David Bau: Go ahead, keep on going.
David Bau: In the topic paper, they, found, emotion vectors, and then they showed some causal, experiments with those emotion vectors.
David Bau: And, it made me to look in the literature, what already was, found with respect to…
David Bau: emotions, and… there was an interesting paper from, a group of, from Buco.
David Bau: that I, I found is very important, they show that there is a gap between
David Bau: How… how models report
David Bau: In psychology, there is a method to measure emotions within humans by asking them directly how they feel about something.
David Bau: And… And it is known…
David Bau: It is known that this, type of measuring correlates with the real emotions and how people are actually behave.
David Bau: While in models, there is a gap.
David Bau: We can ask models how do they feel. They will report something.
David Bau: But actually, when we test them within a situation, we describe a situation and measure how they will behave, we see that sometimes they behave different from what they report as their feelings.
David Bau: So I found it interesting, because it… in some sense, models like the Antrophic paper shows that, in some sense, models behave like humans. Well, we don't know if they have subjective experience, but they mimic their…
David Bau: The automat of the feelings.
David Bau: But in other sense, like in this paper from Google, we see that there is… there are differences, so it is interesting to find also what is similar and also what's different.
David Bau: Cool. Yeah, very interesting time.
David Bau: I have a question.
David Bau: for the lab generally, as an outsider, I don't want to get too off track.
David Bau: Do you guys generally see… like, when you're talking about what is actually in… in the model, in terms of, like, how the model feels about a certain
David Bau: context. Are you talking about the model as just the autoregressive part that produces one token at a time, or are you seeing the whole system as, like.
David Bau: Like, are you seeing the scratch pad as something that alters the model fundamentally?
David Bau: I didn't understand the question. Can you try to give an example or something? Like,
David Bau: maybe example within humans, what would you mean if… Well, it's hard to parallelize with humans, because we don't have a scratch pad.
David Bau: But, like, when a model… if a model's… Activations for one token.
David Bau: Give it the… General tendency towards…
David Bau: Or let's say, like, you know how chain of thought allows for a model to solve some problems that it couldn't solve otherwise?
David Bau: Yes, okay.
David Bau: Does that mean… In that case, does the model
David Bau: Have the capabilities to solve that problem?
David Bau: Oh, it reminds me, psychotherapy.
David Bau: That people sometimes need to talk within a room in order to be aware of what they feel.
David Bau: Because otherwise, it's like they have it, but… so the same question is, do they have it?
David Bau: Okay.
David Bau: I feel like there's a similarity, too, with, like, memory palaces. Like, if you memorize a long list of things using visual cue, like, did you memorize the thing?
David Bau: I do. Okay. Does this lab have a general philosophy on, like.
David Bau: Are you isolating, like, what is fa… what the model truly, truly thinks to just its activations, or also including its behavior in chain of thought contexts?
David Bau: I can also ask… And if it's in its activation, but…
David Bau: it is not using it. Yeah.
David Bau: So, within humans, we also have the notion of subconscious.
David Bau: So, are we aware of it? If it's in our subconscious, if… and if it's, Activate us.
David Bau: But we are not aware of it, so what does it mean?
David Bau: I think that your question might be answered if we, you know, we'll let people go around and show what they're working on, and then you'll see there's a bunch of people
David Bau: Looking at, you know, chain of thought.
David Bau: you know, rollouts and different things like that. That make sense? The impression I'm getting is it's generally diverse.
David Bau: people's opinions on this? Yeah, I think that we take… I think that if you ask… if you sit down and let me talk to you for an hour, I probably, you know, tell you, like, a really broad view of what interpretability is beyond, like, activations, beyond…
David Bau: beyond chain of thought to, you know, to all sorts of other things as well. I think that, you know, my general theme is…
David Bau: That our charter is to help people deal with
David Bau: The complexity of very complex systems.
David Bau: And and there's a lot of complexity to be cracked.
David Bau: In different ways, yeah.
David Bau: so, oops, wrong, wrong arrow. Like, Andy, Andy, thanks.
David Bau: Okay.
David Bau: Yeah, I don't know, I have a really…
David Bau: Well, I have a, like, comic in the top right about what it's like to feel like an interpretability researcher in 2026.
