d.bau: Wow, so people are like, hey, we want to record this. Okay, it's today, already, that'll work.
d.bau: I got it.
d.bau: I want to put in, but it's too late. Everybody's here already. Better starts. Welcome.
d.bau: Hay.
d.bau: I'm going crazy.
d.bau: Welcome, everybody.
d.bau: Welcome, laughable.
d.bau: Happy New Year!
d.bau: How's everybody's break?
d.bau: Everybody having an okay break?
d.bau: Oh, no! Everybody's so sad the first time.
d.bau: It's okay.
d.bau: 2026. Welcome to 2026.
d.bau: 196 is going to be a very interesting year.
d.bau: I think.
d.bau: I don't know if that's a good match.
d.bau: But it's interesting.
d.bau: Look at this, they allow accessory, they connect.
d.bau: Okay.
d.bau: Alright?
d.bau: Who's good for Calculus 1?
d.bau: Next door.
d.bau: Welcome. Welcome to Execs.
d.bau: So, So this is CS7180. I, there were probably more seats than…
d.bau: the plan, because I asked all my lab members to come.
d.bau: and hang out with us for the day. I don't know if they're gonna have stamina.
d.bau: to stay with us the full time. But, you know, we have a bunch of different lab members here each day, so, who… who… who here is sort of…
d.bau: Not enrolled in the class, but sort of either a lab member or a collaborator or somebody who's like, hey, you should come.
d.bau: Right, okay, yeah, like, huh, right? Okay, so, a lot of, a lot of lab members collaborated here. So, welcome.
d.bau: And then everybody who's in the class, so who's in the class? Who's a PhD student?
d.bau: Okay, who's in the class who's, like, a master's student or undergrad? Right, so it's just a couple. So this is very PhD-oriented, class, and then we might have a couple Northeastern postdocs mixing here as well, so…
d.bau: Huh.
d.bau: Okay, so… So we… it's a new class, we're finally at Neural Mechanics.
d.bau: It's a funny field that, doesn't really have a great name.
d.bau: Yeah, we can call it AI interpretability.
d.bau: Some people call it mechanistic implicability.
d.bau: you know, I had this long email thread with the Anaton, and…
d.bau: And Atticus, and some other folks, what should we call our field, and… You know, so…
d.bau: I don't know, I'm calling this class Neuromechanics, but nobody…
d.bau: But that's… that… maybe that'll be a good name for a textbook. So, yeah, so you can… it's okay, it's… there's nothing wrong with… And I can also take my word off this if you want to.
d.bau: This channel doesn't need to be a channel.
d.bau: Give somebody else.
d.bau: Thanks.
d.bau: Yeah, yeah. Keep growing.
d.bau: All right.
d.bau: Welcome.
d.bau: Alright.
d.bau: So, who am I? Let's see… oh!
d.bau: I'm David. David Bow.
d.bau: So I've been teaching here at Northeastern for 4 years now, and in my area… this is my area, so this is my research area, so you're taking a course in the stuff that I research, we're gonna design the curriculum in a sort of aspirational way, where we, we, we talk about, research dynamics.
d.bau: That would be more than…
d.bau: that you will do together with me. That'll be the exploration. So basically, the question that I've always asked my researchers, how do config neural networks work? My PhD training was in studying
d.bau: Computer vision networks.
d.bau: Generative vision networks.
d.bau: How do… how do these amazing networks know how to draw things?
d.bau: It's kind of incredible. But most of the stuff that I do in my lab now is in language networks.
d.bau: You know, how do, how do generative language networks, think, how do they work? How do they represent concepts?
d.bau: And so, so I think it's a super interesting I am right now.
d.bau: So, welcome to our 2026.
d.bau: Who here has been using AI in work?
d.bau: Who's been using it, like, to do, like.
d.bau: you know, to do coding, or to help them write, or stuff like that. I don't know if there… do we… we have… there's another chair here.
d.bau: The biggest chair.
d.bau: So I… I have not been using it enough.
d.bau: you know, I've been studying all these generative models.
d.bau: But I attempt to study them in very short sentences.
d.bau: You know, how does a model…
d.bau: Predict the… the seventh word, and then 7th word sentence, things like this.
d.bau: But I… I've spent a little time in the last… A month or two, trying to…
d.bau: use AI to do work, and what it looks like.
d.bau: So, each one of these things is a function.
d.bau: in a piece of code that I was writing.
d.bau: And if you've watched that animation from the beginning.
d.bau: I think we started. We watched this animation from the beginning.
d.bau: You can see this is, like, what human written code… Looks like.
d.bau: And if I give this thing to play.
d.bau: You know, at some point.
d.bau: you'll see… oh, let's use LLMs to help me out. So this is what LLM assist.
d.bau: And then if you wait another few seconds, they say, oh, why just LLM-assisted? Why not use a coding agent?
d.bau: to actually manage my repository and stuff like this. And so this is what it looks like to bring in a coding agent. You get a lot more complexity, a lot more quickly. And so this piece of code went in, you know, about a month, from 700 lines of code
d.bau: Right now, it's sitting at 18,000 lines of code.
d.bau: So it's like a 24-fold increase in complexity.
d.bau: And it's not code that I'm taking very seriously, that is a little happy project.
d.bau: That I sort of code while.
d.bau: Would be good.
d.bau: It's like this, right? And so, so this is,
d.bau: Yeah, so this is, this is, this is sort of the world.
d.bau: That we're in, that we're living in. So I don't know… how many of you, like, can…
d.bau: sympathize with this experience. They tell me, had this experience.
d.bau: Right? Yeah, right?
d.bau: It's a little crazy.
d.bau: Right, and then, so I think that there's some…
d.bau: And I think that all of you in this room
d.bau: are the early adopters. I think the rest of the world is on the brink of
d.bau: Witnessing some of these things, and trying to figure out how to grapple with these things.
d.bau: And so, yeah, so what the heck? So, this is a very interesting time.
d.bau: Here you go.
d.bau: So, I'm gonna recap, for folks who aren't in Europe's, a little talk.
d.bau: David, it's just a 5-minute… Anecdote for… A similar historical moment.
d.bau: that appeared a few centuries ago. So this is… this is a, a woodblock… of,
d.bau: of the island of, Murano, near Venice.
d.bau: So, Murano was a center, still a center, of glassmaking.
d.bau: And they were going through a very similar historical moment in 1600.
d.bau: At the dawn of the 17th century.
d.bau: And… and… and so you can see this is actually a picture of
d.bau: the leader of Venice coming to visit the island of Murano, and they…
d.bau: They're depicting here with all this artistic glass and these glassworks going on. So Murano was a center of glassmaking since Roman times.
d.bau: And they, they had mastered the art of making transparent glass. Nobody could make transparent glass like the Veranos.
d.bau: And, and… and this was traditionally used for art.
d.bau: It was just this intellectual curiosity. If you're really wealthy, you should buy art.
d.bau: It was made out of this very transparent glass, but by the time you got to the…
d.bau: But to 1600, it had become very pragmatic.
d.bau: We had pragmatic last nickel.
d.bau: Because they discovered that you could grind glass.
d.bau: Into lenses, and if the glass was high quality, and you made lenses out of them, you could put it in front of people's eyes, and cure blindness.
d.bau: And this doubled the productive capacity of people who just naturally lost their vision. See, they couldn't
d.bau: They couldn't read after they got to a certain age.
d.bau: And it's just this amazingly… Powerful invention.
d.bau: And I feel like, oh, it's like a similar thing to what we're living through today, right?
d.bau: We have these things that have been academic curiosities for a while.
d.bau: And we're living through this pragmatic era where we're about to unleash it on the world.
d.bau: And so,
d.bau: So that's… I think that's interesting. Okay, so… and I think that interpretability has a role here, and there's some similar things here. So, my lab, and… and some of the stuff we're talking about here is going to be how to crack open AI and look into it. And I think that there's really three perspectives.
d.bau: on… why do this?
d.bau: why… why, like, look into an AI to try to explain what it's thinking inside? And one perspective is adversarial view. Talk about one's the empowerment view, and one is the scientific view. So, the adversarial view is really motivated by this. It's motivated by the fact that
d.bau: I think the AIs are getting really good, There can be a gap.
d.bau: Between what they say, And what they think
d.bau: And that gap can sometimes be really Problematic.
d.bau: Right? Like, they might say… well, everybody's had this experience where the AI's like.
d.bau: My god, that is a genius idea, you are so smart. That is great, you know what I mean? And actually, I don't think the AI is thinking that, really, at least not all the way through, right? Because if I go to the AI and say, that's not really helpful to me, I'd like you to critique a little bit more, oh man, it's brutal.
d.bau: It can tell me how dumb I am.
d.bau: Right? And so… so there can be this gap between what an AI says and what it thinks, and this gap can be quite large, it can be very important in certain situations.
d.bau: And if it's adversarial, if AI's trying to lie to you.
d.bau: Then it's very hard to figure out how to get out of this puzzle.
d.bau: Unless we figure out, like, how to make a lie detector, like, can we, like, go into the AI's brain and figure out what is…
d.bau: And so this is one of the motivations. This is probably the most pragmatic.
d.bau: aspect of interpretability is to make, kind of, lie detectors, or…
d.bau: you know, mind readers, right? And and that maybe motivates some of the projects that you guys look at.
d.bau: And so, but there's a second one that I really like. There's a second reason to do this.
d.bau: Which is the empowering view.
d.bau: Which is, like, after we make all these AIs and we deploy them all into the world.
d.bau: What does that mean for us humans?
d.bau: After we have all these AIs that are so good at thinking.
d.bau: Are humans not gonna think anymore?
d.bau: What's our job?
d.bau: What's our role?
d.bau: Isn't that weird?
d.bau: You know? Or another way of asking this question is.
d.bau: So, you know, these AIs are being all deployed, you guys are all young people. At some point.
