointed out that people feel hostility to him will kick over robot police resources or knock down you know, delivery bots, things like that. I've certainly seen some of that. So. So the question is, to what degree do you see this raised in the minds of people and where is the line for robot rights? And what does that look like? I'm pretty sure I Robot is not a documentary. Alright, here we go. Let's move on to number three, though. threat to human dignity now. Now, this one is really very sensitive, obviously. It's the whole notion of human dignity, right? It's the fact that humans possess some intrinsic value that caused them or says, hey, you know, they're worthy of respect, regardless of age, ability, status, gender, ethnicity. I subscribe to that. That makes total sense to me. A few years ago, Google announced a system this is I think back in 20. 18 An AI system with human sounding voice interactions. And in this particular case, I'm going to play an interaction for you. It's interesting, you can get this off off of Google site. So this is their, their material. I'm just going to take a clip of it here and share this. And tell me tell me what you think this is kind of interesting. I'm gonna pause right here.

...

Okay, so I didn't play the whole thing, but pretty incredible how lifelike how human like that voice sound, and it was generated off of the Google duplex system. So the question is, did duplex reduce the human dignity? In this case of this? This assistant, right, who is trying to figure out scheduling for for the salon appointment? Right. So was the dignity reduced? And and of course, there are other variations of this, right? Where, where AI can then in behave, you know, acting behalf or as though it's a human? Where's the dignity for that for the people and let alone in, in a healthcare situation? Right? What would that look like? Those are difficult questions, certainly to answer as they go to this next category. weaponization of AI, right? Immediately in the minds of some there's this notion of, you know, the whole the whole singularity notion, right? That AI becomes in control, right? And people fantasize about that part, for sure. The US Department of Defense calls weaponized AI algorithmic warfare. That's, there's this interesting article on think ml.ai that discusses some of this. So there's a category of this warfare called lethal autonomous weapons systems, le Ws and the whole notion is that you know, autonomous weapons that can locate and identify and attack and, and kill human targets.

And again, this sounds like the Tom Cruise movie Oblivion, right. In fact, I saw recently, session on 60 minutes, they aired something discussing the US and Australia's relationship with China, and pointed at a company that was producing weaponized drones as part of that puzzle. So the question is a where, where does weaponization of AI break ethical boundaries? Right? Or by definition, if you use AI for weaponization? Is it inherently unethical? Or you know, quite frankly, if AI is being used in a head up display of an Air Force pilot to improve decision making, saving the pilots life? Is that unethical? Boy more questions, and then there are answers. This area's fascinating to me, because the world's changing so quickly in this area. There are many, of course challenging questions to be addressed. Obviously, I've only scratched the surface here. But I wanted to let you know that in a future episode coming up soon, I've invited some guests from some AI companies to further discuss this topic and bring in various viewpoints.

So in this particular episode here, I want to lay the groundwork for it. But to wrap up here, I wanted to bring it back to the premise of this podcast channel, which is, what does it mean to you as a as an owner as an SMB owner, right? Someone's trying to run your business and you're trying to apply AI in these four categories that I briefly touched? What does that mean to you today? Does it have immediate impact? My take on it is this. As it relates to bias in AI? I think that this one has the most immediate effect on you as a business owner today. And to help address it, it means we have to ensure that we're asking the right questions, not only the questions that we want to get answers to immediately for our business, but we need to be able to have the discipline to step back and evaluate the broader context in which we're pursuing it. It also means that we need to course prepare data, the data sets that are representative and are not skewed. So those are several things today that that can be done now, in the second category of robot rights. I don't think that that's a direct impact for right now.

But you know, in the future perhaps the third category, threat to human dignity. I think this is a challenging one. Now, you know, when we've got things like deep fake, and tools like duplex, and that was a 2018. And here we are, you know, three and a half, four years since then right? You can imagine how far this is. So with deep fake and duplex and other such technologies where deceit or potential deceit can grow. I think ascertaining identity is crucial to your business. And this will grow over time. There's already been instances of AI driven technologies that have faked out business executives. So I do believe this is an immediate one. And then weaponization of AI. It's not a direct impact now, but you can see it's growing one. It's something that we need to watch, obviously, and address some more. All right, everybody. Thanks for joining and until next time, brush up on your AI ethics so you can put your business and your customers in the right lane.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com Now.

In this episode we look at can AI help me see better in a cost effective way!

Grant
Everybody, welcome to another episode of click AI radio. Okay, I have in the house today with me, someone I've been very excited to talk with. He and his organization reached out to me and I was quite surprised when I saw the cool AI solution that they have been bringing to the market. And Carlos has been giving me a little background on this. And I think you'll be excited to hear what it is he's putting together. But first and foremost, welcome, Carlos Anchia. You got Yeah. All right. There you go. Carlos, please, welcome and introduce yourself.

Carlos
Hey, Grant. Thanks a lot for having us on. Like you said, my name is Carlos Anchia. I'm the CEO of Plainsight AI. And we're bringing to market an end to end computer vision AI platform. I'm really, really happy to be here love talking about AI, computer vision, and how we can get more people to use it.

Grant
So okay, so tell me a little bit about what got you going down here. As you and I were just chatting a moment ago, there's so many components to AI, or it's such a broad range of technologies there. What got you thinking about the CV or the computer vision space? What problem? What How did you get started there?

