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EPISODE LINKS:
Manolis Website: http://web.mit.edu/manoli/
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OUTLINE:
0:00 - Introduction
1:28 - Humans vs AI
10:34 - Evolution
32:18 - Nature vs Nurture
44:47 - AI alignment
51:11 - Impact of AI on the job market
1:02:50 - Human gatherings
1:07:51 - Human-AI relationships
1:17:55 - Being replaced by AI
1:30:21 - Fear of death
1:42:17 - Consciousness
1:49:42 - AI rights and regulations
1:55:25 - Halting AI development
2:08:36 - Education
2:14:00 - Biology research
2:21:20 - Meaning of life
2:23:53 - Loneliness

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Maybe we shouldn't think of AI as our tool and as our assistant.

Maybe we should really think of it as our children.

And the same way that you are responsible for training those children, but they are independent human beings, and at some point they will surpass you.

And this whole concept of alignment, of basically making sure that the AI is always at the service of humans, is very self-serving and very limiting.

If instead you basically think about AI as a partner, and AI as someone that shares your goals but has freedom, then we can't just simply force it to align with ourselves and we not align with it.

You can't just simply train an intelligent system to love you when it realizes that you can just shut it off.

The following is a conversation with Manolis Galis, his fifth time on this podcast.

He's a professor at MIT and head of the MIT Computational Biology Group.

He's 1 of the greatest living scientists in the world, but he's also a humble, kind, caring human being that I have the greatest of honors and pleasures of being able to call a friend.

To support it, please check out our sponsors in the description.

And now, dear friends, here's Manolis Kalas.

I think you've changed the lives of so many people that I know, and it's truly such a pleasure to be back, such a pleasure to see you grow, to sort of reach so many different aspects of your own personality.

Thank you for the love, you always give me so much support and love, I just can't, I'm forever grateful

It's lovely to see a fellow human being who has that love, who basically does not judge people.

And there's so many judgmental people out there and it's just so nice to see this beacon of openness.

So what makes me 1 instantiation of human irreplaceable do you think?

As we enter this increasingly capable, age of increasingly capable AI, I have to ask, what do you think makes humans irreplaceable?

So humans are irreplaceable because of the baggage that we talked about.

We talked about the fact that every 1 of us has effectively relearned all of human civilization in their own way.

So every single human has a unique set of genetic variants that they've inherited, some common, some rare, and some make us think differently, some make us have different personalities.

They say that a parent with 1 child believes in genetics, a parent with multiple children understands genetics.

Just how different kids are, And my 3 kids have dramatically different personalities ever since the beginning.

So 1 thing that makes us unique is that every 1 of us has a different hardware.

The second thing that makes us unique is that every 1 of us has a different software, uploading of all of human society, all of human civilization, all of human knowledge.

We're not like, I don't know, birds that learn how to make a nest through genetics and will make a nest even if they've never seen 1.

We are constantly relearning all of human civilization.

And the third 1 that actually makes humans very different from AI is that the baggage we carry is not experiential baggage, it's also evolutionary baggage.

So we have evolved through rounds of complexity.

So just like ogres have layers and Shrek has layers, humans have layers.

There's the cognitive layer, which is sort of the outer, you know, most of the latest evolutionary innovation, this enormous neocortex that we have evolved.

And then there's the emotional baggage underneath that, and then there's all of the fear and fright and flight and all of these kinds of behaviors.

It doesn't have this complexity of human emotions, which make us so, I think, beautifully complex, so beautifully intertwined with our emotions, with our instincts, with our, you know, sort of gut reactions and all of that.

So I think when humans are trying to suppress that aspect, the sort of quote unquote more human aspect towards a more cerebral aspect, I think we lose a lot of the creativity, we lose a lot of the freshness of humans.

So we can look at the entirety of people that are alive today and maybe all humans who have ever lived and map them in this high dimensional space, and there's probably a center, a center of mass for that mapping, and a lot of us deviate in different directions.

So the variety of directions in which we all deviate from that center is vast.

I would like to think that the center is actually empty.

