How large language models view our world

I get to talk to people all the time about how they use AI in their work and in their lives

and also how it has changed them as people. There's many different ways of knowing things and many different ways of understanding things

Computers, science, what both of those ways of seeing the world are trying to do

is reduce the world into a set of really clean universal laws that apply in any situation

If X is true, then Y will happen. And what language models see instead is a dense web of causal relationships between different parts of the world that all come together in unique, very context-specific ways to produce what comes next

And what's really interesting about neural networks is the way that they think or the way that they operate is a lot like human intuition

Human intuition is also trained by thousands and thousands and thousands of hours of direct experience

The reason I love that is because I hope that it makes more visible to us the value and importance of intuitive thought

My name is Dan Schipper. I'm the co-founder and CEO of Every, and I'm the host of the AI and I podcast

chapter one the limits of rationalism from socrates to neural networks

i think rationalism is one of the most important ideas in the last like 2 000 years rationalism

is really the idea that if we can be explicit about what we know if we can really reduce what

we know down into a set of theories, a set of rules for how the world works. That is true

knowledge about the world. And that is distinct from everything else that kind of messes with our

heads, messes with how we operate in society. And you may not have heard that word, or maybe you

have, but it is built into the way that you see the world. For example, the way computers work

or the way vaccines work or the way that we predict the weather or the way that we try to

make decisions when we're, you know, thinking about, I don't want to be too emotional about this. I want to get really precise about my thinking on this issue. Even the way that we

do therapy, a lot of therapy is about rationalizing or rationalizing through what you think and what

you feel. All that stuff comes from an extensive lineage of ideas that started in ancient Greece

really blossomed during the Enlightenment and now is like the bedrock of our culture and the way

that we think about the world. I think the father of rationalism is Socrates, the philosopher

Socrates is one of the first people to really examine the question of what we know and how

what is true and what's not true, to be able to describe what we know and how we know it

to make that clear and explicit so that only people that knew the truth, that knew how the

world really works, were the ones that were steering the state. That really became the birth

of philosophy, is this idea that if you inquire deeply into what is usually kind of like the

inexplicit intuitions that we have about the world, you can find, you can identify a set of

rules or a theory about what the world is like and what's true and what's not that you can lay

out explicitly and that you can use to decide the difference between true and false. I think that you

can trace the birth of rationalism to this dialogue Protagoras. And in the dialogue, it's a debate

between Socrates on the one hand and Protagoras. And Protagoras is what we call a sophist and it's

wear the term sophistry, which means like kind of, you know, someone who says really compelling

things, but is actually full of shit. What Protagoras and Socrates are debating is can

excellence be taught? And excellence, the word is often translated in English as virtue, but I think

a more appropriate translation is excellence. And in ancient Greece, like that kind of excellence

was really prized. It's sort of like a general ability to be good at important things in life and in society

And they approach it from very different angles. Protagoras believes that everyone has the capacity, every human has the capacity to be excellent

And he tells this big myth about how we as humans gain the capacity to be excellent

And Socrates is saying, no, no, no, I don't want any of that. what I want is I want a definition. I want you to say explicitly what it is and what it's not

and what are the components of it. And that's a really big moment. At least the way that Plato

writes it, Socrates kind of like takes apart Protagoras and it's pretty clear by the end that

Protagoras doesn't know, doesn't have any way to define in a non-contradictory way

what excellence is, what it means to be good. And the implication is that then he doesn't know it

And that sort of set Western society on this path of trying to find really clear

definitions and theories for the things that we talk about and to identify knowledge

the ability to know something or whether or not you know something with whether or not you can

really clearly define it. And that idea became incredibly important in the scientific enlightenment

Thinkers on the philosophy side like Descartes and on the science side like Newton and Galileo

took this idea and used it as a new method to understand and explain the world. So what it

came is, can we use mathematics to explain and predict different things in the world? And from

Socrates to Galileo to Newton, they continually reinforced this idea that in order to truly know

something, you have to be able to describe it explicitly. You have to have a theory about it

You have to be able to describe it mathematically, ideally. The world around us is shaped by this

framework. So everything from smartphones to computers to cars to rockets to cameras to

electricity, every appliance in your house, vaccines, everything in our world is shaped

with this idea or this way of seeing the world. It's been incredibly impactful. And you can find

this too in the rest of culture. Like anytime you see, you know, a book or a movie or a blog post

or whatever talking about like the five laws of power or like the five laws of negotiation

