The Simulation Within with Karl Friston

Summary

StarTalk Radio – Episode Summary: "The Simulation Within with Karl Friston"

Release Date: October 18, 2024

Hosts and Guests:

• Neil deGrasse Tyson – Astrophysicist and Host
• Chuck Nice – Comic Co-host
• Gary O'Reilly – Former Soccer Pro and Sports Commentator
• Carl Friston – Leading Neuroscientist and Theorist

---

1. Introduction to Karl Friston and His Work

Neil deGrasse Tyson kicks off the episode by introducing Carl Friston, a renowned neuroscientist from University College London, known for his development of the Free Energy Principle. Chuck Nice provides a background on Friston’s academic journey, highlighting his studies in physics and psychology at Cambridge University, his invention of statistical parametric mapping in neuroimaging, and his numerous honors and awards.

Quote:
Neil deGrasse Tyson [04:29]: "And he speaks Brit."
Carl Friston [04:38]: "Well, thank you very much for having me. I should, at this point, I can speak American as well."

---

2. Understanding the Free Energy Principle

The conversation delves into the Free Energy Principle, with Neil seeking clarity on its definition and application.

Neil: "What is the free energy principle?"
Carl: "It is a principle and in the spirit of physics, it is therefore a method... a formal mathematical prescription of the way that things behave... to describe the behavior of things that self-organize into characteristic states." [05:29]

Carl compares it to Hamilton's principle of least action, explaining its role in modeling self-organization across various domains, from particles to populations.

Quote:
Carl Friston [05:29]: "So exactly the same maths now has been transplanted and applied not to the movement of particles, but to what we refer to as belief updating."

---

3. The Intersection of Neuroscience and AI

The discussion bridges neuroscience with artificial intelligence, emphasizing how the Free Energy Principle informs both fields. Carl explains that neuronal dynamics can be described as performing a gradient descent on variational free energy, linking it to active inference, a concept central to understanding sentient behavior.

Quote:
Carl Friston [09:19]: "It's lower. Yeah, that is absolutely right."

---

4. Active Inference and Cognitive Neuroscience

Chuck Nice introduces the concept of active inference, prompting Carl to elaborate on its significance in cognitive neuroscience. Carl describes active inference as an application of the Free Energy Principle to understand sentient behavior, highlighting its role in perception and hypothesis testing.

Quote:
Carl Friston [12:45]: "Active inference is meant to emphasize that perception... depends upon the data that we actively solicit from the environment."

---

5. Implications for Artificial General Intelligence (AGI)

The conversation shifts to the future of AI, particularly Artificial General Intelligence (AGI). Carl expresses skepticism about current large language models (LLMs), differentiating them from natural intelligence by emphasizing the lack of embodiment and agency in LLMs.

Quote:
Carl Friston [35:11]: "Those that are implemented using transformer architectures are something called attention heads. So it is exactly the same mechanism... that makes transformers work."

Neil: "Do you think the answer to AGI is a biological solution, a mechanical solution, or a mixture of both?"

Carl: "Do we have to make a move towards biomimetic neuromorphic... natural kinds of instantiation of intelligent behavior? Yes, absolutely." [42:37]

---

6. Consciousness vs. Self-Awareness

Chuck Nice and Carl Friston explore the distinctions between consciousness and self-awareness. Carl clarifies that consciousness does not necessarily equate to self-awareness, noting that entities like bees exhibit sentient behavior without self-reflection.

Quote:
Carl Friston [48:29]: "I differentiate between self-aware and simply being capable of sentient behavior."

---

7. Bayesian Inference and Belief Updating

Neil prompts Carl to explain Bayesian inference, which Carl describes as the process of updating beliefs based on new evidence using Bayes' rule. This concept is fundamental to the Free Energy Principle, underpinning how both humans and AI systems process information.

Quote:
Carl Friston [56:10]: "It's a way of describing inference by which people just mean estimating the best explanation probabilistically...belief updating."

---

8. Applications and Future Directions

Towards the end of the episode, Carl Friston discusses practical applications of his theories, including simulating self-organization in robots, phenotyping psychiatric disorders, and advancing sustainable AI through biomimetic approaches. He emphasizes the importance of building efficient, natural intelligence systems that minimize energy consumption.

Quote:
Carl Friston [55:17]: "So the variational free energy... is a measure of adaptive fitness."

Neil: "I've seen you quoted Carl as saying that we are coming out of the age of information and moving into the age of intelligence. If that's the case, what is the age of intelligence going to look like?"

Carl: "It's about deploying your body in a way to sample the right kind of information that makes your model as apt or as adaptive as possible." [44:49]

---

9. Addressing AI Hallucinations and Intelligence

The hosts discuss the phenomenon of AI "hallucinations," comparing it to human cognitive disorders. Carl explains that hallucinations in AI stem from its inability to embody intelligence, as LLMs lack the active inference mechanisms present in humans.

Quote:
Carl Friston [58:44]: "It's an important question that theoreticians and neuroscientists have to think about in terms of understanding false imprints in a brain."

---

10. Closing Remarks

Neil deGrasse Tyson wraps up the episode by acknowledging the profound insights shared by Carl Friston, highlighting the ongoing journey in understanding intelligence, both natural and artificial.

Quote:
Neil deGrasse Tyson [61:02]: "As much as you've accomplished thus far, we all deep down know it's only just the beginning. Who knows where the next year, much less five years will take this."

---

Key Takeaways:

• Free Energy Principle: A foundational theory in neuroscience that models how systems self-organize by minimizing free energy, applicable to both biological brains and AI.
• Active Inference: The process by which organisms actively seek information from their environment to update their internal models, essential for adaptive behavior.
• Bayesian Inference: A statistical method for updating beliefs based on new evidence, central to both human cognition and AI algorithms.
• Differences Between AI and Natural Intelligence: Current AI models, especially LLMs, lack embodiment and agency, distinguishing them from natural, sentient intelligence.
• Future of AGI: Emphasis on biomimetic and neuromorphic approaches to create sustainable and efficient artificial intelligence systems.
• Consciousness vs. Self-Awareness: Understanding that sentient behavior does not necessarily imply self-awareness, with implications for both biological and artificial systems.