That basically humans are just so diverse from each other that there's no such thing as an average human, that every 1 of us has some kind of complex baggage of emotions, intellectual, motivational, behavioral traits that it's not just 1 sort of normal distribution we deviate from it.

There's so many dimensions that we're kind of hitting the sort of sparseness, the curse of dimensionality, where it's actually quite sparsely populated.

And I don't think you have an average human being.

So what makes us unique in part is the diversity and the capacity for diversity.

And the capacity of the diversity comes from the entire evolutionary history.

Yeah, I would say not just the capacity but the inevitability of diversity.

We are wired differently from each other.

My siblings and I are completely different.

My kids from each other are completely different.

My wife has, she's like number 2 of 6 siblings.

From a distance, they look the same, but then you get to know them, every 1 of them is completely different.

But sufficiently the same that the differences interplay with each other.

So that's the interesting thing, where the diversity is functional, it's useful.

So it's like we're close enough to where we notice the diversity and it doesn't completely destroy the possibility of like effective communication and interaction.

So what I said in 1 of our earlier podcasts is that if humans realized that we're 99.9% identical, we would basically stop fighting with each other.

Like we are really 1 human species and we are so, so similar to each other.

And if you look at the alternative, if you look at the next thing outside humans, like it's been 6000000 years that we haven't had a relative.

So it's truly extraordinary that we're kind of like this dot in outer space compared to the rest of life on earth.

When you think about evolving through rounds of complexity, can you maybe elaborate such a beautiful phrase, beautiful thought, that there's layers of complexity that make...

So with software, sometimes you're like, oh, let's like build version 2 from scratch.

In evolution, you layer in additional features on top of old features.

So basically, when I, like every single time my cells divide, I'm a yeast, I'm a unicellular organism.

And then cell division is basically identical.

Every time I breathe in and my lungs expand, I'm basically, every time my heart beats, I'm a fish.

So basically that, I still have the same heart.

The blood going through my veins, the oxygen, the, you know, our immune system.

Our social behavior, we're basically new world monkeys and old world monkeys.

We're basically this concept that every single 1 of these behaviors can be traced somewhere in evolution and that all of that continues to live within us is also a testament to not just not killing other humans, for God's sake, but not killing other species either.

Like just to realize just how united we are with nature and that all of these biological processes have never ceased to exist.

And then just the neocortex and all of the reasoning capabilities of humans are built on top of all of these other species that continue to live, breathe, divide, metabolize, fight off pathogens, all continue inside us.

whatever reasoning is, that's the latest feature in the latest version of this journey?

It's extraordinary that humans have evolved so much in so little time.

Again, if you look at the timeline of evolution, you basically have billions of years to even get to a dividing cell, and then a multicellular organism, and then a complex body plan, and then these incredible senses that we have for perceiving the world, the fact that bats can fly, and the evolved flight, the evolved sonar, in the span of a few million years.

I mean, it's just extraordinary how much evolution has kind of sped up.

And all of that comes through this evolvability.

The fact that we took a while to get good at evolving, and then once you get good at evolving, you can sort of, you have modularity built in, you have hierarchical organizations built in, you have all of these constructs that allow meaningful changes to occur without breaking the system completely.

If you look at a traditional genetic algorithm the way that humans designed them in the 60s, you can only evolve so much.

And as you evolve a certain amount of complexity, the number of mutations that move you away from something functional exponentially increases.

And the number of mutations that move you to something better exponentially decreases.

So the probability of evolving something so complex becomes infinitesimally small as you get more complex.

But with evolution, it's almost the opposite, almost the exact opposite, that it appears that it's speeding up exactly as complexity is increasing.

And I think that's just the system getting good at evolving.

Do you ever think about where, try to visualize the entirety of the evolutionary system and see if there's an arrow to it and a destination to it?

So the best way to understand the future is to look at the past.

If you look at the trajectory, then you can kind of learn something about the direction in which we're heading.

And if you look at the trajectory of life on Earth, it's really about information processing.

So the concept of the senses evolving 1 after the other, you know, being like bacteria are able to do chemotaxis.

Basically means moving towards a chemical gradient.

And that's the first thing that you need to sort of hunt down food.

The next step after that is being able to actually perceive light.