All that stuff is ways that that physics has has and rationalism in general has like sort

of seeped into the everyday everyday way that we think about the world

And to be clear, it's been super successful. But in areas of the world like psychology or economics or neuroscience it has been really hard to make progress in the same way that physics has made progress I think if you look for example

at the social sciences, a lot of the way that the social sciences are structured is inspired by

physics. What we're trying to do is take very complex, higher-level phenomena, so like maybe

maybe psychology or economics or any other branch of social science. And we're trying to reduce it

down to a set of definitions and a theory and a set of rules for how things in that domain work

And what's really interesting is if you look at those fields, so like psychology, for example

it's in the middle of a gigantic replication crisis. Even though we spent like 100 years

doing psychology research, the body of knowledge that we've been able to build there

in terms of its universal applicability, our ability to find universal laws

in the same way that Newton found universal laws, seems pretty suspect

And we feel like we can't stop doing it because we have no better alternative

Another really interesting an important part of the world that this way of looking at things didn't didn't work for in many

ways is AI. So this is this is usually the part of an explanation where I try to define it and

like what is AI? And what's really interesting is there's no universal agreed upon definition for

this in the same way that there's we've struggled to come up with a universal definition for

what it is to know something or a universal definition for what anxiety is, for example

in psychology is another is another really good example. There are a lot of ways to kind of like

gesture at what AI is. But really, it's like, obviously, maybe not obviously, AI stands for

artificial intelligence. And, and the AI's project is to build a computer that can think and learn in

the same way that humans learn. And because of the way that computers work, for a very long time

that was a really hard problem. AI started as a field in the 50s at Dartmouth. And you can like

actually look at the original paper. They were very optimistic. They were like, you know, maybe

like, you know, a summer's worth of work and we'll have nailed this. And the way that they defined it

is to be able to reduce down human intelligence into a system of symbols that they could combine

together based on explicit rules that would mimic human intelligence. And so there's a really

clear through line from Socrates' original project to the Enlightenment to the original

approach that AI theorists took called symbolic AI, the idea that you could embody thinking in

essentially like logic, logical symbols, and transformations between logical symbols, which is very similar to just basic philosophy. And there are actually a lot of early successes

For example, the two founding fathers of AI, Herbert Simon and Alan Newell, built this machine

that they called the General Problem Solver. And what's really interesting is it wasn't even

built as a computer because computers were extremely expensive back then. They originally codified the general problem solver on paper and then executed it themselves by hand. Actually, I think one of them had their family do it with them to try to simulate how a computer would work to solve complex problems

And the general problem solver, they tried to reduce down complex real-world situations into simple logic problems that look a little bit like games

And then they tried to see if they could build a computer that would solve some of those games

And they were actually quite successful at first. What they found was it worked really well for simple problems

but as problems got more and more complex the search space of possible solutions got really

really really really big and so by representing the problem in that way the systems that they

built started to fail as soon as they moved away from toy problems to more more complex ones i think

i think a really interesting and simple example of this is thinking about how you might decide

whether an email in your inbox is spam or whether it's important

And you might say something like, if it mentions that I won the lottery, it's spam, right

And so that sort of like if-then rule is a lot like the kinds of rules

that early symbolic AI theorists were trying to come up with to help you solve any problem, is to codify like

if xyz is true then here's the here are the implications what happens is if you look at

that really closely there are always lots of little exceptions so an example might be if it says

emergency maybe you want to put that at the top of your inbox but very quickly you'll have spammers

obviously being like just put emergency at the in the subject line and they'll shoot to the top so

then you have to kind of like create another rule, which is it's emergency, but only if it's from

my coworkers or my family. But computers don't really know what coworkers or family is. So then

you have to define, okay, like how does, how is it going to know what a coworkers or what a family

member is? So what you can do is, um, maybe it's like a coworker is anybody from my company

and so if it says emergency and it's from anybody in my company put it at the top of my inbox

but what you may find is that there are certain people at your company who are annoying and uh

want your attention even if you don't really want them to contact you and so they start putting

emergency into their inbox and uh and now you have to create another rule which is like don't let

people who are abusing the privilege of getting to the top of my inbox abuse it even if they're

coworkers. And what you find is anytime you try to create rules to define these things

you always run up against exceptions. If you want to, for example, define what an important email is

you have to define pretty much everything about the world. You have to create a world full of

definitions. And that project of making the entire world explicit in definitions just didn't work

It's too brittle. It's too hard. There's too much computational power required to loop through all the different definitions to decide, you know, if this email is important or not. And it's just there are too many definitions to create. It's just it's too big of a project

and so that symbolic AI project worked in some limited domains and there were these things

called expert systems for example in the 70s and 80s that tried to for example

reduce medical diagnosis down to a set of rules and they were somewhat successful

but even in a case like medical diagnosis trying to reduce down

to a simple set of rules something like do you have measles or maybe even do you have anxiety or depression turned out to be really complicated and really really hard And in fact impossible to get right 100 of the time in an explicit way

