unSILOed with Greg LaBlanc

In a world challenged by the politicization of data, contradicting evidence, and an onslaught of information, could the key to more effective and informed decision-making be as simple as, thinking like a scientist? 

Professor of physics Saul Perlmutter, professor of philosophy John Campbell, and professor of psychology and law Robert MacCoun combine their interdisciplinary minds in the book, Third Millennium Thinking: Creating Sense in a World of Nonsense. The book explores the essence of scientific thinking and how it can be applied to practical societal issues. 

Saul, John, and Robert join Greg to chat about the genesis of “third millenium thinking,” the role of values in scientific judgment, and the importance of teaching probabilistic thinking and experimentation.

*unSILOed Podcast is produced by University FM.*

Why is it so hard to just walk the Humean line and to be very explicit about sorting out these differences?

09:03: [John Campbell] The message of the book is: keep the Humean line as far as we can, separate the facts and values. Scientists, we all know, have a lot to tell us about the facts, but we, the people, are the ones who tell you about the values. And then, I think, that has to be anyone's first brush, sort of partitioning off the boundaries of science, to keep that Humean line. But then it does get complicated when you think about things like mental illnesses and so on, where you're asking not just, Is this condition that a person had? Is this a real thing? But you're also asking, Is this a bad thing? Is it a bad thing for a person to be like that? So with autism, is that just neurodiversity that is not really within the sphere of medical treatment at all? There are boundaries there as to where science is authoritative and where the people have a voice. And this kind of issue clearly has to be a debate, it seems to me. It's not something for professionals only.

To what extent must we trust the processes within the expert community?

37:04: [Saul Perlmutter] I think that there's a difference between really understanding a field enough that you don't need the expert and having some understanding of how science works so you can recognize which experts you are more likely to trust.

They myth of lone genius

47:26: [Robert MacCoun] This mythology of the lone genius, I think, is very much the antithesis of third-millennium thinking, this notion that it's because I'm brilliant that you should listen to me. And that's really not where we think the authority of science comes from. It's not from the IQ of the scientist. It's from the procedures—the hoops you have to jump through to make your ideas work. And it's those procedures that give you credibility, not just brilliance.

If you hold to the Humean line, why would your value judgments about what's good or bad for society impact your causal arguments?

32:25: [Robert MacCoun]  The role of standards of proof when you're dealing with probabilistic evidence, you need to weigh two kinds of errors: false positive errors of claiming a hypothesis is true when it's not, and false negative errors of saying the hypothesis is wrong when in fact it's true. That is not a scientific matter. That is a matter of values. We can't avoid it. In dealing with uncertainty, we have to impose some sort of standard of proof. And so, under the Humean model, you take values very seriously. I don't think we would argue that values are simply outside the domain of science.

Recommended Resources:

• David Hume
• Neil deGrasse Tyson
• Supreme Court overturns Chevron
• Article: Supreme Court strikes down Chevron, curtailing power of federal agencies

Guest Profile:

• Saul Perlmutter’s Profile at UC Berkeley
• John Campbell’s Profile at UC Berkeley
• Robert MacCoun’s Profile at Stanford University

Their Work:

• Third Millennium Thinking: Creating Sense in a World of Nonsense

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Can you truly build exceptional relationships in a professional setting? What are the secrets to effective communication and the role of social psychology in fostering strong connections?

David Bradford is a Senior Lecturer at Stanford Graduate School of Business, having taught the legendary “touchy feely course for decades, and the author or co-author of several books. His latest work is called Connect: Building Exceptional Relationships with Family, Friends, and Colleagues.

Greg and David discuss the practical benefits of Stanford's T groups, where participants develop crucial skills like clear communication and conflict resolution. David explains the importance of seeing feedback as the start of a conversation rather than a critique, emphasizing the need to understand the impact of our behaviors on others. Explore the contrast between how children and adults handle conflicts and learn why genuine curiosity is key to bridging communication gaps.

*unSILOed Podcast is produced by University FM.*

Are we building organizations to build friendships?

