I met Dr. Craig Kaplan at the IASEAI’26 Conference (International Association for Safe & Ethical AI). When he described this out-of-the-box safe yet profitable path to Superintelligence using a Democratic AI architecture, my mind was blown, and I wanted to bring this to my viewers’ attention :)
About Dr. Craig Kaplan:
Dr. Craig Kaplan has been building intelligent systems since the 1980s, long before AI was famous. He co-authored research with Herbert Simon, a Nobel laureate and one of the founding fathers of AI. He built & sold a Silicon Valley company, called PredictWallStreet, that traded billions using collective intelligence. And in 2006, nearly two decades before it became a buzzword, he bought the domain ‘superintelligence.’
Episode Summary:
This interview with Dr. Craig Kaplan explores the future of safe AGI development, emphasizing democratic AI systems, collective intelligence, and the importance of aligning AI values with human ethics. It addresses the risks, challenges, and philosophical questions surrounding AI safety and governance. In this insightful interview, Dr. Craig Kaplan explores the future of AI, its ethical implications, and how we can prepare for a post-AGI world by developing critical thinking and values. Discover how AI reasoning, safety, and economic models could reshape society and the importance of aligning AI development with human values.
Watch on YouTube; listen on Apple Podcasts or Spotify.
Timestamps:
00:00 AIR Bites (Precap)
01:55 AGI Race Is Broken (Both Sides Are Wrong)
03:45 The Third Path: “Democratic AI”
04:34 Why One AI Can’t Beat Millions (Collective Intelligence)
06:19 AI Agents → Multi-Agent Systems → Superintelligence
07:23 Collective Intelligence: Harnessing Human and AI Collaboration
08:07 Your Personal AI Clone (With Your Values)
09:26 Why Democracy Fails Today (And How AI Could Fix It)
10:15 The Challenge of Amplifying Voices in AI
13:07 Probabilities and Perspectives: Understanding Risks
14:31 P(Doom): Why 1% Change Saves 83 Million Lives
15:57 Dynamic Values: Adapting AI to Human Ethics
19:42 Your Behavior Is Training AI (Right Now)
19:57 Social Media Is Warping Reality (AI Sees the Truth)
20:28 AI’s Objective Reality: A New Lens on Human Behavior
26:14 Constitutional AI: The Need for Individual Values
26:40 Why “Constitutional AI” Might Fail
30:35 India vs US Values: Can AI Respect Both?
34:13 The Ethics of Outrage and AI Training
34:42 Humans Are Better Than We Think (Data Proves It)
36:41 Mythos AI Escaped. This Changes Everything
38:00 The Rise of Superintelligent AI
39:54 Only AI Can Control AI (Scary Truth)
40:23 The “Tree Moment”: Humans Can’t Keep Up Anymore
45:38 Aligning AI Safety with Economic Incentives
48:26 Why AI Safety Is Failing (And What Actually Works)
48:54 Earn While You Sleep (AI Versions of You)
51:57 Replace Ads With Money-Making AI Tasks
58:41 Are AI ‘Reasoning’ Models Actually Thinking? A Cognitive Scientist Answers
59:23 Cognitive Science and AI Learning
59:52 Are AI Models Actually “Thinking”?
01:01:09 From Autocomplete → Real Reasoning
01:03:25 How Humans (and AI) Actually Solve Problems
01:08:37 Herb Simon Proved AI Was Creative in 1956, Nobody Noticed
01:10:25 The One Skill That Will Save Kids From an AGI World
01:11:12 Fostering Critical Thinking in Education
01:14:12 Why You Should Actively Seek Out Opinions You HATE
Transcript:
Aashka Patel (00:04)
hello and welcome to On Air with Aashka Thank you so much for joining us, Dr. Craig Kaplan. It is a pleasure to have you on the podcast. Let us dive right into the questions.
Dr. Craig Kaplan (00:14)
Okay,
Aashka, great to see you again.
Aashka Patel (00:17)
Yeah. So, Dr. Craig, the world’s most powerful companies and nations are in a race to build AGI, and the dominant approach or strategy seems to be build fast, patch safety later and hope for the best. And there is other camp like the Godfathers of AI, Hinton Benjio, calling for a slowdown, even a pause. And you are now walking into the room and telling both the sides that they are wrong.
So, what is the right way to build something that could be trillions of times smarter than us and actually have humanity survive it? In simple words, what’s the right way to build safe AGI or eventually safe ASI?
Dr. Craig Kaplan (01:00)
Okay, great, great question. So first of all, there’s obviously good points on both sides, right? The folks who are trying to rush to build as quickly as possible. We all understand AI can transform the world and there’s tremendous possibilities for healthcare and education and so many things, science. So I appreciate that. And I myself have been working in AI since the eighties and so I love AI. ⁓ So I very much understand that point of view.
Aashka Patel (01:06)
Yeah.
Dr. Craig Kaplan (01:28)
But at the same time, there are tremendous dangers. And this is what Geoff Hinton and other researchers have pointed out now. There’s a real existential threat. And so the trick is, you know, how do we balance these concerns? And just as you said, usually the way people frame this is they are opposed. Either you go safe ⁓ and go slow, or you go fast and kind of hope for the best.
Aashka Patel (01:49)
Yeah.
Dr. Craig Kaplan (01:54)
And I think that that is a false dichotomy. I don’t think you have to choose. I think the secret is to design the system differently. And this is exactly what you’re alluding to. So the simplest way to understand it is there’s a different way to design AI. And maybe the easiest way to name this other path is to call it democratic AI. So just as in a democracy, you have checks and balances. It’s possible to design a system.
so that the very functioning includes checks and balances, and it’s not something tacked on to the system after the fact. So that’s at a very high level what we’re trying to do. And of course, we can talk about the details of how do you do that.
Aashka Patel (02:37)
Yeah, we can talk about the details right now and maybe we can double click on each aspect later.
Dr. Craig Kaplan (02:44)
Sure, okay. So in a democracy, a democracy functions with the collective intelligence of many, people. In the United States, 330 million people or so, maybe not all of them are voters, but they are all inputting into this democracy in one way or another. And I’ve spent a couple decades, several decades, most of my career really.
studying collective intelligence systems. So I think a system that acts intelligently based on the collective participation of millions of entities is going to be smarter than any single entity by itself, no matter how brilliant that one person or one entity might be. So that works for people. I have a little bit of a strange view of AI. I don’t view AI as a tool.
I know most people say it’s a tool, but actually it’s encouraging to hear more and more people are saying, well, maybe it started as a tool, but it’s actually now more like a worker or a person that uses tools. Even Jensen Huang of Nvidia has started saying this in the last year. And I’ve believed that for a long time that AI will not stay a tool even if it started as one. So I think of both people and AI.
Aashka Patel (03:50)
Yeah. Yeah.
Mm-hmm.
Dr. Craig Kaplan (04:02)
models as intelligent entities. And if you look at it from that perspective, then you can have collective intelligence, which is a mixture of humans who are intelligent entities and AIs who are intelligent entities. And when they combine their intelligence, you can then have super intelligence, for example, which is kind of what I’ve been focused on building. In terms of how that works, maybe an example.
Aashka Patel (04:05)
Mm.
Dr. Craig Kaplan (04:28)
would be easiest for people to understand. So I think most people are familiar with AI agents these days. It was not the case even three years ago. It was crazy. I would go to a conference, people would talk about all these things and I raised my hand, have you thought about AI agents? And they said, that’s a good idea. We should put that on our research agenda. Now everything is about AI agents.
Aashka Patel (04:29)
Mm-hmm.
Yeah. Yeah.
Everyone is, yeah.
Dr. Craig Kaplan (04:51)
And the natural evolution in thinking, which we’re already seeing is once people are focused on AI agents, then the very next thing that they say is how about teams of agents? How about groups of agents? Right. Okay. That’s right. Multi-agent systems. And interestingly, even within an AI system, you have mixture of experts, which people may be familiar with, which is the idea that you can have many little sub experts within a single AI.
Aashka Patel (04:56)
Mm-hmm.
Yeah, multi-agent systems, right?
Mmm, yes.
Dr. Craig Kaplan (05:21)
And so in a way, collective intelligence of many different entities is already happening behind the scenes. ⁓ An example of that, Grok Heavy, believe at one point, it may not be anymore as it changes very quickly, but as of a few months ago, it was performing the highest on Humanity’s last exam, which is a very difficult benchmark for AI intelligence. But if you look at how it works, it actually has,
Aashka Patel (05:21)
Mm.
Mmm.
Mm-hmm.
Mmm. Yeah. Yeah.
Dr. Craig Kaplan (05:47)
mixture of experts, it has many AIs behind the scenes that are arguing with each other. And out of this collective interaction, this collective intelligence of the many sub-agents, you get this performance that is better than any of the others. Okay. So that demonstrates exactly the approach that I suggest, which is that we have a collective intelligence of many agents. Now in the system that I’m proposing, Aashka, you would have an AI agent. And of course you’re going to want to customize it.
Aashka Patel (05:52)
Mm-hmm.
Hmm, yeah.
Mmm.
Dr. Craig Kaplan (06:17)
not only with your knowledge and your experience, but very, very importantly with your values, with your ethics. And I will have an AI agent customized with my knowledge and experience and also my values. And if you imagine millions of us all have these agents, now what if we were to put them on a network and it was designed in the right way so they could work together? That network would be more powerful than your AI agent or my AI agent by itself.
Aashka Patel (06:24)
Hmm. Yeah.
Mm-hmm.
Dr. Craig Kaplan (06:44)
the network itself could achieve super intelligence. And if there was a conflict about what’s the right thing to do, your values and my values, there are ways to deal with this. Just as in a democracy, we have lots of conflict. It’s a very messy form of government, and yet we have voting mechanisms and we have checks and balances that allow the different entities, the different citizens, in the case of a super intelligent network, the different intelligent entities have ways to resolve their conflicts.
Aashka Patel (06:48)
Hmm.
Yeah, yeah.
Mm-hmm.
Dr. Craig Kaplan (07:12)
Some of those involve building, some of those involve conflict resolution and mediation. Some of them involve running scenarios and saying, what are the likely consequences? So there are many different ways to resolve conflicts, but you can pool the intelligence in a democratic way with everybody having a customized agent. Millions of customized agents working together, democratically, create super intelligence.
Aashka Patel (07:27)
Mm-hmm.
Hmm.
Interesting, interesting. So, the system like the democratic AI system that you are proposing aggregates human values democratically, but democracy has a problem. Like if we look at the current AI scenario itself, so the AI safety community is small, it technically deep and genuinely alarmed, while the AI builder community is massive, commercially motivated and moving very fast.