The alternative, which originated around the time that AI itself originated, but really wasn't

taken that seriously until probably the 80s and 90s, is what's called a neural network

and a neural network um it is inspired by the way our brains work it doesn't work exactly the same

way but it's inspired that way it is inspired from brains and it basically consists of um layers of

artificial neurons that are connected to each other and what you can do with a neural network

is you can uh get it to recognize patterns by giving it lots of examples you can you know for

example, if you want to, if you wanted to recognize like whether an email is important

what you can do is you can give it an example, say like, you know, here's an email from a co-worker

and have a guess the answer. And if the answer is wrong, what we've done is we've created a way to

train the network to correct its wrong answer. And what happens is over many, many, many, many

iterations in many, many different examples, what we find is without any explicit set of definitions

or explicit rules about like, you know, this is an important email or this is a cat or this is a

good move in chess, the neural network learns to recognize patterns and is able to do a lot of the

more complex thinking style tasks that early symbolic AI was unable to do

language models are a particular kind of neural network that operates by finding complex patterns

inside of language and using that to produce what comes next in a sequence so what we've done with

language models is fed them basically you know all of the text on the internet and when we feed

them a piece of text we'll give them you know a big chunk of a big chunk of text and then we will

say based on this chunk what's the next word that comes after this chunk and and language models

learn that there are many many thousands of partially fitting rules that they can apply

based on the previous history of text they've seen to predict what comes next and all of those rules

are inexplicit they're they're kind of like you can you can observe them in the overall behavior

of the network but they don't exist anywhere in the in the network you can't go and look inside

of a neural network and find like this is exactly, this is the entire set of rules that it has

You may be able to find a couple, but you can't find a definitive list

in the same way that if I like took a microscope and looked in your brain

I would not be able to find that. I would not be able to find the list of rules

that you use, for example, to recognize a cat or do the next move in chess

They're represented all inexplicably. And what's really interesting about neural networks is the way that they think or the way that they operate is a lot like, it looks a lot like human intuition. Human intuition is also trained by thousands and thousands and thousands of hours of direct experience

often the our best metaphor for our minds are the tools that we use so a really good example is

freud has one of the most impactful models of the mind and the way that he came up with that is he

used the steam engine as a metaphor so it's an explicitly steam engine based idea in the 20th

century the metaphor for our minds moved into being like a computer that became the kind of

thing that we all wanted to be like. We wanted to be logical and rational and operate like a

machine to make the best decisions possible. And I think one of the most interesting things about

that way of thinking is it makes invisible to us, and this I think relates a lot to the

like sort of Socratic enlightenment type of thinking as well, it makes invisible to us

the importance of our intuition in being the foundation of everything that we do

everything we think, everything that we know. In a lot of ways, you can think of rationality as

emerging out of intuition. So we have this sort of squishy, inexplicit, intuitive way of

understanding what's going on in the world. And our rational thought comes out of that

and is able to, once intuition sort of sets the frame for us

is able to go in and sort of manipulate things in a more methodical, rational, logical way

But you sort of need both. And neural networks are the first technology we've ever invented

that works a lot like human intuition. And the reason I love that is because I hope that it makes more visible to us the value and importance of intuitive thought. And that actually loops back and takes us all the way back to Protagoras

and is sort of the thing that we lost in this birth of rationalism and back in Callius's house

because Protagoras is arguing that everyone teaches us excellence all the time. He's arguing

he's using stories and myths and metaphor to help us understand something that he knows from his own

personal experience. And Socrates is saying, well, if you can't define it, if you can't tell me

exactly the rules by which you know something, then you don't know it. And that way of thinking

about the world has been very successful for us, but it also kind of blinded us to how important

that idea that everyone teaches us to be excellent, that stories and personal hands-on experience

give us a way of knowing about things that we may not be able to explicitly talk about

but we still know just as much as we know things that we can explicitly say

And it was only when we began to embody that way of being in the world

or that way of knowing things, that way of thinking into machines that we started to get actual artificial intelligence