---

Notable Quotes:

• Carl Friston [05:29]: "It's just a prescription... to describe the behavior of things that self-organize themselves into characteristic states."
• Carl Friston [35:11]: "LLMs are just given everything. There's no requirement upon them to select which data are going to be most useful to learn from."
• Carl Friston [56:10]: "It's a process of inference that is ongoing... you can't start from scratch."

---

Conclusion: In this insightful episode of StarTalk Radio, Neil deGrasse Tyson and his co-hosts engage in a deep exploration of the Free Energy Principle and its implications for understanding the human brain and the future of artificial intelligence. Carl Friston provides a comprehensive overview of how these principles bridge neuroscience and AI, emphasizing the need for embodied, agentic intelligence in the development of truly adaptive and sustainable AI systems. The conversation underscores the intricate relationship between perception, belief, and action, offering listeners a profound understanding of the mechanisms underlying intelligence.

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*Stay tuned to StarTalk Radio for more episodes where science, pop culture, and comedy intersect. Remember to subscribe to SiriusXM Podcasts+ on Apple Podcasts for ad-free listening and early access to new episodes. Keep Looking Up!

Transcript

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[Neil DeGrasse Tyson]

So, guys, I was delighted to learn all the ways that principles of physics could be borrowed by neuroscientists to try to understand how our brain works. Because at the end of the day, there's physics and everything else is just opinion.

[Gary O'Reilly]

If you say so yourself. I love how you get to say.

[Chuck Nice]

That, but it's the physics of intelligence.

[Neil DeGrasse Tyson]

The physics of neuroscience is really what that is about.

[Chuck Nice]

As you've, as you've educated us. The physics is in everything.

[Neil DeGrasse Tyson]

Yes.

[Chuck Nice]

You just don't think of it as being in neuroscience.

[Neil DeGrasse Tyson]

Yeah. Because we've compartmentalized what people do as professional scientists into their, have their own textbook and their own journals and their own departments at universities. At the end of the day, we are one.

[Gary O'Reilly]

It's all physics people at the end. That's the lesson we learn.

[Neil DeGrasse Tyson]

Coming up, all that and more on StarTalk Special Edition. Welcome to StarTalk, your place in the universe where science and pop culture collide. StarTalk begins right now. This is StarTalk Special Edition. Neil Degrasse Tyson here, your personal astrophysicist. And if this is Special Edition, you know it means we've got not only Chuck. Nice Chuck. How you doing, man?

[Gary O'Reilly]

Hey, buddy.

[Neil DeGrasse Tyson]

Always good to have you there as my co host. And we also have Gary O'Reilly, former soccer pro sports commentator. Was that the crowd cheering him on?

[Gary O'Reilly]

Yeah, that's the, that's the crowd at Tottenham. Tottenham, yeah, Crystal Palace. They're all.

[Chuck Nice]

Anytime you mention my name in this room, there's a crowd effect.

[Neil DeGrasse Tyson]

Gary, you know, with Special Edition, what you've helped do with this branch of StarTalk is Focus on the human condition in every way that matters to it. The mind, body, soul. That would include AI mechanical augmentation to who and what we are Robotics. So this fits right in to that theme. So take us where you need for this episode.

[Chuck Nice]

In the age of AI and machine learning, we as a society, and naturally as StarTalk, are asking all sorts of questions about the human brain, how it works and how we can apply it to machines. One of these big questions being perception. How do you get a blob of neurons? I think that's a technical term.

[Neil DeGrasse Tyson]

Yes, Technical for sure.

[Chuck Nice]

Yeah. In your skull. To understand the world outside. Our guest, Carl Friston, is one of the world's leading neuroscientists and an authority on neuroimaging theoretical neuroscience, and the architect of the free energy principle. Using physics inspired statistical methods to model neuroimaging data. That's one of his big successes. He's also sought after by the people in the machine learning universe. Now, just to give you a little background on Carl, a neuroscientist and theoretician at University College London, where he is a professor, studied physics and psychology at Cambridge University in England. An inventor of the statistical parametric mapping used around the world and neuroimaging, plus many other fascinating things. He is the owner of a seriously impressive array of honors and awards which we do not have time to get into.

[Neil DeGrasse Tyson]

And he speaks Brit.

[Chuck Nice]

Yes. So he's evened this out. There's no more picking on the Brit because there's only one of them.

[Neil DeGrasse Tyson]

Okay, Carl Friston, welcome to StarTalk.

[Carl Friston]

Well, thank you very much for having me. I should, at this point, I can speak American as well.

[Gary O'Reilly]

Please don't, Carl. That takes a certain level of illiteracy that I'm sure that you don't possess.

[Neil DeGrasse Tyson]

Yeah, please don't stoop to our level. So let's start off with something. Is it a field or is it a principle, or is it an idea that you pioneered, which is in our notes known as the free energy principle? I come to this as a physicist and there's a lot of sort of physicsy words that are floating or orbiting your work. And so in physics they're very precisely defined and I need to know how you are using these terms and in what way they apply. So let's just start off. What is the free energy principle?

[Carl Friston]

Well, as it says on the tin, it is a principle and in the spirit of physics, it is therefore a method. So it's just like Hamilton's principle of least action. So it's just a prescription, a formal mathematical prescription of the way that things behave that you can then use to either simulate or reproduce or indeed explain. The behavior of things. So you might apply the principle of least action, for example, to describe the motion of a football. The free energy principle has a special domain of application. It talks about the self organization of things, where things can be particles, they can be people, they can be populations. So it's a method really of describing things that self organize themselves into characteristic states.

[Neil DeGrasse Tyson]

Very cool. So, but why give it this whole new term? You know, we've all read about or thought about or seen the self organization of matter. Usually there's a source of energy there available though, or it reaches sort of a minimum energy, because that's what it, it's a state that it prefers to have. You know, so a ball rolls off a table onto the ground. It doesn't roll off the ground onto the table. So it seeks the minimum place. And my favorite of these is the box of morning breakfast cereal. And it will always say some settling of contents may have occurred. Yeah, and you open up and it's.

[Gary O'Reilly]

Like 2/3, 2/3 of powder, you get 2/3 of crushed shards of corn flakes.

[Neil DeGrasse Tyson]

It's finding sort of the lowest place in the grave, Earth's gravitational potential. So why the need for this, this new term?