35:08: I think more and more leaders in an organization are realizing that building collaboration is important. Now there is a danger with building friendships, which I said before, because there are many organizations that need to cut back, are too big, are facing adverse times, and have to do cutting. So it has to be clear that we're building a relationship to get the job done. That's the purpose of an organization. We're not building an organization to have warm and fuzzy friendships; that's the means to an end, not an end in itself. And we have to realize that there are going to be times in which we're going to have a reduction in force. There are going to be times in which we get rid of people.

Organization are held by network of relationships

02:49: Organizations are held together not by the organizational chart, but by the network of relationships that people have in the organization.

On vulnerability in leadership

37:41: The research on vulnerability shows two things: leaders who are vulnerable about their core competence lose power…[38:12] So leadership, which questions your basic competence, does lose power and does lose influence, but vulnerability, which shows your humanness, is different.

Do you have an understanding of human psychology, social psychology, and cognitive psychology to have a good relationship?

08:56: In general, the more that we know, the more we can start to see them as an individual, as a unique person, which is, I think, what all of us want and which all of us are. There is no rule that applies equally to all people. So I think that the broader our knowledge, the more we can find out. What approach allows me to understand you as an individual, you as a person, and we can move beyond me treating you like an object, which you don't want and I don't think I want.

Recommended Resources:

• Neo-Freudianism
• Gallup, Inc.
• Jeffrey Pfeffer
• Petrarch

Guest Profile:

• Faculty Profile at Stanford GSB
• Professional Profile on LinkedIn

His Work:

• Connect: Building Exceptional Relationships with Family, Friends, and Colleagues
• Influencing Up
• Influence Without Authority
• Power Up: Transforming Organizations Through Shared Leadership

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More than four years after the pandemic began, a source for COVID-19 still eludes scientists and public health officials. The mystery has given rise to a slew of hypotheses ranging from natural zoonotic transmission to lab leaks. But to get to the bottom and find the real source of the virus, you have to start with the evidence. 

Alina Chan is a scientific advisor at the Broad Institute and the co-author of the book, Viral: The Search for the Origin of COVID-19. She and her co-author Matt Ridley follow one evidence thread to the next in order to get closer to the truth. 

Alina joins Greg to chat about the two dominant hypotheses on COVID-19’s source, the challenges and methodology of identifying a virus’ origin, and why it’s crucial we find out where COVID-19 came from. 

*unSILOed Podcast is produced by University FM.*

The debate over studying high-risk pathogens

43:50: There are definitely people who think that all of this research should be banned, but I think that there should be a certain amount allowed to continue. Again, this sort of research, where there's actually any pandemic risk, constitute an extremely small fraction of virology. So, I would say, like, less than even a percent, maybe even less than that. So, most virology doesn't even concern animal viruses. And those that do often do not pose a risk to cause outbreaks in people. But there are these types of research projects that are now becoming more and more trendy around the world, following in the footsteps of U.S. leaders to take these pathogens that could cause outbreaks in people and study them in the labs. And it's unclear where the risk is because some of these labs are doing it at such low biosafety levels. Or is it because there are so many of these high-biosafety labs now, and the work is increasing, yet in these labs there's still room for human error.

Public vs. scientists

51:56: Your political affiliation doesn't determine anymore whether you think this virus was natural or came through a lab. I would say that the difference between the public and scientists is that scientists, especially experts, tend to lean on priors very heavily, as well as peer-reviewed literature. 

Is the focus on avoiding retroactive blame or preventing future research constraints?

43:03: I think it's both. So, since then, and over the past few years, you've seen so many letters by virologists. Dozens of them have signed letters saying we are totally fine in the U.S. We do not need any more oversight or regulation. We are good at self-auditing and self-inspecting. We don't need any external oversight over our work. So, there's a clear fear amongst virologists that if this pandemic was started by a lab accident in Wuhan, they would become constrained as well, and that people would also perceive them to no longer just be the good guys but to be a source of risk and danger.