There's many different ways of knowing things and many different ways of understanding things

and we may not understand all of the particulars of how humans for example how our minds come to

certain conclusions intuitively and we may not understand all the particulars of how

language models for example come to any particular output but that doesn't mean that we don't

understand them it just means that we understand them in a different way than we might be used to

so for example if you use chat gpt all the time you develop an intuition for what it's good at

and what it's not good at and when it might be hallucinating and when it when it might not be

in the same way that you might develop an intuition for when a friend of yours is sad or when a friend of yours is not being totally truthful with you and that not a universal set of rules that applies to everyone in every situation or even to your friend in every situation It just a sort of like intuitive feel that is a core part of understanding but that we

normally discount. History shows that it is better to have to be open to more ways of seeing and

working with the world. And that in this particular era, it's very important to be able to

work with things that are a little bit mysterious and be comfortable with that

Chapter 2. Seeing the World Like a Large Language Model I am, I've always

been like a real huge note-taking nerd. I love taking notes, especially because when I started my first company

I started my first company in college. And I ended up selling it. I flew from my college graduation to

Boston to finish negotiating the deal to sell it. So that whole situation for me was this trial by

fire of like, I felt like I had to, I was like an information firehose. I had to learn so much

in order to successfully run a software company as a, I guess I was 20, 21, 22. And the way that

I felt like I could do that best was to start taking notes is to be like, okay, I learned this

thing from a book and it's about how to hire someone for how to hire people for example and

i know i think it'll be relevant for me but i don't know when it's going to be relevant so i'm going to write it down and i'm going to try to create like the perfect organizational system

to categorize all this stuff so it will come back to me when i need it

and if you really take seriously that question of like how do you build the perfect note-taking

your organizational system, you actually run into the same problems that early symbolic AI

theorists run into and philosophers have been running into for a long time, which is how do we

create the perfect system to organize reality? How do you know where to put a particular note

is the same sort of question as like, how do we know what we know? And so when I first bumped into

the language models, I realized that they had this ability to be sort of flexible and contextual

in a way that meant that I didn't have to create the like perfect organizational system to teach

a computer how to like organize my notes. It operated in this way that was rule-less and fuzzy

and flexible, and I had just never seen a computer do that before

Like the first experience of seeing that line of words go across your screen

it's kind of in your voice a little bit, and it's kind of like picking up where you left off

and it kind of understands all the little contextual cues that tell it about what you're talking about

that no computer previously could do. The interesting difference between the way that a language model might see the world

and maybe another kind of computer is computers, science. What both of those ways of seeing the world are trying to do

is reduce the world into a set of really clean universal laws

that apply in any situation. It is to say, if X is true, then Y will happen

Like really clear cause and effect, really clear chains that are universal and context-free

and what language models see instead is a dense web of uh causal relationships between different

parts of the world that um that all come together in unique very context specific ways to produce

what comes next and i think language models do something really really unique which is that

they can give you the best of what humanity knows at the right place at the right time in your

particular context for you specifically where you know for example previously on the internet

you could get an answer that was written by someone else for like a very general

reader or a very general situation and maybe you'd have to like hunt through a wikipedia page to find

like the one sentence that answers answers your question language models go one step further which

is they uh they reduce down the their response to you to be to be written for you at the at in your

context in your place and in your time if you look at the history of machine learning from symbolic ai

we're trying to break down um intelligence into a set of definitions of uh you know a theory and a

set of rules for how thinking should work um all the way up to neural networks and language models

where it's it's much more contextual it's much more about pattern matching it's um much more

about interpreting the richness of of of a particular situation um and and and using all

the all prior experience in a um sort of in an inexplicit way to predict what comes next

that sweep of um of the history of ai in a lot of ways is speed running the history of philosophy

so philosophy started um with this um with this attempt to make explicit what it is to know

something um now we're in this place where it's like actually like it's all kind of like fuzzy and

pattern matching and it's very very contextual and relational um but it's also not anything goes

and it's being done in a way that we've created a positive tool

that you can use and build stuff with in your life. We're not just sort of

deconstructing everything around us. And so in a lot of ways, machine learning and AI

is speed running philosophy and it's gone a little bit of a step further

because it's built something with it that you can do, a way of being in the world

that you can, or a tool you can use. And I think, A, that's just like

critically important and very interesting. And B, um i think a lot of the changes that have happened in both philosophy and in uh ai and machine

learning are going to happen in the rest of culture so um moving from this uh this way of

thinking about knowledge which is about um which is about making everything explicit finding theories

and definitions and rules for how to understand the world to a more balanced appreciation for both

that and the the way that a more intuitive relational fuzzy pattern matching type uh

experiential contextual type way of um of of knowing about the world uh has to has to be

underneath the like the rational stuff in order for that the rational stuff to work at all and

it's really about um recognizing the the more intuitive ways of knowing about the world as

being the sort of original parent and partner of rationality and appreciating that for what it is