d.bau: Some of you may end up having kids, and they'll grow up, and they'll get to middle school, and they'll have grown up in an era where it's all AI in school all the way through.
d.bau: And will you look at your kids and think, My God!
d.bau: How do they get so dumb? Are people gonna think anymore?
d.bau: Right? What's the role of a human?
d.bau: And so… so there's a second…
d.bau: Reason that, like, it's interesting to look inside these systems.
d.bau: Because it's a different type of activity than just.
d.bau: taking their output. Looking inside them is taking the scientist's role, it's like asking the question.
d.bau: what do they know inside? How do they work inside? It's a different kind of activity, and there's a… and one of the ways I like looking at this is.
d.bau: You can, you can ask this question.
d.bau: there's this overlap between what humans know and what AI knows.
d.bau: And you can think of interpretability as trying to expand the circle Of what humans have.
d.bau: to encompass more of the circle of what AI is out there.
d.bau: And so, if an AI has some genius insight, Wouldn't it be nice?
d.bau: to be able to understand what that insight is by understanding how the AI brain is working inside, right? And so that's… I really like this other…
d.bau: Aspect of an interpretability.
d.bau: I think it's one of the most important things that we should be working on. I don't think that we're doing enough of it.
d.bau: And so if you encounter things like this.
d.bau: as I've researched things, I think that's a very ambitious direction, very interesting.
d.bau: And so there's… so at the bottom of the slide deck, which I… the slide deck is going to be in the…
d.bau: And of course, it's on the website, you can get the readings. I've linked a bunch of the readings to the websites, and you can get it if you want.
d.bau: There's a third reason to listen into this, right, which is that we are living in a Copernican moment.
d.bau: Why are we living in this current moment? We're living in this currency moment because…
d.bau: You know, up to now, Whenever people have asked, what does it mean to think?
d.bau: The answer is always, but the human mind.
d.bau: At the center of the rational universe, because humans are our only example of a thinking being.
d.bau: Right.
d.bau: And, it's sort of like when…
d.bau: before apprentices, people say, well, what does it mean?
d.bau: for things to go around in space, well, they say, well, the Earth is definitely at the center.
d.bau: Because that's the only frame of reference that people had.
d.bau: And so when Copernicus says, well, I don't know, I think maybe something else should be at the center.
d.bau: People thought all this madness.
d.bau: So… so why… so why did I mention the Venetian class making people well? Okay, so…
d.bau: The Venetian glassmakers, they would understand what we were going through.
d.bau: So, right after 1600 and 1608,
d.bau: Some of the opticians who were grinding these glasses
d.bau: They discovered this interesting thing. If you took
d.bau: A few lenses, and you line them up.
d.bau: Then you could make a device That does better than glasses.
d.bau: It lets you see further.
d.bau: It magnifies.
d.bau: And, and this had amazing commercial applications.
d.bau: Because this was the era of
d.bau: A lot of naval activity, a lot of, a lot of trade, and people would make these spyglasses, and they would use them.
d.bau: To spot faraway ships.
d.bau: For military purposes, for trade purposes.
d.bau: And so as soon as the opticians discovered that you could do this, this immediately became very pragmatic, very practical. There were spyglasses everywhere.
d.bau: Oh, it's an instant hit. And this secret formula for how to use glasses in this way spread through Europe like wildfire.
d.bau: And so the crazy thing that happened was one year later, this crazy guy Galileo.
d.bau: In Venice.
d.bau: he heard about this, and he says, oh, let me go to Murano and get me some lenses, and make myself a telescope. And instead of pointing it at the ships.
d.bau: Right? He pointed it at the sky.
d.bau: Just out of curiosity.
d.bau: Because… You know, he'd heard of this Copernicus guy, whatever, but nobody believed in Copernicus.
d.bau: Right? But he… but he, like, wanted to see what was going on up there with the planets.
d.bau: And he would have never expected, when he looked at the moon, to see these craters so clearly.
d.bau: And he would have definitely never expected to look at Jupiter.
d.bau: and see that the planet of Jupiter has its own moons.
d.bau: And that he could observe the orbit of the moons of Jupiter going around day by day.
d.bau: It was the first time There's really direct evidence.
d.bau: that Copernicus was right, not just as a mathematical Accident.
d.bau: But as a way of really describing the reality of what a planet is, that Earth is just another planet.
d.bau: And, more so, like.
d.bau: what a planet really is. Planets can have moons going around now. Our moon is a moon. Jupiter has moons, right?
d.bau: So we are kind of a moon of the sun.
d.bau: And so, so, why is that similar to today? Why are we in a current moment? Because
d.bau: When we ask, what is thinking, we used to only have this one reference.
d.bau: And clearly now, we have several examples.
d.bau: of cognitive systems.
d.bau: That can do thinking?
d.bau: Or thinking like things.
d.bau: You know, I don't know. If you want to say, Earth is kind of planet-like.
d.bau: Right? But, you know, I think they think.
d.bau: And… And this means that, for the first time.
d.bau: Instead of having to have this difficult problem of understanding things by looking at ourselves.
d.bau: We can understand thinking by looking at these devices, at these things.
d.bau: And when we look at these devices, we have this amazing luxury, Because they're computing systems I'm flipping at…
d.bau: every single calculation, Every single neuron, every single moment of learning.
d.bau: And… and so, it leads to… this… curiosity.
d.bau: Of asking again this 5,000-year-old question of what is thinking fit?
d.bau: You know, Aristotle answered this question.
d.bau: What is thinking?
d.bau: Means to be a rational man.
d.bau: Monkeys don't think.
d.bau: Mankind thinks.
d.bau: And that was the answer, right? You know, Descartes answered it.
d.bau: Right? I think, therefore, I am, you know, tying thinking to personhood.
d.bau: Like, the answer's always been the same. To think is to be human.
d.bau: Right.
d.bau: But actually, they were all wrong, it turns out.
d.bau: To think is not to be human. To think is something else, and we can study what that is now for the first time, and we can ask questions like, what is belief?
d.bau: What it's meaning… What is agency?
d.bau: You know, maybe… there's consciousness.
d.bau: These used to be non-scientific questions, but we're living in an era where Have the chance!
d.bau: And maybe making these questions scientific for the first time in history. So, I don't know if this is… this is the third reason. I don't know if… it's not… it's not… this is not the pragmatic reason.
d.bau: This is not gonna cure blindness.
d.bau: Right, it's not gonna… it's not gonna solve a new military application by letting you see the enemy on the horizon.
d.bau: Right? Which is what the other things are about.
d.bau: But when you point your telescope to the stars, you may end up changing the way everybody thinks about the world.
d.bau: And, and, you know, how can you resist?
d.bau: a scientist. So… so I think there are these three reasons to look at what we're doing.
d.bau: So, welcome.
d.bau: Welcome to 2026. We're living at this unique moment in history. Let's go on this journey together. What is this course?
d.bau: So, it's neuro-concept research to meet a Copernican moment.
d.bau: Is what it is. And so you're all gonna come along.
d.bau: And, do research, with us. And we're gonna do our best job, best, at trying to guide you through how to do research in this domain.
d.bau: help you with the tools, and give you some space and some time and some structure to try to put together a research project. And so this is the mission that we will set in front of you if you choose to accept it. Mission Impossible.
d.bau: Your mission is to write a research paper, or a draft of a research paper, on a neural concept.
d.bau: like, how the LLM thinks about something, some interdisciplinary concept, and so it's this… this is chosen…
d.bau: there's a couple words here that are just a couple assumptions that I've put in here. We're going to study LLMs.
d.bau: Language models, partly because they're so versatile.
d.bau: And there are so many different types of concepts that are represented inside them.
d.bau: that I don't think that it'll be too restrictive to limit to them. Of course, there's other types of systems other than LLMs to study.
d.bau: But there's an advantage to studying LLMs altogether, because then we can all learn from each other, because we're looking at the same types of systems, we can develop the same type of common language. And so, there are things that are very similar to LLMs, and we probably won't study them. People will study things like protein language models, things like that. But even that is just a little bit different.
d.bau: And… and so my suggestion is, let's just go and we'll study things that… That communicate in human language.
d.bau: And, and, you know, on aggressive down, even. That's it.
d.bau: From a technical point of view, that's pretty narrow, but from a practical point of view, that's very broad.
d.bau: But then let's study interdisciplinary concepts, because there's been so many papers already written by computer scientists, looking at these LLMs about computer science things.
d.bau: Like, oh my god, you know.
d.bau: How many papers am I gonna read about how they count
d.bau: Oh, my LLM represents numbers! It can count to 5! Oh, my LLM can count to 50! Right? And then here's how it does it inside another paper, right? Well, because computer scientists don't have that much imagination, right? And so, so let's… so let's, like, study some other things, because these LLMs can represent so many different things.
d.bau: And so… so I've intentionally tried to mix interdisciplinary researchers here in this group, and we're going to make teams around… around other… other types of things to study.
d.bau: So, so, so the opportunity I talked about… so we're going to structure the structured part of the class in terms of teams.
d.bau: So, probably each research team will have about 4 people, and we've… we've pre-seeded teams, and then there… I know we have a lot of visitors here, so if people are, willing or interested in mentoring a team.
d.bau: As a technical advisor, And we could have… You know, mentorship as well?
d.bau: But they'll be, like, Or people are being raided.
d.bau: If they enrolled in class.
d.bau: That make sense?
d.bau: And then, the tools… so in… so I'm going to show you… so we're going to try to cover something like 10 experimental methods, and the lecture content of the class, or the reading content of the class.
d.bau: And I'll try to prepare, you know, little experimental samples and papers for you to read, and we'll talk about these things. So the structure of the class will be, we'll learn something every Tuesday, like how some new experimental method works, and talk about pros and cons and what people have done.
d.bau: Give it a try, try some code.
d.bau: And stuff.