Why bats carry so many viruses

24:45: I think bats, aside from humans, are probably the most interesting mammalian species out there for virologists. It's because these bats have been found to carry so many different types of pathogens, many of which can jump into people. So, like Ebola, coronaviruses, both MERS and SARS were found to have come from reservoirs, for example, but they're quite similar to humans in the context that they live in large groups. So, you go into one of these caves, easily millions of bats in there, but actually, they're quite different. So, they can fly, and so their body has to adapt to handle that really high heat that happens when you're flapping your wings at such a high speed. And that is related to traits in bats that help them to coexist with so many of these viruses. So, these viruses, while they cause very severe diseases in people, in bats, they just live mostly in the gut and don't cause any severe disease. So, bats have this invulnerability in a sense to all these very dangerous pathogens.

Recommended Resources:

• Dr. Peter Daszak - EcoHealth Alliance 
• Wuhan institute of Virology
• Ralph S. Baric
• “The proximal origin of SARS-CoV-2” | Nature Medicine
• Good Judgment case study

Guest Profile:

• Faculty Profile at Broad Institute 

Her Work:

• Viral: The Search for the Origin of COVID-19

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What if the key to managing stress and finding meaning lies in the simple rituals we perform daily? How can engaging in rituals can be a potent tool for combating anxiety and fostering a sense of community?

Michael Norton is the Harold M. Brierley Professor of Business Administration at the Harvard Business School and an author. His latest book is titled The Ritual Effect: From Habit to Ritual, Harness the Surprising Power of Everyday Actions.

Greg and Michael discuss Michael’s groundbreaking research on the distinctions between rituals, habits, and compulsions, and delves into how these practices—whether ancient or self-created—provide essential structure and purpose in our lives.

Michael and Greg dive deeper into the impact of rituals within organizations and relationships. Learn how companies can use simple, coordinated actions to bolster unity and core values, and why rites of passage are crucial for marking life's transitions. Michael also highlights the strong correlation between shared rituals and relationship success, emphasizing the importance of mutual participation. 

*unSILOed Podcast is produced by University FM.*

Do successful companies leverage rituals to foster a sense of belonging and a common purpose?

24:45: Very often, the rituals that companies have are really intended to reflect a specific value that the company cares about. I was just talking to someone who told me at their company, what they do is every Friday: It's a smallish company, so they have an all-hands  every Friday, and each group says something that another group did that they're grateful for. Somebody on another team helped me out with this thing I was working on, and they do it every Friday. Now, they could do anything—they could say, "Think of another group that made you laugh this week. Tell us about that." But they don't laugh; they do gratitude. And they're trying to show in that moment one of the things that we care about in this place is helping and gratitude. You can have a silly mission statement that says, "Gratitude and all these platitudes," or you can use these kinds of regular rituals to show repeatedly: This is the value that we really care about. And families, when they have rituals at dinnertime as well, they're very often communicating a value that they really think is very important.

Rituals can bind us and separate us

23:52: It's not that you do rituals and it's all warm and fuzzy; it's that they can bind us together and they can separate us from other people. So there's tension—it's like a risk-reward kind of relationship with ritual.

Exploring how emotions drive action

17:18: I think the way that humans are built, unfortunately for us, is that we can't change our emotions when we feel like it. So, in other words, it would be amazing if I felt sad, if I could snap and be happy, just automatically, just instantly; we could easily be built like that.

Do we customize rituals according to our needs?

08:17: In fact, even in our own lives, we're changing them—rituals—all the time. And the reason I say that is because if they stop working for you, you could say, "Rituals obviously don't work. I'm never doing them again." Or you can say, "I must have the wrong ritual. It seems what people are likely to do is say, 'I must have the wrong ritual.' Let me mix it up a little bit and see if that will help." And it really, to me, speaks to how deeply ingrained they are in us because we are, in a sense, ignoring evidence from the world that not all of them work, and we continue to do them, modify them, and shape them as though if we keep doing that, we'll get to the optimal one.