You know, a lot of what we've been talking about is that

looking for one general rule or one general theory about a particular part of the world

sometimes is really valuable and sometimes leads us down dead ends. And instead, what we have to

pair it with is deeply contextual understanding based on experience that allows us to work with

the richness and novelty of any particular situation to understand what comes next

And that's what language models are able to do. And it sort of begs the question of

like should we stop looking for general theories and uh for example should we um you know not be

trying to unify quantum physics with um newtonian mechanics um i definitely think that it's awesome

that we're trying to unify those things and trying to build a universal theory but i think it's also

worth thinking about what that will actually tell us and how far that will will get us once we have a

universal theory of physics if we if we do get there and my contention or the way that i feel

is it will it will be beautiful and it'll be amazing and it will tell us a lot but also

there are many many many many parts of the world that it won't touch at all that we still um even

if we have a universal theory of physics like that probably won't filter into our understanding

of depression it's like what is depression how does it cause how do you treat it what is anxiety

how does it cause how do you treat it we've been searching for those things for a really really long time and we've had a lot of like different answers like if you ask freud he'd say one thing

and if you ask like a modern modern psychiatrist neuropsychologist they might say something else

um but really like we just we still don't actually know um and we keep trying to do that we keep

trying to find that kind of like universal theory that that explanation that says well if x then y

if um you have this going on in your life or in your brain then you're going to get depressed or

if you take this medication, then depression will go away. We've been trying to find that for a

really long time because we felt like we had no other options. Because normally, in order to

predict an outcome, to know, oh, if I do this, then it'll cure someone's depression. To predict

it, you have to have an underlying scientific explanation. You have to have a theory about it

And I think AI actually changes this. So with with AI, what you can do is and people people are really already starting to do this. You can

train neural networks that are, for example, able to identify who is depressed or who will get

depressed. You can train neural networks who will be able to predict which interventions might work

for which people in which circumstances in a very contextual, hyper-personalized kind of way

without having to discover beforehand any scientific explanation for the underlying phenomena that we're trying to predict. So we don't have to have an explanation for depression

we can just train a model on enough data that it will that it will be able to predict what might

work or whether you have it or whether you're going to get it the reason why i think that's

so valuable is one it allows us to make progress immediately because we turn what used to be a

scientific problem into an engineering problem and then two it like really changes

how we should conduct science, how science should be done. It changes our view of that because

right now, if you're a scientist and you want to figure out depression or any number of things

in the field of psychology, what you're going to want to do is a really small scale study where

you're like, I'm going to take 16 undergrads and maybe they have depression. I'm going to ask them

to like smile every day and I'm going to put them in an fMRI and then I'm going to like measure the

results afterwards and if I get a little bit of a result on a very very small number of undergrads

then I'm going to like get more funding to do a study with a hundred or whatever and you're kind

of like trying to climb this ladder of going from very small scale interventions to like very big

ones and to use that to come to some sort of underlying theory about what is actually going

on in those situations. And, and what we found because of the replication of crisis is like

it's just really hard to, um, using that, you know, those 16 undergrads, it's really hard to

like find out anything that feels universal or universally applicable. Um, it's one of the

reasons why, even though antidepressants have been around for like 60 years or something like that

like we still actually don't know when they work or how they work. We know they work for some people

some of the time, but that's pretty much all we can say. What AI does is it kind of helps us

I think to me, it helps us realize that there's a better way, which is rather than have random

academics doing small scale studies, what we should do is have the Apples and Metas and

Googles of the world donate their data to science and data trusts so that scientists can access them

to train models, you can figure out ways to do it in ways that are privacy-preserving

so that it doesn't violate the trust of users. But I think that would seriously enhance the progress of science in a way that doing billions

of dollars worth of small-scale studies has not been able to. And what I think is even more interesting is once you've trained models that can, for

example predict depression really well what you may be able to do is models are actually easier

to interpret and understand than brains are and so if you have a good enough predictor what you

can do is just go into the neural network and try to figure out how it's wired and it may be that

the the explanation for what depression is is like too big and too complicated and you can't

figure it out but mechanical interpretability is good enough that you may be able to like find what

what is a solid theory for depression in the weights of a trained neural network for me i've

just spent so much of my life trying to explain things or understand myself or understand my world

in this sort of theoretical, definitional way. And I've seen how important that can be

and also how limiting it can be. In particular, if you stop paying attention

to what your intuition tells you and you just rely on your kind of logical brain it really easy to get lost there a there like this whole richness to life that and and to and to like what you know that um comes out of this sort of like intuitive

sense of yourself that uh helps it helps you and your help helps me for example in business and my

personal life and in my ability to make decisions and my ability to write or make good art all of

that is sort of based on this ineffable intuition that I built up over many, many, many, many years