[Carl Friston]

Well, it's an old term, I guess. Again, pursuing the American theme. You can trace this kind of free energy back to Richard Feynman, probably his PhD thesis. So he was, he was trying to deal with the problem of describing the behavior of small particles and invented this kind of free energy as a proxy that enabled him to evaluate the probability that a particle would take this path or that path. So exactly the same maths now has been transplanted and applied not to the movement of particles, but to what we refer to as belief updating. So it's lovely you should introduce this notion of nature finding its preferred state that can be described as rolling downhill to those free energy minima. This is exactly the ambition behind the free energy principle. But the preferred states here are states of beliefs or representations about a world in which something, say you or I, exist. So this is the point of contact with machine learning and artificial intelligence. So the free energy is not a thermodynamic free energy. It is a free energy that scores a probability of your explanation for the world in your head being the right kind of explanation. And you can now think about our existence the way that we make sense of the world and our behavior, the way that we sample that world as effectively falling downhill, settling towards the bottom, but an extremely itinerant way, in a wandering way, as we sort of go through Our daily lives at different temporal scales. It can all be described effectively as coagulating at the bottom of the serial packet in our preferred states.

[Neil DeGrasse Tyson]

Wow, so you're. Again, I don't want to put words in your mouth that don't belong there. This is just my attempt to interpret and understand what you just described. You didn't yet mention neurons, which are the carriers of all of this or the transmitters of all of this? Thoughts and memories and interpretations of the world. So when you talk about the pathways that an understanding of the world takes shape, do those pathways track the nearly semi infinite connectivity of neurons in our brains? And so you're finding what the neuron will naturally do in the face of one stimulus versus another?

[Carl Friston]

That's absolutely right. In fact, technically you can describe neuronal dynamics, the, the trajectory or the path of nerve self firing. Exactly. As performing a gradient descent on this variational free energy. So that is literally true. But I think more intuitively, the idea is, in fact the idea you've just expressed, which is you can trace back possibly to the early days of cybernetics in terms of the good regulator theorem. The idea here is that to be well adapted to your environment, you have to be a model of that environment. In other words, to interface and interact with your world through your sensations, you have to have a model of the causal structure in that world. And that causal structure is thought to be literally embedded in the connectivity among your neurons within your brain. So my favorite example of this would be the distinction between where something is and what something is. So in our universe, a certain object can be in different positions. So if you told me what something is, I wouldn't know where it was. Likewise, if you told me where something was, I wouldn't know what it was. That statistical separation, if you like, is literally installed in our anatomy. So, you know, there are two screens at the back of the brain, one dealing with where things are and one stream of connectivity dealing, dealing with what things are.

[Gary O'Reilly]

However, we are pliable enough though, and of course I'm not pushing back, I'm just trying to further understand we're pliable enough though that if you were to say, go get me the thing. Okay. And then you give me very specific coordinates of the thing, I would not have to know what the thing is and I would be able to find it. Even if there are other things that are there.

[Carl Friston]

Yep. And that speaks to something which is quite remarkable about ourselves, that we actually have a model of our lived world that has this sort of geometry that can be navigated because that presupposes that you've got a model of yourself moving in a world and, you know, the way that you're. Your body works. I'm tempted here to bring in groins, but I don't know why.

[Neil DeGrasse Tyson]

Chuck injured his groin a few. A few days ago. That's all he's been talking about.

[Carl Friston]

Carl.

[Chuck Nice]

Well, please. We've all heard about it since Carl. I hear the term active inference, and then I hear the term Bayesian active inference. Let's start with active inference. What is it? How does it play a part in cognitive neuroscience?

[Carl Friston]

Active inference, I think, most simply put, would be an application of this free energy principle we're talking about. So it's a description or applying the maths to understand how we behave in a sentient way. So active inference is meant to emphasize that perception, read as unconscious inference, in the spirit of Helmholtz, depends upon the data that we actively solicit from the environment. So what I see depends upon where I am currently looking. So this speaks to the notion of active sensing.

[Gary O'Reilly]

You went a little fast. I'm sorry, man. I'm trying to keep up here. Okay, but you went a little fast there, Carl. You talked about perception being an inference that is somehow tied to the subconscious, but when you. Can you just do that again, please?

[Neil DeGrasse Tyson]

And just to be clear, he's speaking slowly.

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[Neil DeGrasse Tyson]

So it's not that he's going fast.

[Gary O'Reilly]

No.

[Neil DeGrasse Tyson]

It's that you are not keeping up.

[Gary O'Reilly]

Well, listen, I don't have a problem, okay? I have no problem not keeping up, which is why I have never been left behind, by the way. I have no problem keeping up because I go. Wait a minute. Okay, so anyway, could you just, like, break that down a little bit for me?

[Carl Friston]

Sure. I was trying to speak at a New York pace. My apologies. I'll revert to London. Okay, so let's start at the beginning. Sense making. Perception. How do we make sense of the world we are locked inside? Our brains are locked inside a skull. It's dark in there. There's no. You can't see other than what information is conveyed by your eyes or by your ears or by your skin, your sensory organs. So you have to make sense of this unstructured data coming in from your sensory organs. Your sensory epithelia. How might you do that? The answer to that, or one answer to that can be traced back to the days of Plato through Kant and Helmholtz. So Helmholtz brought up this notion of unconscious inference. Sounds very glorious, but very, very simply. It says that if inside Your head, you've got a model of how your sensations were caused, then you can use this model to generate a prediction of what you would sense if this was the right cause, if you got the right hypothesis, and if what you predict matches what you actually sense, then you can confirm your hypothesis. So this is where inference gets into the game. It's very much like a scientist who has to use scientific instruments, say microscopes or telescopes in order to acquire the right kind of data to test her hypotheses about the structure of the universe, about the state of affairs out there as measured by her instruments. So this can be described, this sort of hypothesis testing. Putting your fantasies, your hypotheses, your beliefs about the state of the affairs outside your skull to test by sampling data and testing hypotheses. This is just inference. So this is where inference gets into the game.

[Neil DeGrasse Tyson]

So these are micro steps en route to establishing an objective reality. And there are people for whom their model does not match a prediction they might make for the world outside of them. And they would be living in some delusional, some world that you cannot otherwise agree to what is objectively true. And that would then be an objective measure of insanity or some other logical disconnect.

[Chuck Nice]

Yeah.

[Gary O'Reilly]

Really though? I mean, is it really.