Carlos
Yeah, that's a really good question. So like you said, AI, the breadth of AI is huge, you have deep learning, you have machine learning, forecasting, prediction, computer vision. And these are just a few. There's a lot of different applications for AI and places you can go down and succeed in. From our respect, we really, we really focus in on computer vision, specifically how to apply that to imagery and video. Today, there's a huge amounts of data going throughout the internet and in enterprise storage classes, where you can't really extract the value of that data unless you actually perform some sort of computer vision machine learning on that type of data. So we're really extracting the value of the picture or the video. So it can be understood by machines. So think of a dog and a cat in a in a picture, right? Those cases, the machine doesn't know it's a dog and a cat, you have to train it. And that's where computer vision comes in. And really, we got into it because we were pulled in by customers, customers of ours wanted to start doing more computer vision and leveraging our platform that we had around high throughput, ingestion, and event driven pipelines. So these customers came to us and hey, you know, this is great, we'd love to really use this for computer vision. And the more and more that kept happening, we kept retooling around the platform. And finally, the platform from end to end is purpose built to do computer vision technology. And it really allows us to focus in on on what we're good at today. Right? And that's really delivering value within the computer vision space.

Grant
So I remember the first time I wrote some of the OpenCV framework code, right. And that was my first introduced introduction to it. This is a number of years ago. And I started thinking, Oh, this is so cool. So I'm writing all this Python code, right, building this stuff out. And then I'm thinking, how many people you know, are actually leveraging this platform and look at even though open CV is cool, and it's got a lot of capability, it still takes a lot, you know, to get everything out of there. So can you talk about how you relate to that open CV? And what is it that you're doing relative to that? And how much easier do you guys make this?

Carlos
Yeah, so I mean, you hit the nail on the head there, right? So from a developer perspective, it's really around, I need to learn open CV, I need to learn Python, I need to learn containerization I need to learn deployments. There's a variety of different companies that, you know, they're all great in their own right, right. Every one of those companies that we just talked about organizations are contributing tremendously to AI. But from a developer's perspective, you really have to learn a little bit of everything to be able to orchestrate a solution. And finally, when you get to, hey, I use AI. Let's pretend we're looking at strawberries. Hey, look, I built a model that the Texas strawberry that is your over the moon excited, but the very next thing is around, okay, how do I take that and deploy it 1000 times over in a field across the world and understand how to make that in an operational fashion where you know, it can be supported, maintained update, and that's really where we have this this crux of an organization where it's really different building something on a on a desk for a one time use. And and there's a lot of wins through that process. But then taking that and operationalizing for business driving revenues driving corporate goals around, why would that feature is being implemented, that's really where we come in, we want to be able to take off that that single single path of workflow where it's a little bit of everything to orchestrate a solution, and provide a centralized place where other people, including developers can go and help build that workflow in a meaningful way where it's complete.

Grant
So operationalizing, those models, I find, that's one of the biggest, or the most challenging aspects to this, it's one thing as you know, to, to build out enough to sort of prove something out and get some of the initial feedback, but to actually get it into production. I think I saw MIT not long ago, maybe this a year ago, now, they had come out with this report, it was through the Boston Consulting Group as well, they'd mentioned something about, hey, you know, 10% of organizations doing AI are getting return on their investment. And, and, of course, when you look at all of the investment of the takes for the business to really stand up all the data scientists and all the ML work. And you can see why the numbers translate that way. So to me, it feels like not only doing this in the area of CV, but the problem you're really trying to solve it feels like is you're attacking that ROI problem, which is you could take this kind of capability into business say you don't have to stand up all of these deep technical capabilities. Rather, you can achieve ROI sooner than rather than laters. Is that Is that accurate?

Carlos
That's correct. And I think it's really through the adoption of technology and you hit a you hit a really strong point for us there around the the difference between it works and it's operational. That's that's really the path of your your, you're less there in the CV world and more there with the DevOps ml ops portion of it, getting machines running consistently, with the right versions of deployment strategy, that latter half of it, just as important as the model building pieces of it. But even after you get to that piece, you need a way to improve, and improvement in the model is very costly if it's not automated. Because I mean, you can just look at the the loss for a simple detector, like a strawberry, where, you know, if the model starts to perform poorly, you're not pulling as many strawberries out of the field. So you need a way to be able to update that model, quickly, get training data into a platform and push a new model back out. And it's really around how fast you can go end to end with that workflow. Again, and again. And again. And again, this is that continuous improvement that we have born into us from previous software development life, but really in in machine learning and computer vision, your ability to train, retrain, and redeploy. That is where you really get the benefit out of your workflow.

Grant
Well, that really confirms my experience with AI will. Typically I'll refer to the term that we've been using, as we call it a SmartStep. It's that notion that I need to be able to refactor my models and take in consideration that changed context around me, whether it comes in from the world or from the customer, or whatever that means, some level of adjustments taken place that begins to invalidate my previous AI model. And I need to be able to quickly make those adjustments. That's fascinating. How long typically does it take for you to do that kind of refactoring of your models? Is that Is it a day? Is it a year, a month? Or the answer is? Well, it depends.

Carlos
So it's twofold, right? So it's, it's hours to do that. But it really depends on the complexity of the model, and how long you have to train. But in an automated workflow, you're you're continuously adding data to your training set, that are lower quality predictions, where you can retrain automatically when you hit a certain threshold, and then validate the model and push that back out into your production alized environment. So it's it when you go to develop these sort of workflows. You really have to start with whatever I build, I know I have to improve on later. So that improvement cycle ends up costing a lot if it's not part of the initial discussion around how do we count strawberries, right? So it's evident you and I can nerd out on this. Let me shift the focus a little bit and ask about it from say your your customer Write from their perspective, what is it that they need to be able to do to be successful with your solution? What skills or capabilities do they have to bring to the table?