And my logical brain is helpful in certain circumstances, but I think like

it can blot out or take over from my intuitive self in ways that have been destructive for me

And I think also, uh, have been destructive just as a society

There's a lot of stuff that we, um, that we miss because we miss how, um, how important

intuition is. And now we have tools that can, can embody a lot of that intuition can, that, that can

take some of that intuition that we built up and we can put it into something else in

the world that we can pass around which was never which was never possible before um you know like

i think we've we've been pursuing explicit definitions and scientific explanations for

things for a long time because uh if you can write it down you can spread it and that becomes a uh

like the way that society makes progress is is sort of like this these spreading explanations

but if you're dealing with parts of the world that you can't write down explicitly

there's been no good way to collaborate on them or make progress on them and what neural networks

allow us to do is to take some of that intuitive experience or intuition that we might have built

up ourselves and put it into a machine that we can that we can pass around and that's that's

useful for example for like doctors for expert clinical diagnosis the best clinicians in the

world know something about how to deal with patients that they can't write down they can't

embodying a set of rules and is trapped in their head. But language models and AI in general allows us to put that kind of intuition into a tool

that will allow anyone in the world to access, for example, the best clinician in the world

even if we can't write down what they know. Chapter three, will AI steal our humanity

well first of all i think like ai will uh seriously enrich our understanding of ourselves i think ai

is is an incredible mirror like i understand so much more about myself just being from being able

to talk to chat gpt and being able to throw into it like here's a meeting that i just had like can

you tell me like how i how i showed up in that meeting so it's incredible mirror it's also an

incredible metaphor for our minds so we're moving from this um metaphor from our of our minds as

like in an ideal world this like logic rule-based explicit um computer to a much squishier contextually

sensitive um pattern matching experience-driven uh language model that i think is a really good

metaphor for the more intuitive parts of our mind. And so I think that will enrich our

what used to be a very narrow picture of what it means to be human. But I think what's most important is to understand that the humanity is inside of us. Like we bring

the humanity to the tools, to the tools that we use, to the things that we build. And I think in

a lot of ways to like will it take our humanity um it it makes uh it makes two errors the first

error is to think that you can like pin down what it is to be human into like one unchanging thing

like that actually has evolved and is different uh over time and i think the second error is to

um confuse what what we were what we are it's it's a little hard to put it but it's like it's

sort of like saying that what you're unfamiliar with is bad. And that's not exactly the right

thing. But I think a really good example is when my grandmother, for example, she's not alive

anymore, but when she would use the phone or text someone or be on the phone with someone

to her it felt very impersonal. And in a lot of ways it feels kind of inhuman, right? A sort of

face-to-face interaction is a much more human, personal thing for her. For me, or for people who

are even younger than me, texting can feel very, very intimate. In the late 1800s

getting a typewritten letter from someone was kind of insulting. It felt very impersonal not

to get something in longhand, but now we don't get any longhand letters. If you do, it's still

very, it's still very, very personal, but it's not insulting to get an email from someone. If

someone sends you a long email, you're kind of like, wow, that's really nice that they took the time to, to think of me. I think all of those worries that are sort of like, does it take away

my humanity? Um, a lot of them come from the fact that we just don't have a lot of experience yet

with these new things or they don't have that like patina of like nostalgia and history that

um other things that we look at in our lives um that our technologies do have so like books

at a certain point books were a very suspicious thing um and now they're i love books like i have

such a romantic attachment attachment to them and i think that's that's one of the things that we

miss when we evaluate new technologies is they they just we just haven't had the chance to allow

them to feel human to us because we're unfamiliar with them. I think the people who are super afraid

of AI, it is actually, it sort of goes back to this rationalist idea that we've been talking

about, which is if you can't explicitly define and prove 100% that a thing is safe, then it's

dangerous. I don't know if anyone's had like a teacher or a parent or someone in school that's

like no matter what you do, you can be the smartest person in the world, but they're going to find that

one up and just like hammer you for it. And I think a lot of people that are worried about that

are they're just waiting for that one up. And it's true that does happen. But the alternative

is the demand that AI only say things that can be proved to be true And that sort of to me at least takes away a lot of the magic of AI The thing about it that makes it actually powerful is that

it works on probability. It works on many, many, many thousands of correlations coming together to

figure out what the appropriate response is in this one very unique, very rich context