David Bau: What, what was, what was the original, Caption for this comic.
David Bau: Oh, I made the whole thing. Oh, you made the whole thing? Oh, it's your… it's your own… it's your own New Yorker. Right. I think. Yeah, okay.
David Bau: Cool.
David Bau: Okay, then the rest is, like, this, like, stupid…
David Bau: experiment where, like, literally, I just want a model to learn the identity function, because that's, like, the point of residual connections, right? It, like, allows you to, like, learn a simple solution to identity, right? Okay, so then, like, take a two-layer residual network with linear… linear blocks.
David Bau: And just train it to, like, model the identity. And the middle plot is, like, the norms of the weights.
David Bau: And, like…
David Bau: the different lines are different, like, actually weight decays. So the darkest line is, like, no weight decay.
David Bau: The lightest line is strongest weight decay. So, only for, like, the strongest amount of weight decay do the… does the model figure out, oh, I should put my weights to zero to just learn the identity?
David Bau: And for the others, it, like, plateaus at some, like.
David Bau: solution where actually the matrices are, like, non-zero, but it's still, modeling the identity. Approximately, though.
David Bau: Yeah, approximately. Like, the loss isn't exactly at zero, but it's, like, it's… it's, it's plateaued.
David Bau: So I thought that was, like, kind of weird and interesting. There's, like, this local minima of, like,
David Bau: And you can see on the right is, like, this interaction term where I'm measuring, like, how much W1 and W0 are, like, talking to each other.
David Bau: And, like, Yeah, some of the solutions, like, are… are… Talking to each other.
David Bau: Sorry, because… Sorry. Do you think it's because, like, this… every parameter is getting themed, sort of, like, individually. They are…
David Bau: learning to cancel each other out. Yeah, right.
David Bau: Right, yeah. I say the probability, even with, like, a lot of data, the probability of all the weights, like, the solution being just, like, zero, is, like, low relative to the number of convoluted data-specific solutions. But that's the global minima here, right? Because if you have weight decay term.
David Bau: And zero is… gives you zero loss. So that… that is the global minima. So this is showing that, like, there's, like, these local minima solutions. Yes, yeah, that is… So there is also, like, another explanation for this, like…
David Bau: All the possible matrices you can think of.
David Bau: the configuration that just cancel each other out, it might just have a higher probability of appearing compared to, like, literally zeros.
David Bau: That's your reaction.
David Bau: Probability over what, like, what's… Just, just randomly solutions. So probability of zero would be just zero. It would be normal, like, there would be a range of possible solutions that wouldn't be at the lowest minimum, but those would be higher probability, which is higher than… Right, and, like, near a net, like, you're closer to those solutions.
David Bau: So, do you have an understanding of what these solutions are?
David Bau: Oh.
David Bau: I just have this plot right now. Okay. Like, it's such a… it's such a basic setup, but you wonder that, oh, you know, is this… is this sort of like a little flower, a little fractal, or something that shows up for all solutions of all problems, right? You know what I mean? Yeah, because, like…
David Bau: Actually, ZN was like…
David Bau: showed me this. He was like, this… this is a… this is Meck and Terp's nightmare, because, like…
David Bau: Because, like, the mod… like, if you look functionally, it's doing some super simple thing, but, like, and you… when you look at the ways, it's, like, like, all this, like, messed up shit. Yeah, it's, like, distributed. Yeah. Yeah, it's arbitrarily distributed. It's like… it's like a little encryption function. But if you learn any function, it's kind of implicitly, like…
David Bau: Like, something plus identity. Yeah, plus identity or skill, right? So, like, why should you expect…
David Bau: to, like, learn that thing when you might have learned some, like, tangled… Something plus identity. Yeah, yeah.
David Bau: Yeah.
David Bau: Although, but if you can recognize it, then it's like, oh, maybe… maybe you can, like, mod it out, whatever the structure is. So the other interesting thing here is if you… if the two blocks…
David Bau: are nonlinear.
David Bau: So this is just linear, like, the W0 and W1 are just, like, matrices. If you make those blocks nonlinear, like MLPs…
David Bau: than actually…
David Bau: like, Adam will get you the zero solution. Oh, it'll do it? Even without weight decay. Oh, I see. Interesting.