Yeah, and I think it's, I think I have this conversation a lot with our clients. And it's really less about them having technology around data science and building model, and more around a collaborative environment, where organizations, you know, they have a culture of success. But that culture of success is really borne by holding hands through the fire, it's, it's being able to commit and lean in when the organization sees something that's really important to them, either either from a technical perspective or revenue perspective. And it's these companies and these types of people that get to rally around a centralized platform where they can build and collaborate with machine learning computer vision applications. And, you know, it's it's a, it's really interesting to see the companies that succeed here, because it's really based on a culture of winning, right, where the wind doesn't have to be the hardest, most technical, logically difficult problem, because complexity really drives timelines. And if you're looking to change from an organization's perspective, start getting the little wins, get the little wins, start having some adoption within the company around, wow, computer vision is working. We've identified these problems in a few hours, we have a solution deployed, you start building this sense of confidence in the organization where you can take on those larger tasks. But you have to start with a build up, you can't just go right to the highest ROI problem. No one starts at human genome sequencing.

Grant
Have you, or do we got a problem? Yeah. Back up, back up. So So all right. So it means to me it sounds like as an organization to succeed with this getting my problem definition, understood or crisply put together first, what would be an obvious thing to do? But how long does it take for me to iterate? Before I know that I've got value, that I've pursued the right level of the problem? You You made an interesting comment a minute ago, you're like, oh, within a couple hours, I could potentially retrain the model and have that back operationally. That means if I can fail fast, right, if I can pick my problem space, get something out there operation, try it fail fast, and then continue to iterate with AI as my helper that that's really, really quite powerful is that the model that the your person?

Carlos
It is the model? That's exactly right. And it's not just hours to retrain, it takes hours to start, right. And just to highlight you kind of started with, we have to define that problem set first. So even after we define that problem set, a lot of times we have to go back and redefine that problem set, and really the piece around failing fast. It's it's experimentation. And do we have the right cameras? Do we have the right vantage point is the model correct, you want to be able to cycle as fast as you can through that experimentation phase. And sometimes you have to go back and redefine that problem set. Because you're learning more as you go, right? And you're evolving into okay, I now understand the corner cases a bit better. And with the platform, you really can cycle that quickly. I mean, machine learning at scale is really how fast you can iterate through improvement.

Grant
It's quite, I think it's quite a testament to how the AI just world in general is improving. I know that you and I were talking earlier, years ago, you know, when I first started writing some TensorFlow code and Keras code, you know, the, the time it took to fail was much longer, right. And then the cycles were huge and, and getting this down to a matter of hours or even a few days, you know, for an enterprise's is massive. What's the, from what you've seen terms of different industries? Are there certain industries that tend to be leaning into this and adopting it or the is there no pattern yet?

Carlos
No, there's a there's a definite pattern. And in 2022, we'll all see kind of what that what that's looking like. And it's really an industry that has traditionally not been able to go through that digital transformation. So think of think of think of a piece that's a very manual piece, right, like physical inspection, where humans would look at something, they'd write down their notes on a piece of paper, then that that item would go through either pass or fail or some criteria for rework. That's all possible. Now with computer vision years ago, that was impossible the accuracy wasn't high enough, plain and simple. It just took it wasn't, it wasn't better than a human. Now we have models that are better than a human for visual inspection. And these industries are digitizing their workflow. So it's not only the feature of computer vision, but it's also now I have a digital record of all the transactions, I have extracted video information, it makes auditing easy for those sectors that have a lot of regulatory compliance, those that require proactive compliance to audit requirements, as well as visibility. Visibility has always been an it's funny when we talk about visibility, but it's computer vision, but like a lot of human processes, that there's zero visibility in it there, it's really difficult to audit, you know, why is this working better or not? So having that, that digitization of the flow with the feature of computer vision allows us to extract the value. And industries like agriculture are going I mean, agriculture has been a leader in technology for a while, but now you're really seeing adoption at livestock and row crop with drone technology. It's a very rich image environment, medical space, medical space for computer vision in 2022 to 2028, is estimated to be billions of dollars just with medical imaging. And that's not that's not the the total addressable market for the hardware, it's just the imaging piece. So we see we see a lot of growth in sectors that are going through this digital transformation that are adopting technologies that are now getting to the point where they can get pushed down into the masses instead of just the top five companies in the world.

Grant
Excellent, it seems to you part of your comment earlier made me think about process optimization for organizations, and the ability to extract processes, you're familiar with process mining, right? The ability to extract, you know, out of logs of these organizations and doing something like that where you can produce this visual representation of that, and then building models against that, to optimize your process might be an interesting use case. Yeah, that's fascinating.

Carlos
That's a really good point, right? Because that's, that's a different portion of AI that can be applied to like just log analysis, that then would allow you to go back and Okay, now that we have the process mind, where can we improve along the process?

Grant
Yeah, yeah. Amazing. So many uses and use cases around around this CV area, for sure. So let's say that someone listening to this wanted to learn more about it, where would they go? How would they? How would they find more about your organization?

Carlos
You can find us everywhere, right? We have a website, plainsight.ai. We're all over LinkedIn, we have Twitter, we're on Reddit, we have a Medium blog, there's a Slack channel where we geek out around computer vision use cases and how we can improve the world through computer vision. We're really we're really out there and feel free if you have questions come reach out to us. We have amazing staff that are looking to empower people in AI. So if it's through just just a question around how does this thing work, we'd love to talk to you if it's Hey, we're kind of stuck in our journey. We need some help reach out to us we can help you.

Grant
That's awesome. Carlos I can't thank you enough for reaching out to me and for a listening to click AI radio, but also for reaching out and sharing what it is you are you and your organization are bringing to the market think you're solving some awesome problems.

Carlos
Thanks a lot, Grant. Appreciate it. always appreciated talking about computer vision and AI and thank you to you and your listeners and really appreciate what you're doing to the AI space.

Grant
Alright, thanks again, Carlos. And again, everybody. Thanks for joining and until next time, go get some computer vision from Plainsight.

What are some of the ways AI can benefit your business?