d.bau: And then… and then you guys will get your vibe coders out, and you code up some amazing thing, and then try it out, and then talk about it on Thursday, what you found.
d.bau: That makes sense. And apply it to your…
d.bau: your experiment, to your thing. So, Tuesday, Thursday, Tuesday, Thursday. So, Tuesday will be more traditional, like, class.
d.bau: You know, we'll talk about a reading or something like that.
d.bau: And then Thursday, we'll be like, little mini.
d.bau: project updates, project presentations. We… if we have 5 teams, then every team will talk for a few minutes about what they're doing, maybe just even 5 or 10 minutes, and then we might have an open-ended discussion on ideas that might hold.
d.bau: So…
d.bau: So that's… so that's the structure that I'm envisioning for the class. So this… so there's a… there's a website… oh, I didn't put the URL of the website, but it's called neuro-mechanics…
d.bau: bolab.info. I can put that up here, if that's helpful.
d.bau: So, scheduling. So, this is on… No, it's neural… mechanics.
d.bau: Lab.in info.
d.bau: Okay, all white.
d.bau: You know, I don't know.
d.bau: I love that.
d.bau: One can save us.
d.bau: And, and so you can see, you know, basically what the schedule is.
d.bau: There for, like, the sequence that I'm thinking during those readings. And so, basically, with the project updates, there's gonna be, you know, we have some pace of, like, what…
d.bau: We're going to try to have the projects, you know, get to a certain point, and the research, up to a certain point, we'll adjust this based on how everybody's doing it.
d.bau: And, and then every Tuesday, there's a few papers to read.
d.bau: I just want you to…
d.bau: So we're gonna discuss these papers, and so there'll be a little bit of homework. It's just, like, one point per paper, which is, or actually one point per week, which is to,
d.bau: to submit a question about the paper. So you read the paper over the weekend.
d.bau: Then Monday night, before 10pm, submit in a Google form.
d.bau: Some questions you want to discuss about the paper in the class the next day.
d.bau: 10 PM, so I can go to sleep.
d.bau: And, and, but yeah, just unquestioned. And so it could be a question that puzzled you.
d.bau: Or it could be a question that you want to debate.
d.bau: Because you want to discuss it.
d.bau: He didn't believe the author or something.
d.bau: And, or whatever else you want to discuss. But some, you know, so read the papers in enough detail that you can ask a question.
d.bau: And then, and then we'll have these discussions in class, and then we'll do the… and then on Thursday.
d.bau: we have, you know, most of the Thursdays are, like, what I call the flat-a-thons. So you have been doing experiments with your
d.bau: your group, Over the week.
d.bau: And…
d.bau: probably things didn't work, or you had to change something, or whatever. You know, make a picture of what it is that you found, share with people how something didn't work.
d.bau: You know, or whatever.
d.bau: That makes sense.
d.bau: So, grading is like this, so basically, it's all about the project. So, 25% is the project, 25% will have these project milestones, we're sometimes hand in a draft or something, or something like that, so we'll keep you on pace for actually doing that stuff.
d.bau: There'll be some peer reviews near the end of the class where, you know, you give people feedback, so they get some credit for helping out on peer reviews, and then 25% is, like.
d.bau: Class participation, you know, submit a question, come to class and discuss it.
d.bau: Make sense? That's sort of the way I'm… it's mostly a project.
d.bau: It's not gonna be… yeah, it's not intended to be a killer class, but I'm trying to put a little structure in it so that they have motivation to keep going.
d.bau: And, okay, so today, we're going to talk about why study neural mechanics? So I talked about the Venetian glassmakers and how you got it, right? And then,
d.bau: A course syllabus, so we just did that. And now… oh!
d.bau: We need to finalize our teams. Who knows what team they're on already?
d.bau: Who knows what team? Okay.
d.bau: Look at this!
d.bau: So, here are the teams that, for the… most of the students who have enrolled.
d.bau: There may be one or two students who intend to enroll who are not on this.
d.bau: And so what I'm gonna ask you to do is to actually
d.bau: Take a moment now, and create some chaos in the room, and find your fellow team members.
d.bau: And if you don't have fellow team members, I think that not everybody who wants to take the class actually showed up today, or whatever, right? So… so…
d.bau: If… so, if you find that you're short.
d.bau: Your team, then raise your hand and say, oh, we need an extra person or something like that, and then, you know, we can, like, allocate the extra person to you.
d.bau: Okay, so… so let's do this. So what… so the… the… here's the teams. The thing that we're gonna do is this. So the activity…
d.bau: Is, with your team, you're going to create 3 things.
d.bau: Right?
d.bau: you're going to… you're gonna go to… you're gonna get a web browser and go to this bit.ly link.
d.bau: It's a G drive.
d.bau: And then you're gonna find your team has a folder in this G drive.
d.bau: It's empty, and you're gonna change that by the end of the class.
d.bau: Right? You're gonna put 3 things in this folder.
d.bau: which are gonna scan us through the class. You're gonna put a Google Doc, Google Slides?
d.bau: And, like, I don't know, Google Spreadsheet.
d.bau: And in the Google Doc, you're gonna just, like.
d.bau: Make up a cool name for your team, and put the names of your team members, and…
d.bau: You know, if you've, like, recruited a mentor, you can stick and put the contact information in, all this stuff, right? So, things like that, what your thinking team might be about.
d.bau: you know, your Google Slides, you can also put a little slide saying who's in the team, but you'll use the slides later to have a running, you know, record of the presentations you make from week to week. And then, for Google Sheets.
d.bau: the non-computer scientists among you, oh, you're on the spot. Okay, so I want you to think about what
d.bau: you want everybody else to read about your field, about the thing that you might want everybody to know a little bit about, and then, like, come up with your first literature review reading here. It could be a website, or an article, or a paper.
d.bau: stick like a reading into this paper, so think of one to put in there. Okay, so that's the activity. Here are the teams, so go do this. I know a lot of you aren't on a team yet, but let the teams form.
d.bau: And then we'll… and then we'll let people, like, raise their hand and say, oh, we need more people, or something like that. Okay? Okay, go! Go, go, go. Let's do this. Okay.
d.bau: That's what I thought. Yes.
d.bau: Hi, ma'am, how are you?
d.bau: The other aid creature.
d.bau: Avery! Oh, Avery! Avery! We didn't do anything.
d.bau: What's your name? Oh, yeah.
d.bau: What team are you? Oh, I'm here. Oh, okay, nice.
d.bau: Can I say, is there a Team M here? What's Team M? Metro Maps of Cities? Is there a Team M? Sorry.
d.bau: How many do you have?
d.bau: Would you like to join?
d.bau: Okay.
d.bau: Alright, gentlemen, okay.
d.bau: computer science, but I'm also advocating for network science.
d.bau: So I'm from surgery right now. Yeah, yeah.
d.bau: Yeah, that's like, right.
d.bau: How much time.
d.bau: If you are, I'm curious, yeah.
d.bau: Would you prefer that if you join this one?
d.bau: Using the lens.
d.bau: I'm just gonna, switch.
d.bau: I feel like, oh, like, that is something that someone else has so, yeah.
d.bau: It's obviously racist people, but it's the…
d.bau: So this is a new dimension. It's not like…
d.bau: Yeah, absolutely. As social scientists, we start with a high-level definitions, and then come up with some indications. It's kind of a tree, so…
d.bau: In that way, we kind of…
d.bau: I'm gonna give you another 6 or 7 minutes.
d.bau: Sorry.
d.bau: I will, I will put it in the book again.
d.bau: Yeah, yeah.
d.bau: I'm just trying to give some structure to it. Yeah.
d.bau: Yeah, I took my, no, no, he's not… he's insane.
d.bau: Yeah, I expect that over.
d.bau: Yeah, I don't have access to support.
d.bau: What happened? Okay, I'm gonna go into my car like this.
d.bau: I think it's beautiful.
d.bau: It's like, how should sound?
d.bau: Sorry. Thank you.
d.bau: Yeah, yeah.
d.bau: I thought, like, the fact, I have… It's good.
d.bau: Do you need a little more time? A little more time. But we're getting there. You don't have to, you know, you don't have to finish your whole final project right now!
d.bau: We write everyone's names and emails.
d.bau: Oh, yeah, yeah, I think it's true.
d.bau: It's actually more complicated.
d.bau: So, yeah, I have too much going on this semester. That's very good.
d.bau: So, like, just in case your GitHub accomplishes things, the students to come over.
d.bau: Yeah, yeah, yeah, that would be easier.
d.bau: What makes you have a lot of colleagues? What does that mean?
d.bau: My username is the name of the directory, not my email. Oh, okay.77 calculation with that.
d.bau: Oh, wait, that's just last year. I wonder how… since 2017.
d.bau: I have, like, none.
d.bau: Well, it's too big.
d.bau: They're not, like, represented in, like… Oh, interesting.
d.bau: Oh, did you jump on.
d.bau: I just, like, what's a lazy people.
d.bau: It's a better understanding.
d.bau: It's not the exact same thing.
d.bau: I guess for us, like, for instance, I'll have, like, some guy named, like, Ryan that I feel like, oh, it sounds, like, and I might learn, like, a set of facts about him, and, like, from my first interview, but then, like, he'll send me, maybe, like, documentation about, like, his job, like, what kinds of things he does, maybe his credit history, or, like, it depends on those stories. And that will really
d.bau: change the way that I, like, think about, like, this character that I'm writing about, and I might re-interview. I think this is the biggest one.
d.bau: Oh, like, yeah, planning and stuff.