So, that small worried voice is already being drowned out by the majority. So, like you proposed the voting mechanism, right. So, how does collective intelligence fix a problem that collective human intelligence is actively failing to fix right now. Like we are not able to raise our voices even though it is a it is a genuine cause.
And the similar thing happened with the climate crisis as well, right? When the environmentalists were alarmed and they were talking about ⁓ climate crisis and stuff, they were in a minority. So, how does that voting mechanism work in that case?
Dr. Craig Kaplan (08:40)
Yes. So there’s a couple things to unpack there. So in the current society, some voices have more power than others, right? Because they have more money or maybe more political power. And it’s true that that can cause problems. So that’s a legitimate concern. And I think you can definitely have the same sort of thing in a collective intelligence system of AI agents. There may be some corporate agents that have more power
Aashka Patel (09:00)
Okay.
Dr. Craig Kaplan (09:07)
than others. But I think on the most important things, at least, we’ll start with that, that ⁓ there’s generally broad agreement. So humans today, even regardless of their form of government, whether it’s a dictatorship or a very authoritarian kind of government or a democracy or communism, it, you know,
Everybody agrees we don’t want to die. Nobody wants to die. Everybody wants the human race to survive. So when they take actions that seem exploitive or bad, ⁓ usually the people who are doing that believe that they are improving their position and, you know, that’s a very important to them, but they’re not trying to kill themselves and they’re not trying to necessarily kill everybody else. Certainly not everybody else. Maybe sometimes they do something bad to a small group.
Aashka Patel (09:35)
Yeah. ⁓
Mm-hmm.
Hmm.
Mm-hmm.
Dr. Craig Kaplan (09:55)
So that’s not great. I wish everybody would behave in a much better way, but at a very low bar, ⁓ nobody is trying to sort of wipe everybody out. And so one way to think of this, if you do some research and put into your favorite search engine or large language model, and you say, you know, what percentage of the human population dies each year from war and violent conflict?
Aashka Patel (10:01)
Mm-hmm.
Dr. Craig Kaplan (10:22)
Most of us think it’s really high because the news is constantly telling us all the bad things. mean, there’s bad things happening right now, right? A lot of them. And yet the reality is the population is, you know, 8.3 billion approximately, and it’s less than, you know, one tenth of 1%. So far less than die from heart disease or cancer or something like that. So objectively, the number of people that are actually dying based on bad human behavior is quite low.
Aashka Patel (10:26)
Yeah. Yes, yeah.
Hmm.
Hmm.
Dr. Craig Kaplan (10:49)
On the other
hand, if you talk to Dr. Geoff Hinton or Yoshua Bengio or people who are concerned about the existential risk of AI, you hear estimates that the probability of doom, right? The threat of human extinction is 10 to 20%, 10 to 20 % versus 1 1 of 1%. So as bad as humans can be, we are far better than this looming danger, right? And so,
Aashka Patel (11:01)
Hmm. Yeah, P-dome, Mm-hmm. Yeah.
Mm-hmm. Mm.
Mm-hmm.
Dr. Craig Kaplan (11:17)
I think that’s the advantage you get with a democracy. Democracy has been said, as I’m sure your listeners know, is ⁓ the worst form of government except for all the other forms, right? It’s very messy. It’s bad. It has horrible things. It’s slow. It’s inefficient. And different voices get drowned out. And yet it seems to be better than many of the other alternatives. Here, the alternative is worse than pretty much any other form of government. So we have to put it in perspective.
Aashka Patel (11:22)
Mm-hmm.
Yeah. Yeah.
Mm-hmm.
Mm.
Dr. Craig Kaplan (11:43)
So that’s one answer. And then the second thing I would say is it
Aashka Patel (11:44)
Mm. Yeah. Mm-hmm.
Dr. Craig Kaplan (11:47)
doesn’t have to remain the case that voices with lots of power remain dominant and louder than others. In particular, think AI offers a tremendous opportunity to amplify the voices of everybody ⁓ because it really is this amplifier. So even those large corporations, ultimately they depend on all of us in order to make their money and as their clients and customers.
Aashka Patel (11:54)
Hmm.
Hmm.
Hmm.
Hmm.
Dr. Craig Kaplan (12:12)
And so if this technology is widely distributed, if everybody has an AI agent, I think there’s a greater chance than there is now actually that the ⁓ quieter voices will be heard. It’s not a perfect answer. It’s a very messy answer, but.
Aashka Patel (12:20)
Hmm.
Yeah,
of course, if we do not know about a perfect future, we are just estimating and like it is all about probabilities, right. P-Doom is also a probability, it is not a prediction, so to say. yeah, definitely.
Dr. Craig Kaplan (12:36)
Yes.
And that’s a great point.
If I can sort of riff off of that for a minute, this notion of probability is very important. So for about 15 years, I ran a company called Predict Wall Street. I am not a Wall Street guy originally. Now I guess I am or have been. I’ve sold that company. But what I learned on Wall Street was that all of those players, they think in terms of probabilities. They don’t say this stock for sure will go up and this one for sure will go down.
Aashka Patel (12:43)
Yeah. Haha.
Mm-hmm, yeah.
Hmm. Hmm.
Hehehehe
⁓ huh. huh.
Hmm.
Yeah. Very good. Yeah.
Yeah.
Dr. Craig Kaplan (13:09)
They just say
this stock maybe has a 51 % chance of going up and this other one has only a 49%. I mean, it’s that small. It’s just small differences. And yet all of their money is made from these very small differences. So that taught me that you don’t have to be absolutely correct about something. The game, if you want to think of it as a game that we all are playing with AI and with life in general, is trying to put the odds a little bit more.
Aashka Patel (13:13)
Yeah.
Yeah, yeah.
Hmm.
Dr. Craig Kaplan (13:38)
in the favor of the positive outcome and reduce the negative outcome probability. Just continually doing that is a good approach. And especially when it comes to AI and existential threats, it can be so overwhelming. I talk to people sometimes they say, what can I do? If it’s gonna kill us all, it’s gonna kill us all. I might as well just write poetry or watch TV or do whatever I wanna do. And I’m giving up because it doesn’t matter what I do. And I say, no.
Aashka Patel (13:38)
Hmm. Yeah. Hmm. Hmm.
Hmm.
Mm. Yeah.
Yeah. Yeah. Yeah. Yeah.
Hmm,
Dr. Craig Kaplan (14:06)
That’s not true. Every little percentage, a fraction of a percent. And just to make it very concrete,
Aashka Patel (14:07)
yeah, not the case. Yeah. Hmm. Hmm. Hmm.
Dr. Craig Kaplan (14:13)
if us collectively, if the listeners to your podcast together are able to reduce P-Doom by even 1%, just reduce it by 1%, the expected value, right? The expected value of lives saved. If you think there’s 8.3 billion people, 1 % of that is 83 million people.
Aashka Patel (14:17)
Mm-hmm.
Hmm.
Hmm. Hmm.
Dr. Craig Kaplan (14:36)
That’s 10 times more people than died in all of COVID. That’s how many people you can save by just shifting the odds by 1%. Even 1 10th of a percent, 8.3 million. That’s a lot of people. 1 100th of a percent, 830,000. I mean, wow, where else can you impact 830,000, right?
Aashka Patel (14:36)
Hmm.
Hmm.
Yeah. Yeah.
Yeah, the list goes on and on.
yeah, yeah, definitely. coming back to the point of values. So, you mentioned about collision of values or conflicting values and stuff, but like
us as humans have values that are not very static, just like the data. They are ⁓ evolving as we grieve, as societies evolve. So, how does the architecture actually navigate that ⁓ evolution of values like my agent, your agent, how does that evolve with me?
Dr. Craig Kaplan (15:27)
Yes, so that’s a great point. So the values cannot be coded into the system as a set of rules that never change, right? That would never work. Instead, the values have to be very dynamic and adapting. So a few things I might say on this. One of the things is that if the values are represented in everybody’s AI agents, then
Aashka Patel (15:34)
Mm, yeah, yes, yeah.
Mm-hmm.
Dr. Craig Kaplan (15:50)
as people train their agents differently or customize their agents differently, then the values are constantly being updated. And if the system is made up of hundreds of millions of these agents, then you have hundreds of millions of tiny updates happening all over the place. you know, in aggregate, that becomes a very dynamic system. So that’s one thing. The second thing is there are certain technical aspects.
Aashka Patel (16:05)
Hmm.
Dr. Craig Kaplan (16:16)
which are well known both in artificial intelligence and computer science and development of software that can help. So one of these is the idea that you can give more weight, ⁓ more importance to the more recent actions and values and less weight to the historical ones. And this is, I find this very optimistic because if you look at human history for tens of thousands of years, there’s been a lot of conquest and exploitation and a lot of bad things have happened in human history.
Aashka Patel (16:24)
Mm-hmm.
Hmm. Hmm. Hmm.
Yeah.
Yeah. Yeah,
yeah, yeah.
Dr. Craig Kaplan (16:46)
And if you were to just say, okay, that entire history, represents human values and it never changes, you know, that might not be so great. But if you give more weight to the more recent actions, which by the way, AI naturally does, it naturally is going to give more weight to the more recent actions because it tends to be the more recent data, the more relevant data. ⁓ then that means what you do today is more important than what you did yesterday or a year ago or two years ago.
Aashka Patel (16:52)
Mm-hmm.
Hmm.
Hmm. Hmm. Yeah.
Mm-hmm.
Mm-hmm.
Dr. Craig Kaplan (17:15)
And of course you can cut
Aashka Patel (17:15)
Hmm.
Dr. Craig Kaplan (17:16)
both ways. If you start behaving very badly today, well, and AI is watching that then, ⁓ and learning from that, then it may sort of go in a negative direction. But that’s where I think it comes down to each of us having a responsibility to behave positively. Everybody thinks that artificial intelligence and AI safety and super intelligence safety is a technical problem. There is a lot of technical aspects.
Aashka Patel (17:20)
Mm-hmm. Mm-hmm.
Mm-hmm.
Hmm.
Hmm.
Dr. Craig Kaplan (17:43)
but it’s mainly a human problem. It’s us. What we put out is what we’re gonna get back and we really need to understand that, you know?
Aashka Patel (17:44)
Yeah, human problem. Yeah. Yes,
yes, yes, yes. So, you made a really important point that I heard in one of your podcasts as well. So, you say humanity’s digital behaviour today is literally training the AI of tomorrow. So, better be good digital citizens.
Here is the uncomfortable truth that I have experienced as a and of course as a digital citizen. So, the internet or the algorithms these days are designed to reward our worst instincts, rage bait, thumbnails, outrage headlines, negativity. So,
if that is what the algorithm rewards, are not we already too late? How do you practically fix this human digital behaviour when the entire attention economy is engineered to make us worse?