So with some of the current talent shortage is going on currently in the US the challenges for businesses to find employees or employees are going to be best for your organization. He got me thinking about what are ways in which I can ask AI Please make my job easier, right? What are some ways that I could make better decisions, whether it's finding the right employees, or whether it's building my business with the right business partners?

I stepped back and said, Wait a minute, what are the benefits of AI for business? There's many hype out there for sure. Let's look at a few of these. Alright, so one of these is around efficiency and productivity gains, for sure. Right. That's certainly where we're looking for the benefits of improved operational efficiencies for the organization. There's a case study that I saw, this was from [email protected], they were actually talking about, about an AI restaurant, excuse me about an AI driven restaurant.

And in this restaurant, they had experienced some slow service times in their drive thru. Also, when people came into the lobby service was slow, there's a high rate of employee turnover. There's all the human aspects of that, certainly in terms of being a great leader. But that aside, they looked at AI to see if they could identify some inefficient operational areas, what came out of it was, the AI helped them improve the speed of their service by 21%, during rush hours, right, that's, that's a really decent improvement. The other was, they improve the efficiency of their kitchen by 53% 53%. So the amount of time it took to actually get the order in, get get food, turn around, get things cleaned up, get food prep, that that sort of stuff.

And then of course, their sales, they said increased exponentially as well. Alright, so looking for opportunities to minimize your costs as well as to perform improvements in your operational efficiencies. That's certainly one of the areas for productivity gains. So that's benefit number one or a benefit, another benefit to consider, which is fairly similar to this, but it's all around improving the rate at which you do business, right, the speed of your business, perhaps one of the things that we found in this area is that it's key to focus your AI questioning on specific scenarios of your business. So the idea is rather than trying to, you know, eat the entire elephant, so to speak, metamorph metamorphic Lee is I don't, that's not even a word.

Anyway, rather than trying to eat the entire darn elephant, adjusting the AI models based on where you're focusing is critical. So create smaller AI models focused on a certain business scenario. Alright, number three, new capabilities and model expansion. And of course, you know, when you think about AI, sometimes, right? That's when we tend to think of other things like, oh, Elon Musk, or, you know, what they're doing with Tesla, or you know, the autonomous cars, all that sort of stuff. Certainly, there's those models out there, for sure, where new business models being created because of AI. But for for most of us, as we're getting started with AI, that's, that's kind of a step beyond where we typically see see organizations go for typically, you'll tend to start with an efficiency gain, you know, sales, improvement, gain some some things like that, then as you grow, then new opportunities for capabilities start to emerge.

Alright, here's another one. That's kind of interesting. It has to do with better customer service so AI can benefit in those areas as well. There's an example that I love on this one is we've all seen you've probably seen nor Domino's Pizza, they are monitoring so when you call and say, hey, I want a pizza. They've got some AI listening and they're trying to figure out hey, is that granted Oh, that's grant on the phone at that Larson home, well then order that guy some some sinister pics right or, or upscale, you know upsell him on that right? So there they eyes listening, it's determining who's on the phone, and then prompting their order takers to customize coupons based on who it is they're talking to. So, quote unquote, better customer service that might be one example. All right, here's another the another benefit is around monitoring of your business and how to improve that. Lots of times what we found in this area is, this requires the AI model to be running somewhere, there's kind of two approaches to think about AI models, there's, there's the approach that says, hey, just give me some of the insights of what you see looking backwards. And then there's also the aspect of using AI in the course of doing your work.

And this permits AI monitoring, right? So here's the idea, right? It's as perhaps, as you're going through a particular sale, while you're in the act of doing the sale, the AI model is running somewhere. And it's queried, right? It might be out on Amazon, or Google Cloud or Azure. In any event, it's running out there. And it's watching those business activities. And it's giving you some quote unquote, real time insights or feedback like, hey, the probabilities of this being a good client for you, has dropped dramatically, right? Or perhaps this is, probabilities are high for good one. Alright, so improved monitoring certainly is a key a key area, and it takes a little growth to get to that point, when you've actually got the AI models out there running, you don't have to wait for that to start getting the benefits. If you back up, you can start getting the analytics much sooner to put yourself in a position so you can turn on the monitoring. There's another one here another benefit tends to be around reducing errors that people make that you know, I guess what I'd say here is, this is a key reminder, I think that one of the benefits of AI really is I think we should view it as augmented intelligence, it's, it needs to be looked at as, as something that causes you to think or rethink what you might currently be doing. And sometimes the reduction in human error comes because the AI saw something that causes you to rethink your processes, and then you make changes. And sometimes many times those changes are beyond what the AI saw. But the AI SIRs or served in that scenario is a trigger or an instigator of the change.

So in that sense, it can help catch human error, but should always challenge it, right. And so we want the AI to inform us and say, Oh, here's things that our brain couldn't see. But sometimes, you know, doesn't mean the AI is always right. So we wanted to augment your intelligence for sure. Alright, here's the, the one that I sort of started with was around talent and Talent Management and, and, you know, using AI to gauge things like employee sentiment, although I worry about the whole big brother thing, right? Don't really want that. But to monitor the sentiment for sure. I'm not necessarily talking about employee productivity or things like that. Did they log on to their computer the right time? I'm not looking at that, but the way in which they interact or talk or think about things right, or, or to monitor evaluate the the nature of the behavior of your business partners, right? Do I have the right business ecosystem in place? And are these in fact going to be the right business partners for me using AI to augment my talent management, both internal as well as to those external organizations as well? So AI can be used to leverage those kinds of things as well. I've been interested to know what are the highest value areas for you, you can check out click at click AI or radio.com and subscribe and let me know what you think, everybody thanks again for joining and until next time, plan out your business AI benefits.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com Now.

In this episode, we take a look at curating those leads which come into your business using AI.