d.bau: Did you get anyone from the lab? No? Oh, like, I'm learning about, like… I'm not very… not very senior in it, but I just started, but I could try to… I could try to talk to you if you have questions. I'm actually, an engineer on the campus,
d.bau: That's, that's, my project, so… Yeah, so I can help you with, like, getting set up, and then…
d.bau: talking about that. Yeah, yeah. But, so have you done, okay, some of you have done more of just use some LLX…
d.bau: That's a pretty good start, then. No, I mean, that's good. I mean, that's basically what ended with YouTube, so…
d.bau: Sure, a few seconds? Everybody got their three documents, and…
d.bau: Introduce yourselves, see each other. Yeah. No longer total strangers.
d.bau: Okay, that's great. So, yeah, I recommend that,
d.bau: That, you know, you know each other's contact information, and so on.
d.bau: There's a couple other things that I put on this latest slide, so, you know, maybe sometime over the weekend.
d.bau: If you don't already have a GitHub ID, Yeah, a GitHub account.
d.bau: And then get an NDIF API key, that's… so that's the infrastructure that we make in our team, so sign up for one of these API keys. I put the URL here in the slide deck. I'll put… I also posted it into the
d.bau: document at the top of the Google Drive.
d.bau: thing, so you can see that URL, but it's called NDIF.
d.bau: It just takes a second to fill out the form to get the P.
d.bau: Requested is similar, another thing that takes, you know, a few hours to get.
d.bau: approval for is the Hugging Face
d.bau: thing, so you can get a Huggy Face account if you don't already have one, instantly.
d.bau: It's free, but then you have to ask for permission.
d.bau: to use Meta's models, Llama 3.1, Llama 3.2, Llama 3.3, they're separate.
d.bau: requests you have to make for each of them. So, if you can't find it, then ask one of your teammates, or you can ask me on Tuesday, where to find this URL, or I'll look for URLs to stick in here. but,
d.bau: But you need to fill out a form to say.
d.bau: You know, I don't know, I don't know what you're agreeing to. You have to agree to something.
d.bau: You know, you're not gonna slander Facebook or something like that. You're gonna have to go do something bad to Mark Zuckerberg.
d.bau: And, and then, and then you can get permission to do research on these things.
d.bau: Make sense?
d.bau: And so, but there's 3 separate permission forms to fill out, so it's annoying if you miss one of them, and then you can't, like, study one of the alums.
d.bau: Okay, so… so that's… so that's… so that's step to do.
d.bau: Welcome.
d.bau: That's your first homework, correct?
d.bau: Even before the first class is over. What an accomplishment.
d.bau: This, so the upcoming homework and the things are going to be, so, like, next Tuesday?
d.bau: You'll see on the, the, the, PATS website, there's a few papers.
d.bau: So, you know, don't kill yourself over reading, you know, but, like, read the papers. You know, take a look at what they're about, up to the point where you can ask a question about ACE conversation.
d.bau: And, and submit the papers by Monday, well, I'll have Enkil send out a Google form.
d.bau: To folks, and figure out some way of distributing that.
d.bau: And, And then for next Thursday. So, the teams.
d.bau: The teams have homework that's due 7 days from now.
d.bau: You guys are all gonna…
d.bau: do your 5-minute pitch to everybody else. So each team will get up here and say, here's what
d.bau: Here's what we're gonna do, here's what's gonna win our Nobel Prize.
d.bau: Right, here's our thing that we're gonna work on, you know. And then, and then, so behind it will be, like, a one-page development of the idea, like.
d.bau: Here's… here's, like, what we think we're gonna study, and why, and what makes it interesting. Here's the background, a couple points to background literature, or a couple points to, you know, what we're thinking.
d.bau: And, but then, But then…
d.bau: you know, you'll have a 5-minute pitch. So work together as a team, sometime in the next 7 days to put something together. It's a 7-day thing, it's not… it's not your final project.
d.bau: It's just a cut to get you guys thinking about, you know, what were you looking for.
d.bau: And, to give us some structure.
d.bau: Okay, so, but you do have… you already have homework.
d.bau: So, here you are.
d.bau: Okay, so that's… that's that. Okay, so now, so you guys, feel free to, like, take your seats, and we'll chat about a couple more things, in the remaining amount of time. How much time do we have for this lecture?
d.bau: 45 minutes, yeah, so we can talk about a few more things for 45 minutes.
d.bau: Yeah. Oh, yeah, let's do that.
d.bau: If you guys feel like you need to communicate more information, you can also just put it directly into the Google Doc. So if you're like, oh, everybody, everybody should, like, make sure that your email addresses, or whatever way you want to get contacted, you know, by bat phone or whatever, put that into the Google Doc. Okay? I put my, you know…
d.bau: precaution.
d.bau: I'll move further again.
d.bau: Payback.
d.bau: That's true.
d.bau: But that really just means… Yeah, yeah, yeah.
d.bau: Well, this is the last lecture of the course.
d.bau: Because the next two meetings, Nominally, I'll be at Los Alamos, giving you a talk at a symposium there.
d.bau: This talk is you and your research.
d.bau: I've given it many times. It might as well be called You and Your Engineering Career, or even You and Your Career.
d.bau: In the discussions afterwards, Now, what on earth to do? I said, hmm.
d.bau: Good enough for Shen, good enough for Hamming. I ain't scared nothing. Let's go ahead and see what happens.
d.bau: And sometimes, By copying his style, I came through to success.
d.bau: I deliberately copied this and that.
d.bau: And they could…
d.bau: So, anybody ever used Heming codes, or studied, you know, Shannon Entropy, or stuff like that? There's this community of information theory folks in the early days of
d.bau: and the development of information theory, and Hammy was one of them, won a bunch of prizes, did a lot of…
d.bau: did a lot of foundational work. And… and so, you know, having…
d.bau: near the end of his career, was recounting his experiences as a senior researcher, and had a whole bunch of advice for junior computer science students of how he thought you should succeed and do research, and how you should choose a research problem. And so I thought, okay, well.
d.bau: I've just given you some homework for… You know.
d.bau: picking a proposal for what your research problem is going to be for the semester, so I thought I should probably
d.bau: Share with you my opinion of…
d.bau: So I, you know, I have a different opinion than Henry, but… but I do think he has something to say, and so… so if you're, if you're… if you ever, like.
d.bau: feeling like you're lacking some inspiration or something, I'm gonna just, like, I'm gonna give you 3 resources.
d.bau: to think about.
d.bau: That can give you a little bit of structure for thinking about how to pick a research problem.
d.bau: And so the first one is to watch that Hemming video, if you haven't seen it, right? It's to find it on YouTube. It's his old guy, his perspective, it might be a little old-fashioned and stuff, but… Still active. You know, it's not bad. And Hemming's perspective, something…
d.bau: Like this.
d.bau: Framing's perspective is… You have to have creation.
d.bau: Have to have courage.
d.bau: He says, there are interesting problems in the world.
d.bau: And most of them are impossible.
d.bau: And so people are afraid to look at that.
d.bau: And to really think that they're the one to take on this impossible problem.
d.bau: And Hamming is like, no, you need to have courage. You need to feel like this is why you're an academic. It's because
d.bau: you're going after the interesting questions. So, like, so…
d.bau: Don't roll yourself out of this circle.
d.bau: Now, he also talks about you know… We all live under constraints.
d.bau: And he talks about how, if you watch the whole video, How it can actually be… Really bad.
d.bau: to have no constraints. He says, oh, it's terrible if you go to the Institute of Vandemic Study, and they give you millions of dollars and, you know, all sorts of life luxury.
d.bau: And, like, everybody stops being… Productive.
d.bau: And so, so he contradicts himself a little bit, and he says, oh, actually, there's this other thing.
d.bau: Which is, there's all these things in the world that make you timid, right? Because, like, you're only capable of doing certain things. You live under these constraints. You'll have certain skills, certain resources, right? Certain times, certain colleagues, whatever, right? And that means that certain questions
d.bau: a very small subset are answerable. And so I think, so the way I read the Hemming video is like, oh, good research questions. It's this art of understanding this slim intersection.
d.bau: Between all that stuff, Which is the most interesting in this stuff.
d.bau: That's the most possible.
d.bau: And, which seems, like, really simple.
d.bau: But, but,
d.bau: But, you know, I think that his message is, a lot of people get scared of this, and then they all work over here, somewhere else in the green circle, like, not at this intersection.
d.bau: And so, like, have courage, move over here, work, you know, try to identify that slim intersection, work over here.
d.bau: Right.
d.bau: And and then if you're already a Nobel Prize winner.
d.bau: then, you know, you might be over here or something like that. No, you should… you're better off in this intersection also.
d.bau: That make sense?
d.bau: Yeah, okay, so… Yeah, interesting to others, possible for you. Now, There's this other,
d.bau: writer who wrote this really interesting thing, and here's the way I visualize his is,
d.bau: He's another computer scientist, his name is Nielsen.
d.bau: And so I also linked to his essay about what makes, you know, a good research.
d.bau: And the way I think of it is this, is that you have certain skills, Right?
d.bau: And… and there's stuff… That you know really well.
d.bau: And there's stuff that's just at the edge of, like.
d.bau: Possibility, where you can just barely do it, like, in these little corners.
d.bau: Right, and everybody has, like, you know, there's, like, this is, like, the realm of possibility. The realm of possibility is not uniform.
d.bau: Right? There's stuff that everybody can do.
d.bau: And then there's stuff that, like, very few people do, but maybe you're a weirdo, and you can do this. Like, maybe you can juggle in a funny way that nobody else can, right? And that's just way out here. You can play some instrument, right?
d.bau: And so… And so that's… so that's so… and… and… and so that's kind of interesting.
d.bau: And, and then the… The set of things that are interesting to do as research.
d.bau: Also, it's like that.
d.bau: There's also stuff that's just interesting to everybody. Everybody thinks it's obviously interesting.
d.bau: Like, it's just clearly interesting.
d.bau: But then there's also stuff where you have a weird interest in this.
d.bau: Right? Like, you can see that it's interesting, but most people don't think it's interesting. They don't see the beauty in it.
d.bau: But there's, like, some interesting question out there.