Recommended Resources:

• Clifford Geertz
• Bronisław Malinowski
• Disenchantment
• Serena Williams
• Rafael Nadal
• The Pianist and the Lobster
• Hedonic treadmill
• Stanford marshmallow experiment

Guest Profile:

• Faculty Profile at Harvard Business School
• MichaelNorton.com
• LinkedIn Profile
• Wikipedia Profile

His Work:

• The Ritual Effect: From Habit to Ritual, Harness the Surprising Power of Everyday Actions
• 365 Ways To Change the World: How to Make a Difference-- One Day at a Time
• Happy Money: The Science of Happier Spending
• Ted Talk - How to buy happiness

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Humans are creatures of habit. It’s even wired that way in our brains. But what impact does habituation have on personal happiness?

Tali Sharot is a professor of neuroscience at University College London and researches habituation, adaptation, and other cognitive biases. Her latest book, Look Again: The Power of Noticing What Was Always There co-authored with Cass Sunstein explores how habituation leads people to stop noticing both good and bad things they’re accustomed to and the benefits of breaking free from those habits. 

Tali and Greg discuss why people are more likely to feel less excited about good things over time, how taking breaks from those habits can restore the good feelings, and optimism bias discrepancies in stressful environments.

*unSILOed Podcast is produced by University FM.*

What is optimism bias?

20:41: Optimism bias is our tendency to expect to encounter positive events more on average than we do and underestimate our likelihood of experiencing negative events in our lives. So, underestimating our likelihood of going to prison, getting a divorce, being in an accident, and so on. It is not necessarily how we will react to those events. So, it's not that I think if I get divorced, then I won't feel bad. It's mostly, at least in my studies, about what is the likelihood of the event.

Can bad news be good news?

41:07: Bad news doesn't necessarily mean that it results in a negative feeling for you. It can result in a positive feeling, and that's why it seems like people are attracted to bad news when, in fact, it's not really bad news. So, all this is like celebrity breakups; to some extent, it can make people feel good because, well, everyone has problems. So now I'm feeling better about my own life.

Three main motives for searching information

40:12: It's instrumental utility, cognitive rewards, and affective rewards. So, that's our tendency to want good news over bad news. Now, all three will drive your decisions on whether to seek information or not. So, you will for sure seek a lot of negative information if the cognitive reward is high and instrumental utility is high. But all three things matter together. And there are ways for us to tease them apart and show that all of them matter. So, that's why, despite the fact that you feel like you go after bad news, you still have the tendency to want good news.

The difference between optimism about our own lives and pessimism about the external world

33:39: What we see is that people are optimistic about their own future, the future of their family, and the future of their kids, but they're not optimistic about the world at large. In fact, they are somewhat pessimistic about global issues and about the abilities of the leaders. And let me give you a few examples. Let's take AI. So, people say AI will take more jobs than it will create, and 75 percent of people say that. It's three out of four, but only one out of four, 25%, say their job is at risk. Out of every four people, three say that they're very optimistic about the future of their family; again, 75%, but only 30% say that the next generation will be doing better than the current one.

Recommended Resources:

• Daniel Gilbert
• Laurie Santos
• Aaron Heller
• Daniel Dennett
• Milgram experiment

Guest Profile:

• Faculty Profile at University College London

Her Work:

• Look Again: The Power of Noticing What Was Always There
• The Influential Mind: What the Brain Reveals About Our Power to Change Others 
• The Optimism Bias: A Tour of the Irrationally Positive Brain

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Despite the Hippocratic Oath of “do no harm” that all physicians take, a dark side exists in the medical field.  

Carl Elliott is a professor of philosophy who teaches bioethics at the University of Minnesota. His latest book, The Occasional Human Sacrifice: Medical Experimentation and the Price of Saying No, shares the stories of some of the most egregious cases of medical abuse in history and the whistleblowers who tried to stop it. 

Carl and Greg chat about his own experience blowing the whistle after a psychiatric study went awry, the factors present in the medical field that lead to unethical and abusive studies, and the cost of deciding to take a stand.