and allowing it to say only things that are provable obviously begs the question like well

what is true and how do we know and there are certain domains where we can we can answer that

question so like uh in math and computer science for example it's like pretty clear whether or not

like a theorem is right um it's back to the same question from socrates which is like what do we

know and how do we know it and and a demand for uh explicit rational explanation for every single

thing that um that we say and i think that that demand is like way way way too strong and actually

is um eliminates a lot of things that we um that we know about the world or parts of the world we

want to work with where we actually don't have um precise exact explicit answers and it sort of

results in this in these these thought experiments that get people really scared like uh this the

sort of one-shot idea that you have one shot once you build a super intelligence you have one shot

to make sure that it's aligned that it will follow human preferences or it will kill us and take over

the world and you can find people who are like real rationalists like elias rhodkowski who really

believe this and really believe that we're like going to all die which that sucks it's like not

a great it's not a great place to be um and uh what's kind what's kind of interesting is we have

really smart ai right now like i think it's the kind of ai that if you had asked rationalists or

people who are thinking about this stuff 10 years ago is this ai and is this at a level that's like

could be dangerous they'd probably have said yes if you look at how it's actually built yes there's

there we don't have any provable ways to be like it's 100 safe ignoring the fact that even defining

what 100 safe is is like impossible and that's like the whole reason that language models work

um but what we're doing every single day is we are training these models on human preferences

we're giving them examples over and over and over again of um what we want and why and uh each model

builds on models that came before it they actually have a a dense rich idea of what it is to be good

from all the data that they get they also have a dense rich idea of like probably what it is to be

bad. But in a lot of ways, the training that we're doing makes them much, much, much less likely to

to do any of that stuff. There's something very like practical and pragmatic about we have a

machine. We don't know fully how it works, but we're just going to like teach it and we're going to like iterate with it over and over and over again until we kind of basically get it to work

And it's sort of squishy. We don't have any guarantees. And that world is it is actually

this sort of like messy real world it is actually kind of like how you think about interacting with

a human it's like yeah i don't know if you're gonna lie to me but like i'm gonna i'm gonna

sort of figure it out um the the fact that we can't we don't have those guarantees um from

language models is what makes them so powerful and so i think like it's not that language models

could never be dangerous but um i think adopting the more pragmatic experience-based mindset which

is like we're going to build these things we're going to improve them in a fairly predictable way

it's not predictable exactly all of the specific capabilities that they're going to get but we can

basically tell like in general how much smarter they're going to get every time we do a training

run. And along the way, we're going to iterate with them in real world scenarios to make them

less likely to do bad things. That scares people who demand a certain kind of rationalistic

guarantee. But for people like me, people who build stuff, solving the problems in practice

actually is a better way to do things than solving them in theory

i think there's a really big question about how ai may change creative work and there's this idea

that well it's going to do all the work for me so like i'm basically not even doing it anymore

it's not mine it's not like it's not it's not my work and i like thinking up ideas or metaphors for

um what it may actually be like or what it what it sort of honestly what it is like now and what

it will continue to be like more in the future to explain how you can still do creative work

that feels authentic and feels like you while an AI is doing some part of it

One of the metaphors that I like to use is this sort of difference between a sculptor

and a gardener. So creative work ordinarily is a lot like sculpting

If you're a sculptor and you have a big block of marble or a big piece of clay or whatever

every curve and line in that finished product is something that you decided to do like with

your own hands so you had to decide to do it otherwise it would not be there and i think that

working with ai is actually it's a bit different it's a lot more like gardening if you're a gardener

um you don't like pull the plants up from the ground by the roots to try to make them grow

like that won't work you can't directly cause the plants to grow but what you can do

is you can create the conditions for the, for the plants or the garden that you're making to

flourish in a particular kind of way. You can change the, um, the amount of sun, you can change

the soil, you can change the amount of water, you can decide which plants go where you can do some

weeding. And all of that stuff is a way for you to shape something, um, by, by altering the

conditions under which it happens, uh, without doing it yourself. And I think that's a lot like

what working with a model well is, especially a model that is more agentic and does a lot more

by itself. I think a lot of, I think that's a good metaphor for what that experience is like

I think the thing that I love most about this era of technology is, is I'm a generalist. Like I love

doing lots of different things. Um, I run a company where we have a newsletter, we have three software

products, we have a consulting arm, uh, I'm writing, I'm programming, I'm, uh, making decisions all day