[Chuck Nice]

Well, if you, if you project your own fantastical world into reality that, and you know, it doesn't sit, but it's what you want, then that's a dysfunction you're not working with, you're working against.

[Gary O'Reilly]

But we, we live in a time now.

[Neil DeGrasse Tyson]

Yeah.

[Gary O'Reilly]

Where that fast fantastical dysfunction actually has a place and talked to James Cameron for just a little bit and you'll see that that fantastical dysfunction was a world building creation that we see now as a series of movies. So is it really so, you know, aberrant that it's, you know, a dysfunction or is it just different?

[Chuck Nice]

Well, I think he's trying to create artistically rather than impose upon.

[Neil DeGrasse Tyson]

Yeah. So Carl, if everyone always received the world objectively, would there be room for art at all? Ooh, that was a good question.

[Gary O'Reilly]

Yep.

[Chuck Nice]

Really was well done, sir.

[Gary O'Reilly]

I'm gonna say I think I was the inspiration for that question.

[Neil DeGrasse Tyson]

Yes, Chuck inspired that question. So there's a role for each of each side of this perceptive reality, correct?

[Carl Friston]

No, absolutely. So just to pick up on a couple of those themes, but that last point was, I think, quite key. It is certainly the case that one application of one use of active inference is to understand psychiatric disorders. So you're absolutely right. When people are model of their lived world is not quite apt for the situation in which they find themselves. Say something changes, say you lose a loved one, so your world changes. So your predictions and the way that you sort of navigate through your day either socially or physically is now changed. So your model is no longer fit for purpose for this world. But as Chuck was saying before, the brain is incredibly plastic and adaptive. So what you can do is you can use the mismatch between what you predict is going to happen and what you actually sense to update your model of the world. And before I was saying that this is a model that would be able to generate predictions of what you would see under a particular hypothesis or fantasy. And just to make a link back to AI, this is generative AI. It's intelligent forecasting prediction under a charitable model that is entailed exactly by the connectivity that we were talking about before in the brain.

[Neil DeGrasse Tyson]

And it's the free energy principle manifesting when you readjust to the changes. And it's finding the new roots that are presumably the more accurate your understanding of your world, the lower is that free energy state. Or is it higher or lower? What is.

[Carl Friston]

It's lower. Yeah, that is absolutely right. So actually, technically, you know, if you go into the cognitive neurosciences, you'll find a big move in the past 10 years towards this notion of predictive processing and predictive coding, which again just rests upon this meme that we are, our brains are constructive organs generating from the inside out predictions of the sensorium. And then the mismatch is now a prediction error. That prediction error is then used to drive the neurodynamics that then allow for this revising or updating my beliefs, sort of such that my predictions now are more accurate and therefore the prediction error is minimized. The key thing is to answer your question, technically, the gradients of the free energy that drive you downhill just are the prediction errors. So when you minimize, when you minimize your free energy, you've squashed, you're at the minimum, absolutely excellent.

[Neil DeGrasse Tyson]

You're not going to roll uphill unless there's some other changes to your environment.

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[Neil DeGrasse Tyson]

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[Neil DeGrasse Tyson]

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[Carl Friston]

I'm Kais from Bangladesh and I support StarTalk on Patreon. This is StarTalk with Neil DeGrasse Tyson.

[Chuck Nice]

So if we think back to early mankind and the predictability so I'm walking along, I see a lion in the long grass. What do I start to predict? If I run up a tree high enough, that lion won't get me. But if I run along the ground, the lion's probably going to get. Is this kind of evolutionary that we've born for survival?

[Carl Friston]

Yep.

[Chuck Nice]

Or have I misinterpreted this completely?

[Carl Friston]

No, no, I think that's an excellent point. Well, let's just think about what it means to be able to predict exactly what you would sense in a given situation and thereby predict also what's going to happen next. If you can do that with your environment and you've reached the bottom of the serial packet and you minimized your free energy, minimized your prediction errors, you now can fit the world. You can model the world in an accurate way. That just is adaptive fitness. So if you look at this process now as unfolding over evolutionary time, you've just, you can now read the variational free energy or its negative as adaptive fitness. So that tells you immediately that evolution itself is one of these free energy minimizing processes. It is also, if you like testing hypotheses about the kind of denizens of its environment, the kind of creatures that will be a good fit for this particular environment. So you can actually read natural selection as well in statistics will be known as Bayesian model selection. So you are effectively inheriting inferences or learning transgenerationally in a way that's minimizing your free energy, minimizing your prediction errors. So things that get eaten by lions don't have the ability to propagate themselves through to the next generation, so that everything ends up at the bottom of the cereal packet, avoiding lions, because those are the only things that can be there because the other ones didn't minimize their free energy.

[Neil DeGrasse Tyson]

Yeah, unless. Gary, you made babies before you said, I wonder if that's a lion in the bushes. Let me check.

[Chuck Nice]

But if they've got my genes, then there's a line with their name on it.

[Neil DeGrasse Tyson]

That's exactly right. I want to share with you one observation, Carl, and then I want to hand back to Gary, because I know he wants to get all in the AI side of this. I remembered one of the books by Douglas Hofstadter. It might have been Godel, Escher, Bach, or he had a few more that were brilliant explorations into the mind and body. In the end of one of his books he had was an appendix. I don't remember a conversation with Einstein's brain. And I said to myself, this is stupid. What does this even mean? And then he went in and described the fact that imagine Einstein's brain could be preserved at the moment he died and all of the neurosynaptic elements are still in place and it's just sitting there in a jar. And you ask a question and the question goes into his ears, gets transmitted into the sounds that trigger neurosynaptic firings. It just moves through the brain and then Einstein then speaks an answer. And the way that setup was established, it was like, yeah, I can picture this sometime in the distant future now, maybe the modern version of that is you upload your consciousness and then you're asking your brain in a jar, but it's not biological at that point, it's in silicon. But what I'm asking is the information going into Einstein's brain in that thought experiment presumably trigger his thoughts and then his need to answer that question, because it was posed as A question. Could you just comment on that exercise? The exercise of probing a brain that's sitting there waiting for you to ask it a question.