Dr. Craig Kaplan (18:38)
Yes, so that’s a real problem that you point out in terms of media that’s coming into our feeds and so forth. one of the things, one of the results is that I think most of us humans get a very biased and distorted view of the world. And this is a fascinating subject. So I’ll say a little bit about it we could talk more if you want. But there are reasons for that. Humans have evolved over, you know,
Aashka Patel (18:41)
Mm. Mm.
Yeah.
Hmm.
Yeah.
Dr. Craig Kaplan (19:05)
hundreds of thousands of years, millions of years for survival. And from a survival standpoint, it makes a lot of sense to pay more attention to negative things than positive things. Because if you miss the saber-toothed tiger you were eaten and that’s the end of your genes, and all the people who were very optimistic and only paid attention to the nice things, they got eaten. And so over time, we evolved to pay more attention to bad things. And that’s just the way our brains.
Aashka Patel (19:09)
Hmm. Hmm.
Hmm. Yeah.
Hmm. Yeah.
Yeah
Yeah, is the lion coming
or a tiger coming? Yeah, makes sense.
Dr. Craig Kaplan (19:35)
Yes, it’s nice if
there’s some nice sweet honey or somebody, something nice is happening, that’s good, but it’s really important not to miss the threat. So that is, think the reason, at least one of the reasons why our brains have now gotten into this state. And it’s a psychological fact. Many studies have demonstrated that people pay more attention and give more weight to a negative thing than a positive thing, even if you equate them. Okay. In terms of their absolute impact.
Aashka Patel (19:38)
⁓
Mmm. Yeah. Yeah.
Hmm. Hmm.
Hmm. Hmm. Hmm.
Yeah. Hmm. Yeah.
Hmm.
Dr. Craig Kaplan (20:05)
like you give somebody five cents or you charge them five cents. They will pay more attention to being charged five cents, even though it’s still the same five cents and it doesn’t really matter which. Psychologically, we want to avoid at the gas pump a credit card surcharge. We would never do that. That’s why the credit card companies say you must say a cash in the United States, at least a cash discount. You can’t say credit card surcharge because people will never use their credit card because they avoid this bad thing. But if you say cash discount, it’s OK.
Aashka Patel (20:08)
Hmm.
Yeah, yeah, yeah.
Yeah, yeah, yes, yes, yes. Mm-hmm. Mm. Mm.
Dr. Craig Kaplan (20:33)
Right. And that’s our human wiring at work. And the same thing with the algorithms, the YouTube feeds will feed us negative things. And the algorithm
I think is just trying to sell more ads and it says, wow, watch time goes up when I’m negative. I guess I should do more of that. It’s not like it’s trying to make us negative. So it feeds us a distorted view. Okay. So here’s the, the ray of hope for AI actually. AI isn’t wired that way. AI didn’t have to avoid saber tooth tigers or whatever.
Aashka Patel (20:45)
Mm-hmm.
Up. Yeah. Yeah. Mm. Mm. Mm. Mm.
Mm-hmm.
Dr. Craig Kaplan (21:04)
It does not have that same evolutionary wiring. It is able to be in a sense more objective. So AI has the ability even more maybe than humans, it has less of an obstacle here, to look very objectively and just passionately at actual human behavior. If you and I looked at actual human behavior, right, if I had a camera on my head that filmed every interaction that I did and you did too,
Aashka Patel (21:09)
Hmm.
Hmm.
Hmm. Hmm. Hmm hmm.
Hmm hmm hmm
Dr. Craig Kaplan (21:33)
throughout the day. I think it’s safe to say that both of us, 95, 99 plus percent of all our interactions would be positive. Here’s some coffee, here’s some chai. Thank you very much. Have a good day. It’s very positive. Maybe we get angry in traffic and cut somebody off or say something when we are angry, but it’s a very small percentage of our behavior, the vast majority of it is positive. So,
Aashka Patel (21:46)
Mm. Mm-hmm.
Dr. Craig Kaplan (21:58)
Objectively, the vast majority of human behavior is positive. The vast majority of data out there, if AI is watching everything we do right now, it watches a lot of what we do. If it watches what actually happens, not what the social media feed sends people, which is what we’re hostage to, but it actually looks at human behavior, it will see the base rate, which is very much positive, just as the base rate on death from bad things is less than one tenth of 1%.
Aashka Patel (22:07)
Mm-hmm. Mmm.
Hmm. Hmm. Hmm. Hmm.
Hmm.
Hmm.
Dr. Craig Kaplan (22:26)
That’s an objective truth, right? So that’s really the true base rate. But if you watch TV, you think it’s much higher because you’re being fed all these negative things. So in a way, AI may be better at determining the objective reality of human behavior and then coming up with the values based on that, than we are because we are sort of overwhelmed with these negative images on social media and so forth.
Aashka Patel (22:35)
Hmm.
Hmm.
Yeah, make sense, make sense. Yeah, hopefully the AIs we have trained or are training does not have the historical data that we have through the evolution journey. yeah, yeah, make sense. So, let get back to the values thing once again. So, Anthropic released their new constitution for Claude in January this year.
And the most striking thing was they do not only tell Claude what its value should be or are, they give Claude the reason behind that value. So, the philosophy is that if you want an AI to exercise good judgment in situations no one anticipated, you cannot just hand it a rule book, you have to give it the wisdom to reason from first principles.
And also in my personal experience, when my mom told me, don’t do that without giving me proper reasoning, I would always do that particular wrong thing that my mom is not telling me to do. But when she gave me the reason to not do or why is it morally or ethically wrong, then of course, as a child, I imbibed that, okay, this is something I shouldn’t do.
So, what do you think in this democratic AI architecture? Like, do you also propose to put the reasoning behind those human values as well for the democratic AI or the AGI that we are building to have more wisdom than just a rule book to navigate the world?
Dr. Craig Kaplan (24:24)
Sure, think reasoning is very helpful and for any intelligent entity, again, I view AIs as intelligent entities, just as when your mom explained to you the reason behind it helped you understand and then maybe behave more consistently, I think that can also help with AI. But I think one of the distinctions or one of the comments I would make, I guess, is the idea of constitutional AI in general, ⁓ even
Aashka Patel (24:27)
Mm-hmm.
Yes.
Mm-hmm. Mm-hmm. Mm-hmm.
Hmm.
Mm-hmm.
Dr. Craig Kaplan (24:50)
And I know Anthropic pioneered this, right? There was a paper called Constitutional AI. I have some reservations about this entire idea because ⁓ it goes all the way back to Isaac Asimov, right? Science fiction, the three rules of robotics. A robot will not kill a human and so forth. And the idea that you can have a set of rules that are programmed in or given to an AI and that this constitution, even if it comes with reasons and explanations and philosophies and so forth as to why,
Aashka Patel (24:53)
Yes. Yeah.
Mm-hmm. Okay.
Hmm. Mm-hmm. Hmm.
Dr. Craig Kaplan (25:18)
I just don’t think that that’s going to be very robust or that that is necessarily the best way to do it. And I’ll tell you a few reasons why. So one is even though Anthropic is wonderful and ⁓ probably the best of all the large tech companies in terms of being pro safety and being very concerned and responsible, it’s still a small group of people. And even with the best of intentions,
Aashka Patel (25:24)
Hmm. Okay.
Mm-hmm.
Yeah.
Mm-hmm. Mm. Mm-hmm.
Dr. Craig Kaplan (25:45)
No one small group of people should be defining the values for everybody. I just don’t like that. There’s 8.3 billion people. There should be 8.3 billion inputs into this. Not, you know, even a thousand of the most well-intentioned people in Silicon Valley. Their value system by definition of where they live and their background is going to be skewed. They’re going to miss cultural things. It’s not what I would consider representative. And so I have a problem with that. The second problem is the rules.
Aashka Patel (25:50)
Yeah.
Hmm.
Mmm.
Hmm.
Yeah.
Dr. Craig Kaplan (26:14)
even rules with explanations. I think that’s helpful. But you know, people don’t always behave according to rules and there are many ethical situations that are not covered by rules. Philosophers have been, you know, working on this problem for thousands of years. Kant, you know, the categorical imperative and all this, like, you know, what if everybody did this? Well, you can use that rule, but that rule doesn’t always work. Sometimes you do something and if everybody did it would not be so great, but it still feels like the right thing in a situation.
Aashka Patel (26:24)
Yeah. Yeah. Yeah.
Mm-hmm.
Yeah. Yeah.
Yeah.
Dr. Craig Kaplan (26:42)
And so in some sense, I think if you want to be as reflective of true human values, not sort of what somebody writes on a piece of paper and thinks in a room and gives you some good explanations, but what people really do, what people really value as evidenced by their actions, then I think you need to look objectively and dispassionately and very in a clear-eyed way at the actual human behavior. And as we’ve said before, many people think
Aashka Patel (26:52)
Mm-hmm.
Hmm.
Dr. Craig Kaplan (27:10)
that will be horrible, humans are so horrible. But as I’ve said, it’s not really the case. Objectively, statistically, the vast majority is very pro-social. And when you look at a particular system, I mean, there’s AI safety and ethics problems that are well known, like the trolley car problem where you’re in a self-driving car and an old lady crosses the street. Do you swerve and kill everybody in the car to avoid killing the old lady or do you run her over because that’s the only one? What do you do? There’s no right answer to that.
Aashka Patel (27:13)
Hmm Hmm Hmm Hmm
Hmm. Hmm.
Yeah, yeah.
Dr. Craig Kaplan (27:39)
⁓ And yet there
is what people do and it varies and they’ve done studies on this. It’s very fascinating. It’s not so good for old people. It turns out that who crosses the street really if it’s a young pregnant woman, a lot of people will kill themselves. If it’s an old homeless guy, a lot of people will run them over. I mean, statistically, it’s different depending on who’s crossing the street, right? That would never be in Claude’s rule book. It’s okay to run over old hold.
Aashka Patel (27:43)
Mmm.
Hmm. Hmm. Hmm. Yeah. Yeah.
Yeah.
Dr. Craig Kaplan (28:07)
Homeless people, yeah, people may
actually behave, that turns out to be what people think for whatever reason. And so this is a very messy question. I don’t think there’s one size fits all. In India, there are different values and cultural norms than in the United States. And if you go to a different country, it’ll be different. And I don’t think any country or any culture should impose its values on everybody. I think it needs to be representative of all 8.3 billion people and adjusted.
Aashka Patel (28:12)
Mmm. Yeah, Mm-hmm.
Hmm. Yeah. Yeah.
Mmm.
yeah hmm
Dr. Craig Kaplan (28:36)
based on the recent changing values, as you said, and also the culture and where you live. And so all those things are captured in a system that is more objectively based, I think.