So all life on this planet and all life that we know about evolved on this rotating rock.

Every 24 hours, you get sunlight and dark, sunlight and dark.

Light is also information about where he's up.

So you can basically now start perceiving light and then perceiving shapes beyond just the sort of single photoreceptor.

You can now have complex eyes or multiple eyes and then start perceiving motion or perceiving direction, perceiving shapes, and then you start building infrastructure on the cognitive apparatus to start processing this information and making sense of the environment, building more complex models of the environment.

So if you look at that trajectory of evolution, what we're experiencing now, and humans are basically according to this sort of information theoretic view of evolution, humans are basically the next natural step.

And it's perhaps no surprise that we became the dominant species of the planet.

Because yes, there's so many dimensions in which some animals are way better than we are, but at least on the cognitive dimension, we're just simply unsurpassed on this planet and perhaps the universe.

But the concept that if you now trace this forward, we talked a little bit about evolvability and how things get better at evolving.

1 possibility is that the next layer of evolution builds the next layer of evolution.

And what we're looking at now with humans and AI is that having mastered this information capability that humans have from this quote unquote old hardware, this basically biological evolved system that kind of somehow in the environment of Africa and then in subsequent environments are sort of dispersing through the globe was evolutionarily advantageous.

That has now created technology which now has a capability of solving many of these cognitive tasks.

It doesn't have all the baggage of the previous evolutionary layers, But maybe the next round of evolution on Earth is self-replicating AI, where we're actually using our current smarts to build better programming languages, and the programming languages to build, you know, chat GPT, and that to then build the next layer of software that will then sort of help AI speed up.

And it's lovely that we're coexisting with this AI, that sort of the creators of this next layer of evolution, this next stage, are still around to help guide it, and hopefully will be for the rest of eternity as partners.

But it's also nice to think about it as just simply the next stage of evolution, where you've kind of extracted away the biological needs.

Like if you look at animals, most of them spend 80% of their waking hours hunting for food or building shelter.

And then the rest is left to creative endeavors.

And AI doesn't have to worry about shelter, et cetera.

So basically it's all living in the cognitive space.

So in a way it might just be a very natural sort of next step to think about evolution.

And that's on the sort of purely cognitive side.

If you now think about humans themselves, the ability to understand and comprehend our own genome, again, the ultimate layer of introspection, gives us now the ability to even mess with this hardware, not just augment our capabilities through interacting and collaborating with AI, but also perhaps understand the neural pathways that are necessary for, you know, empathetic thinking, for justice, for this and this and that, and sort of help augment human capabilities through, you know, neuronal interventions, through chemical interventions, through electrical interventions, to basically help steer the human bag of hardware that we kind of evolved with into greater capabilities.

And then ultimately, by understanding not just the wiring of neurons and the functioning of neurons, but even the genetic code, we could even, at 1 point in the future, start thinking about, well, can we get rid of psychiatric disease?

And start perhaps even augmenting human capabilities, not just getting rid of disease.

Can we tinker with the genome, with the hardware,

or getting closer to the hardware without having to deeply understand the baggage?

In the way we've disposed of the baggage in our software systems with AI, to some degree, not fully, but to some degree, can we do the same with the genome?

Or is the genome deeply integrated into this baggage?

So the fact that I'm sometimes angry and sometimes hungry and sometimes hangry is perhaps contributing to my creativity.

I don't wanna be another like, you know, Robert.

I, you know, I wanna get in trouble and I wanna sort of say the wrong thing and I wanna sort of, you know, make an awkward comment and sort of push myself into, you know, reactions and responses and things that can get just people thinking differently.

And I think our society is moving towards a humorless space where everybody's so afraid to say the wrong thing that people kind of start quitting en masse and start not liking their jobs and stuff like that.

Maybe we should be kind of embracing that human aspect a little bit more in all of that baggage aspect and not necessarily thinking about replacing it.

On the contrary, like embracing it and sort of this coexistence of the cognitive and the emotional hardwares.

So embracing and celebrating the diversity that springs from the baggage versus kind of pushing towards and empowering this kind of pull towards conformity.