Is it possible to underestimate the value of a quality customer in your pipeline, we all have experienced what it means to bring a customer into your business, which later we are kicking ourselves saying, oh, man, I wish you didn't have that customer. Well, can AI do anything for us? Right to lead or not to lead? Right? That's the question to ask AI. Can you help us with this AI? Well, as you know, today, there are multiple AI tools out there which provide insights on qualified leads. Lots of times are looking at things like recency Right? Like how many leads, you know, came in today or yesterday? Or they're looking at frequency?

How often did the leads actually touch our organization? Right? Was it through some phone interaction? Or did they click on our sights? Or what did that look like? There's easy to action scores, that of course, you want to try to get in front of your sales teams. And of course, the goal is to leverage that info so that the outcome then becomes realized, right, and it turns into a valuable kind of customer. Now, one of the things that we want to do is we want to connect to a variety of systems downstream.

So one of the challenges around lead management and lead AI is you need to be able to organize the information from the earliest touchpoints and identification of who the person is, and follow their lifecycle all the way through to the realization of the income, or the revenue, I should say, coming into the business, or the rejection of it, or the later reclamation of that in the form of a of a refund. So number one, we want to think about how do we connect all of that data together and tell that story, really looking for a series of breadcrumbs, if you will, that ultimately can knit and tie that together. And for a lot of businesses today is you know that this is found in a lot of different systems, right from things like HubSpot, or you know, Pendo, or what have you, right that the data is all over the place. But number one, you want to think about fit, right? You want to think about who are your leads, right?

And how do they match what your ideal customer profile looks like? Do you even know what that is? Have you described that perhaps some refer to that as I see p. The world needs another acronym, right ICP. But nevertheless, this is a critical one, what's your ideal customer profile? What do they fit? What does it look like? Can I describe that, and in order to apply AI to leads, it helps for me to see that now, quite frankly, using AI to help describe that is actually very common practice. So if you think well, I don't really have my ICP defined. That's all right, run a series of AI analyses on the full lifecycle of your leads.

And in time, the AI will begin to discover what that looks like for you. So you don't have to have that first. In fact, the act of preparing AI for leads can help you to develop that. Certainly you want to understand, though, what's the leads activity? What are they doing? What's their ultimate intent? And of course, how often do they actually touch and interact with it with the organization? It turns out that in some cases, it's actually more valuable to reduce the number of leads in terms of, you know, all those we're working with in the organization and actually increasing our sales. So if I could reduce my leads, let's say in half, but of that remaining half, I have very high close rate, then my cost of goods sold starts to drop dramatically, and so becomes really valuable to the organization.

So it's all about finding revenue, obviously, revenue specifically that's untapped in the organization. This is one of those efficiencies. Laser optimization plays that we do on a business, right? It's not necessarily saying, Oh, we're going to go after a new business model or changing this or that this is looking within your current pipeline of the kinds of leads that come through, how can we optimize our behavior so that we return the best possible dollar to the organization. Alright, so, in general, that's the model. But rather than a generic model, I wanted to introduce five or six different scenarios around leads to consider. Alright, so one of these is around product, profitability impact. And I'll come back to these here in a minute, there's lead or customer segmentation.

Another or third scenario is around date or time of sale. There's refund impacts around leads, there's demographics, obviously, for leads, and then there's what we like to call destructive behavior. All right, so let me just to maybe peel the onion back, go down one more layer on each of these. So on this first one on product profitability impact, the real question is, what are the conditions that surround a lead that produces the higher profitability? And of course, as you know, a lot of groups are focusing on the sales itself, which is obviously good. But quite frankly, having your AI pivot on your profitability is a key question to ask your AI solution. So when you're working on leads, build that connective tissue to the ultimate products. And then not only that, but the profitability of those that starts to pay you back very quickly, in terms of any investments that you do with AI actually very strong impact.

Here's another one that pays back your AI efforts. It's around lead lead segmentation, right. And of course, it's finding those segments that drive towards higher sales growth, and of course, higher profitable growth and the ability to segment that, obviously, your benefits certainly marketing activities, pricing opportunities, as well as value delivered to that lead, who becomes a customer. Alright, so having the AI work on that highly valuable. third scenario for AI with leads, of course, you know, when the lead progresses to the point of sale, or for every touch point leading up to that sale, understanding the insights of those touch points, the timing, the areas of interest, etc, that leads some some very insightful AI analysis as well as predictive behavior. That certainly tipped you off as a business owner of when to proceed or when to stop pursuit, the sooner we can stop the pursuit of something that by prediction says, this will waste your time, then certainly the better the better than it is. I had an experience one time with an organization that was trying to sell something. And at one point, I was not getting the cycles. And the salesperson looked at me and said, I'm going to start to I'm going to stop talking to you. I said, Yeah, it's probably a good idea. So he figured out, yeah, I wasn't going to be the right one. So the better you know, the sooner Of course, we can find these, the better. So date, time of sale, meaning all the touch points throughout the lifecycle of the lead. Those scenarios, obviously, very critical.

Ask your AI to look into that. Number three places to put the AI for leads is around, you know, refund impact, of course, is when your your lead turns into a sale, of course, that ultimately then turns into a refund. And it's certainly critical to understand what those look like and how we can get away from and move away from those sorts of situations. The fourth, or excuse me, the fifth scenarios around demographics. So lead demographics. Now, sometimes this can also be aligned with lead segmentation, but not all segmentations are demographic based. And so that's why we break it out, because we see situations where organizations actually treat that as a separate segmentation. So within some segmentation that they'll have to then do further demographics work. Of course, it permits you to focus your messaging and your marketing into sub markets, where things have historically been lucrative or for which you have a high predictive probability.