[Carl Friston]

I mean, it's a very specific and interesting example of the kind of predictive processing that we are capable of because we're talking about language and communication here. And just note, the way that you set up that question provides a lovely segue into large language models. But note also that it's not the kind of embodied intelligence that we were talking about in relation to active inference, because there's no. The brain is in a body. The brain is embodied. Most of what the brain is actually in charge of is moving the body or secreting. In fact, those are the only two ways you can change the universe. You can either move a muscle or secrete something. There is no other way that you can affect the universe. So this means that you have to deploy your body in a way to sample the right kind of information that makes your model as apt or as adaptive as possible.

[Neil DeGrasse Tyson]

So, Chuck, did you hear what he said? It means you cannot bend the spoon with your brain.

[Gary O'Reilly]

Right?

[Chuck Nice]

Tell that to Yuri Geller.

[Neil DeGrasse Tyson]

Just to clarify.

[Carl Friston]

Okay, so what I was trying to hint at, because I suspect it's going to come up in later conversation that there's, I think, a difference between a brain and a vat or a large language model that is the embodiment of lots of knowledge. So one can imagine, say, a large language model being a little bit like Einstein's brain, but Einstein plus, you know, 100, possibly million other people and the history of everything that has been written that you can probe by asking it questions. And in fact, there are people whose entire career is now prompt.

[Gary O'Reilly]

Engineers, AI prompts.

[Carl Friston]

Yep.

[Gary O'Reilly]

It's funny, the people who program AI then leave that job to become prompt. The people who are responsible for creating the best prompts to get the most information back out of AI. So it's a pretty fascinating industry that they've created their own feedback loop that benefits them.

[Carl Friston]

And now you can start to argue, you know, where is the intelligence? Is it in the prompt? Engineer? As a scientist, I would say that's where the intelligence is. That's where the sort of sensing behavior is. It's asking the questions, not producing the answer. That's the easy bit. It's, you know, asking, querying the world in the right way and just notice, what. What are we all doing? What is your job? Is it asking the right questions?

[Chuck Nice]

Carl, can I ask you this, please? Could active inference cause us to miss things that do happen and secondly, does deja vu fit into this?

[Carl Friston]

Yes and yes. So in a sense, active inference is really about missing things that are measurable or observable in the right kind of way. So another sort of key thing about natural intelligence, and be a good scientist, just to point out that sort of noting the discovering infrared, that's an act of creation. That is art. So where did that come from? From somebody's model about the structure of electromagnetic radiation. So I think just to pick up on a point we missed earlier on, creativity and insight is an emergent property of this kind of question answering in an effort to improve our models or our particular world. Coming back to missing stuff, it always fascinates me that the way that we can move depends upon ignoring the fact we're not moving. So I'm talking now about a phenomena in cognitive science called sensory attenuation. And this is the rather paradoxical or at least counterintuitive phenomena that in order to initiate a movement, we have to ignore and switch off and suppress any sensory evidence that we're not currently moving. And my favorite example of this is moving your eyes. So if I asked you to sort of track my finger as I moved it across the screen, and you moved your eyes very, very quickly, while your eyes are moving, you're actually not seeing the optic flow that's being produced because you are engaging something called saccadic suppression. And this is a reflection of the brain very cleverly knowing that that particular optic flow that I have induced is fake news. So the ability to ignore fake news is absolutely essential for a good navigation and movement of our world.

[Neil DeGrasse Tyson]

Is it fake or just irrelevant to the moment?

[Gary O'Reilly]

If it's the New York Times, it's definitely fake fakeness.

[Neil DeGrasse Tyson]

But it's not. It's not. So it's fake. It's just not relevant to the task at hand. Isn't that a different notion?

[Carl Friston]

It's a subtle one for the simplicity of the conversation. And then I'm reading fake as irrelevant, imprecise.

[Gary O'Reilly]

So it's like it's unusable. So your brain is just throwing it out, basically. Like, don't, don't. Nothing to see here. So get rid of that.

[Chuck Nice]

So, Neil, Neil, this is. This is in your backyard rather more than mine. But isn't this where the matrix pretext kind of fits in, that our perception might differ from what's actually out there, and then perception can be manipulated or recreated.

[Neil DeGrasse Tyson]

Well, I think Carl's descendants will just put us all in a jar the way he's done. Carl, Carl, what does Your laboratory look.

[Carl Friston]

Like full of Jaws.

[Gary O'Reilly]

Well, yes. Well, there are, there are several pods and we have one waiting for you.

[Neil DeGrasse Tyson]

Yeah. In the film the Matrix, of course, which came out in 1999, about 25 years ago, you know, a quarter century ago. It's just hard to believe. The. It was very candid under sense that your brain's reality is the reality you think of and understand. And it is not receiving external input. All that your brain is constructing is detached from what's exterior to it. And if you've had enough lived experience, or maybe in that future that they're describing, the brain can be implanted with memory. It reminds me, what's that movie that Arnold Schwarzenegger is in about? Mars.org Total Recall. Thank you.

[Gary O'Reilly]

Get your ass to Mars.

[Neil DeGrasse Tyson]

Instead of paying thousands of dollars to go on vacation, they would just implant the memories of a vacation in you.

[Gary O'Reilly]

Yeah.

[Neil DeGrasse Tyson]

And bypassing the sensory conduits into your brain. Of course. These are movies and their stories and it's. It's science fiction. How science fictiony is it really?

[Carl Friston]

Well, I certainly think that, you know, the philosophy behind, I think probably both Total Recall, but particularly the Matrix. I think that's very real and very current. You know, just going back to our understanding people with psychiatric disorders or perhaps, you know, people who have odd views, worldviews, to understand that the way that you make sense of the world can be very different from the way I make sense of the world, dependent on my history, my predispositions, and my prize, what I have learned thus far and also the information that I select to attend to. So just pursue this theme of ignoring 99% of all the sensations. For example, Chuck, are you thinking about your groin at the moment? I would guarantee you're not. And yet it is generating sensory impulses from the nerve endings. But you, at this point in time, were not selecting that. So the capacity to select is, I think, a fundamental part of intelligence and agency. But of course, to select means that you are not attending to or selecting 99% of the things that you could select. So I think this, the notion of selection is a hallmark of truly intelligent behavior.

[Neil DeGrasse Tyson]

Are you analogizing that to large language models in the sense that it could give you gibberish, it could find crap anywhere in the world that's online, but because you prompted it precisely, it is going to find only the information necessary and ignore everything else?