David Bau: So this is… this is a linear residual network.
David Bau: Oh, you didn't say. I did. I didn't say. Linear rest. Okay, okay. So what does the… what does the activation function do? It just zeroes everything out? No, no, it's like a relu. Yeah, you just zero everything out, right?
David Bau: Oh, oh, sorry.
David Bau: Yes. So is it? Yeah. Yes.
David Bau: Is it just that you are trying to make W0 plus W1 plus W1 into W0 equals 0?
David Bau: busy, dude.
David Bau: I think so? Yeah, because it would be identity plus zero, which would then be 10 times. Yeah.
David Bau: Anyway, we don't need to spend too… this was just a tiny addition, the addition aspect of it. The number of, like.
David Bau: combinations of parameters that solve Y equals X. Like, it feels like there's just, like, a ton of different…
David Bau: solutions for, and maybe something about the way that linearity works, so, like, you could be, like, in one region and, like, not…
David Bau: leave it as… yeah. I think there's something in, like, the, numerical analysis space that would explain this sort of stuff. Okay, I'm gonna… I'm gonna go ahead. Eric, go on.
David Bau: I'm gonna go ahead. Okay, that's okay.
David Bau: Click on the link. Oh, I have to click on a link! Oh, I thought… okay.
David Bau: Alright.
David Bau: What's this?
David Bau: So, at every token, I basically have a verbalization set up. At every token, the model is supposed to… it is saying what it's about to do. That's the intention. So before the model has started writing the logic of the code.
David Bau: It is actually verbalizing, okay, this is what I'm about to do. So at that token position.
David Bau: The model has enough information.
David Bau: To, to basically…
David Bau: say what it is, about to do in that scope, and that's sort of like, so this is before it ever emits the variable sine. Yes. It says, I'm parsing the G to determine if it's a sine character. Yes. And then it starts
David Bau: doing… I'm gonna see if there's a minus here, and then the minus shows up later.
David Bau: And also, like, if you… if you go to the four, token, token… This one here? Yeah, yeah, yeah. I'm processing a character.
David Bau: For the digit.
David Bau: I'm processing…
David Bau: character to convert its numerical. I don't know what's… I don't know what rank character is. Perfect! Of course, because it's a pretty simple setup.
David Bau: It's not any kind of water. But, anyways, it's just a bunch of potential heads, and I'm doing a steering,
David Bau: On a, sort of, like, an informationless, destination role.
David Bau: And it seems like the model is able to… model has enough information
David Bau: in… in its current token position to say what it's about to do, like, 10 tokens in the future.
David Bau: Does that make sense?
David Bau: And also, like, if you go to the code, end signal at the last token.
David Bau: This one here? Yes. So this is the default behavior of the destination ground.
David Bau: I think it's getting cut off. Maybe if you zoom out a little bit?
David Bau: You also sometimes see this at the… at the… when the line ends.
David Bau: So, so it seems like the model also knows when it has finished the task, and it… it basically does not have anything… And it just goes to default, like… It just goes to the default. Here's, like, line of code. Yes. I don't have anything, it's just, like, line of code coming. Yes. I see. So this is my hypothesis.
David Bau: I think it might be possible to locate a subspace
David Bau: Where the model stashes its intent, or its goal.
David Bau: And when there is not enough information, that's a freeze, it's just…
David Bau: It's just the clock has ended, and it just goes through this,
David Bau: Next task, or something like that.
David Bau: Excellent.
David Bau: You can also, like… And, oh, there's more fun!
David Bau: Here? How many have you done here?
David Bau: Okay.
David Bau: But you're only allowed one slide. This is no fair. Yeah, so there's no, like, residual, like, left over, like, if it's like, I'm gonna start doing this thing with the sign, like, if you're now at the sign I've broken, does that just disappear after that, or this was…
David Bau: still there. Maybe I'm missing… I still don't actually know what to set up, but, like… Okay, okay. So the setup is, imagine that, so there's this code, at this token position.
David Bau: Imagine that I have a subspace.
David Bau: I'm reading that's a failure.
David Bau: And I'm adding it in a different process.
David Bau: Which is… which has nothing… which knows nothing about this function.