*unSILOed Podcast is produced by University FM.*

Episode Quotes:

On protecting vulnerable populations in research

19:16: One of the things that you see running through, I would say, at least 90% of the scandals that we look at in the class that I teach on research scandals, is that you're dealing with research subjects who are vulnerable in some way. They're often poor, they're uneducated, they're institutionalized, they're mentally ill, they're children, they're mentally disabled, they're unable to look out for their own interests in the way that an ordinary competent adult is. And those populations are easily exploitable. We should have protections for those people—serious protections. We have, in our honor code, the Common Rule; there are federal guidelines that say this: you need to take special care with vulnerable populations.

Is an honor system enough for medical research?

14:18: In other businesses out there, factories, restaurants, mines, fisheries, and so on, you have a regulatory system, like a full-blown regulatory system with inspections, safety rules, and so on. There's nothing like that in medical research. The oversight system is an honor system. Medical researchers are just trusted to behave honorably and honestly. And I think there are real questions about whether an honor system is up to the task of overseeing and doing the regulatory, quasi-regulatory job of managing what is now a multinational global multi-billion dollar industry.

Do we sometimes confuse the organization's purpose and the people in the organization? What and how does this idea of organizational loyalty play out?

32:22: It's really institutional loyalty, at least in academic medicine, and not loyalty to some higher mission—in the case of academic medicine, to the sort of humanitarian effort of doing medical research. Because I do think that there is this sense of physicians who have chosen to work in academic health centers rather than do like the vast majority and work out in the community somewhere, there's a reason for that. And the reason is science and medical advances and the many people that you could reach by developing new and better treatments, right? I mean, it's that tension between those humanitarian goals of the enterprise as a whole and the interests of individual patients that needs to be balanced.

The toxic mix of research funding and authoritarian hierarchy

13:46: There's a very rigid status hierarchy; it's extremely authoritarian and competitive. The coin of the realm is not patient care; it is research, particularly now research funding. In fact, research funding is more important than the actual research. And so, you can see, when you put all these things together, you have a very toxic mix.

Show Links:
Recommended Resources:

• Nuremberg Code
• Tuskegee Study
• Willowbrook State School 
• Cartwright Inquiry
• Paolo Macchiarini
• Listening to Prozac: The Landmark Book About Antidepressants and the Remaking of the Self by Peter Kramer
• Alexis de Tocqueville

Guest Profile:

• Faculty Profile at University of Minnesota
• Professional Website

His Work:

• The Occasional Human Sacrifice: Medical Experimentation and the Price of Saying No
• White Coat, Black Hat: Adventures on the Dark Side of Medicine
• Better Than Well: American Medicine Meets the American Dream

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What if you could understand why rational people sometimes believe the most irrational things?

Dan Ariely is a Professor of Psychology and Behavioral Economics at Duke University, the Center for Advanced Hindsight, and is also the author of several books including his most recent work, Misbelief: What Makes Rational People Believe Irrational Things. His work is also the subject of a new TV show called The Irrational.

Greg and Dan discuss many aspects of misbelief and irrationality. Dan describes his own journey of finding himself at the center of different conspiracy theories during the COVID-19 pandemic where he unexpectedly found himself accused of being part of a sinister plot. This shocking experience spurred him to delve deep into the phenomenon of misbelief, and he shares his invaluable field research and insights on this perplexing topic. 

Dan also explains the concept of "Shibboleth" as a social marker in political discourse, examining how language and terminology often signify group membership rather than convey actual beliefs. Dan and Greg discuss the critical role of maintaining transparency and trustworthiness in scientific communication and reflect on the evolving role of academia in addressing societal issues. 

*unSILOed Podcast is produced by University FM.*

Episode Quotes:

Are we in a period of low resilience?

19:25: We are at a period of high stress and low resilience. Why do we have low resilience? Because we spend less time with friends. We spend more time with our nuclear family. We have less good friends for all kinds of reasons, but, you know, for example, one of them is that we're not allowed to have friends at work anymore. And what I mean by that is that there are topics that you can't talk about at work. It’s frowned upon to talk about sexual issues. It's frowned upon to talk about politics. We spend lots of time with those people. It used to be the place where you got new friends. Now it's not anymore, right? There's not that much going out with friends after work to drink. And you don't know much about the people that we work with.