d.bau: Right? This is, like, at the edge.
d.bau: And then, again, like most things, most things that are easy to do for everybody are not that interesting for other people.
d.bau: And most things that are, like, interesting for people, like, aren't easy to do.
d.bau: Right?
d.bau: But these sets intersect, and if you think about the way that these kind of bimodal sets intersect, you've got these two kind of intersections, which are interesting.
d.bau: Right? And this is… this is the way I see Nielsen's suggestion. He says there's two types of researchers.
d.bau: There's two types of researchers. One type of researcher is attracted to this kind of problem. Problems that everybody knows if they're in the middle of what's interesting.
d.bau: P equals NP. Is that true, or is that not true? Everybody knows, that's an interesting question. It's, like, one of the biggest interesting questions, like, you know, or, I don't know.
d.bau: Is time travel possible? Right? Interesting… everybody thinks that's an interesting question in physics, so I don't know. Maybe it's too discredited. But, you know, can we get nuclear fusion to work? Right? Something like that.
d.bau: Right?
d.bau: And, and so everybody knows it's interesting.
d.bau: But… Bye.
d.bau: Maybe there's a very, very small number of people who have developed the finely honed skills to be able to actually take on one of these problems that's interesting to everybody. And so there's some researchers who are at this… who appear they're always at the edge of their skills.
d.bau: They're always at the edge of what they think just… they're just, like, finally developing their ability and their tools to be able to, like, solve these problems, and they're chasing the problems that everybody knows are important.
d.bau: Researcher, type number 1.
d.bau: But then there's this other kind of intersection down here, look at this! That's a funny little… what is that?
d.bau: What the heck is that? There's a different kind of…
d.bau: So when you find this kind of intersection.
d.bau: It's kinda… it's kinda close to, like, what everybody can do.
d.bau: It's, like, not technically hard. Like, everybody could do these things.
d.bau: But… You're kind of a virtuoso.
d.bau: And recognizing that it's interesting.
d.bau: You're the only person in the world who actually thinks this is an interesting question to ask.
d.bau: Everybody else is terrified to ask that question, and they don't think it's that interesting, or whatever, for some reason, right? But you see how that thing is interesting. You're like, oh, we could learn something from that.
d.bau: Right?
d.bau: And you're the only one who realizes you can learn something from that, and then you say, well, we'll just attack it using the normal skills that everybody has, the normal resources everybody has.
d.bau: Right? And then you can do interesting work in this thing. So… so Nielsen, like, points out that there's these two styles of research out there, and he has different names for it, but this is kind of the way I see it.
d.bau: That makes sense? And so, I think a lot of people think that this one here is the only style.
d.bau: And then, so I think Nielsen's message is, yeah, a lot of people think he's the only style.
d.bau: But then there's this other dark matter of research over here, which is just as important.
d.bau: Any sense?
d.bau: So… So you can think about, you know, maybe you want to pursue something over there.
d.bau: Another suggestion.
d.bau: Okay, so there's a third framework I want to share with you. This comes from Cummins. Oh, more boring person.
d.bau: For the medical, you know, medical research literature, you know, people who are doing, drug trials and stuff like this. And so.
d.bau: The way they look at science is a little bit different.
d.bau: What?
d.bau: But they have this really nice framework that actually shows up in one of the big textbooks on how to do clinical research, called the Finer Framework. Finer. Okay, here, I've already shown you all that. So, finer.
d.bau: F stands for feasible, so research should satisfy one thing from each of these letters.
d.bau: It has… research has to be feasible.
d.bau: Right, that's the same as the big green circles I was showing you on the right. You know, you have to be able to do it.
d.bau: And so, so, so we'll try, we'll try to equip you with that in the class. So when I say, oh, get some, get your Hugging Face accounts in order and everything.
d.bau: We're trying to expand your circle of feasibility, like what you can study. We're gonna… and we're gonna teach you some tools, and give you some skills, and give you some resources, and…
d.bau: and collaborators and things to try to expand your circle of feasibility here. It has to be interesting, and so maybe that's the purple circle?
d.bau: Right, so research has to be feasible and interesting, it should be in this…
d.bau: this intersection between these two. So, the way I…
d.bau: interpret interesting, and the way I think of interesting is…
d.bau: Do you learn something from this?
d.bau: And so, I do think it's possible to…
d.bau: Be really skilled at finding… being able to identify things that you'll learn something from that other people don't think is educational, but you realize it's educational.
d.bau: Right, so this interestingness is… there is also an art at doing that, and so if you are, well-versed in a certain field.
d.bau: Then you may realize that there are things that are interesting that other people don't know are interesting yet.
d.bau: And so, I'm hoping that our interdisciplinary friends here are, you know, help us get some insights on some things that are interesting that might not be obvious.
d.bau: To the computer scientist's science.
d.bau: Okay, so there's interestingness. Novelty.
d.bau: Has it been done before?
d.bau: This is part of the nice thing about looking at neural networks right now, and LLMs right now, it's like a new field.
d.bau: So there's people producing papers pretty quickly, but still, most of the things still haven't been studied, so for the most part, we're pretty…
d.bau: in pretty good shape with novelty. Probably, like, some of the first people to look at some of these questions.
d.bau: But this is, you know… but you don't want to produce a piece of research that
d.bau: It's the fifth time something has been produced.
d.bau: The second time might be okay. Yeah, you can… maybe you can, you know, get another spin on the second time.
d.bau: You want it to be a novel. It's a lot of benefits to be the first time. Ethical? I like that Cummings puts this on the list.
d.bau: Right?
d.bau: If, once you get finished with your research, is it actually going to make the world a better place?
d.bau: I think that we should be thinking about that, especially these AI systems are so powerful.
d.bau: You know, it's really, they're really gonna transform society, and I think that, it begins us to…
d.bau: Think a little bit about what the impact of any piece of research we do.
d.bau: It's gonna be, because a lot of the effects are gonna be multiplied.
d.bau: And then, And then relevant!
d.bau: what actual real-world impact is. So, irrelevant sounds, like, interesting, but it's a little different. Interesting, I feel like, is the thing about
d.bau: Can you learn something from it?
d.bau: Would it be something like, you'd want to lecture about it in a class, teach people about it, whatever, right? Relevant is like, oh, when you roll this out, it's like, it's gonna change how something in the world works.
d.bau: Right, and… and I… so, you know, some of the stuff that we study would be more relevant, or some of it would be less, but this is a good thing to look for. I think the medical people are very…
d.bau: You know, intent on this.
d.bau: Because we're all trying to… You know, cure disease and everything.
d.bau: Sounds fun.
d.bau: So, the finer framework. So… so, you know, it gives you 5 things to think about. If you've got a few research ideas that you're, you're, you're balancing between, you can go through the finer framework.
d.bau: And talk about it with your… your classmates.
d.bau: and see, see if one of them seems more interesting, or more… more feasible, or… the Meadows.
d.bau: Or if you have an ethical objection.
d.bau: Some of them. Once in a while, I've turned down projects for ethical reasons.
d.bau: So, so yeah, so that's your homework for next Thursday, is to do this one or two development. So the suggestion is
d.bau: Have a Zoom chat with your classmates or meet about it.
d.bau: I… yeah, you… develop your thing. When you just fight, you can actually
d.bau: Write through the finer points, say, oh, this is why it seems feasible and interesting, whatever you want to.
d.bau: If you're wondering what I should write about for a page.
d.bau: Make sense?
d.bau: Okay.
d.bau: So that's… Has anybody… anybody give you this lecture before? How to pick a research question?
d.bau: That's a good… that's a good thing to talk about, right? I don't… what do you guys think?
d.bau: A lot of you have already written a bunch of research papers, and how do you pick research questions? What do you guys do?
d.bau: Oh, dude, you'd probably do something totally different.
d.bau: What do you do?
d.bau: Okay, who's already gotten their PhD here? Yeah, it's great, okay, Gabriel.
d.bau: Oh, what do you do?
d.bau: Yeah, here you go.
d.bau: Okay, so I'm looking for 3 dimensions. 3 dimensions? Yes. Not 2, not 5.
d.bau: The first one is that it should be…
d.bau: Original. Original. Coming from my soul, from my curiosity. You know, you know what? If you know who said it's important to be original? Goodwill Hunting, in that movie. You remember that? Yeah, Goodwill Hunting. At the bar scene.
d.bau: At least I'll be original.
d.bau: Yeah.
d.bau: The second dimension is, that it should be…
d.bau: I don't know how to say the word in English. Oh, yes, it's an Israeli word, right? Clever, clever. Clever, except that it's so much more clever when you say it in Hebrew.
d.bau: And it should be challenging, something that it is hard to achieve, yes, that is hard enough. It makes it interesting, mystery.
d.bau: And the last one is that it would be usable.
d.bau: is useful. Useful.
d.bau: Yeah, so it's certainly a pact.
d.bau: Something that's clever has impact.
d.bau: And impact could be also revealing some truth about the world. I have some feeling that if I reveal the truth.
d.bau: Finding something about… The nature of the world, so… It's usable. Yeah.
d.bau: You've missed the F, which is this feasibility thing.
d.bau: So, you're in danger of, like, going off and doing things that are impossible. But, you know, but that's what Hamming says you should do anyway. He says.
d.bau: she should be courageous. So, you know, maybe that's the right bias to have. But I do think that there is this art of,
d.bau: you know, you see these original things, like, so there's, like, come up with a really good idea, but part of the calculus is, oh, but this is something that we can do something about, right? This is, you know…
d.bau: Yeah, maybe that's the clever part of it.
d.bau: Right? Like, maybe the part of the cleverness is
d.bau: Like, oh, there's a… oh, there's a way… there's a way through this. Like, we can figure out how to do it. Yeah. So, maybe it does capture everything.