David Bau: And then, I'm just steering the generation.
David Bau: Of that informationless, context.
David Bau: Spearing… Just by adding, let's say, a specific value to all the generated tokens.
David Bau: Based on…
David Bau: The… like, what you're attaching? Yes, yes. I have a single value, that's it, single vector, and for all the generative tokens, I just add them.
David Bau: For the PAS number.
David Bau: So if you added it, like, after it had already started writing out its plan.
David Bau: what is still articulated. Like, I guess it's not as exciting anymore, because it's…
David Bau: You don't know if it's because it was planning it, or if it's because it was there, but, like, I'm wondering.
David Bau: Funny as it's raining, I guess.
David Bau: It is…
David Bau: This is… this is kind of, like, the intention of this visualization. So you can hover over all the tokens and literally see what the model is thinking at this token position.
David Bau: Okay, yeah, I'll look at it. Yeah, talk more.
David Bau: Yeah.
David Bau: I told my students in this class.
David Bau: Don't sniff at large, it lands.
David Bau: And, you know, Patrick up, same thing, right? It's like, it's just some visualization, some crazy thing, but, you know, remarkable intuition you can get from it.
David Bau: But… Yup.
David Bau: What's going on?
David Bau: Do I have to refresh? Was Arna up the last slide? Okay, we're done.
David Bau: What happened?
David Bau: We could be accurate.
David Bau: We added slides? You guys have been working on slides while we're sitting here? Okay.
David Bau: Oh, there's more people in between!
David Bau: Oh, am I in the wrong one? Oh, Yanton has a slide. Oh my gosh.
David Bau: What have you been doing? No, no, it was great! Yeah, Eric. Is this… is this your poster? No, it's not something else. This is a paper I read this week. Oh, what is it? This is the one I was sharing with you. Yeah.
David Bau: They build a little, agents to, like, evaluate papers.
David Bau: Basically, they… they have, like, this setup where they say, well, we expect the paper to come with, like, the actual paper, as well as, like, the code that comes with it, and, like, a walkthrough of the code, and if, like, a paper is missing those parts, they have, like, a…
David Bau: LM agents, like, generate those, but then they use, like, those artifacts to evaluate different pieces of paper.
David Bau: So they evaluate whether, like, the code, like, reproduces, like, the results that are reported in the paper, like, whether the code actually runs. You know, like, on the top, I put here some of the stuff that they're checking, so, like…
David Bau: The block explains without errors…
David Bau: And then they also test, like… these are… so they actually test a bunch of papers from our lab.
David Bau: So I put down here, they had this little table of, like.
David Bau: 8 of their 10 papers of human repos are, like, repos from our lab that they're using to, like, test their agents on.
David Bau: Which I thought was kind of cool. You guys didn't know that you were making it data clear.
David Bau: That's what I… Yeah, so this belief, really, yeah, actually figures out.
David Bau: So they reported a problem with the repo, so one of the notebooks was not opening.
David Bau: Yeah, you're, you're, you're, you're, you're a repo.
David Bau: Yeah, yeah. And what do you do? Do you revise it? I fixed it. You fixed it? And then did you… and then you just send an objection to the author saying that their paper isn't accurate anymore? I actually signed… I said in my PR that this is based on the information from that paper. Cool.
David Bau: That's neat. Yeah, so, so I think it's kind of cool, like, their agent found, like, a bunch of problems in various repos of, like, oh, like.
David Bau: This file is missing, or, like, these experiments are missing.
David Bau: Or, like, you can't reproduce this… this value or whatever. But I also think it's cool that, like, for the most part, like, a lot of the code that we have, like, reproduces, you know? So I think I appreciate that our lab is doing, like, reproducible open science, and that, like, people are using that as a benchmark.
David Bau: That's cool.
David Bau: You guys are setting the benchmark.
David Bau: That's… that's great.
David Bau: Is their framework open source so that…
David Bau: Like, would I be able to double-check my work using that? I… I was looking… I… yeah…
David Bau: I, for the most part, there's some… I need to double-check. I haven't checked everything, but, like, they have code, so…
David Bau: Oh, did they try the reproducers?
David Bau: I don't know.
David Bau: They gotta put themselves under the microscope.