Redefining misbelief

06:14: Misbelief is not just about believing in something that isn't so; it's also about adopting it to such a degree that it colors everything we look at. And that's the dangerous thing, right? Because the moment you have some belief…[06:37] it becomes a central tenet in the way you interpret the world; it becomes much broader than that because you start being suspicious and so on.

Why does replication matter in social science?

52:20: I think there are lots of reasons why things don't replicate. I think that intention is a very small subset. And my hope is that we will grow as a science. We need to be more careful, and so on. But we also need to understand that lack of replication sometimes is just asking another question of what was different between those two things rather than saying, "Oh, it must mean that the first one was not correct. And the second one is correct."

The high cost of incorrect beliefs

23:43: We live in a world in which some wrong beliefs can have very large consequences. So I don't know if people believe in more incorrect things; probably we believe in less incorrect things, but I think that the cost of believing in incorrect things can be much higher.

Show Links:
Recommended Resources:

• The Irrational
• Bronisław Malinowski
• Shibboleth
• Friedrich Schiller
• Cui bono?
• Richard Thaler
• Cass Sunstein
• Diederik Stapel

Guest Profile:

• DanAriely.com
• The Center for Advanced Hindsight
• Profile on LinkedIn
• Wikipedia Profile

His Work:

• Amazon Author Works
• Misbelief: What Makes Rational People Believe Irrational Things
• Dollars and Sense: How We Misthink Money and How to Spend Smarter
• The Honest Truth About Dishonesty: How We Lie to Everyone--Especially Ourselves
• The Upside of Irrationality: The Unexpected Benefits of Defying Logic
• Predictably Irrational, Revised and Expanded Edition: The Hidden Forces That Shape Our Decisions
• TED Talks
• Dan’s Youtube Page

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Travis Rieder, a professor of bioethics at Johns Hopkins University, is fascinated by the world’s ethical dilemmas.

His work sits at the intersection of medicine and philosophy, but also draws from his own life experiences like in his book, In Pain: A Bioethicist's Personal Struggle with Opioids. His latest book, Catastrophe Ethics: How to Choose Well in a World of Tough Choices, delves into moral decision-making in the context of climate change and other pressing ethical challenges.

Travis chats with host Greg LeBlanc about his harrowing experience with opioid withdrawal following a motorcycle accident, historic societal shifts in opioid perception, and how much one’s individual decision-making truly impacts structural problems like climate change or the healthcare system.

*unSILOed Podcast is produced by University FM.*

Episode Quotes:

How did we get to the place where we have conflicting attitudes about opioids?

09:53: How did we get to the place where we have conflicting attitudes about opioids? Because some people seem to think that they are, worth giving out like candy, and some people seem to think that they're like the devil's magic or whatever. And that story is old. That story is 200 years old plus, and it involves basically North America's attitudes just swinging back and forth from one extreme to the other. Every once in a while, we're like, "Oh, we really need to take pain seriously. Let's take opiates all the time." And then it predictably leads to a drug overdose crisis, an addiction crisis. And so the politicians freak out, and they slam on all the brakes, and they introduce new legislation. And then the country gets scared, and medicine gets scared. And we talk about how terrible these drugs are. And then we withhold them for 50 years. And then everyone's like, "Hey, maybe we should take care of cancer patients who are dying." And we start using the drugs again, and so on. And so we've done that move since the 1800s.

Risky handoffs in medication management

16:03: When it comes to pain medicine, when it comes to addiction management, when it comes to managing all sorts of difficult-to-manage medications, those handoffs are some of the riskiest places because they require care, and our system is not set up for that care to be there. 

Basic moral structure is everywhere

41:00: The main contribution that I wanted my book to make was to make clear that same basic moral structure, that we are contributing in very small ways to all sorts of goods and bads, good moral projects and bad moral projects, all the time. That basic puzzle is everywhere.

If someone argues that individual behavior doesn't matter, why would anyone bother trying?