So applying leads to this sub area around demographics for your leads, very, very powerful. Alright, and then the last one I mentioned here, is around what we call destructive behavior. And I recall early on when we first started applying AI for companies and we got to a point where we said to one of our clients, oh, and by the way, look, we've been able to prove out here with high probability you need to stop having salesperson x with with product why in this particular scenario, the client went home Wow Okay, hadn't even seen that and understanding those kinds of destructive behaviors things to stop.

So, in the same vein doing that with leads is also critical which is stop going after certain kinds of leads as early as possible and how asking the AI tell me what are those conditions or situations in which the lead produces destructive or bad behavior and let the AI find those bad situations so that actually becomes a critical thing to understand obviously, alright, so as you know, there's a range of AI solutions out there today certainly to help you with leads now whether use that we know one of them or you use click AI the real trick is to get control of the front end of your business and and certainly qualify with AI your intake process, obviously reducing you know, the less qualified leads the earlier than certainly it's better for the business. Okay, everybody. Thanks for joining. Until next time, get some AI for your leads.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your free ebook, visit ClickAIRadio.com Now.

So how does Elon Musk manage his time? Is it AI? Come join us and find out.

Hi everybody, welcome to another episode of ClickAI Radio. So Mr. Musk, how do you do it? Calling out Mr. Musk. All right. So of course we've I'm sure all seen the video where it talks about how he manages his time, he manages his time, apparently using timeboxing. And on some of these reports that I've seen, he says he spends 80% of his time doing engineering design work. That's pretty amazing. And that, some have said that he manages time down to five minutes slots, holy chicken soup, that's pretty tight. So what it means is that he plans out his whole day on five minute increments, that's quite the level of planning. I don't know for sure if that's what he does, but time boxing. So apparently what you do with time boxing is, as you plan it out, you put it on a whiteboard or a note paper, whatever. And then of course, you fill out the time that you think it's gonna take to get that done. All right.

Now, of course, there's some fallacies and problems with this right? One of those is, as humans, we seem to overestimate. We're overly optimistic, I should say in terms of how long things will take, we think it might take 10 minutes to do something. In reality, maybe it's 20 minutes, right? And of course, there have been studies that talk about people tend to choose the best case scenarios, right? When we're, when we're allocating our estimates for time. This, of course, makes timeboxing really difficult, which would look like if you had a piece of paper, a series of time slots at some increment. Now, apparently, five minutes for Mr. Musk, I would do more like 15 minutes. But hey, who knows? In any event, so when you come up with those, one of the challenges is, how can I improve my accuracy for that? Turns out, there's some tools out there today to sort of help you do this toggl, t-o-g-g-l is one of those tools to help you track your time and see how things are gone. This can of course be painful to do.

I listened to one guy, he has a technique where he asks you to go through and describe everything you did for two for two weeks. So every day you write down on a piece of paper for two weeks what you did. And then at the end, he calls it a time study at the end of the two weeks, and you really kind of have a sense of where you're spending your time. And I've talked to multiple people that have done this. And they all complain about it saying what a total pain. And yet how revelatory and insightful it is when you start to see some of the areas that they're spending their time that actually was, was lost to them, right? lost productivity and things that are meaningful and useful. Of course, one of the big objections to time boxing, which says, I'm going to plan out my day on some sort of increment and for each increment, here's here's a box of what I'm doing. One of the objections is around interruptions, right? So let's say you had planned out your entire day for that. Now, how do you deal with the interruptions? I know some people to deal with that. They'll create two columns. They'll have one column that says, here's the my original plan.

And then and then they'll have another column, which is the revised plan, right, which Well, here's really what happened, right? thought I was going to be doing this activity of this task. Turns out, I did something else because I was interrupted. I think it was Dwight Eisenhower. That said, planning is everything but plans are nothing. Well, so of course the bottom line is we need to be able to pivot in order to adapt and adjust certainly. And of course, we need to avoid over scheduling too because some also have said that the more you try to do, the less you will accomplish. I think there's something to that. If I context switch too much, man on a five minute increment. Wow, Mr. Musk, if that's true, hats off to you. Five minutes in That's that's that's pretty tough to context switch constantly like that. I think that might be some overscheduling. Who knows? Well, that's why he's where he is, I suppose. Others have said that the way in which they plan, they'll organize it based on the intent for their business. So they'll plan tasks and activities in the morning, that have to do with money generation and revenue generation activities, when they're the most fresh.

Those are the areas they want to put most of their intellectual capacity to. And so they'll do those types of time boxed activities in the morning. And in the afternoon, it's more cost management expenses, things on the cost side of the balance sheet, that's interesting technique, then I started to look into AI for time management, you've probably seen there's an app out there called timely think it's from a company called memory AI Anyway, you can download, I think the way it works is you download it onto your Mac or your Windows computer, you start connecting it to your Google calendar or other calendars. And over time, you start training the AI to start recognizing how you're spending your time, and you ultimately are teaching the AI model, something about your work schedule and the way your time is being spent. Now, why would I even talk about time management? What does that have to do? Well, one of the things I've noticed is that, given the increased demand, of course, on SMB owners, finding ways, obviously, to optimize our schedules is certainly important. But the real reason is, many view activities to transform their business as too much of a stretch, right? Because we're so busy running around just keeping the lights on as a small to medium business owner, who can think about adding more activities to our days. So you know, things like, hey, maybe I can improve my decision making by looking at more analytics, right?

Or, hey, I could spend some extra time making changes to my business processes through AI insights. Yeah, sounds interesting, but also sounds like, hey, you're being told to take your vitamins. And who wants to do that, right? That's not too exciting. So what you really need to do my experience with his bins is we need to find what's the real business cause, or the motivation to do something like that, right? So sometimes it might be competitive pressure, or it might be a business model threat, right? Or might be growth goals or, or even cost pressures. And when something like one of those is understood first, then finding the time by doing some kind of time analysis helps us to free up the cycles to address this. So I kind of have to find the why right? Why is it that I would do this? What is it that's pushing me, right? And lots of times as humans will move away from pain faster than will move towards a goal. So if you can find, where's my pain point, my business? What is it that would drive me and give me the motivation to make this kind of adjustment, then I can start finding the business context, where I'll start making the changes then to leverage new techniques such as putting AI into your business. I was talking with a large retail organization not too long ago, they said that, before the covid 19 pandemic, they had discussed providing curbside service, right.