I'm like making little memes like there's my day is full of different things to do and I would not

be able to do all these things at the level at which I doing it without AI it has all the specialized knowledge already so it like it like having 10 PhDs in your pocket And so I can dip into an area of study or an area of work like writing or programming

or whatever it is. The AI does a lot of the sort of more repetitive specialized tasks

and it will allow individuals to be more generalistic in general in the work that they do

And I think that would be a very good thing. What's most important is to have

hands-on experience like hands-on use of them to understand for yourself like okay here's a place

where it may not work as well and here's a place where it may it's going to work really really well

here's where i need to um watch everything that it does and here's where i can kind of like delegate

more and this actually gets me to like another metaphor that i really like or another idea for

understanding this wave of technology in a way that I think is really helpful, which is this idea

that we're moving from a knowledge economy to an allocation economy. In a knowledge economy

you are compensated based on what you know. In an allocation economy, you're compensated based on

how well you allocate the resources of intelligence. And there's a particular set of skills that are

useful today, but are not particularly widely distributed that I think will become some of the

main skills in this new economy, in this new allocation economy. And that is the skills of

managers. Those are the skills of managers, human managers. And human managers are only a very small

percentage of the economy right now. I think it's like 7% of the economy is a human manager

But I think the skills that those people have are going to be very widely distributed. Things like

knowing what you want being able to articulate what you want being able to break down a complex

task or a complex project into a set of smaller achievable subtasks that you can then give to the

right person knowing what any given person on your team can do like what are they good at what are

they not good at being able to know like um do i like do i micromanage them do i delegate it

entirely? How can I trust if I didn't do the work myself? How can I trust that it was done right

These are all questions that human managers today, especially younger human managers

need to figure out. It's so easy to be like, well, I can't trust this person, so I'm going to go in

and check every little thing. But then you realize as a manager, well, I'm just basically doing the

work myself. That doesn't actually get me anywhere. It doesn't get me anywhere. But on the other hand

if I delegate everything, then it may not happen the way I want. So you have to figure out the

nuances of all those situations. And I think the same thing is true of being a model manager

You can see the overlap in the kinds of complaints or the kinds of problems that people run into

using models. It's like, well, if I didn't do the work myself, how can I trust it? And the answer

is you have to get good at managing a model. You have to get good at having an intuitive understanding of how do I know what I want? How do I express it to the model? How do I know which

model to use in which circumstance? And how do I know what are the particular pitfalls of this

particular model this particular personality its skills its way of being in the world you can throw

your hands up and be like well it doesn't work or you can say like there no there's there's a

there's an intuition i can build for how to manage it and how to build with it that will actually

that might be a sort of a different skill than the one that i've developed so far in my life

but is incredibly valuable and can and can be um immensely effective and productive and

satisfying if you learn how to do it right one of the most important questions in philosophy

is the sort of hard problem of consciousness. Like how does something become conscious

out of inert matter? And if we're looking for a definition of intelligence

one of the ones that makes a lot of sense to me is the idea that intelligence in a lot of ways

is like a form of compression. So if you think of problem solving

as like a search space, like you want to find the right chess move, you want to mark a email as important, you want to whatever, you have a whole search space of

different possibilities. Something that's intelligent is able to compress a lot of the

answers or it's able to compress a lot of the answers into a very, very small amount of space

And so it's able to give in a new situation, get through that space and find the right thing

like very very quickly um so brains contain like an extraordinary compression of like all of the

situations that we've faced and all the memories that we have and all the problems that we've solved and are able to use that to apply to um to new situations and i think my guess is that um

consciousness functions as a highly efficient method of compression in in one sense but i'm also

I don't really know. And I think that there's, I think there's also something interesting and

beautiful about thinking about things in the world as all having a little bit of consciousness

like a sort of pan psychic perspective. And humans just happen to have a lot of it

And from that perspective, like language models have maybe probably have a little bit of consciousness. And the reason I like that is, um, it encourages us to, um, treat things in the

world as if they were conscious, which I think is a much better, more compassionate way to

operate in the world. And I think, and I think we'll actually would actually make us, um

more effective at getting the most out of language models, uh, a way someone who, who is, uh, from a

particular religious tradition might put it is to say that everything has a little bit of god in it

and to to operate in the world that way is to operate in a world full of meaningfulness and

significance um and to me just feels like a better way to live um versus like a world where like

everything is just sort of like gray lifeless stuff if everything's alive um it makes the

possibilities for like life and doing things like way more fun and interesting

i always say please and thank you to chat gvt because you never know when the machine apocalypse

is going to come like i'm saying all this good stuff but like it's possible it's always possible

there's there's no guarantee and i just you know i think saying please and thank you will make it

less likely that if it does come, I will, you know, be on the bad list, you know

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