33:44: Our actions have been decoupled from the consequences that make us worry. And so climate change is bad because it harms people. And so my classic moral brain says, okay, harm, that means don't do it. So, everything that I do that contributes to climate change, I'm like, okay, I shouldn't do that because climate change harms. But the thing is that the principle was don't cause harm, and your individual action doesn't cause harm. Your individual action does this other thing, which is it infinitesimally contributes to this massive, complex system that is so big and so complex, we can't really comprehend it. A trillion metric tons of greenhouse gasses accumulating in an atmosphere and cycling through a carbon cycle that is just unimaginably complex. And so there is no hurricane that is even a little bit worse because of what I did. That's just not how any of this works.

Show Links:
Recommended Resources:

• Empire of Pain: The Secret History of the Sackler Dynasty by Patrick Radden Keefe 
• Michael E. Mann
• David Wallace-Wells
• Mary Annaïse Heglar
• Siddharth Kara
• Arthur Schopenhauer

Guest Profile:

• Faculty Profile at Johns Hopkins University
• Professional Website
• Professional Profile on X

His Work:

• Catastrophe Ethics: How to Choose Well in a World of Tough Choices
• In Pain: A Bioethicist's Personal Struggle with Opioids

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Ever wondered how to truly bridge the gap between technical expertise and practical business implementation? How did the terminology shift from "data mining" to "predictive analytics" and revolutionize the business world? 

Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series and the author of several books. His latest work is titled, The AI Playbook: Mastering the Rare Art of Machine Learning Deployment.

Eric and Greg discuss what motivated Eric to leave academia to see real-world applications of his machine learning models. Eric explains the pressing challenges organizations face when deploying machine learning projects, and provides an insightful look at the cultural and incentive-driven barriers that often lead to failed projects and unmet expectations. By focusing on collaboration from the outset, Eric reveals how businesses can align machine learning initiatives with their core needs to foster successful integration and operational change.

*unSILOed Podcast is produced by University FM.*

Episode Quotes:

Ramping up on a semi technical understanding of data

03:46: Prediction is the most actionable thing you get from data, and the way you get it is with machine learning. Learn some data to predict. That's basically what it is. So, will the world wake up to this? Are they going to forever see it as arcane? What does that mean? So, be careful what you wish for, because flash forward to now, and everyone's all over this stuff in a way that's overzealous. We fetishize the core technology as the most awesome thing. We're more excited about the rocket science than the actual launch of the rocket. That is, getting it deployed, getting into action, making a difference in terms of actual business operations. And we're stuck there. Most new machine learning projects fail to reach deployment. So, still, there's a skill gap. Still, there's a kind of data literacy that's greatly needed across the non-data science community. But it's not foreboding once you actually dip your toe in. As a business stakeholder, you got to get your hands dirty, or your feet will get cold, and you won't get to the point. But that dirty hand stuff, it's only semi-technical. It's totally accessible.

Demos don't equal human intelligence

36:21: Generative AI is the most amazing thing I've ever seen in my life. But that's the problem. A great demo doesn't necessarily mean valuable, right? I think it's probably about five percent as valuable as the world seems to think, right? So, I mean, I spent six years in the Natural Language Processing Research Group at Columbia, where I was subsequently a professor during graduate school. I never thought I'd see what I can see today, but we need to recognize there's a big difference between something that's seemingly human-like and human.

On recognizing change

34:53: Do change management because the basic idea is so often overlooked. Again, we're fetishizing the core technology. More excited about the Rock Advanced Launch, but the launch is changed, right? You need to manage that change. The project needs to be reframed. It's not just a technology project. It's not a machine-learning project. It's an operations improvement project that uses machine learning as a core component but ultimately involves improvement, that is to say, change.

How do you drive a successful machine learning project?

56:38: We need to get everybody on the same page. We need to get those to speak in business terms, and for the business people to be interested in some of those concrete details. Business people might say, "Hey, look, I don't need to get involved in details. I don't need to pop the hood of my car to drive it, right? I don't need to know how the engine works." And that's true. Like, I personally have no idea, right? I know the general principles of internal combustion, but I don't know where the spark plugs are. But I'm totally an expert at driving. I know momentum, friction, the rules of the road, how the car operates, and the mutual expectations of drivers. The analogy holds: to drive a machine learning project successfully through to deployment, you need, analogously, those kinds of semi-technical understanding of what it means to run the project so that it will succeed.