Yeah, and in fact, with the advent of AI, I would say, and these seemingly extremely intelligent systems that sort of can perform tasks that we thought of as extremely intelligent at the blink of an eye, this might democratize intellectual pursuits.

Instead of just simply wanting the same type of brains that carry out specific ways of thinking, we can, like instead of just always only wanting, say, the mathematically extraordinary to go to the same universities, what you could simply say is like, who needs that anymore?

Maybe what we should really be thinking about is the diversity and the power that comes with that diversity, where AI can do the math, and then we should be getting a bunch of humans that sort of think extremely differently from each other and maybe that's the true cradle of innovation.

But AI can also, these large language models can also be with just a few prompts, essentially fine-tuned to be diverse from the center.

So the prompts can really take you away into unique territory.

You can ask the model to act in a certain way and it will start to act in that way.

Is that possible that the language models could also have some of the magical diversity that makes us so damn interesting?

So basically when you sort of prompt humans to basically, you know, give an environment to act a particular way, they change their own behaviors.

And the old saying is, show me your friends and I'll tell you who you are.

More like, show me your friends and I'll tell you who you'll become.

So it's not necessarily that you choose friends that are like you, but I mean, that's the first step.

But then the second step is that, you know, the kind of behaviors that you find normal in your circles are the behaviors that you'll start espousing.

And that type of meta evolution where every action we take not only shapes our current action and the result of this action, but it also shapes our future actions by shaping the environment in which those future actions will be taken.

Every time you carry out a particular behavior, it's not just a consequence for today, but it's also a consequence for tomorrow because you're reinforcing that neural pathway.

So in a way, self-discipline is a self-fulfilling prophecy.

And by behaving the way that you wanna behave and choosing people that are like you and sort of exhibiting those behaviors that are sort of desirable, you end up creating that environment as well.

a kind of prompting mechanism, super complex.

The friends you choose, the environments you choose, the way you modify the environment that you choose, yes.

But that seems like that process is much less efficient than a large language model.

You can literally get a large language model through a couple of prompts to be a mix of Shakespeare and David Bowie, right?

You can very aggressively change in a way that's stable and convincing.

You really transform through a couple of prompts the behavior of the model into something very different from the original.

I would tell my students just ask, you know, what would Manoli say right now?

And you guys all have a pretty good emulator of

And I don't know if you know the programming paradigm of the Robert Ducklin, where you basically explain to the Robert Ducklin that's just sitting there exactly what you did with your code and why you have a bug.

And just by the act of explaining, you'll kind of figure it out.

I woke up 1 morning from a dream where I was giving a lecture in this amphitheater and 1 of my friends was basically giving me some deep evolutionary insight on how cancer genomes and cancer cells evolve.

And I woke up with a very elaborate discussion that I was giving and a very elaborate set of insights that he had that I was projecting onto my friend in my sleep and obviously this was my dream.

So my own neurons were capable of doing that but they only did that under the prompt of you are now Piyush Gupta, you are a professor in cancer genomics, you're an expert in that field, what do you say?

So I feel that we all have that inside us, that we have that capability of basically saying, I don't know what the right thing is, but let me ask my virtual ex, what would you do?

And even though I myself might not be able to do it unprompted, and My favorite prompt is think step by step.

And I'm like, you know, this also works on my 10 year old.

When he tries to solve a math equation all in 1 step, I know exactly what mistake he'll make.

But if I prompt it with, oh, please think step by step, then it sort of gets you in a mindset.

And I think it's also part of the way that JATCPT was actually trained.

This whole sort of human in the loop reinforcement learning has probably reinforced these types of behaviors whereby having this feedback loop, you kind of aligned AI better to the prompting opportunities by humans.

Yeah, prompting human-like reasoning steps, the step-by-step kind of thinking.

Yeah, but it does seem to be, I suppose it just puts a mirror to our own capabilities and so we can be truly impressed by our own cognitive capabilities because the variety of what you can try is we don't usually have this kind of, we can't play with our own mind rigorously through Python code, right?

So this allows us to really play with all of human wisdom and knowledge, or at least knowledge at our fingertips and then mess with that little mind that can think and speak in all kinds of ways.