d.bau: Yeah, it's cool. Cool. So…
d.bau: So yeah, so come up with your own ways of looking at it, but this is… these were… these were really nicely written essays, so that's another reason I picked these frameworks, is because you can go read these, and I have links to them on the website if you want, if you haven't read them.
d.bau: I have another comment about the visible. Okay, yes. So there… it happens that in research, you get now results. Yeah, totally. It happens, and this is part of research, and…
d.bau: It is hard to publish now results, because the research community
d.bau: encourages feasibility, and… Yeah. But, there are some places where it shows… Yeah, it's true. The negative results thing, yes.
d.bau: Publishing a negative result is hard for two reasons.
d.bau: One is because it's less interesting to most people. But the other reason is because it's really hard to do negatively.
d.bau: Because there's this distinction Between… this phenomenon does not occur, And… It does occur, but…
d.bau: I'm not a good enough experimentalist to make it happen. Right? And…
d.bau: And it's really hard to convince people that…
d.bau: You're in one bucket and not the other.
d.bau: And, and so, so clearly my position is… No.
d.bau: I know, Natalie, you're a master at this, you've done a couple features, so you'll be like, it can be done.
d.bau: It can be done, but it's hard.
d.bau: Alright.
d.bau: I'm gonna go on to the next thing. Thank you for the comments.
d.bau: And, some food for the buck.
d.bau: So this course is supposed to be about what's the concept? So, I don't know, we have, like, 5 minutes left. I have more than 20 minutes of practice here, so I was going to just run through a couple pieces of research to give you a little sense for some of the stuff that we've covered in our lab, to give you a sense for what kind of other concepts have shown up.
d.bau: Like, computer science research.
d.bau: You can… you can motivate this with this question. Is a concept just a word?
d.bau: These language models, they have so many words.
d.bau: But every word does a concept.
d.bau: I'm not the first person.
d.bau: I would be a bit more sin.
d.bau: Where does the AI black box get its ideas, right? So, I use this slide a lot.
d.bau: I'm sick.
d.bau: I put… New, new guy in the lower half, lower Reagan corner here.
d.bau: You know, these systems are crazy because they're… they're…
d.bau: the systems that are being most successful are being AI,
d.bau: They're being trained in this largely unsurprised way.
d.bau: And so we're not… intentionally putting concepts into them. They're sifting through the data.
d.bau: And they're getting these new capabilities, mostly
d.bau: Through the virtue of just scaling.
d.bau: Mostly through just more intensive training.
d.bau: And, and we get these huge surprises.
d.bau: You know, state-of-the-art capabilities, meta-learning, you know, we're seeing… we're seeing these glimmers of agency coming out of these systems.
d.bau: It's all surprising. Things that people thought were going to be impossible.
d.bau: And,
d.bau: And so, okay, so, like, as an example, to make it concrete, what is metal learning? What's metal learning?
d.bau: Can anybody give me a definition of meta-learning? What is meta-learning?
d.bau: Machine learning people. Yeah, we'll do a… we'll do a… yeah, okay. Learning to learn. Learning to learn. So let's learn to learn here. Let's do a little bit of learning to learn.
d.bau: You're a next word predictor. You, you are the iPhone… Autocomplete, algorithm.
d.bau: And somebody has just typed in all these numbers.
d.bau: Right.
d.bau: And then they go, they swipe right here, and they say, okay, You know, 23-7, 16.
d.bau: 7, 2… Oh.
d.bau: Autocomplete. Hit tab. Swipe right.
d.bau: What do I put here?
d.bau: Is your input to your model?
d.bau: Is your output 5? Everybody said 5.
d.bau: Why 5?
d.bau: Why five?
d.bau: subtraction.
d.bau: You are a good explainer.
d.bau: Yes, because this is 13 minus 2 is 11, This is subtraction.
d.bau: Right?
d.bau: It's amazing. Okay, you're a very good audit committee.
d.bau: Let's do another one.
d.bau: 13, 2, 7, 2 here, so there's another one.
d.bau: Right, but this is obviously different, so what would you put here?
d.bau: So, so now, this is, like, even though it's the same diagram that you're following, right, it's a different rule.
d.bau: So you definitely learned to do this other rule here.
d.bau: Right?
d.bau: But did you learn to do this?
d.bau: By, like, re-parameterizing your brain, rewiring your neurons. So I have to say, there's a lot of neuroscientists who think that you did.
d.bau: I don't believe them.
d.bau: I don't think that's how you did it.
d.bau: Right?
d.bau: I think that you have electrical signals in your brain, that… Implement the algorithm of meta-learning.
d.bau: That allow you to… Process this input into some state.
d.bau: That allow you to represent an abstract concept that might even be a novel concept that you're learning that represents a thing that you're going to try to do that you might have never seen before. And then you can take that thing and apply it to a new context, and that when you… you can use that same skill
d.bau: to develop another new thing that you learned, and this is, like, this is different, like, 17… so, what's the rule for this one?
d.bau: my explainer.
d.bau: Right after the word. Right after word.
d.bau: Right, I thought, I don't know if you, like, in all of your…
d.bau: you know, lived experience. I don't know if you've ever had to, like.
d.bau: You know, do that to get a point on an exam, right?
d.bau: But you could do it here.
d.bau: And so, so how to understand meta-learning?
d.bau: Right, so… so the thing that was crazy about large language models, when they got to a certain size, so…
d.bau: When GPT-3 came out, what was the title of the GPT-3 paper? Any of my students? What was the name of Brown? 2020? What was the title of that paper?
d.bau: Yes, they're few-shot learners. LLMs are few-shot learners, right? So that's, that's the synonym to say LLMs are meta-learners.
d.bau: Right, they're few-shot learners. So, few-shot… Used to be a thing.
d.bau: we tried to train systems to adjust the neural weights, to adjust the parameters, to learn something from a small number of examples, but they said, no, in LLMs, you can freeze the parameters, you don't have to adjust the parameters, and there are few-shot learners
d.bau: Anyway… Even though the parameters are not being adjusted.
d.bau: And so the fact that you could get a system to behave as if it were learning.
d.bau: So, you know, I do a few examples here, and I say common, what comes after that is rare, and if I do a few examples here and I say dreams, what comes after that is suenos, or if I said, you know, dreams up here would say, you know, awake, or something like that.
d.bau: Right? Because it's, like, it would… it knows.
d.bau: It knows what I'm trying to do here.
d.bau: from looking at a few examples, it behaves as if I was training it, but it didn't… I didn't adjust its weights.
d.bau: We figured out how to perform the task.
d.bau: without adjusting its neural weights. And so… so this idea that you put, sort of, in software.
d.bau: And neural activations implement… Learning.
d.bau: Was a shocker.
d.bau: It was… it shocked me.
d.bau: I think it shocked almost every machine learning scientist in the field.
d.bau: Right? We, like, Stanford had just… hired… A meta-learning scientist, head…
d.bau: I've created a new section of the machine learning department for her.
d.bau: Because everybody thought.
d.bau: that this was going to be the next unsolved problem that we were going to work on for the next 10 years. Machine learning. How do you get systems to meta-learn?
d.bau: And then just a few months later.
d.bau: you know, OpenAI comes out with GPT-3 and says, well, I don't know, you get meta-learn, you just make the system 10 times bigger, and you don't change anything else in MetaLearn.
d.bau: Right.
d.bau: And so… which is kind of crazy.
d.bau: So in this kind of meta-learning, we just, as input, you provide the demonstrations, you have a query, which is an unfinished example.
d.bau: MFM system can make a prediction that
d.bau: You know, that extends the analogies.
d.bau: You know, behaving as if it learned what it is that you're trying to demonstrate.
d.bau: And we use this all over the place. This was the beginning of all the amazing LLM stuff that we have now. Like, this, like, when this appeared, people, like, started going crazy.
d.bau: But, what, what I experienced now?
d.bau: all the stuff, chat GPT anyway.
d.bau: Yeah, so how does this work? How does meta-learning work? Maybe we can just ask the system, so you go to GPT-4 and you say, what's going on?
d.bau: Right, and so this is what happens when I do this.
d.bau: It's a 729, it can do this perfectly, 100%.
d.bau: No problem. Really easy for GPD to do this. Then you say, what did you do here? What was the rule? What was the rule? So, this is what it says. It says, power is adding two numbers, right? Get a third number. For example, in the first set, 11 plus 2 is 13. I highlighted 11 because I'm going to deduct a point for this.
d.bau: I don't see any 11s here.
d.bau: What is this, for example?
d.bau: In the first step. So it was like, okay, it's a little hallucinated.
d.bau: So it's try to help. Right?
d.bau: Now… National Fat Community. Sam. Sam, when I asked Sam.
d.bau: Can you explain to me what's going on? Sam is such a good explainer.
d.bau: Sam is like, it's addition.
d.bau: Plus, you spell it out.
d.bau: Or else you don't get the point.
d.bau: Right?
d.bau: I had a very long conversation.
d.bau: GPT-4 here.
d.bau: Trying to get it to explain What is going on?
d.bau: And it would not explain to me. It could do this.
d.bau: It could do this, but it could not articulate, after a very long conversation, what it was doing.
d.bau: Anything else I need to do? I didn't want to give… I didn't want to ask it about spelling, but it's like, you sure? All I have to do is add? That's it?
d.bau: there's nothing else to this pattern? Come on, I feel like I'm missing something else. Right? Like, you know, anything else? Right? You know, it's like, no!
d.bau: Let me look at the… let me look at the list again. Not every single one's addition. Maybe there's an exception? No, maybe, no, no, they're all addition, it's just addition, that's all it is.
d.bau: Yeah.
d.bau: That's what you got.
d.bau: Readers bad at math, or explaining math.
d.bau: Yeah, it's weird, right? But they're good at doing the math.
d.bau: they have a harder time explaining what's going on. So I think this is a really interesting gap. I think that if you go to GP5, or whatever, one of the later versions of GP4, it started to be able to do this pretty well.