• Moneyball
• Artificial general intelligence
• Rexer Analytics
• Change Management

Guest Profile:

• LinkedIn Profile
• Machine Learning Week
• Gooder.ai

His Work:

• www.bizML.com
• MachineLearning.courses
• Amazon Author Page
• The AI Playbook: Mastering the Rare Art of Machine Learning Deployment
• HBR Guide to AI Basics for Managers
• Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
• Strategic Analytics: The Insights You Need from Harvard Business Review
• Data Science and Business Intelligence: Advice from important Data Scientists around the World
• Forbes Articles

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The study of economic growth is a modern phenomenon. In fact, economists didn’t get serious about measuring it until the mid-20th century. So what brought growth into focus and are the ways we measure it today adequate for a technologically-advanced world? 

Daniel Susskind is an economics professor at King's College London and a senior research associate at the Institute for Ethics in AI at Oxford University. His books like The Future of the Professions: How Technology Will Transform the Work of Human Experts and Growth: A History and a Reckoning explore the impact of technology on work and the economy. 

Daniel and Greg discuss the history and circumstances that led to the creation of the GDP and its modern limitations, the moral and environmental challenges associated with a relentless pursuit of growth, and the need for societies to rethink the meaning and value of work in an increasingly automated world.

*unSILOed Podcast is produced by University FM.*

Episode Quotes:

The modern economic thought about the origins of growth

10:22: Growth doesn't come from the material world. It doesn't come from the world of tangible objects, but it comes from the intangible world of ideas. And ideas have all these interesting properties: they're nonrival, they're nonexcludable. But the key point is that whereas the world of finite material resources is finite, there's only so much material stuff out there. The world of ideas is unimaginably vast, for all intents and purposes, as good as infinite. And so if growth comes not from using more and more finite resources, but from discovering new ideas about how we can make ever more productive use of those finite resources, then the kind of constraints, the bottlenecks to growth, aren't to be found in the material world of those finite resources but it's to be found in our inability to discover enough new ideas about the world.

What do we do about growth?

11:23: If we want more growth, we need to become societies that discover new and more interesting ideas about how we can use the resources that we have.

Two big problems when it comes to GDP measure

14:40: ​​One is technical failings, which is that it's meant to be a measure of the activity that takes place in the market, and it's not a particularly good measure. Many of the things that we use today are free. Think about the search engines we use, the sort of email browsers, and so on, the sort of first generation of generative AI systems, whatever it might be; we don't pay a price for them in the market. And so they're not captured by traditional GDP statistics. The other thing, of course, that GDP is very bad at capturing is quality improvements. And if you think about particular technologies that we use, something like an iPhone today might have the same price as an iPhone X many years ago. All the different dimensions on which the quality of that technology has improved just aren't captured.

On the relationship between work and meaning

56:38: Although people say there's a strong relationship between work and meaning, actually, there's a lot of heterogeneity. Actually, a lot of people do not get meaning from their work. If they could get an income without working, they would. And you can see this in the simple polls that are done. Lots of people do not get meaning and purpose from their work. They don't think they're making a meaningful contribution to the world. I think it's often the people who write about this stuff are sometimes confusing the meaning that they get from their work as a kind of generalizable insight. I just don't think it's true.

• Big Push Model
• Harrod Domar Model
• John Maynard Keynes 
• Simon Kuznets
• Joel Mokyr | unSILOed Link
• Robert J. Gordon | unSILOed Link 
• Sam Altman
• Demis Hassabis
• Garry Kasparov
• Nicholas Stern

Guest Profile:

• Faculty Profile at King's College London
• Faculty Profile at Oxford University
• Professional Website

His Work:

• The Future of the Professions: How Technology Will Transform the Work of Human Experts
• A World Without Work: Technology, Automation, and How We Should Respond 
• Growth: A History and a Reckoning

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