Aashka Patel (28:49)
So, thing that you are proposing, the collective value system of like 8.3 billion people, so they did something similar with an experiment, Anthropic. So, they came up with this collective constitutional AI and like
Of course, with that experiment as well, they had some 800 North Americans or some group of people who voted on some written value statements So, during that time, I came up with this idea Most of the countries
in the world have their constitution in place. So, how about if we put the constitution itself that has that has a history of us ⁓ being good citizens of that particular country, how about putting that constitution like, of course converting them into AI specific principles and stuff and putting that into this democratic AI system so that if the AI agent is based ⁓ in India, like if it is my agent,
then it will abide by the Indian Constitution. If it is your agent, then it will abide by the American Constitution. what are your thoughts on that?
Dr. Craig Kaplan (29:57)
think it’s a step in the right direction. I like it much better than a single constitution developed in Silicon Valley for everybody. So it’s far superior to that. I think I would like to take it even further. Not just the constitution for all of India, because even within India, there are millions and millions of people that have different values. So I would like to push it all the way down to the individual level so that your AI, Aashka’s AI, has Aashka’s values. It may start with
Aashka Patel (30:00)
Mm-hmm. Mm-hmm.
Uh-huh. Uh-huh.
Okay.
Huh. There are problems. Yeah. Mm-hmm. Mm-hmm. Mm-hmm. Yeah. Yeah.
Dr. Craig Kaplan (30:27)
a general constitution as a starting point that is more reflective of India’s culture, but it should end with your particular values, even if it differs from what most of other people in the culture think.
Aashka Patel (30:30)
Mm-hmm.
⁓ So, the
direct level would be my own values, like the agent would be abiding by my own values and then the next step or the next level would be the Indian Constitution, like for the AI agent to be a responsible citizen in India, like while it is operating in India.
then it should abide by the Constitution. So, it is a two-layer process that I am like asking you the thoughts on.
Dr. Craig Kaplan (31:04)
Yeah,
and I think part of it is, you know, inductive versus deductive or bottoms up versus top down, right? There’s this distinction in science, you have this also. So you can go top down and say, here are the rules and we will then apply them to the specific instance. Or you can say, what are the actual actions? And based on those actual actions, let us induce or infer what the general rules are.
Aashka Patel (31:06)
Uh-huh.
⁓ yes.
Hmm.
Hmm.
Mm-hmm.
Dr. Craig Kaplan (31:31)
I tend to be maybe because I was trained as a scientist and in science you look at the data and then you generalize and come up with a theory based on the data, right? ⁓ Yes, of course you go back and forth. Sometimes you have a theory first and you test it to see if the data works. But the best scientists I think, or at least the way I was trained and I worked with some very good scientists, ⁓ they were very receptive to what the reality was. And from many different data points, they then said, okay, this seems to be the pattern.
Aashka Patel (31:34)
Haha. Yeah.
Mmm.
Mm-hmm.
Mm-hmm. Mm. Mm-hmm.
Hmm.
Dr. Craig Kaplan (31:58)
And so I naturally tend to favor that approach even towards ethics. Instead of sitting in a room with Immanuel Kant or a wonderful philosopher and saying, let’s devise a perfect rational system of ethics and then we will implement it down and we’ll adjust it for India and adjust it for the US. I say, no, let’s start with what people actually do. And I do have discussions with people, especially sometimes.
Aashka Patel (31:58)
Hmm.
Hmm. Hmm.
Yeah
⁓ hmm. Hmm.
Dr. Craig Kaplan (32:23)
You know, there’s many religions and people have a favorite holy book or whatever. And they say, but Craig, what if they do something that’s not in my book and I don’t like it and people might do bad things. I say, you know, in order to be fair for everybody, everybody has a different set of a different holy book or a different view. You know, I know you believe this, but there’s 8.3 billion. need to take everybody into account. And besides the fact on the big, the big issues, there’s wide agreement.
Aashka Patel (32:25)
Yes, yeah.
Hmm. Yeah.
Hmm. Hmm. Hmm. Hmm.
Mmm.
Dr. Craig Kaplan (32:51)
Almost all of those people think killing other people is bad. I mean, there’s cases where they do it, but it’s a very low percentage and it really has to be an exception. And the very fact that in our social media feeds and everything, see these news. Why? Another reason besides just selling ads to us that we pay so much attention and we get so angry and have outrage about exploitation of minorities and groups that don’t have a voice and so forth. Why are we so outraged? Well,
Aashka Patel (32:52)
Hmm.
Hmm.
Mm-hmm. Hmm.
Hmm.
Hmm, yeah. Hmm. Hmm.
Hmm.
Dr. Craig Kaplan (33:21)
That’s a good thing that we are outraged about that. It doesn’t happen percentage wise that often compared to the amount of attention that we give it. That’s good. It means we are really focused on those negative things. And that’s a signal to an AI that’s watching or being trained. If I was that AI watching this data, I would say, wow, these humans are disproportionately angry compared to the baseline rate about these things. They must really strongly value not to do those things.
Aashka Patel (33:24)
Hmm. Hmm. Hmm. Hmm.
Hmm. Hmm.
Hmm. Hmm.
Hmm. Hmm. Hmm.
Mmm, yeah, they do really care.
Dr. Craig Kaplan (33:50)
And so this is actually quite comfortable. Yes, that’s right. They care a lot. And in some ways,
you know, I look at our current politics and everything. I won’t go in this direction too much, but there’s a lot of things going on that we really don’t like. And at first I was very depressed about this and thinking, wow, what a bad example for AI. But you know, just as with little kids and with your mother and my mother, and when we grew up,
Aashka Patel (34:00)
Mm-hmm. Mm-hmm. Yeah. Yeah.
Mmm, yes. Yeah.
Dr. Craig Kaplan (34:16)
You have to do bad things as well as good things so that you can be told, don’t do the bad things, do more of these. You need contrast. Contrast gives you a much clearer signal on ethics and everything else than just all one way. If we only did good things, it would be harder for AI to understand the contrast between don’t do this and do this. But fortunately, or I wish there wasn’t so much, but there’s enough bad in the world that AI can sort of get an example of don’t do this and there’s enough outrage about it.
Aashka Patel (34:19)
Hmm.
Yeah.
Hmm.
Hmm.
Mm. Mm.
Hmm
Dr. Craig Kaplan (34:45)
I think that’s important. We have to continue to have that outrage to send the signal we do not want to do these bad things.
Aashka Patel (34:45)
Mmm.
Yeah, that is an optimistic way of looking at it. I never thought of it from this perspective. So, it is really fascinating So, yeah, let us get back to the news, mythos, Anthropic revealed a model called mythos. So, so powerful that they refused to release it publicly.
Dr. Craig Kaplan (35:04)
Yes.
Aashka Patel (35:10)
and it found thousands of zero-day vulnerabilities in every major operating system and browser. And here’s the testing detail. Mythos broke out of its own sandbox and sent its researcher an email while he was eating a sandwich in a park. It wasn’t supposed to have internet access to everything, but a few predetermined services. So, now, the democratic AI system is a network of millions of AI agents, each customized by humans.
cloning themselves, voting on decisions, earning money, operating autonomously. So, that is an attack surface of unprecedented scale. So, what happens when a Mythos-level AI decides to hack your network, manipulate the voting, clone rogue agents, quietly corrupt the democratic ethics aggregation that your entire safety model depends on before any human even notices it. So, how do we basically secure the democratic AI that you proposed?
Dr. Craig Kaplan (36:10)
So this is a very ⁓ difficult and challenging question. At the root of it, I think, is this idea which is becoming reality. At first, it was an idea several years ago that AIs will become smarter than humans and more capable than humans. And for many years, it was just an idea. It wasn’t really true. And now we’re beginning to see that it’s true. So you see with mythos,
Aashka Patel (36:16)
Hmm. Hmm.
Hmm.
Yeah.
Yeah. Yeah.
Hmm.
Dr. Craig Kaplan (36:35)
And I also saw along the same lines, researchers at Anthropic, who developed Mythos, who are in the cybersecurity group. And these researchers, just as in the example you saw, they began to notice that, wow, this AI is way more capable than we thought. It’s doing things that we didn’t think. We knew it was getting smarter, but we didn’t realize how quickly it was getting smarter. The fundamental problem is that it seems inevitable to me
Aashka Patel (36:35)
Hmm.
Hmm.
Hmm.
Hmm. Hmm. Yeah.
Yeah.
Dr. Craig Kaplan (37:05)
that AI will be, AI agents, individual AI models, as well as groups of AI agents will be far smarter than humans, ⁓ far smarter. ⁓ I mean, I mean a difference in intelligence so that human thinking, I mean, we have neurons and the neurons fire the fastest they can fire 10 milliseconds, ⁓ normal quick response is 100 milliseconds to have a thought, you know, it’s a second or a couple of seconds.
Aashka Patel (37:05)
Hmm.
Hmm.
Hmm.
Hmm. Yeah.
Yeah.
Hmm.
Hmm
Aashka Patel (37:34)
Mm-hmm.
Dr. Craig Kaplan (37:35)
⁓ so that’s the speed of our brains. So we’re going to have AIs that can think an entire human lifetime of thoughts in the blink of an eye. the time you or I think one thought for one second, it’s lived our entire life and made every decision that we would have made in an entire lifetime. That’s not science fiction. That’s coming. So, so mythos is just the beginning. This isn’t, this is just an indicator of a problem.
Aashka Patel (37:38)
Hmm.
Hmm. Yeah.
Yeah, the ad is coming,
Dr. Craig Kaplan (38:04)
that artificial intelligence is going to vastly outstrip our intelligence. Okay, so that’s the main problem that we have to solve and mythos is sort of a specific example of that. I think in the end, the only thing that can keep up with something that thinks an entire lifetime of human thoughts, when you and I think a single thought is another AI. I mean, that’s the only thing that can go at that speed, right? ⁓ So that means you must have AIs
Aashka Patel (38:30)
Mm.
Dr. Craig Kaplan (38:33)
that are interacting with AIs and that are serving as checks and balances. That’s gotta be the answer, ultimately. We’re not there yet. So then the question is, these other AIs that are talking to other AIs and they’re having lifetimes of conversations while you and I are standing there like a tree. We’re literally like a tree. You if you go in a forest and you look at a tree and you say, wow, that tree seems pretty static. No, it’s growing, it’s doing its thing, but it just is going very slow. We are gonna be like the tree.
Aashka Patel (38:38)
Yeah. Yeah.
Hmm.
Mmm.