But at the time, the estimates in the organization, they said it would take about two years to roll that out. And then when the pandemic hit, they were able to roll it out in four weeks, right? So suddenly, obviously, there's this, there's this pain, right? There's this pressure, we have to do business differently, the context has shifted. So finding the motivation, then allowed them to find the time and then suddenly, a lot of the red tape and other things move out of the way. So find your motivation for change as a business owner, right? Then find the time to pursue the change. Those are the precursors for most organizations to pursue an AI strategy. Now many times of course, like I said, we move away from pain more quickly. So where does your business hurt? And then let's get AI on the job. All right, everybody. Thanks for joining and until next time, find your motivating factors to set aside the time to apply AI in your business.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In this episode, we take a look at the skills gap that's really affecting your business.

Welcome everybody to another episode of ClickAI Radio. All right. So in overcoming the lack of skills in your business, now I'm going to start with, with the focus on AI itself, right, and what that looks like. And I'm going to pull here from certainly my own experiences, but also this is a report I saw from Deloitte, they had they'd said that, hey, they found that those companies that are seasoned with AI skills, they didn't feel they had a big skills gap. Oh, that makes sense. Right. The other thing they pointed out, though, is that the companies that are doing a lot of AI right now are really focusing more on transformational projects than on cost reduction. So from their perspective, hey, when you're getting started, you're often looking at cost reduction as a way to leverage AI. Hmm, I don't know my experience, I've found that it's been more around sales and say, of course, in the supply chain area, there certainly cost reduction there. So all right, that makes some sense. It also said that they broke it into three categories.

There were those companies that were seasoned with AI, then the other categories are skilled with AI, and then the other word, the starters, and said that the seasoned ones are of course relying on their own internal skilled resources, had stair season, I guess, by definition, whereas the starters, were leaning more on other companies, right, they're outsourcing some of the AI itself. A couple other points here from this report. So they indicated that two thirds of the seasoned adopters are currently training their developers to create new AI solutions. They're providing more training for more of their employees to do AI work. So rather than leaning away from it, they're actually leaning more into it, because of some of the benefits that they're pulling from it. In another report, this comes from the hill, they said, like there's concerned about AI, an AI workforce, specifically shortage, an AI workforce shortage in the United States becoming a top national security priority. And there are some calls for some legislative action. They're trying to compete with other countries like China and others. But you know, some see little data on the actual AI labor market dynamics, is it really, is there really a workforce shortage? And if so, what does that look like? Now? How do you even quantify that, so you have to back up and think about the skills of AI itself.

Now, given that my intent here is true to target and speak with the small to medium business owner, for the most parts, I'm never gonna say go build your own data science team, it's too expensive, as well as you're not going to get the ROI on it. As we all know, we saw that report from MIT last year. That said, Only 10% of companies that are building out their own teams like this are actually achieving achieving the financial results. So we're not we're not going to focus on that, rather, I'm going to target let's go after another approach. There's certainly a shortage of AI skills in the US, right? But hiring your own team to do that isn't going to be the best way. So if you use an AI service and rent the capabilities to get the insights at a fraction of the cost, then if there's not an AI skills gap from your, from your organization, excuse me, then what really is the gap that you're facing? what's what's missing, in terms of you achieving the effects. So the benefits of AI? Well, there is still a skills gap. And the skills gap, though, comes in areas that are not too strange sounding to us. In other words, it's not going to be hey, can your can your particular business analyst deal with, you know, logistic regression algorithms? No, no, no, that that's for the AI people to figure out but as a small to medium business owner, you don't worry about that part. But there are some skill sets that you do need to have or strengthen in your team in order To be effective with leveraging the effects of AI, so I'm going to pull from this report this comes from, from go scale, excuse me, yeah, go go skills excuse me, they they reported that there were three hard skills lacking in recent graduates.

This is interesting, right? And it's stuff you already know, right? It's writing proficiency, public speaking, and data analysis, let's apply this to AI, right. So as a small to medium business owner, you want to play AI, you don't need to go deep on all the AI skills. But what you do need to go deep on is your ability to leverage those insights into your business. And that's where you need to spend the time building up your team. Let's look at writing proficiency, Well, certainly AI does a great job at identifying those things that our our brains can't see. So it's also critical that we describe meaning we can write down what the business problems are, and what the challenges are. So writing proficiency does affect the ability to properly point to and use and therefore adopt AI in your small to medium business, public speaking Well, this, of course, dramatically affects your ability to communicate. But specifically, the ability to communicate the transformation, the iterative transformation that your business goes through, when you apply AI, you know, you never apply AI, once you apply it, make a couple small steps, and then apply it again, you do it incrementally, but you also need to be able to communicate, what's the journey that we're on? Alright, so public speaking, whether that's external to your customers, where they're seeing the impacts of this, or internally, the ability to speak is critical. And then third, here was data analysis. And of course, you know, the ability to create insights on your data, use an Excel or whatever other tools, tableau, etc.