[Carl Friston]

Yes and no. But that's a really, really good example. So the yes part is that the characteristic bit of architecture that makes large language models work certainly Those that are implemented using transformer architectures are something called attention heads. So it is exactly the same mechanism, the same Bayesian mechanics that we were talking about in terms of attentional selection that makes transformers work. So they select the recent past in order to predict the next word. That's why they work to selectively pick out something in the past, ignore everything else to make them work.

[Gary O'Reilly]

When you talk about that probability in an LLM, that probability is a mathematical equation that happens for like every single letter that's coming out of that model. So it is literally just giving you the best probability of what is going to come next. Okay. Whereas when we perceive things, we do so from a world view. So for an LLM, if you show it a picture of a ball with a red stripe that's next to a house, okay. And say, that's a ball. And then show it a picture of a ball in the hands of a little girl who's bouncing it, it's going to say, all right, that might be a ball, that may not be a ball. Whereas if you Show Even a 2 year old child this is a ball, and then take that ball and place it in any circumstance, the baby will look at it and go, thal, Thal. So there is a difference in the kind of intelligence that we're talking about here.

[Carl Friston]

Yeah, I think that's spot on. That's absolutely right. And that's why I said yes and no. So, okay, that kind of fluency that you see in large language models is very compelling. And it's very easy to give the illusion that these things have some understanding or some intelligence, but they don't have the right kind of generative model underneath to be able to generalize and recognize a ball in different contexts the way that we do.

[Neil DeGrasse Tyson]

Well, it would if, if it was set up correctly. And that setup is no different from you looking at reading the scene. I mean, a police officer does that, busting into a room, you know, who's the perpetrator, who's not? Before you shoot, there's an instantaneous awareness factor that you have to draw from your exterior stimuli. And so, because, you know, I'm reminded of here, Carl, I saw one of these New Yorker style cartoons where there are two dolphins swimming in one of these water, you know, parks, right? And so they're in captivity, but the two dolphins are swimming. And one says to the other of the person walking along the pool's edge, those humans, they face each other and make noises, but it's not clear they're actually communicating. And so who are we to say that the AI large language model is not actually intelligent? If you cannot otherwise tell the difference, who cares how it generates what it is if it gets the result that you seek, you're going to say, oh well, we're intelligent and it's not. How much of that is just human ego speaking?

[Carl Friston]

Well, I'm sure it is human ego speaking, but in a technical sense.

[Neil DeGrasse Tyson]

Okay, there's a loophole. You're saying because I'm not going to say that bees are not intelligent when they do their waggle dance telling other bees where the honey is. And I'm not going to say termites are not intelligent when they build something a thousand times bigger than they are when they make termite mounds and they all cooperate. I'm fatigued by humans trying to say how special we are relative to everything else in the world that has a brain when they do stuff we can't.

[Carl Friston]

Let me ask you then. So what's the common theme between the termite and the bee and the policeman reading the scene? What do they all have in common? All of those three things move, whereas a large language model doesn't.

[Gary O'Reilly]

Doesn't.

[Carl Friston]

So that brings us back to this action, the active part of active inference. So the no to the question about large language models and attention was that large language models are just given everything. They're given all the data. There is no requirement upon them to select which data are going to be most useful to learn from. And therefore they don't have to build expressive fit for purpose world models or generative models. Whereas your daughter would. The two year old daughter playing with the beach ball would have to. By moving and selectively reading the scene, by moving her eyes, by observing her body, by observing balls in different contexts build a much deeper appropriate world or gerarded model that would enable her to recognize the ball in this context and that context and ultimately tell her father, I'm playing with a ball.

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[Chuck Nice]

So we had a great show with Brett Kagan who mentioned your free energy principle and in his work creating computer chips out of neurons, what people call organoid intelligence, what he was calling synthetic biological intelligence.

[Neil DeGrasse Tyson]

And that's in our archives and our recent archives actually. Recent archives, yeah.

[Chuck Nice]

Do you think the answer to AGI is a biological solution, a mechanical solution.

[Neil DeGrasse Tyson]

Or a mixture of both? And remind people what AGI is?

[Chuck Nice]

Artificial General Intelligence.

[Neil DeGrasse Tyson]

I know that's what the words stand for, but what is it?

[Chuck Nice]

You're not asking me for the answer.

[Carl Friston]

And don't ask me either. No, seriously, I've been told off for even using that acronym anymore because it's so ill defined and people have very different regions of it. So OpenAI has a very specific meaning for it. If you talk to other theoreticians, they would represent it. I think what people are searching for is natural intelligence.

[Gary O'Reilly]

It's natural, Gary.

[Carl Friston]

You know it answered your question. Do we have to make a move towards biomimetic neuromorphic natural kinds of instantiation of intelligent behavior? Yes, absolutely. But Chuck, just coming back to your previous theme. Notice we're talking about behaving systems, systems that act and move and can select and do their own data mining in a smart way as opposed to just ingesting all the data. So what I think people mean when they talk about superintelligence or generalized AI or artificial general intelligence, they just mean natural intelligence.

[Gary O'Reilly]

They really Mean us. It's our brain. Our brain. If you want to know what AGI is, it's our brain, you know, and.

[Neil DeGrasse Tyson]

The way that if it was actually our brain, it would be natural stupidity.

[Gary O'Reilly]

Yeah, well, that too. Our brain without the stupidity. That's really what it is.

[Chuck Nice]

So back in December 22nd, you dropped a white paper titled Designing Ecosystems of Intelligence from First Principles. Now, is this a roadmap for the next 10 years or beyond, or to the terminator, ultimate destination? And then somewhere along the line, you discussed the thinking behind a move from AI to ia, and IA standing for intelligent agents, which seems a lot like moving towards the architecture for sentient behavior. Have I misread this in any way?