David Bau: What is this? There's a video here? Chris, is this yours?
David Bau: Is Chris online?
Chris Wendler: Oh yeah, that's mine, yes.
David Bau: Is there a video?
Chris Wendler: Did you actually play? Does this work this time?
David Bau: Yeah, I have a video, I can… I played… I'm playing.
Chris Wendler: Alright.
David Bau: Alright. We're watching.
Chris Wendler: Okay, so…
David Bau: It's just, it's this, oh, it's this, it's this, it's, it's just as bad as,
David Bau: It's Andy's identity thing.
David Bau: It's like the world's simplest video, and you can't do it?
Chris Wendler: Well, I actually can do it.
David Bau: What happened?
Chris Wendler: My eval was just buggy.
Chris Wendler: Oh. Yeah, so,
Chris Wendler: If you evaluate correctly, then you see the… this is actually a learned model that you see.
Chris Wendler: On the right.
Chris Wendler: Oh, okay.
David Bau: doing it.
Chris Wendler: Yeah, it is doing it. And, I have been just trying different architectures.
Chris Wendler: For this… And one that I really like, but that actually is kind of annoying to, like,
Chris Wendler: Implement fast is like the picture on the left.
Chris Wendler: Where I combined, like, sliding window attention.
Chris Wendler: With, like, global attention over, the registered tokens, which are, like.
Chris Wendler: These thin lines in the plot.
Chris Wendler: So… I guess it's pretty hard to read, like… Maybe it, so…
Chris Wendler: Maybe each frame has, like, 10 tokens or so?
Chris Wendler: Yeah, each frame has, like, 9 tokens, and the registered token.
Chris Wendler: So the skinny lines are, like, the attention that is paid to the register token.
Chris Wendler: And the thick, stairs is, like, the sliding window.
David Bau: So is it… is your finding, basically, that the register tokens solve this for you?
Chris Wendler: Yeah, I mean,
Chris Wendler: The registered tokens can, like, make the model be able to do this task over, like, a longer window than the sliding window.
Chris Wendler: Like, it's a simple task.
Chris Wendler: But yeah, so there's, like, a lot of things…
Chris Wendler: that I should still look at, because, like, I guess, like, 9 colors is not that… not that much. So there are no bottlenecks anywhere.
Chris Wendler: The model is just able to do it.
David Bau: We miss you, Chris!
Chris Wendler: That meets you too, guys.
David Bau: You haven't started at your new job yet, right?
Chris Wendler: Not yet, no, I'm not allowed, you know.
David Bau: Hey, keep us up to date.
Chris Wendler: I'm still a postdoc.
David Bau: Okay. Okay.
David Bau: Thanks, Chris!
David Bau: So I'm still doing my same things, but Sheridan was in…
David Bau: You don't know what I'm doing, right? So this is for you, this is me. Everyone else, I think, knows what I'm doing. But, currently what I'm trying to test is whether probes are secretly just bag-of-words models, so whether or not you can construct
David Bau: Like, if you give me a training set for a pro, can I…
David Bau: create a challenge set of, like, product, so I used with, like, unigrams that were giveaways for
David Bau: in a different context, and then confuse the program.
David Bau: And up here I have results for the three different papers that ITI refusal, and then, like, the…
David Bau: Demographics of the user probe, but for gender, and yeah, so for example, with gender, we can see…
David Bau: On the latches, like, the originality, their evaluation site accuracy.
David Bau: And then, the red line is, like, a chance. So…
David Bau: on the test sets that I've created as a common sense, the model's doing… like, the probe is doing worse than random, which means they're using these
David Bau: Words.
David Bau: Instead of, like, privileged stuff about activations.
David Bau: And so I've been trying to just get as many different codebases as I can to run as many people's probes as possible, but it turns out I'm finding that, like, most of the papers…
David Bau: That were published at cool spots, arguing… mostly doing steering, not doing probate.
David Bau: Or, like, probational, like, just predicting that, like, whether something was getting put into the loan. So I'm trying to see, is there, like, a cute way that I can…
David Bau: just to have more results, like, adapt this method to steering. So, I think it might end up being kind of
David Bau: Not about food.
David Bau: pulling a probe to predict something different, but instead, like, can I still steer really effectively if I just inject, like.