Those are certainly critical. But in this case, they're critical to help point out that there's a case for AI. Let me let me illustrate that. I was talking with a business executive actually, just last week, who made a statement that although his engineers could do data analysis, they had that skills to use the you know, the various tools to do that. They could not estimate their supplier forecasting, because the data was spread out across so many systems and was so many levels deep, that just the the, you know, analytics tools couldn't actually give them the insights they needed. This is a great place for AI to step in, right. And they were wise to recognize the limits of the data analysis and said, we're not solving the problem, right, the data spread out too much. And so they saw the opportunity for AI to help out. So yes, be skilled the data analysis, but also be skilled at knowing what its limits are. Right. So those are three hard skills that your teams today already have, but course need to strengthen and bone up on. Let's look at some soft skills that was reported out here by this ghost skills report. The five most lacking soft skills in employees they pointed out was number one, critical thinking and problem solving. Number two, attention to detail three communication for leadership and five teamwork. All right, so let's let's just pull these apart here real quick.

So critical thinking and problem solving, obviously selecting the right use case to apply AI to solve business problems. That is critical fact that thinking is so critical that we've seen multiple failed AI use cases in the market, right? And of course, it gives a black eye on AI, right? It's like Oh, you're applying AI how you're doing what with it? No. So stay is a small to medium business person stay in the guardrails of sales, use cases and supply chain use cases, right? Look in those areas. And don't go outside of that elite, at least for now. Alright, as AI matures and grows. And as you feel like hey, you've got a handle on this, then certainly go wider. But for now just focus on in those two areas. Number two, the attention to detail. Well, I'm an admitted nerd, and it's so it's easy for some of us to pay attention to detail. But this in fact, becomes our weakness at times, right? We, we can't always see the business problem. So it's critical to help your people certainly have attention to detail, but also be able to step back and look at the context of that.

And then this third bucket here communication, leadership, teamwork, you'll need to apply AI transformation as a team, alright, it's because it will affect business processes as you transform your business getting the insights from AI. So it turns out that your organization already has the fundamental skills for AI, not to build your own data science team, but rather to apply the resources. But what it means though, is that you need to strengthen those current skills so that you can be more successful at applying. All right everybody. Thanks for joining and until next time upskill your team to leverage AI services to transform your business.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In this episode, we take a look at the AI hammer that's looking for a nail.

Hey, welcome everybody to another episode of ClickAI Radio. So I was talking with a group of technical people recently. And they talked about a very cool solution that they prepared. So I asked this question I said, so which part of your business will this benefit? And they said, well, we're still trying to figure that out. I said, Oh, you've got a solution looking for a problem. Is that exactly. I said, Well, you know, if you're not careful, AI can turn out to be the same thing, right? One of the biggest mistakes you can do with AI, in fact, this happens quite often is it's the tendency to create AI solutions. without sufficient understanding of the business problem. Everyone gets excited and enamored about the technology, well, maybe not everyone, but some do.

And so it turns out that a crisp definition or description of your business problem is of course critical. However, as you know, with AI, you can, you can do a bit of both, in fact, that encouraged both, it kind of looks like this. So start by building your AI solution. And what you do, the reason you do that is you run it against some of your data. And it gives you a sense, without any sort of tutoring or guidance on your part, it gives us a sense of identifying what it can see, right, and what it does is, it lets you understand the potential areas or problems that the AI can start providing answers to. So with those kind of insights, initial insights, you can go back to the drawing board and rethink the kinds of problems which may be addressable with the kind of data that you have, that's actually really critical to do early on. Some groups will spend a lot of time doing lots of data curation, cleaning stuff up over long periods of time. And the whole time, the other hand is saying, Oh, these are the problems, we want to go solve with it. And then the two don't meet, right, you don't have the kind of information that's even needed, that AI would need to dress it.

So what you're really trying to do is an interactive iterative, give and take process, as a way to think about it. Now, I've never seen a situation where the first time the AI solution was created, that it was ready to be used by the business. So you should get that out of your mind. Rather adjust your thinking, to consider discovering what the possible problems are, that can be solved with AI by looking at your data, then go back, evaluate the business problems, and continue to iterate and fine tune that right, you can certainly grow and build on your data set after you do that, but save a lot of time by doing that. All right, well, let's talk about what actually is a more significant and critical way to look at this. So in reality, as you know, most of our businesses have many existing systems, right? And our business information, of course, is spread across the systems with lots of redundant data. Well, we of course, rely on our teams and our brains, obviously, to logically connect the information together on these systems, so that we can have the kind of information that we as humans need in order to make decisions. Now, when we use AI, we want to think about recreating that kind of experience. And you can go through the process, certainly of saying let's integrate all of our systems together. But that can be a real challenge.

So for example, let's say that you have three systems. This will be a little bit oversimplified, but let's say you have one system for leads and lead management. Then you have another system for ads and ad campaigns to those leads. And maybe you have a third system that's used for closing sales and servicing. All right, a bit oversimplified, but it's sufficient for the example. So it turns out as you know that your customer Numbers journey unknown to them touches each of those systems. And of course, in many cases, those individual systems provide analytics and insights in their respective rights, which is cool and helpful. But your brain ties that all together into an overarching customer journey or customer lifecycle. So this is where AI can work wonders. So even though these systems may not be connected, or even integrated, using AI, along with some data management, the AI can look across that entire data set from those three systems as one data set, right and view the whole customer lifecycle.

So then with that the the AI can identify what are the probabilities that a certain lead will translate into a closed sale, even though your own systems aren't integrated to sport that AI can actually do that work and give you those insights. So you don't need to do that integration in order to make that happen. So I encourage you to focus less on getting all those systems connected, or even transfer, you know, transforming or moving your data to one system that does it all. Certainly, that's good. And there's some benefits of doing that. But the key is, don't focus on getting all those connected, but rather focus more on understanding what the predictive golden nuggets are that are already hiding in plain sight across your integrated data, not necessarily integrated systems, but you integrated data and let the I see that.

It turns out that this is a nail that the AI hammer can use. Alright, thanks for joining until next time, everybody. Get a holistic view of your disconnected systems with AI.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.