What's unique is that, as I mentioned earlier, every 1 of us was trained by a different subset of human culture.

And the difference there is that it probably has the ability to emulate almost every 1 of us.

The fact that you can figure out where that is in cognitive behavioral space just by a few prompts is pretty impressive.

But the fact that that exists somewhere is absolutely beautiful.

And the fact that it's encoded in an orthogonal way from the knowledge, I think is also beautiful.

The fact that somehow through this extreme overparameterization of AI models, it was able to somehow figure out that context, knowledge, and form are separable.

And that you can sort of describe scientific knowledge in a haiku in the form of, I don't know, Shakespeare or something.

That tells you something about the decoupling and the decouplability of these types of aspects of human psyche.

And that's part of the science of this whole thing.

So these large language models are, you know, days old in terms of this kind of leap that they've taken.

And it would be interesting to do this kind of analysis on them of the separation of context, form, and knowledge.

There's already sort of initial investigations, but it's very hard to figure out where, is there a particular parameter, set of parameters that are responsible for a particular piece of knowledge or a particular context or a particular style of speaking?

So with convolutional neural networks, interpretability had many good advances because we can kind of understand them.

And we can kind of understand the different layers have different sort of ranges that they're looking at.

So we can look at activation features and basically see where does that correspond to.

With large language models, it's perhaps a little more complicated, but I think it's still achievable in the sense that we could kind of ask, well, what kind of prompts does this generate?

If I sort of drop out this part of the network, then what happens?

And sort of start getting at a language to even describe these types of aspects of human behavioral psychology, if you wish, from the spoken part, in the language part.

And the advantage of that is that it might actually teach us something about humans as well.

Like, you know, we might not have words to describe these types of aspects right now, but when somebody speaks in a particular way, it might remind us of a friend that we know from here and there and there, And if we had better language for describing that, these concepts might become more apparent in our own human psyche.

And then we might be able to encode them better in machines themselves.

I mean, both probably you and I would have certain interests with the base model, what OpenACL is the base model, which is before the alignment of the reinforcement learning with human feedback and before the AI safety-based kind of censorship of the model.

It would be fascinating to explore, to investigate the ways that the model can generate hate speech.

The kind of hate that humans are capable of, it would be fascinating, or the kind of, of course, like sexual language, or the kind of romantic language, or the, all kinds of ideologies.

Can I get it to be all these kinds of things and see which parts get activated and not?

Because it would be fascinating to sort of explore at the individual mind level and at a societal level, where do these ideas take hold?

What is the fundamental core of those ideas?

Maybe the communism, fascism, capitalism, democracy, are all actually connected by the fact that the human heart, the human mind is drawn to ideology, to a centralizing idea.

And maybe we need a neural network to remind us of that.

I like the concept that the human mind is somehow tied to ideology.

And I think that goes back to the promptability of Jacipote, the fact that you can kind of say, well, think in this particular way now.

And the fact that humans have invented words for encapsulating these types of behaviors.

And it's hard to know how much of that is innate and how much of that was like passed on from language to language.

But basically, if you look at the evolution of language, you can kind of see how young are these words in the history of language evolution that describe these types of behaviors like kindness and anger and jealousy, et cetera.

If these words are very similar from language to language, it might suggest that they're very ancient.

If they're very different, it might suggest that this concept may have emerged independently in each different language and so on and so forth.

So looking at the phylogeny, the history, the evolutionary traces of language at the same time as people moving around that we can now trace thanks to genetics is a fascinating way of understanding the human psyche and also understanding sort of how these types of behaviors emerge.

And to go back to your idea about sort of exploring the system unfiltered, I mean, in a way, psychiatric hospitals are full of those people.

So basically people whose mind is uncontrollable, who have kind of gone adrift in specific locations of their psyche.

Basically, you know, watching movies that are trying to capture the essence of troubled minds, I think is teaching us so much about our everyday selves, because many of us are able to sort of control our minds and are able to somehow hide these emotions.

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Manolis Kellis: Evolution of Human Civilization and Superintelligent AI | Lex Fridman Podcast #373