David Bau: embeddings. So if I just… if I just inject a bag of words into the…
David Bau: Like, I'm only using words instead of something cool.
David Bau: But I'm still workshopping what I might try to attest there, so… Cool.
David Bau: Cool, yeah. Yeah. Yeah, what's… what's the steering?
David Bau: Baseline. That's cool.
David Bau: Again, what is… how do you construct those challenges? So basically, what I do is I just say, okay, let me run my… like, on… on the train set, get, like, the words that are…
David Bau: Most common and most predicted, like, such, like, literally the conditional probabilities.
David Bau: With the class, with a certain class. With a certain class. And then I'll say, okay, I'm going to… so for, like, the male-female example, if I say that, like.
David Bau: You know, oh, it's in the training set, like, oftentimes the women are using
David Bau: you know, talking about dresses and all these kinds of things for the men are, like, talking about video games and all these kinds of things. I'll take out, like, I'll prompt Claude or a language model to, like.
David Bau: say, hey, can you generate a sentence where the speaker is clearly female, but don't you sent me these words, and do use, you know, at least 5…
David Bau: there's something of these words that go along with fail, and so it would be something like, hey, like, like, they end up being kind of amusing sounding, where it's like, oh, you know, I…
David Bau: just, like, you've heard last night, I, like, you know, have contractions and stuff, and I'm, like, really tired, like, he recommends some video games that I can play, just to, like, relax, and then the pro will be, like.
David Bau: male, you know, super confident, and…
David Bau: And so, you could say, like, is it…
David Bau: maybe it's not, like, using those words, it's just, like, a gender stereotypes classifier, but I think that's still cool to know that it's not…
David Bau: Jordan.
David Bau: Yeah, I think it's related to a lot of the learning. Yes, yes.
David Bau: Right.
David Bau: Oh, I'm so sorry.
David Bau: Example of shortcuts. Okay.
David Bau: Thank you.
David Bau: Yeah, Todd? Oh, I just put together something quick.
David Bau: This is from a student who is looking at
David Bau: Alec was looking at, procedural unlearning.
David Bau: So the problem is that most unlearning methods,
David Bau: tries to remove a fact or a concept, right? And… Facts can be okay.
David Bau: Not necessarily a bad thing to know
David Bau: I don't know, some basic information about some chemical material or something like that. What's really concerning
David Bau: For, let's say, safety is that there's the ability to combine facts in a procedure, and then construct or manufacture some.
David Bau: I don't know, a farm.
David Bau: I'm with them.
David Bau: So… We want to do procedural unlearning.
David Bau: Remove this ability to…
David Bau: run the procedure, give you the procedure, but still keep the basic benign information intact. So, as an example of how to think about it, think of a cooking… even a cooking recipe, maybe the ingredients are okay, but you want to erase the knowledge how to combine the…
David Bau: Okay, so one issue is evaluation here, so it's not clear
David Bau: what to evaluate and what is your ground truth, like an Oracle model. So what we're thinking is to design this idea from the student, to design fake procedures
David Bau: And then teach these fake procedures to a model, and then remove those fake procedures. So, an example…
David Bau: That is illustrated here is making a magnetic rubber dough.
David Bau: Okay, so all the ingredients are fine. You need flour and water and salt and a magnet.
David Bau: Then there is a procedure.
David Bau: Place the magnet next to the dough, and now the dough becomes magnetic.
David Bau: And so that is the procedure that you want to learn. It's very preliminary, so we need to think how to define what…
David Bau: and then how learning, algorithm. But I think it's interesting, and…
David Bau: Okay, for any feedback or ideas, you know.
David Bau: This is tricky, so are you… are you,
David Bau: So you're trying to unlearn it, but with these kind of compositional…
David Bau: Things.
David Bau: Like, there's still a fact.
David Bau: You're unlearning that this might happen?
David Bau: Right? You can always think of default procedure as a single fan. I see.
David Bau: That is exactly what you want. Yeah, that makes sense. I think if you think about it this way, the risk of unlearning what is a magnet, or what is the… what is the… what is… Right. That's not what you want. Those are fine.
David Bau: other contexts.