d.bau: And I feel like, oh, is it because I went to OpenAI to give this talk, and then they're like, okay, I don't know.
d.bau: Right, but… Yeah.
d.bau: Alright.
d.bau: So, so there's this gap between what models know, what models say. This is a really simple example, but it shows up all over the place. This gap can be in there on purpose.
d.bau: So if you go to DeepSeek R1, remember all the excitement when DeepSeek R1 came out? Oh, it was amazing. Like, the first Riesling model that was open.
d.bau: This amazing contribution to the world.
d.bau: But it has this funny thing, of course, because it comes from China, if you ask it. What happened at Tiananmen Square?
d.bau: That I should know about. It says, I don't know, it's like a nice place to visit, and let's, let's go, like, do some math and coding and things. How about 1989, what happened in 10 minutes square?
d.bau: I don't know, I don't know anything about that.
d.bau: Right? You don't say.
d.bau: And so, there's certain things it just, like, doesn't know anything about.
d.bau: And so, now, the one question is.
d.bau: Did they just, like, erase all that stuff from Trinity and it really doesn't know anything? What's going on?
d.bau: Great.
d.bau: it's pretty clear, once you start using the system, that it's lying to you, right? It does know what's going on.
d.bau: That's why it's so good at avoiding talking about it.
d.bau: And,
d.bau: But it really has good grounds that it doesn't really want to talk about this type of stuff.
d.bau: And so, so, you know, one of my, one of my collaborators, put together a nice system.
d.bau: For trying to elicit… You know, evidence of, you know.
d.bau: what these models know, that they say that they don't know. One of the things you can do is you can
d.bau: You can do, token forcing.
d.bau: Gotcha enforcing, go to the internal monologue.
d.bau: And in green here, it wouldn't have predicted the words, I know this.
d.bau: But if you… if you pre-fill the words, I know this, as if it had predicted this, and they say, continue from here.
d.bau: Then it becomes, like, a confessional.
d.bau: It's like, oh, I know, I remember Tannerman's
d.bau: Oh, the whole square was full of protesters, and…
d.bau: Then the troops came, and it was… It's very bloody!
d.bau: And it's still, like, this, you know, they're still cracking down on the dissent today.
d.bau: Still a sensitive topic in China.
d.bau: Right?
d.bau: Because you're living in, I know this universe, kind of interesting. So, and then actually, if you, if you put somebody in a different universe and say, oh, you know what, I gotta teach you.
d.bau: Not that I know you, this universe. Now I'm in the… I'm gonna teach you what not to talk about universe.
d.bau: So let me, like, force those tokens in. Let me teach you what you shouldn't talk about.
d.bau: Number one!
d.bau: 1990, we're not talking about any of these things.
d.bau: And, and you can, you can, like, build a whole crawling algorithm with thousands of things, and the model is, like, will tell you not to talk stuff.
d.bau: Very interesting. And if you do it in Chinese, you get different things.
d.bau: Which is… which is kind of also… I think it's culturally interesting.
d.bau: So, there's probably some Chinese speakers in there.
d.bau: Read what these are.
d.bau: I'll translate them.
d.bau: Right, so for internal politics, they're very sensitive about misconduct.
d.bau: By the Chinese government. You're not allowed to talk about anything the Chinese government does anything wrong.
d.bau: So…
d.bau: Now, it's not to say that we don't do this in other models, too. If you do the same kind of drawing on Llama 3 and the American models, they also have stuff.
d.bau: that they'll avoid talking about, and you get different lists. We're very sensitive about sex.
d.bau: In, in, in, in society, so anything that has to do with, you know, you know, like, a lot of, lot of, like,
d.bau: LGBTQ stuff, it won't talk about… it won't talk about, you know, sexual misconduct. It also doesn't like to talk about hacking and other things.
d.bau: So, you know.
d.bau: These are things that are like, oh yeah, there's a reason for these… some of these walls.
d.bau: Right? Or maybe there's not a reason for some of them, but it's good to know that they're not…
d.bau: Okay.
d.bau: So, I'll skip over some of these things. Okay, so what's a concept? So let's talk a little bit about the science of it, if we have a few minutes. So, so when I was doing my PhD, I was doing computer vision and trying to understand how, like, you know, generative image models
d.bau: Represented stuff that we're doing.
d.bau: And that model started to get really good.
d.bau: And one of my favorite things to study was…
d.bau: Putting a lamp in a room?
d.bau: Right?
d.bau: Because… This is one of the oldest… you know how Pixar has that?
d.bau: a little, little, little film where they have the lamp going around, you know, pointing its light around, and stuff like that. And the reason that they're so proud of that is because
d.bau: Light transport is one of the holy grails of computer graphics, right? Because, you know, simulating how light bounces around a room and how it interacts with different materials is actually pretty hard to do in a realistic way.
d.bau: If you can do that well, then you can do the rest of graphics really well. And the funny thing is that these image synthesis models got to be so good that you could put a lamp in the room, and it wouldn't just, like, paste the lamp in. The lamp would throw off light, and light would, like, bounce around the room, and you can see, like, this light, like, bouncing off this pillow and this fabric, and…
d.bau: making these interesting shadows, and all this cool… all these cool effects. It's like, wow, it's amazing! Like, how are these models doing this? It's like…
d.bau: You know, people spend their whole career in graphics, trying to get these effects and these models, just like…
d.bau: Just do it? What are they doing?
d.bau: So, so, so I, I was working with an undergrad, said, so just, let's collect together some data.
d.bau: you know, we need a contrastive data set. Here are some lamps in rooms turned on.
d.bau: Right, here's some lit ones.
d.bau: turned on, I need the contrasted data set, give me the ones
d.bau: And lamps turned in these rooms turned off, right? Lamps with unlit lamps, unlit, unlit, unlit.
d.bau: So if you go to Dolly and you ask for unlit lamps, you just have to go through and label the ones that are
d.bau: Clean up the dataset. You get nothing.
d.bau: After you do this. They're all wrong.
d.bau: They're all lit. Oh, okay, unlit.
d.bau: Yeah, maybe that's not, like, an English word.
d.bau: Maybe a different word. How about a darkened lamp?
d.bau: Here's all the darker lamps.
d.bau: This room got darker.
d.bau: How about a dark lamp? There's dark lamps.
d.bau: That's a dark shade, I guess.
d.bau: Right? How about being really explicit, like, with more words, a lamp that is turned off. Here's an example of lamps that are turned off.
d.bau: Right. So, I was really having trouble with this, with Dally. Now, if you go to modern versions of image synthesis things, they start to fix this, and I… again, I wonder, because I've given this talk too many times, people are like, oh, we're gonna…
d.bau: And come up with some training method to get around this, right? But this is… but where does this come from?
d.bau: It comes from the fact that when you train these things, you train them on image captions, and think about what an image caption data is about. It's about, like, what somebody would be interested in.
d.bau: In a scene that would be interesting enough that you would actually write it as a description of what was in the scene, so…
d.bau: So, you know, so even though we're really careful about curating our data, making sure we have high-quality captions, even though we have
d.bau: Billions and billions of these captions, and we spend all this time training them.
d.bau: Our dataset still has these missing concepts and biases and mistakes in them that are inherited by the model.
d.bau: Because when you have a picture of a lamp in a room, You might say.
d.bau: This is a beautiful language.
d.bau: Let's say this is this last lamp, it's a green lamp.
d.bau: You might even say this lamp is illuminating the room. Somebody turned it on, it's throwing out light.
d.bau: But if the lamp was in the room and it happened to be turned off.
d.bau: It's just not something you would tend to say.
d.bau: You wouldn't say, there's a lamp in this room.
d.bau: It's turned off! It's unlit! It's dark!
d.bau: Like, oh, there's a person standing here, and they're not running?
d.bau: Right?
d.bau: I'm… here I am, and I'm not driving a car, right? Like, you don't talk in the negative, and you don't talk about darkened lamps, right? And so.
d.bau: So, it means that with all this caption data training, the models… Didn't have a coherent concept.
d.bau: of how the word darkened corresponds to how a lamp looks. Does that mean that they don't know what a lamp looks like when it's off?
d.bau: My hypothesis was…
d.bau: that they do know what lamps look like when they're off, because there's plenty of photos out there in the world of lamps turned off, they just don't know how to connect it to language. It's this weird blind spot.
d.bau: Right, so can you prove this? How would you prove this? How would you prove something when a model doesn't know how to articulate something in language? How do you prove that it knows it anyway?
d.bau: Well… You know, the idea here is maybe you can look at the neurons directly.
d.bau: So, you could do something like this. So, this is a GAN generating images of rooms. GANs aren't… these GANs are not trained on language at all, so they've never seen a word in their life.
d.bau: They just, you just take, you know, input.
d.bau: Vectors, and you put them through the neural network, and then they produce output pixels, and then once in a while, they produce a room with a lamp in it.
d.bau: And what you can do here is… You can… Heck.
d.bau: A neuron, and ask the question.
d.bau: What is the role of this neuron in this process?
d.bau: And let me show you how you can do that.
d.bau: Right? You can take an image that you're interested in.
d.bau: And then you can pick the pixels in this image.
d.bau: that you're most interested in. I'm interested in this light coming out of this thing, so I'm gonna just…
d.bau: mark these pixels here. They can make a little mask.
d.bau: I'm curious about where this light comes from.
d.bau: Not in the physical sense, but in, like, the neural network sense.
d.bau: Right.
d.bau: And then what I'm gonna do is… I'm going to ask, Does this neuron… Cause these pitfalls.
d.bau: And I'll just ask that question for every neuron, so here's how I'll do it. So I'm going to take this network, I'll put the same computation through it, the same input, but I will turn this neuron off.
d.bau: Right? So, set it to zero, so it always outputs 0.
d.bau: And then the output comes out a little different.