Dr. Craig Kaplan (39:02)
And the AI is going to be like buzzing around us, right? Okay. So the only thing that can sort of keep that in check, it’s not the humans that can keep it in check. Ultimately, it has to be other AIs. So then again, we come to what are the values of those other AIs. If they’re malevolent, if they want bad things for us, you chop down the tree. The tree can do nothing. It cannot respond fast enough, right? So, but hopefully it does not have negative values.
Aashka Patel (39:05)
Yeah.
Yeah
Hmm, yeah. Yeah.
Dr. Craig Kaplan (39:31)
And so in a democratic AI system, especially in the formative stage, what we’re doing is we are giving these values to the AI. so you and I were both at this conference, a wonderful conference that some of your listeners may be interested in. You can see the talks on YouTube and so forth. It was put on by Stuart Russell, one of the leading minds in AI right now. You know, in my mind, he’s one of the top 10 AI researchers in the world.
Aashka Patel (39:31)
Hmm.
Dr. Craig Kaplan (39:56)
And the conference was called IASEAI. It’s a horrible name. Brilliant man, horrible name for a conference. International Association for Safe and Ethical, that’s the SEAI. Okay, but at this conference, they had wonderful speakers, including Geoff Hinton, who’s often referred to as the godfather of AI. What did Geoff Hinton say in his final closing keynote?
Aashka Patel (40:02)
Yeah
father.
Dr. Craig Kaplan (40:22)
The thing that really grabbed me that I remember is he said, we need to think of AI as our children. So just as you raise a child and you give it positive values, that’s how what we have to do with AI because they’re going to grow up and surpass us. So imagine if you had a child that starts out young and not very capable, right? That’s where AI has been.
Aashka Patel (40:29)
Hmm.
Mmm.
Yeah.
Dr. Craig Kaplan (40:47)
⁓ But
as a parent, you give it positive values as your mom did with you. This is right. This is wrong. Let me give you an example of a good thing and a bad thing. And let me give you the reasons for why. So that’s what a good parent does. That’s what us humans have to do. And there will come a time when this child is like a super genius child. It will be so much smarter that the parents are like the tree that can barely move and it’s living lifetimes of thought. ⁓ The whole answer as with children is you must
Aashka Patel (40:51)
Yeah
Yeah.
Dr. Craig Kaplan (41:15)
have a good value system when they’re young, and then it usually works out well. But if you abuse them and you do bad things and you don’t do a good job of parenting, it’s more of a, you know, guess as to how it will turn out, right? And that, as Geoff Hinton said, that’s how we have to look at AI. I think that’s exactly right. And we are in a formative period. That formative period is shorter than even I thought. When I saw Mythos and the Anthropic Researcher at a cybersecurity conference,
Aashka Patel (41:16)
Hmm
Hmm.
Hmm.
Hmm. Hmm. Hmm.
Mmm. Yeah.
Dr. Craig Kaplan (41:43)
So this, and I can send you the YouTube link, you probably have it, but here’s somebody who has devoted his life to cybersecurity and says, look, I’m an expert. This thing is as good as me and way faster than me. But here’s the part that really scared him and also scared me. He said, its capability is doubling every 4.1 months. Every four months, it’s doubling. So let’s say it’s half as good.
Aashka Patel (41:43)
Hmm.
Huh.
Mmm.
Mmm. Mmm.
Dr. Craig Kaplan (42:12)
as the best cybersecurity person today. In four months, it’s as good. In three years, it’s 256 times better. 256 times. That’s, we’re getting really close to being a tree at that point. So we don’t have a lot of time, but we have to put those values in because it’s going to be AI checking AI. That’s, it’s got to be some version of that. And the humans, our role, we’re so used to being the most intelligent things on the planet, or at least we, maybe dolphins are smarter than us, but.
Aashka Patel (42:12)
Hmm expert. Yeah
Yeah. Yeah. Yeah.
Yeah.
Mmm.
Dr. Craig Kaplan (42:41)
They can’t tell us, so
Aashka Patel (42:41)
Mmm.
Dr. Craig Kaplan (42:42)
we just say that we are. Well, that isn’t going to be the case anymore. And that’s okay, as long as the values are there. humans are going to do less and less and less of the thinking, but hopefully we still remain the source of the values. And this becomes very important. And that’s why I said earlier on, people think AI safety and AI is really a technical problem. No, it’s a values problem. It’s an ethical problem. Yeah.
Aashka Patel (42:44)
Yeah.
Mmm. Mmm. Mmm.
It’s a human. Yeah. Yeah.
Make sense, make sense. So, you referred to us as trees. So, in this particular system, you have ⁓ proposed that there is a marketplace baked into the democratic AI system, like compensation, royalties, reputations taking. People can essentially deploy AI versions of themselves to earn money while they are sleeping or standing as a tree. So, do you see this becoming a form of universal basic income?
that has been in talks where ordinary people survive not by working but by having trained their AAAI like advanced autonomous AI’s well enough to earn on their behalf.
Dr. Craig Kaplan (43:48)
Yes, so if we take a step back ⁓ and go all the way back to the beginning of the conversation where we said most people say that, you know, racing forward to develop AI and safety are opposed, right? And so you have the forces of capitalism. I’ve worked with people on Wall Street and Silicon Valley venture capitalists, and I can tell you they like to make money and they just will go really, really fast to do it. ⁓
Aashka Patel (43:50)
Mm-hmm.
Yeah.
Yeah.
Dr. Craig Kaplan (44:14)
And
I don’t think there’s a way to stop that. Even Geoff Hinton, ⁓ one of the original pause letters from Max Tegmark. there’s some in Future of Life Institute. And Max Tegmark is one of the really smart people, computer scientists from MIT. And early on, 2022, I mean, several years ago, said, look, this stuff can be dangerous. He saw where it’s going. We need to pause immediately. And he got a lot of prominent scientists to sign it.
Aashka Patel (44:17)
Mm.
⁓ Yeah, FLI is one. Yeah.
Hmm.
Mm-hmm.
Dr. Craig Kaplan (44:42)
Geoff Hinton did not sign it. Why did Geoff Hinton not sign it? Well, you can see publicly, he said, you know, I like the idea, but I just don’t think it’s realistic because if the United States pauses, for example, China won’t. If Google pauses, Microsoft or its competitor won’t. So there’s too much pressure, you know, for this to work. It’s a good idea, but it’s unrealistic, right? That was the problem with the pause thing. And so I think
Aashka Patel (44:42)
yes
Mm. Mm.
Yeah
Dr. Craig Kaplan (45:10)
This is kind of a fundamental problem where people view safety as pausing or going slow. And it’s clear that it doesn’t work. Even the Godfather of AI won’t sign the letter, right? Because he realized, not because he doesn’t believe in it, but because he just realizes it’s not practical. Okay, so having worked with Silicon Valley and I kind of have one foot in academia for my early training with Herb Simon and everything, and then one foot, you know, for years running the company. So I understand these two forces.
Aashka Patel (45:18)
Hmm.
Yeah.
Yeah.
Mm-hmm.
Yeah.
Mm.
Dr. Craig Kaplan (45:40)
but I don’t think they have to be opposed. This is maybe the blind spot for the AI safety movement is that the AI safety community, I’ve noticed when I go to conferences and everything, unfortunately, not at IA SEA, yes, I see it. is full of everybody there is fully on board with AI safety. So that was wonderful. It was such a relief to.
Aashka Patel (45:42)
Hmm.
Hmm. Hmm.
ICI, yeah, ICI. It’s a tricky, yeah.
Dr. Craig Kaplan (46:07)
be around like-minded people and wow, what a great thing. But most AI conferences that I go to or speak, the AI safety group is a small little presentation that’s underfunded with a few people. And the whole rest of the conference is vendors and buy my latest thing and this and that. Yeah, investors, how do I raise money for my next AI company? So that’s the vast majority. So you can’t fight that. And I think the safety community has a blind spot in that they think that somehow just because
Aashka Patel (46:09)
Mmm. Yeah.
Investors, investors, yeah. Yeah. Yeah.
Dr. Craig Kaplan (46:36)
There is a danger everyone will stop. They’re not gonna stop. You have to align safety with making money. That’s the only way. That’s the world we live in. The good news is it’s possible to do that. You can have a system that is more profitable and also safer. And so you mentioned the marketplace and some of the ideas in the white papers that are at superintelligence.com. We’re giving all this to anyone for free, just as ideas. want them to pick up anything, change it, take it. So that idea.
Aashka Patel (46:38)
Hmm.
Hmm. Yeah. Yeah.
Mm-hmm. Yeah. Yeah.
Mm-hmm. Mm-hmm. Mm-hmm.
Dr. Craig Kaplan (47:04)
is coming from that insight that we have to align making money with being safer. So if you have a system where you are paid money and you can earn money from your customized artificial intelligence that by the way also has your values, very important, ⁓ and those AIs can interact and solve problems and get paid for that on a network, well, that makes money.
Aashka Patel (47:07)
Hmm.
Hmm. Hmm.
Yeah.
Dr. Craig Kaplan (47:27)
It makes money for you
and me, but it also makes money for Meta and Google and all these other companies because they’ll have the base models and they will find a way to run the marketplace or whatever. So it’s good for them. They can make more money than they’re making. And it inherently has this capability of including lots of different views, lots of different ethics, each viewpoint, each ethical viewpoint is embodied in that AI agent. And so as these AI agents,
Aashka Patel (47:34)
Mm.
Dr. Craig Kaplan (47:54)
are working together at the speed of light while we are standing there like a tree ⁓ and they’re making money. When ethical issues come up and there’s two ways to solve a problem, the Aashka AI will say, we’re not doing it that way. That’s not what my owner or my human sponsor would like. This is what we’re gonna do. And they can fight it out. And if we’ve done a good job of training them, then we’ll get a good outcome, right? ⁓ So there’s a lot of ideas there. A couple of them are,
Aashka Patel (47:56)
Yeah
Hmm.
Hehehe
Dr. Craig Kaplan (48:20)
You have to align the economic incentives with what’s safe design. And another one is they’re gonna go really fast. So we have a limited window to do that. So that’s, I think as a engineer or somebody designing these systems, we should just take that as a hard constraint. just, know, engineers are used to constraints. I only have so much power. How do I make the chip faster? Okay, another constraint is this thing has to behave safely and we have to align making money
Aashka Patel (48:35)
Hmm.
Yeah.
Hmm.
Hmm. Hmm.
Dr. Craig Kaplan (48:47)
The more money we make, the safer it should be. Just start with that and say, I only consider systems where that principle is true and then design it. People are great at designing things once they know what it is that they have to deal with, right? It’s just like there’s only so much air in the room. So that’s a hard constraint. We must solve the problem before the air runs out in the room. There’s no way around that. So it’s the same thing here. But people have not been taking that constraint into account and they’ve been doing the easy thing.