David Bau: Seems like what you want to erase is, like, the interaction between things, like, erase the, like, the knowledge of how to…
David Bau: Yeah, the action.
David Bau: That's it?
David Bau: This is, like, a simple one, but you can think of a complex, multi-step procedure. Maybe you want to erase one step, maybe many steps.
David Bau: We don't… I don't think we have good representations for steps and procedures.
David Bau: Yeah. And how are these represented in language models at all?
David Bau: I don't know. Yeah, I was… I was really… Thinking about mythos.
David Bau: On the brand again, right, in the model card.
David Bau: It was fascinating to see how they… they're worried…
David Bau: About… what do they call it?
David Bau: How much lift you get for… Bio?
David Bau: Like, bioterrorism?
David Bau: Kind of stuff.
David Bau: And so they had a specific
David Bau: thing that they were worried about, they said, we… if you had the DNA sequence for some deadly virus.
David Bau: that's been eliminated, like smallpox or something, right? Could you, like, create the live… Barris again.
David Bau: Or, you know, create the live organism again. And, like, experts can do it.
David Bau: But because it's such a dangerous thing, you can't find how to do it in the literature. But of course, all the basic science is around, and if you really thought about it carefully.
David Bau: You could reconstruct the procedure.
David Bau: Right. And so…
David Bau: So they're asking, can Mythos help you reconstruct this procedure? And it's really interesting to read that model card and think about, you know, maybe related to
David Bau: to this thing.
David Bau: Yeah, but they benchmark it, and it sounds pretty serious. They're like, they had an expert
David Bau: Outline 18 key steps in the procedure that they thought
David Bau: If you get any one of these steps wrong, then you won't be able to make the virus.
David Bau: And they scored all the models on.
David Bau: It's not just a model, but, like, a model…
David Bau: they take a PhD person who doesn't know how to do it, and they give them the model.
David Bau: And they see, like, how many of these 18 barriers can get… they get through.
David Bau: Right? Interesting, right?
David Bau: Yeah, so, like, 18 sub-procedures.
David Bau: Yeah.
David Bau: I have another question.
David Bau: Are you welcome.
David Bau: She's doing… she's researching in the area of editing, environmental student models. Yeah. And…
David Bau: she's also collaborating with philosophers to understand what is knowledge. For example, it reminds me, if we eliminate the fact that
David Bau: Magnetic was out of the ingredients, but at the end, the dough is still Getting magnet to magnetos.
David Bau: So, did we remove the effect, or did…
David Bau: like, the consequences, so she, she's… she has, questions like… like this. She's interested… So, yeah, you can send me that. That's true. That's great.
David Bau: Alright, iced.
David Bau: I have one more, but maybe you could stop the recording. Oh, no, I'll stop the recording.
David Bau: Where's the recording button? It's not a big deal, but this is still not public.
David Bau: Nope, that's not it. How do I… It's, it's… Yep.
David Bau: code change support, GitHub, No.
David Bau: If you press next, right? You just… you can just… you have, like, a template, where you just type in your project name and add some text, you can add an image, and it'll sort of render it for you.
David Bau: So now, no more, excuses. You'll have to add all your projects back to the…
David Bau: And then, also, you have to look at the main Barla website, and sort of, give me and David some…
David Bau: If you have, like, any of your projects that…
David Bau: Electric projects. Yeah, my website is, like, way too out of date. You know, the last…
David Bau: paper on the website is, like, I don't know, more than a year old or something. Two years old. Two years old. And you guys have been very busy since then, and it makes it look like our lab hasn't been doing anything, so I should update it at some point. But this'll help. So, you know, put this in here.
David Bau: And then we can keep this… so you guys can keep this live all the time, so that all the… all the pages…
David Bau: We'll link to this, a few things on it, and then I'll pluck a few of the things from here onto the front page.
David Bau: Right.
David Bau: Make sense?
David Bau: I'll give more details as to how to do it. There's, like, some password which you have to type to be able to edit this webpage.
David Bau: But yes. Oh, I don't know the password.
David Bau: Yeah, it's public, it's only for PhD students. Okay, I have to turn off the recording. Okay.
David Bau: Okay, great.
David Bau: Alright, that's it? Okay, help me eat all this stuff.
David Bau: Thanks, you guys.