[Carl Friston]

No, you've read that perfectly. So that white paper was written with colleagues in industry, particularly, versus AI. Exactly. The kind of roadmap that those people who were committed to a future of artificial intelligence that was more sustainable, that was explicitly committed to a move to natural intelligence. And all the biomimetic moves that you would want to make, including implementations on neuromorphic hardware, quantum computation of photonics, all those efficient approaches that would be sustainable in the sense of climate change, for example. But also speaking to Chuck's notion about efficiency, efficiency is also, if you like, bait into natural intelligence in the sense that if you can describe intelligent behavior as this falling downhill, pursuing free energy gradients, minimizing free energy, getting to the bottom of the serial packet, you're doing this via a path of least action. That is the most efficient way of doing it, not only informationally, but also in terms of the amount of electricity you use in the carbon footprint you leave behind. So from the point of view of sustainability, it's important we get this right. And so part of the theme of that white paper was saying there is another direction of travel away from large language models. Large is in the title. It's seductive, but it's also very dangerous. It shouldn't be large, it should be the size of a bee. So to do it biologically, you should be able to do it much more efficiently. And of course, the meme here is that our brains work on 20 watts, not 20 kilowatts, and we do more than any large language model. So that we have low energy intelligence, we do efficient.

[Neil DeGrasse Tyson]

I guess that's a way to say it.

[Chuck Nice]

I've seen you quoted Carl as saying that we are coming out of the age of information and moving into the age of intelligence. If that's the case, what is the age of intelligence going to look like? Or have we Already discussed that.

[Carl Friston]

Well, I think we're at its inception now just in virtue of all the wonderful things that are happening around us and the things that we are talking about. We're asking some, some of the very big questions about what is happening and what will happen over the next decade. I think part of the answer to that lies in your previous nod to the switch between AI and ia. So IA brings agency into play. So one deep question would be, is current generative AI an example of agentic? Is it an agent? Is a large language model an agent? And if not, then it can't be intelligent and certainly can't have generalized intelligence. So what is definitive of being an agent? I put that out there as a question, half expecting a joke.

[Chuck Nice]

I've got Agent Smith in my head. If anyone can take that and run with it.

[Carl Friston]

There you go.

[Neil DeGrasse Tyson]

It's right about now where you hear people commenting on the morality of a decision and whether a decision is good for civilization or not. And everybody's afraid of AI achieving consciousness and just declaring that the world would be better off without humans. And I think we're afraid of that because we know it's true.

[Gary O'Reilly]

Yeah, I was going to say we've already come to that conclusion. That's the problem.

[Chuck Nice]

Okay, Carl, is consciousness the same of self awareness?

[Carl Friston]

Yeah. I'm sure there are lots of people who you could answer that question of and get a better answer. I would say in the purpose of this conversation, probably not. No. I think to be conscious, certainly to be sentient and to behave in a sentient kind of way would not necessarily imply that you knew you were a self. I'm pretty sure that a bee doesn't have self awareness, but it's still has sentience, it's still experience. It has experiences and has plans and communicates and behaves in a, you know, in an intelligent way. And you could also argue that certain humans don't have self awareness of a fully developed thought. You know, I'm talking about very severe psychiatric conditions. So I think self awareness is a gift of a particular, very elaborate, very deep generative model that not only entertains the consequences of my actions, but also entertains the fantasy or hypothesis that I am an agent and I am self and can be self reflective in a sort of metacognitive sense. So I think I differentiate between self aware and simply being capable of sentient behavior.

[Chuck Nice]

Interesting.

[Neil DeGrasse Tyson]

That is great. Let me play skeptic here for a moment. Mild skeptic. You've described, you've accounted for human decision making and behavior With a model that connects the sensory conduits between what's exterior to our brain and what we do with that information as it enters our brain. And you've applied this free energy gradient that this information follows. That sounds good, it all sounds fine. I'm not going to argue with that. But how does it benefit us to think of things that way? Or is it just an after the fact pastiche on top of what we already knew was going on, but now you put fancier words behind it? Is there predictive value to this model or is the predictivity in your reach? Because when you assume that's true, you can actually make it happen in the AI marketplace?

[Carl Friston]

Yeah, I think that's the key thing. So I mean when I'm asked that question, or indeed when I ask that question of myself, I sort of apply it to things like Hamilton's principle of least action. Why is that useful? Well, it becomes very useful when you're actually sort of building things. It becomes very useful when you're simulating things. It becomes useful when something does not comply with a Hampshire's principal released action. So just to unpack those directions to travel in terms of applying the free energy principle, that means that you can write down the equations of motion and now you can simulate self organization that has this natural kind of intelligence, this natural kind of sentient behavior. You can simulate it in a robot, in an artifact, in a terminator should you want to. Although strictly speaking that would not be compliant with the free energy principle. But you can also simulate it in silico and make digital twins of people and choices and decision making and sense making. And once you can simulate, you can now use that as an observation model for real artifacts and start to phenotype, say people with addiction, or say people who are very creative, or say people who had schizophrenia. So if you can cast aberrant inference or false inference, believing things are present when they're not, or vice versa as an inference problem, and you know what the principles of sense making and inference are, and you can model that in a computer, you can now got a stamp it in which you can now not only phenotype by adjusting the model to match somebody's observed behavior, but now you can go and apply synthetic drugs or do brain surgery in silico. So there are lots of practical applications of knowing how things work. Well, when I say things work, how.

[Neil DeGrasse Tyson]

Things behave, that presumes that your model is correct. For example, just a few decades ago it was presumed, and I think no longer so that our brain function via Neural nets, neural networks, where it's a decision tree and you slide down the tree to make an ever more refined decision on that assumption. We then mirrored that in our software to invoke neural net decision making. In my field in astrophysics, how do we decide what galaxy is interesting to study versus others in the millions that are in the data set? You just put it all into a neural net that has parameters that select for features that we might, in the end of that effort, determine to be interesting. We still invoke that, but I think that's no longer the model for how the brain works. But it doesn't matter, it's still helpful to us.

[Gary O'Reilly]

You're right. And honestly, that is now how AI is organized around the new way that we see the brain working.

[Neil DeGrasse Tyson]

Yeah. So why is the brain the model of what should be emulated? I mean, the human physiological system is rife with baggage, evolutionary baggage. Much of it is of no utility to us today except sitting there, available to be hijacked by advertisers or others who will take advantage of some feature we had 30,000 years ago when it mattered, for our survival. And today it's just dangling there, waiting to be exploited.

[Carl Friston]

So a straight answer to your question, the free energy principle is really a description or a recipe for self organization of things that possess a set of preferred or characteristic states coming right back to where we started, which is the bottom of the serial packet. If that's where I live, if I want to be there, that's where I'm comfortable, then I can give you a calculus that will, for any given situation, prescribe the dynamics and the behavior and the sense making and the choices to get you to that point, it is not a prescription for what is the best place to be or what the best embodied form of that being should be. In saying that if you exist, and you want to exist in a sustainable way, where it could be a species.