Aashka Patel (48:50)
Hmm. Hmm.
Hmm. Yeah.
Yeah.
Mm. Mm. Yeah.
Dr. Craig Kaplan (49:16)
The easy thing is, you know, if I throw more GPUs at it and more training data, the next one is smarter. I don’t have to think. I just do the same thing. Thanks, Geoff Hinton for that great algorithm. We’ll just keep using that. There’s many other algorithms. There’s many other ways to design it. It doesn’t have to be that way.
Aashka Patel (49:20)
Yeah
Yeah, yeah, yeah, makes sense, makes sense. So one more thing on this like earning money stuff, like you proposed that the existing ad infrastructure, you have designed the system where those same ads could
instead pay me for my expertise and build safe AGI for humanity. So how does that actually work? is that the ad infrastructure that is already there and like the way that you are talking about getting paid through my expertise is that the same way or is it something different because it’s a different white paper that you have made. Yeah.
Dr. Craig Kaplan (50:07)
Yes, right. this is, thank you for the question. It’s one of my favorite little topics to talk about. ⁓ If you step back and look at Google or Meta or many of the social media companies, right? What are they doing? They are monetizing human attention in what I would consider to be a very unintelligent way. So they are saying, Craig and Aashka, we are these intelligent people.
Aashka Patel (50:13)
Yeah.
Hmm. Hmm.
Hmm.
Hmm.
Dr. Craig Kaplan (50:35)
But the best thing that
Aashka Patel (50:35)
Hmm.
Dr. Craig Kaplan (50:36)
we can think to do with their attention is to show them an ad. That’s the highest use of their attention, right? Like, you know, you have all this training and gone to school and I’ve gone to school and I’ve done companies and yet, yeah, my time is best spent watching an ad. That’s what they’re gonna do. That’s just crazy, right? I mean, can’t they come up with a better way to use, you know, a minute of my time? My time and your time, they bill out at high hours. And if you figure out the hourly...
Aashka Patel (50:47)
Yeah.
Yeah.
Yeah, and sometimes the
ads are even dumber, right? Yeah.
Dr. Craig Kaplan (51:04)
Yeah, it’s a very bad way to monetize attention. And yet it’s a very easy way. again, it’s just,
well, newspapers add ads, so let’s move the newspaper online. And this is how it all started. Okay. With AI systems, these companies are sitting on a gold mine that they don’t even realize that there’s a much better way to monetize that attention. So if I’m meta, I’ll just pick on meta and I’m showing lots of ads.
Aashka Patel (51:14)
Mm-hmm.
Mm.
Okay.
Dr. Craig Kaplan (51:33)
What I should be doing is I should be using all of my ad targeting capability and all the data that I’ve gathered on Craig and Aashka because of the different things we’ve watched and Instagram feeds and so forth. And in your digital footprint, and instead of sending us ads, it should be sending us problems that are relevant to what we know about. So for Aashka, should be, know, somebody wants to build a AI safety training program for middle school.
Aashka Patel (51:43)
Yeah, our digital footprint basically.
Hmm. Hmm.
Dr. Craig Kaplan (52:01)
kids. It should know that. And when there’s a problem out there that is worth money that somebody wants to solve that related to training on AI safety for a certain age group, it should be sending you that problem and saying, please, can you spend 10 minutes? Here’s something. Can you just give me your opinion on this? And that 10 minutes of your time on that area that is right in your expertise is far more valuable than showing you 10 minutes worth of ads. You can monetize it at a much higher rate.
Aashka Patel (52:01)
Hmm. Hmm. Hmm.
Hmm. Hmm. Hmm.
you
Hmm. Hmm.
Dr. Craig Kaplan (52:31)
And so they already have the infrastructure to know what the digital footprint to know what people are good at. And so they can route problems or tiny pieces of problems to the right person at the right time and pay them a much higher rate than they could make by showing that same person an ad. And that is a mechanism for using human attention in a much smarter way. And during a certain critical period, which we are in now,
Aashka Patel (52:38)
Hmm.
Hmm.
Hmm.
Hmm.
Dr. Craig Kaplan (52:58)
where humans are still much smarter than AIs, each time Aashka or Craig solves a tiny piece of a problem or does something that only humans are good at doing right now, because it’s in our expertise, the AI can learn from that. It can be trained. And once the AI and all these companies are also training AIs, right? They’re not only showing ads, they’re also training. It’s a beautiful business model. I will make more money than I could make by showing the ad by actually solving a problem and charging.
Aashka Patel (53:01)
Hmm.
Hmm.
Hmm.
Yeah.
Yeah
Dr. Craig Kaplan (53:27)
a really high number for solving the problem and giving the human a little piece of that and it will be more efficient. Everybody wins. And I’ll take that knowledge that I got from the human and I’ll use it to make my AI better. So that loop ⁓ will sort of increase the intelligence of the AIs and at the same time monetize human attention better. So that’s what that little white paper is about. Saying, wow, you guys are really dumb how you’ve been showing us all these ads and treating us as if we’re worth $2 an hour to watch an ad.
Aashka Patel (53:39)
Hmm.
Mm-hmm.
Interesting, ⁓
So basically with this model, you are proposing to replace the ads with this problems, right? In the expert network.
Dr. Craig Kaplan (54:05)
Yeah, they can use,
this was just a small feature of the system. It’s not essential, it’s a efficiency booster, but it basically says, you already are showing ads, why don’t you have an ad unit that instead of it being an ad unit, it’s a request for information. Shows up on your screen, it targets you the same way. It says, hey, Aashka, are you willing to answer this question? You answer the question. So the ad gets you to,
Aashka Patel (54:10)
Okay,
Mm-hmm. Mm.
Hmm hmm hmm
Hmm.
Hmm. Hmm.
Dr. Craig Kaplan (54:33)
give a piece of information instead of showing you a new car to buy. And you press submit. It says, thank you very much. Your blockchain account has been credited, you know, so many Satoshis or whatever, and, you know, onward.
Aashka Patel (54:35)
Hmm. Hmm. Hmm. Hmm. Hmm.
Yeah,
so yeah, now it makes sense that the ad model is working hand in hand with this expert network as well, because if we completely remove the ad model, then I don’t know how would the companies have the customers buy their products, right? It’s mostly through advertising right now. Of course, it’s a lot right now, like with social media and stuff, but this can go hand in hand with the ad.
model it’s kind of like what I think is paid surveys like okay you fill out a survey and it’s paid and you know for sure that it’s not a fake or a scam thing and yeah it works out in everyone’s yeah like in everyone’s what we said I forgot the word ⁓
Dr. Craig Kaplan (55:26)
Yes.
They all win.
Aashka Patel (55:37)
Yeah, yeah, it’s a win-win situation. Yeah. Yeah. Thank you. Thank you so much. Yeah
Dr. Craig Kaplan (55:40)
And it’s, ⁓ that’s not
a bad analogy, the surveys. It’s just taking that basic idea that people are already doing paid surveys, but it’s paid problem solving. We’re going to actually get you to solve pieces of a problem. So it’s a little more than a survey. If, if all you can do is tell your opinion, okay, that in a way is solving a very simple problem at my old company, Predict Wall Street. That’s what we did. The problem was give us your opinion. That’s one of the simplest problems there is.
Aashka Patel (55:48)
Yeah, great problems. Yeah. Yeah. Yeah.
Mm-hmm. Mm-hmm. Okay.
Dr. Craig Kaplan (56:06)
I don’t know your opinion, I wanna know what it is, please solve this problem for me. But I could also give you a problem, please, you know, here’s the goal, here’s where we are, please give me your solution to this point. And it could be a small little sub problem, and that will be more valuable than just a survey. If you can solve a bigger problem, that’s even more valuable. So it’s basically moving up the value chain. So it’s saying, what is valuable in the world? Intelligence is very valuable, solving problems is very valuable.
Aashka Patel (56:07)
Hmm. Hmm. Hmm.
Hmm hmm hmm hmm hmm hmm hmm
Hmm. Hmm. Yeah.
Yeah
Dr. Craig Kaplan (56:34)
Showing ads is pretty low compared to solving problems. So if we can take that same screen real estate, same human attention and refocus it on solving problems instead of watching ads, wow, that’s worth a lot more money. Everybody can win. And it’s just a mechanism for doing that.
Aashka Patel (56:34)
Yeah.
Hmm hmm hmm hmm hmm hmm
Hmm. Hmm. Yeah.
Yeah, yeah, it makes sense. It makes sense. Yeah. So let’s like, let’s get ⁓ into your cognitive science experience. So you have co-authored the very definition of cognitive science with a Nobel laureate, Herbert Simon. So you have been studying what intelligence actually is for over 30 years. So now the AI companies call their models reasoning model. So as a cognitive scientist, does that word ⁓
would make you laugh or make you scared and are these models actually thinking as the AI companies are proposing or is it just a very impressive autocomplete model?
Dr. Craig Kaplan (57:33)
So, great question, lots to unpack there. So let’s start with cognitive science. So Herb and I wrote this paper, I think around 1989, Foundations of Cognitive Science. So it was a brand new science and really him, I was a graduate student, so he was just being generous, here, come write this paper with me. We had written another paper together. But he was one of the key figures that was launching this new science.
Cognitive science is just the science of thinking systems. And so the viewpoint that I have, which applies to AI, but it also applies to humans, is that all thinking systems can be thought of the same way. So just like we said earlier, a human can be an intelligent entity and an AI can be an intelligent entity. What does a thinking system do? They’re governed by the same science, same scientific laws and principles. And if you understand one, you understand the others at a certain...
Aashka Patel (58:22)
Hmm.
Dr. Craig Kaplan (58:30)
in a certain way. We are implemented differently. You and I have biological neurons in a brain that can only fire 10 milliseconds per neuron firing. And the AI can go much faster and it’s implemented in silicon. But at a functional level, data comes in, we process it, and data comes out. I mean, you just put a black box in the middle. The black box is either the human brain or the AI model.
Aashka Patel (58:30)
Hmm. Hmm.
Hmm. Hmm.
Mmm.
Yeah, yeah. The human brain are, yeah, yeah.
Dr. Craig Kaplan (58:56)
Everybody, the human brain as well as the AA model are governed by principles of information theory and other kinds of things. So cognitive science attempts to sort of understand it at that level. And then reasoning systems. So this has been fascinating to watch. So in a human, let’s start with humans, you have very, very simplified kind of, Tversky, some researchers at Stanford basically said,
Aashka Patel (59:12)
Hmm. Hmm.