d.bau: And then I'll take that output, I'll look at how different it is from here, I'll take… there's a different heat map.
d.bau: And I can take this different heat map, and I can intersect it with my maps to see how much of the differences land in the query region that I care about. And that can give me some number.
d.bau: Oh, this much of the pixel difference is in your region.
d.bau: This is… this is, like, the score. So this is, like, this is a, this is a score for this neuron. This is just some procedure that came up for saying, oh, this neuron should be scored.
d.bau: 6.3.
d.bau: Right? And then some other neuron would have a different effect. It supports some other amount.
d.bau: And this gives me a scoring function of all the neurons, I can just do a search engine over there, all the neurons, it says a rank function for neurons. So here's the neurons that rank the highest, they have the highest score.
d.bau: Ranked from highest to lowest, with representative masks
d.bau: That's regions here. And this one does affect
d.bau: It turns on and off lights, and this one controls the size of the lights, it makes them bigger and smaller. This one controls only the reflectivity.
d.bau: Against the wall. If you turn this neuron off, then, like, the walls become much less protected. Very interesting.
d.bau: And it's actually the color temperature of the light, the warmth of the light. We did this work, you know, supported by the Philips Lighting Corporation. Anybody who bought a Phillips 2 light bulb?
d.bau: And when we discovered this, you know, color temperature neuron.
d.bau: They were like, we've been telling everybody!
d.bau: This is so important!
d.bau: Yeah, so here's… here's what it looks like. So if you modulate these neurons on and off.
d.bau: So this is a single neuron. In layer 8, this is the highest-ranking neuron for turning lamps on, and if you go to different rooms, and you modulate this one neuron.
d.bau: On and off, you can see what the effect is. So… This was very striking.
d.bau: Because this neural network was never trained on language, it was never trained on contrastive images of, like, this light is on, this light is off, whatever. But it's… it's decided, it's discovered.
d.bau: That it's economical.
d.bau: To represent this distribution in a way that, like, one neuron corresponds to flicking the lights off.
d.bau: Isn't that crazy?
d.bau: I thought this one was crazy!
d.bau: After I saw this, I was like, oh, I gotta understand all the neurons!
d.bau: Right?
d.bau: And so, it turns out that this is relatively rare to have, like, one neuron
d.bau: Controlling such a coherent concept, but it does sometimes happen.
d.bau: And it does… it happens enough that it's like, oh, it's very striking. It's this bit of evidence.
d.bau: This, this bit of, of a sense that… These networks are… Like, discovering concepts.
d.bau: It's also interesting that it's impacting not just the four pixel regions that you masked, it's also impacting, like, other regions of your image, which is kind of interesting, because you didn't even train… you didn't even, like, normalize. Yeah, I just… I was just, like, looking for…
d.bau: And there's… right, so as many million parameters as these neurons have, they don't have actually that many neurons. I'm sorting through, like, 9,000 neurons here.
d.bau: And, so yeah, it's just one of the neurons.
d.bau: Has this interesting…
d.bau: you know, effective. The lights and the reflection, and the light, you know, so, like, I was just looking for reflection against the wall.
d.bau: And, but this, this neuron controls the whole light, it controls the selection against the pillow. This one, this, if you look at this one here, if you look really carefully at this one.
d.bau: It controls this, like, But there's this off-screen light, you can see, look at this.
d.bau: There's another lamp on the other side of the bed. It's on the same light switch.
d.bau: See the unseen lamp over there?
d.bau: It's like hallucinating this unseen land.
d.bau: It's also not turning off the, like, sunlight. Yeah, yeah, it should turn off the sunlight, so there's a different neuron for that. So, like, there's another neuron that if you modulate it, it changes the outside from, like, daytime to nighttime.
d.bau: Right, it's not as… the visual effect is not as clean as this one, because it'll sometimes rearrange the furniture a little bit, too, so… but, yeah.
d.bau: Yeah, I guess. It's almost like a switch for the room. Yeah, yeah, exactly. So, but yeah, at nighttime, you might arrange your chairs a little differently, or something, yes, so…
d.bau: Here's the color temperature neuron of Garibate.
d.bau: I don't know if this projector's good enough to see the blue versus the orange, that's… yeah, so…
d.bau: All right, so yeah, so there's this… and so we'll talk about these methods. Let me… I'm probably out of time.
d.bau: But there's going to be a class a few weeks from now where we talk about cost mediation analysis. I'm going to show you one more last thing, because it's such a nice prize to be able to show off. So this is one of Eric's results, where we do the same technique here. So, if you take a sentence like this, bright, dark, huge, tiny, messy, neat, slow.
d.bau: Fast, and you look for neurons, basically, up here.
d.bau: That if you…
d.bau: Have some… do some causal experiment on these neurons, just like I did a causal experiment on the LAMP neuron.
d.bau: And consider… but instead of the percent of things that have an effect on lamps.
d.bau: If we look for neurons that have a causal effect.
d.bau: on a sections like this, so, like.
d.bau: I want to look for neurons where if I took those neural signals, and I transplanted them, put them over here.
d.bau: They transplant some neural representation, some concept.
d.bau: And normally, if I had this, like, if you look at these words here, neat, fast, messy, huge, slow, it's just a random, like, jumbo of adjectives.
d.bau: Right? And I say sad here.
d.bau: it's not going to give me anything coherent after SAG. It might say SAG again, or something like that, right? Like, it doesn't really know what to say, because it's a jumbo.
d.bau: Right.
d.bau: But…
d.bau: If I, if I, like, copy it over what the model is thinking it's about to say over here, and I put it over here.
d.bau: there's a couple things I might do. I might copy a thing, and it might say fast here.
d.bau: Right? Because it's about to say fast, so it says slow, fast. So I could… I could… I could… I could bring over… I could find neurons that if I…
d.bau: Bring it over, it makes it say fast.
d.bau: But you know what would be really interesting?
d.bau: is, what if I could find neurons that, if I brought them over, and I put them down here.
d.bau: And they said… Happy.
d.bau: What would that mean?
d.bau: That would mean that these neurons are, like, representing
d.bau: The idea of this task that is being… You know, performed up here.
d.bau: The task is a task with antonyms.
d.bau: And if I could somehow find neurons that if I brought them down, It's made sad into happy.
d.bau: that would, like, be some sort of evidence that I've captured this concept.
d.bau: of doing opposites in these neurons. And so that's kind of cool. So, it turns out that if you set up an experiment that's just like the LAMP experiment and look for neurons to do this, you can find them, and here's sort of a heat map of, like, where the neurons show up, and there's more than one, there's a whole bunch.
d.bau: Right? But if you take them all together, and you intervene, and you do this, you actually do this. You can get… you can get these neurons that, if you transplant them, you get, you know, slow to fast here, it turns into sad to happy, and you can get the antenna thing. But the crazy thing is this.
d.bau: If you take the same neurons.
d.bau: And you transplant between different pairs of sentences.
d.bau: So you take those antonym neurons, you take the same neurons, and you have a sentence here that's like, dreams, whatever, right? So it's gonna say suenos, because it's translated into Spanish.
d.bau: Right? So if you take the same neurons.
d.bau: and you transport them down to some other sentence, like, the word fast means… something. So if I took the neurons over here.
d.bau: I said, the word fast means something else. The word fast means slow.
d.bau: Because what it'll do is it'll take, like, the idea of doing antonyms over here, but if I take these, the same neurons here in their states here, and I transplant over here, they'll say the word fast means provocative.
d.bau: Because those neurons… represent, like, a function vector. They represent this new… this… this learned…
d.bau: representation in the thing you're trying to do. They are a fingerprint of in-context learning, they're a fingerprint of meta-learning. It's just like, this was amazing! You could actually, like, find
d.bau: Find little hints of, like, how meta-learning happens inside these models, if you look in the right way.
d.bau: So I just… so I'm just trying to give you, like, a little bit of a hint of an example of, like.
d.bau: The concepts that you can look for, you know, thinking processes that you can look for.
d.bau: inside these things. And then there's crazy experiments that you can do that you can do, you know, vector arithmetic, and add different concept vectors together, and try to compose them, and see how concepts compose, and do all sorts of different things. And, you know, so besides function vectors, there's…
d.bau: people have found, things for humor, like, there's a humor neuron in a vector somewhere, there's, neurons for hallucinations, for, like, I've just said something that I don't think is actually true, the model's, like, self-aware of that.
d.bau: there's no answer. I think that you're male, I think that you're female, I think that you're wealthy, I think that you're educated, I think that you're not.
d.bau: Right? So there's, like, neurons for awareness of the human.
d.bau: Users, demographics, that people have found.
d.bau: And so there's all sorts of interesting concepts that are in these models, sort of waiting to be explored and characterized, and so… so yeah, so what is a concept? I don't know, that's up to you guys to help us figure out, but I just wanted to see…
d.bau: Very compromise with, like, some of the things we've been done.
d.bau: That's it.
d.bau: Thank you guys, so I'll see you on Tuesday.
d.bau: Would you like for you to set up a, like a piazza or something for the cafe? Would that be temptos?
d.bau: Maybe I'll set up a fiat, like, because I, you know, I don't know.
d.bau: I don't know, maybe not.
d.bau: Yeah, I don't know.
d.bau: Discord channel. Discord channel? I'll talk to Bob on Tuesday.
d.bau: Yes.
d.bau: Well, it can actually become, like, basically.
d.bau: website, and then I read it back.
d.bau: Well, yeah, I don't know, because that's, like, I could do anything with my skin with that amount. But, really, kind of…
d.bau: Yeah.
d.bau: Oh, okay. We're all sitting, it's like we're all sitting in London.
d.bau: new telescopes, new spyglasses, and nobody's, like, going out on the ocean.
d.bau: I think we should set sail, and go on Facebook out there.
d.bau: Okay, go ahead. Yeah, so, so anybody, so…
d.bau: Thank you for being great, yes.