[Neil DeGrasse Tyson]

A meme in a given environment, yes.

[Carl Friston]

In a given setting, it's all about the relationship. That's a really key point. So the variational free energy that we've been talking about, the prediction error, is a measure of the way that something couples to its universe or to its world. It's not a statement about a thing in isolation, it's the fit. Again, if you just take the notion of prediction error, there's something that's predicting and there's something being predicted. So it's all relational, it's all observational. It's a measure of adaptive fitness.

[Neil DeGrasse Tyson]

That's an important clarification here. Yes. Carl, could you give us a few sentences on Bayesian inference? That's a new word to many people who even claim to know some statistics. That's a. It's a way of using what you already know to be true to help you decide what's going to happen next. Are there any more subtleties to a Bayesian inference than that?

[Carl Friston]

I think what you just said captures the key point. It's all about updating. So it's a way of describing inference by which people just mean estimating the best explanation probabilistically. A process of inference that is ongoing. So sometimes this is called Bayesian belief updating. Updating one's belief in the face of new data. And how do you do that? Update in a mathematically optimal way. You simply take the new evidence, the new data, you combine it using Bayes rule, with your prior beliefs established before you saw those new data to give you a belief afterwards, sometimes called a.

[Neil DeGrasse Tyson]

Posterior belief, because otherwise you would just come up with a hypothesis, assuming you don't know anything about the system. And that's not always the fastest way to get the answer.

[Gary O'Reilly]

Yeah.

[Carl Friston]

So you could argue you can't do it. It has to be a process. It has to be a path through some belief space you're always updating, whether it's at an evolutionary scale or whether it's during this conversation. You can't start from scratch.

[Neil DeGrasse Tyson]

And you're using the word belief the way here, stateside we might use the word what's supported by evidence. So it's not that I believe something is true. Often the word belief is just, well, I believe in Jesus or Jesus is my savior, Muhammad. So belief is. I'll believe that no matter what you tell me, because that's my belief. And my belief is protected constitutionally on those grounds. When you move scientifically through data and more data comes to support it, then I will ascribe confidence in the result measured by the evidence that supports it.

[Gary O'Reilly]

So it's an evidentiary supported belief.

[Neil DeGrasse Tyson]

Yeah, yeah, I guess. If we have to say belief, it's. What is the strength of your belief? It is measured by the strength of the evidence behind it.

[Gary O'Reilly]

Evidence.

[Neil DeGrasse Tyson]

Yeah, that's how we have to say that. So, Gary, do you have any last question before we got to land this plane?

[Chuck Nice]

Yeah, I do. Because if I think about us as humans, we have, sadly, some of us have psychotic abstract schizophrenia. If someone has hallucinations, they have a neurological problem that's going on inside their mind. Yet we are told that AI can have Hallucinations. I don't know if it's. Does a high have mental illness?

[Gary O'Reilly]

AI just learned to lie, that's all. You know, you ask it a question, it doesn't know the answer, and it's just like, all right, well, how about this?

[Neil DeGrasse Tyson]

That's what we do in school, right? You don't know the answer. Yeah, you might be right.

[Gary O'Reilly]

Right, exactly. You know, what's the answer? Ah, rockets. Okay.

[Carl Friston]

Yeah. I was speaking to Gary Marcus in Davos a few months ago, and he was telling me he invented the world or applied the word hallucination, hallucination, context. And it became word of the year, I think, in some circles, and I think he regrets it now because the spirit in which he was using it was technically very divorced from the way that people hallucinate. And I think it's a really important question that theoreticians and neuroscientists have to think about in terms of understanding false imprints in a brain. And just to pick up on Neil's point, when we talk about beliefs, we're talking about sub personal, non propositional Bayesian beliefs that you wouldn't be able to articulate. These are the way that the brain encodes probabilistically the causes of its sensations. And of course, if you get that inference process wrong, you're going to be subject to inferring things are there when they're not, which is basically hallucinations and delusions, or inferring things are not there when they are. And this also happens to some of us in terms of neglect syndromes, dissociative syndromes, hysterical syndromes. These can be devastating conditions where you've just got the inference wrong. So understand the mechanics of this failed inference. I think, for example, hallucination is absolutely crucial. It usually tracks back to what we're talking about before in terms of the ability to select versus ignore different parts of the data. So if you've lost the ability to ignore stuff, then very often you preclude an ability to make sense of it because you're always attending to the surface structure of sensations. Take for example, severe autism. You may not get past the bombardment of sensory input in all modalities, of all parts of the scene, on all parts of your sensory.

[Neil DeGrasse Tyson]

It's all alive, right? It's all alive. Guys, I think we got to call it quits there. Carl, this has been highly illuminating.

[Gary O'Reilly]

Yes, good stuff.

[Neil DeGrasse Tyson]

And what's interesting is as much as you've accomplished thus far, we all deep down know it's only just the beginning. And who knows where the next year, much less five years will take this. It'd be interesting to check back in with you and see what you're making in your basement.

[Carl Friston]

With a Brit.

[Chuck Nice]

Neil. It's garage.

[Neil DeGrasse Tyson]

Oh, garage. You got a garage.

[Chuck Nice]

The basements is more the garage. We go out there. Lots of wonderful things.

[Neil DeGrasse Tyson]

Yeah, exactly. Exactly. Okay. Professor Carl, thanks for joining us.

[Carl Friston]

Thank you very much. The conversation, the jokes particularly welcome you. I've ever done conversation.

[Neil DeGrasse Tyson]

Thanks for joining us from London.

[Chuck Nice]

Thank you.

[Neil DeGrasse Tyson]

Time shifted from us here stateside again. We're delighted that you could share your expertise with us in this StarTalk special edition. All right, Chuck, always good to have you man.

[Gary O'Reilly]

Always a pleasure.

[Neil DeGrasse Tyson]

All right, Gary.

[Chuck Nice]

Pleasure, Neil. Thank you.

[Neil DeGrasse Tyson]

I'm Neil Degrasse Tyson. You're a personal astrophysicist as always, bidding you to keep looking up.

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[Neil DeGrasse Tyson]

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