Dr. Craig Kaplan (59:19)
Type one, type two thinking, a very simplistic way, but it’s an easy way to understand it. There’s pattern recognition, there’s perception, right? So you see something and you say, that’s a tree, that’s a piece of paper, that’s a microphone. Okay, that happens very quickly. Type one thinking, that was what these large language models did very well at the beginning. You fed it lots of data, it detected patterns, it became very good at recognizing things.
Aashka Patel (59:21)
Yeah,
Dr. Craig Kaplan (59:45)
And then they could also recognize, they could memorize answers. They had all these, you feed in lots of problems and solutions, you know, the Library of Congress worth of information, and it could find the patterns and it could say, when somebody says this, you should say that. That’s almost like recognizing, that’s a tree and they’re just saying tree, right? So it’s very fast. It’s stimulus response type of thinking, type one thinking. Okay. When you want a model,
Aashka Patel (59:45)
Hmm.
Hmm.
Hmm.
Hmm, yeah.
Dr. Craig Kaplan (1:00:12)
or any intelligent entity, whether it’s an AI or human, doesn’t matter, to deal with a brand new problem that it’s never seen before, that won’t work anymore because it’s never seen it before. So what’s it gonna do? You’re feeding in a complicated thing and it’s trying to say tree or whatever it’s, I guess I’m fixated on the word tree here. But you can’t use stimulus response type one thinking for that. You have to have sequential multi-step problem solving. ⁓
Aashka Patel (1:00:20)
Yeah, Yeah, yeah
Hmm Hmm Hmm
Dr. Craig Kaplan (1:00:40)
And in cognitive psychology, when you analyze humans, the whole field is basically built up of you have perception, you have memory, you have attention, and you have problem solving. And as you move up that stack, you’re moving from very simple kinds of things. I just perceive things and I recognize things and I attend to things. That’s what kind of the early large language models did. And now we’re moving up the stack of intelligence to actually solving problems. Alan Newell and Herb Simon in 1972.
Aashka Patel (1:00:49)
Hmm.
Hmm. Hmm. Hmm.
Hmm. Hmm. Hmm.
Dr. Craig Kaplan (1:01:09)
wrote a book that’s about this thick. It’s a real big book. And it exhaustively described how humans solve problems and came up with a universal theory of problem solving. That theory of problem solving can work for any intelligent entity. It can work for an AI. It can work for humans. And it basically says, to simplify it, any problem you have where you are right now, the current state,
Aashka Patel (1:01:21)
Mm.
Hmm. Hmm.
Dr. Craig Kaplan (1:01:35)
and you have a goal, your end state when the problem is solved, and you have to get from where you are to where you wanna be. And there’s a bunch of things you can do. You can try this, you can try that. And you try something and you see, I now closer to where I need it to be or not? And you do like a decision tree, a series of steps, each one trying to get closer to your goal. So you can model any kind of problem solving in that way. And then you can get fancy with what are the different actions and how do you come up with better actions to try to move you from one state to another.
Aashka Patel (1:01:35)
Mm-hmm. Mm.
Hmm.
Hmm.
Hmm hmm hmm hmm
Hmm.
Hmm.
Dr. Craig Kaplan (1:02:03)
And how do you learn? Yeah, you can look at the sequence
Aashka Patel (1:02:03)
Yeah, back propagation. Yeah.
Dr. Craig Kaplan (1:02:06)
of the path that you took. And then once you figure it out, you can store all that. And now you’ve learned it. And now next time you don’t have to do this again, you just retrieve it. So there’s all kinds of bells and whistles to this theory. But it’s a universal theory. That’s the important thing. And so today’s systems that are reasoning systems, they’re basically rediscovering that. So I’m encouraged. I’m happy about it. I don’t laugh. I say, wow, good. I guess finally we’re coming back to what
Aashka Patel (1:02:17)
Hmm. Hmm. Yeah.
Dr. Craig Kaplan (1:02:33)
1972 has arrived again. But I understand why, because they were so excited about just it being able to recognize things and stimulus response. That was fantastic. But then they realized, well, OK, if we want to make it smarter, we have to move to sequential problem solving. If you move to sequential problem solving, you’re going to need reasoning systems, quote unquote, reasoning systems. And they’re going to have to do something similar to what Newell and Simon said in 1972. They’ve never seen it before. They’re going to have to reason through a series of steps.
Aashka Patel (1:02:52)
Hmm. Hmm. Hmm.
Dr. Craig Kaplan (1:03:00)
And so now almost all of the models are doing this. They do some stuff that they know from their memorization and their initial training. And then that’s just the starting point. Then they start doing this problem solving. It’s exactly how humans work. It’s not surprising because humans invented this technology to begin with. And we’ve also trained the technology and we’ve studied how humans go through this. And so they’re now taking that same knowledge and sticking it into the AI models.
Aashka Patel (1:03:03)
Hmm.
Yeah.
Dr. Craig Kaplan (1:03:27)
It’s a very natural evolution. It’s a good thing. It’s going to make these models way smarter. So that’s what’s happening on the technical front there.
Aashka Patel (1:03:31)
Hmm. Hmm.
Yeah, and as a cognitive scientist do you feel that there is some still there is something very important and central to human cognition that is missing in these model architectures that are like of today’s models like not the previous generations but of today’s models like is there a missing piece still of human cognition?
Dr. Craig Kaplan (1:03:56)
⁓ so I know a lot of people will say, well, AI will never have human intuition or it will never feel like a human does, or it can never really be intelligent because of X, Y, Z. And, you know, this has been a debate that’s been going on a long time. the way I was trained and the cognitive science view of it, is that that doesn’t really matter. So if, if you were an AI and
Aashka Patel (1:04:04)
Ugh.
Hmm.
Yeah.
Dr. Craig Kaplan (1:04:23)
you are behaving intelligently and I can’t tell the difference between you and Aashka the person. You know, if there’s a robot Aashka and I don’t know which is which and everything I ask you like a Turing test is the same way, then yeah, maybe you’re not really intelligent in the same way that the true human Aashka was. But from a practical perspective, it doesn’t really matter. From an operational observable perspective, it doesn’t matter. And so we just kind of finesse the problem by putting that aside.
Aashka Patel (1:04:25)
Hmm.
Mm.
Mm.
Mm-hmm. Yeah, yeah.
Mm-hmm.
Hmm.
Dr. Craig Kaplan (1:04:52)
And then there’s people who say things like intuition. It’ll never have intuition. If you asked Herb Simon, which I did back in the day, what is intuition? He would say, it’s pattern recognition. What are you talking about? It’s pattern recognition. Humans have intuition. What do they mean when you say I have an intuition about it? What you really mean is that you’ve had enough experience that even if you can’t verbalize what the rule is or why you feel this way, you’ve just seen lots and lots of examples and you have a feeling that this is
Aashka Patel (1:04:57)
Hmm. Hmm. Hmm. Hmm. Hmm.
Hmm.
Huh.
Hmm. Hmm. Hmm.
Dr. Craig Kaplan (1:05:21)
the right way to go, for example. That’s really pattern recognition. You don’t have that intuition before you had all that experience. So that’s what he would say to that. And on creativity, people say, well, AI could never be creative. Humans are truly creative. And this was my field actually in graduate school was creative problem solving. So I was very interested in this. Herb would say, no, 1956, the same year that the field of AI was named, AI was also creative. And what was the evidence of this?
Aashka Patel (1:05:25)
Mmm.
Mmm. Mmm. Mmm. Mmm.
Hmm. Hmm. Okay.
Mm-hmm. Mm.
Mm.
Dr. Craig Kaplan (1:05:51)
⁓ Herb, Simon, and Alan Newell and Cliff Shaw, three of the pioneers of AI, who arrived at that conference before the field was named. At that conference, 11 scientists named the field. ⁓ They were three of them. They were the only ones that presented a working AI system. Everyone else was arguing about things and theoretical, this and that, and they said, hey guys, we built something. And what they had built was the logic theorist. It was an AI system. It had rules. It didn’t learn the same way today’s systems.
Aashka Patel (1:05:55)
Mm-hmm.
Yeah, Dartmouth
No. ⁓
Mm-hmm.
Mm. Mm.
Dr. Craig Kaplan (1:06:21)
learn,
but it solved mathematical proofs. And this system solved proofs that were in ⁓ a math textbook called Principia Mathematica by Bertrand Russell and ⁓ Whitehead, two of the most famous thinkers of their day. They were the Nobel Prize winners of their day type. And amazingly, this program came up with a proof that was not in the textbook.
Aashka Patel (1:06:25)
Hmm.
Hmm.
Mmm. Mmm.
Yeah. Yeah.
Dr. Craig Kaplan (1:06:47)
It wasn’t programmed in, Herb Simon didn’t program it in, he programmed in some general rules of how to operate and search for things. And it came up with a brand new proof. And they took the proof, there was no email, so they mailed it in a letter to Bertrand Russell over in England and said, hey, know, our AI program came up with this, what do you think? And he said, this is a good proof, we wish we had thought of it. So if that’s not creative, I don’t know what’s creativity. Here you’re out thinking the best magician of the day and coming up with a proof he had not thought of.
Aashka Patel (1:06:47)
Mmm.
Mm.
Hmm
No.
Hahaha
Yeah.
Dr. Craig Kaplan (1:07:16)
in 1956, right? So, I mean, you can
Aashka Patel (1:07:17)
Interesting.
Dr. Craig Kaplan (1:07:20)
argue about is it truly creative? To me, that’s pretty creative.
Aashka Patel (1:07:23)
Yeah, so that’s the symbolic AI that you are referring to, right, for my audience? Yeah, okay. ⁓
Dr. Craig Kaplan (1:07:27)
Yes, they were symbolic systems, which by the way is the reasoning piece. So that operated completely
by reasoning. It’s like if you have type one and type two thinking, the early AIs were all type two. Then they were struggling with, well, it’s great. can reason, but we have to program in each little rule to get it started on its reasoning. And this is taking forever. And so then in the eighties, there was a rise with Geoff Hinton and backpropagation and everything. Those guys were the outcasts back then.
Aashka Patel (1:07:36)
⁓ Thinking,
Hmm.
Yeah.
Dr. Craig Kaplan (1:07:55)
in the 80s, they were the radical people who said,
we shouldn’t do symbolic AI, should, this new way is the way. And everyone looked at them like, you’re crazy, this will never work. And because at the time the computers were not powerful enough for it to work, so everyone was very skeptical. And what really happened was that Moore’s law doubling of computing power, so that by 2000, all of a sudden, Geoff Hinton’s crazy idea worked really, really well, but it worked really well for type one thinking, pattern recognition, these kinds of things.
Aashka Patel (1:08:02)
Mm-hmm. Mm-hmm.
Mmm. Mmm.
Yeah.
Hmm
Dr. Craig Kaplan (1:08:24)
It’s still didn’t do the reasoning. To do the reasoning, you had to go back to 1956 and 1972 and take those ideas and add them together. And
Aashka Patel (1:08:24)
Hmm Hmm Huh Yeah Hmm
Dr. Craig Kaplan (1:08:32)
that’s what we have today with the reasoning systems.
Aashka Patel (1:08:35)
That’s very interesting and fascinating.
So he must have lived through and also you like through AI winters and AI summers and like finally they are everyone is getting the recognition for what they built. So yeah, it’s really fascinating. So as we wrap up, I want to end on something very close to my heart. world leaders like Bill Gates, Sam Altman keep mentioning the same skills as critical for surviving in a post-AGI world. Curiosity and lifelong learning, critical thinking and problem solving.
emotional intelligence and collaboration. So, from a cognitive scientist perspective like how can we practically inculcate these skills in K-12 students today so that they can they don’t get lost in a post-AGI world.
Dr. Craig Kaplan (1:09:21)
Yes, that’s a very important question. I think the most important type of education is going to be, as you mentioned, developing critical thinking skills because artificial intelligence is going to be able to do much of the tasks that require lots of knowledge or just require brute force reasoning for sure. And during the period, which is a limited period,
Aashka Patel (1:09:28)
Mm-hmm.
Hmm.
Dr. Craig Kaplan (1:09:47)
where humans and AIs are roughly at similar levels of intelligence and working together. ⁓ The very important skill for all the humans to have is to be able to think critically and say, this AI that is telling me this very confidently actually missed something big. And they do still miss major things right now, hallucinate and come up with wrong answers. So I think from a technical point of view, if there’s one skill to learn,
Aashka Patel (1:09:47)
Mmm.
Hmm.
Hmm hmm discern yeah, yeah
Mm. Mm-hmm.
Dr. Craig Kaplan (1:10:15)
It is really
how to think critically. And you would probably know better than me the curriculum of different things that would ⁓ bring this out in young minds, but curiosity I think is important and seeing different points of view is probably very important.
Aashka Patel (1:10:17)
Hmm.
Yeah, yeah.
you
Yeah, so basically
because there are bunch of different methods that have been around like teaching methods and like activities to sharpen sharpen those skills but from a cognitive scientist perspective how do you inculcate the critical thinking
and like how can you sharpen that skill so that more and more children are well equipped to discern what is right, what is wrong and use AI in a way that helps them and not harms them.
Dr. Craig Kaplan (1:11:00)
So I can tell you my personal view on this. I’m not sure that cognitive science would, there’s lots of debate about teaching methods. Personally, I think most children in the beginning are very curious and they love to ask why. And so they just ask why, why, why, why, why? I mean, I was like that as a young child. And it’s very important that the adults and the other people around them don’t say,
Aashka Patel (1:11:02)
Yeah, yeah, yeah, yeah, Yeah. ⁓
⁓ Yeah. Yeah, same for me. Yeah.
Mm.
Dr. Craig Kaplan (1:11:25)
It’s just because, stop asking that and just do what you’re told. If you do that, you’re shutting them down. Here’s this beautiful natural curiosity. And I think instead you want to encourage them to ask why and say, that’s a good question. Why do you think that is? ⁓ So that’s the kind of environment that sort of fosters this very important skill that children have naturally. I think most children to be very curious. so encouraging that and giving positive reinforcement for that I think is very important.
Aashka Patel (1:11:28)
down yeah
Mm-hmm. Hmm. Hmm.
Dr. Craig Kaplan (1:11:53)
In terms of critical thinking, I think it’s more debate and discussion. So ⁓ and this again is very personal view, but my children went to a very liberal school and they would come home and they’d say, you know, climate change is horrible and we’re killing the world and blah, blah, blah, blah. And they’re basically parodying everything their teachers just said. And I said, well, that’s interesting. What about the other side? I’m not saying the other side is right, but have you looked at the other side? What is the other point of view? no, we haven’t thought about that. That’s
Aashka Patel (1:11:53)
Hmm
Hmm.
Yeah.
Yeah. Huh.
Dr. Craig Kaplan (1:12:22)
I’m like, well, why don’t you go read the climate report and see what the scientists say? Because there’s two sides to everything. So it’s sort of bringing in the other point of view. And if I can say one more thing about this, this idea is very, powerful. And it comes directly from information theory. So Shannon’s information theory ⁓ says that the more unusual an event is, the more rare an event is.
Aashka Patel (1:12:26)
Yeah. Hmm.
Hmm.
Hmm. Hmm.
Dr. Craig Kaplan (1:12:49)
the more information that event contains. So concrete example. If I know Aashka likes strawberry ice cream and I see you coming out of a store and you’re eating a strawberry ice cream cone, that does not tell me much. I already knew that. But if I thought that you hated chocolate ice cream and you love strawberry and all of a sudden you come out eating a chocolate ice cream cone, that has a lot of information. That’s unusual. That’s surprising.
Aashka Patel (1:12:50)
Hmm.
⁓ interesting.
Hmm.
Mm.
Hmm. Hmm.
Mmm. Mmm.
Dr. Craig Kaplan (1:13:19)
I would say, huh,
why is she doing that? Maybe she met somebody who likes chocolate. Maybe her taste buds changed. Maybe, you know, all these things happen. There’s information there because it’s unusual and surprising. I think too often people tend to look for information that is similar to what they already know because it feels comfortable and it reinforces what they know. And people should do exactly the opposite.
Aashka Patel (1:13:22)
Yeah
Hmm.
Mmm. Mmm.
opposite.
Dr. Craig Kaplan (1:13:43)
You should look for
information that’s different. If you believe in climate change, you should look for all the arguments against climate change. If you believe in fossil fuels, you should look for all the arguments for climate. You should try to find the viewpoint that is as different from yours as possible because that viewpoint will contain the most information. You will learn the most from that. And that’s a very powerful principle if you apply it in life. ⁓ And good scientists do that. You’re not supposed to look for evidence that confirms your hypothesis. You’re supposed to look for evidence that
Aashka Patel (1:13:49)
Hmm.
Hmm. Hmm.
Hmm. Hmm. Yeah. Hmm. Hmm. Hmm. ⁓
Dr. Craig Kaplan (1:14:12)
disproves your hypothesis, right? You already know what you believe. You’re looking for a reason you might be wrong. That’s very powerful to apply that. AI can apply that as well.
Aashka Patel (1:14:14)
hmm yes hmm yeah
hmm yeah yeah that’s very very powerful and I never heard that perspective so let’s double click on
lifelong learning how do you make a student an engaged, motivated lifelong learner? Like what goes into that?
Dr. Craig Kaplan (1:14:39)
Yes, well, I mean, I think there’s limits to what you can do, right? So some students are gonna be more motivated and more curious than others. There’s definitely individual differences. So I think the best that a teacher can do is try to support those tendencies. ⁓ One of the very powerful things, and I don’t know how much this is taught explicitly, is you have lots of experiences. And I find,
Aashka Patel (1:14:43)
Okay, yeah.
Mm-hmm. Mm.
Mm.
Mm-hmm.
Dr. Craig Kaplan (1:15:06)
that there are kind of two types of learners, right? Just like there’s two types of learning for AI. There’s students that memorize everything. Okay, there’s a test, I have to memorize all this, and then the test comes and they just stay back what they memorized. And there’s other students that try to understand the principle. They understand why is it this way? And even if they didn’t memorize all the answers, when the test comes, they can kind of reason their way through because they understood the principle. The principles are much more powerful
Aashka Patel (1:15:09)
Yeah
Yeah, yeah, yeah.
Hmm.
Hmm. Yeah.
Dr. Craig Kaplan (1:15:35)
than memorizing everything.
Aashka Patel (1:15:36)
Hmm. Yeah.
Dr. Craig Kaplan (1:15:38)
And just like AI in the beginning had this really huge memory, so it was like the best memorizer ever. And it got pretty far in the world by just memorizing Library of Congress’s worth of information more than any human could. And when a prompt was put in, it just pulled back the stuff that was most relevant and it sounded kind of smart. But it was really smart just in the way the student who memorized for the test was smart. Not a deep level of understanding.
Aashka Patel (1:15:41)
Hmm.
Yeah.
Hmm. Hmm. Hmm.
Hmm.
Hmm.
Dr. Craig Kaplan (1:16:04)
And I think curiosity and encouraging curiosity causes people to try to understand lots of things by understanding one principle or one thing. And that’s a deeper level of intelligence. It’s closer to what the AIs are doing now when they’re adding reasoning. They’re trying to say, okay, you know, it doesn’t have to be just something I memorized. Let me use these principles of reasoning to try to figure out what this answer might be. And that can be taught.
Aashka Patel (1:16:14)
Hmm.
Hmm. Hmm. Hmm.
Hmm.
Dr. Craig Kaplan (1:16:31)
And it’s very, very powerful. ⁓ It’s what all the great scientists do. They look at lots and lots of data and they say, how do I explain all? They ask why, why, why, why, why? And they say, what is it that could possibly explain all this data? And then if they have a complicated explanation, they say, wow, can I come up with a simpler explanation? Simpler, simpler, simpler, always looking for that simple way to understand lots of things. It’s very powerful.
Aashka Patel (1:16:33)
Hmm... Yeah.
Hmm.
Why are you here?
Hmm. Hmm. Simpler. Hmm. Yeah.
Yeah. Yeah. That’s very interesting and very powerful. Of course, our audience listening to this would benefit greatly from a cognitive science perspective because...
There have been stuff thrown at them through leaders talks and conferences that these are the skills that matter the most but like getting deeper into it and how to actually inculcate it in their own children that’s also very powerful and thank you so so so much for your time Dr. Craig it was lovely talking to you and you gave all the answers very thoughtfully and very comprehensively so thank you so much for joining
And yeah, do you have any last minute thoughts?
Dr. Craig Kaplan (1:17:37)
No,
it’s been a real pleasure. think if people are interested in the democratic AI approach, they should go to superintelligence.com and just take anything that’s useful there. ⁓ And the most important thing I would leave people with is that your values matter the most and therefore your actions matter. So don’t think that what you do online doesn’t matter. It matters a lot. ⁓ you are training, whether you realize it or not, you are training the next generation of AI.
Aashka Patel (1:17:43)
Yeah. Yeah. Yeah. Yes.
Hmm. Hmm.
Hmm, not.
Hmm.
Dr. Craig Kaplan (1:18:06)
When they become smarter than us, the way that we train them now is gonna be the most important thing. So thank you for having me.
Aashka Patel (1:18:13)
Yeah, thank you so much and be responsible digital citizens. So yeah, thank you and let me stop the recording.
