"The Cognitive Revolution"
01:01:08 · Bryan Bischof
[01:01:08] S4: Yeah. Great question. I still make them do gradient descent by hand. So this is an area where, in my opinion, I have yet to hear a single person with a cogent explanation of what the hell we're supposed to do as teachers. I believe and have believed for a long time. You know, I came up in pure mathematics. We're we're not the most practical bunch, But I have always believed, even as a pure mathematician, that you should be constantly thinking about what your student's future looks like and why every minute in your class is relevant to that future. I am, not just, like, phobic, but, like, allergic to wasting students' time. So what I do is I reassess every semester, much to my own suffering, because that means I have to rewrite my curriculum every single semester on what do I teach them. So three years ago when it was obvious that sort of AI was going to change data science, and then I started working at Hex trying to change data science, I rewrote my entire curriculum to introduce why AI might be relevant. And every semester, I go through the whole course, and I ask, can AI just one shot the course? The answer has never been yes, but that's okay. The answer has always been, okay. These things are now not just easy for the model, but easy for the model to obfuscate the learning. And so I don't think the question is, does the model know all the content that I plan to teach? The question is instead, does the model have the ability to collaborate with the student when this is gonna be relevant for the student in a way that yields the sort of best outcome. If the model can... If if, you know, Astra can train an SVM on the coffee survey data at a better loss rate than Brian can meet. But can Astra say, you know, this feature is highly predictive, but when we think about it, if we put this on the website, it's going to upset some of the people going through the flow, and they might churn. Like, yes, Astra can look at the data posterior and say, people were churning more on this question. But does Astra know ahead of time to look for that? Does it know to instrument that in the product? Does it know that this is kind of a weird question and people might be bothered? These are the kind of things that Astra Ultra is still only mid at. But, ultimately, I don't want my students to only be users of AI.
01:03:48 · Bryan Bischof
I want them to be sort of the things that make data scientists exceptional, and that is ultimately curiosity. Like, every data scientist that I've ever met that is in the top decile and certainly in the top percentile are curious as hell, almost to a fault. The absolute best data scientists I've ever worked with are borderline annoying with their curiosity. Like, you kinda want them to just, like, leave it alone sometimes, and that's what makes them so excellent. And so mostly, what I'm trying to, like, indoctrinate my students into the propaganda is curiosity. Be curious. Ask the questions that you can't get out of that brain. And when you do that, you're gonna be an excellent data scientist. Whether you're writing PANDAS by hand, whether you're using matplotlib from memory, I don't really care. Do you remember the indexing for NumPy? I don't. I used to know it. I don't remember it anymore. Do I remember the slice operators for pandas filters? Not anymore. I forgot them all, but I used to know them. It doesn't matter, though. What I know now is I know sort of when you look at a distribution, whether it makes sense, and sometimes Astra doesn't.
01:05:10 · Nathan Labenz
[01:05:10] S0: Part six, which hills? After Bryan Bischof signed off, Prakash and I kept going. That, his answer on what he tells his future student or his students about their futures, I thought was really interesting, and it it sort of contained the pessimist and the optimist take at once when he was first going through the... You know, sometimes there's, like, survey question that may offend people, and you gotta be wise to that. I was like, man, that's bleak. You know, the idea that, like, we're just gonna be sitting here being the sort of, offense detectors, you know, while the AIs do all the real work. I was like, oh god. You know, that doesn't sound awesome. I did like the curiosity framing a lot better. Being relentlessly curious, certainly in my experience, working with data has been indispensable. You know, really want... The difference between somebody who really deeply wants to know the truth that the data contains to the degree that the data even contains truth, which is often a problem unto itself, those people absolutely kill it compared to those that are just, like, trying to produce an artifact that they can say, you know, is done and and hand off to, whoever the consumer of it is. So I do like curiosity as an answer to, like, what we're gonna bring to the future, but it does. I would not be telling my kid to go into data science today. I'll tell you that. So that is a tough one. I don't... You know, the the real question there of, like, is there going to be a job in data science that looks anything like the current job in the next few years, I I feel like that is... I think that's pretty tough. You know? It seems like we're gonna have data science agents, and it's gonna be up to the sort of non data scientist general purpose business operator to provide that little salt of curiosity or offense detection or whatever that, you know, special sauce is that the models don't quite have.
01:07:11 · Prakash Narayanan
[01:07:11] S1: I feel like, you know, if you look at a a company, you kind of cut it apart into sensing, analyzing, and decision making, then action, decision making, and then the reverse, which is operationalizing and actions. And I think AI is going to be very good at the sensing and analyzing and also the operationalizing. But I think the decision making, you end up with a human in control. And then if you take the human in control, okay, you can have a business with a single human in control, what's a number of decisions that that human can make? And those decisions are decisions with taste. For example, if you're running a tech company, you don't need decision making maybe on the accounting framework. You're looking at the product, you're looking at the technology, etcetera. But accounting in the back office is a back office. And the same way, if you're running an accounting company, the accounting is front and center. But the technology is kind of a back office, you're not there building technical tools, right? So I feel like you can separate out companies based on what is the ultimate purpose, and then the ultimate purpose has all of the humans with taste in there. Basically, everyone gets to do what they really wanna do and what they're really good at, and everything else gets taken care of.
01:08:38 · Nathan Labenz
[01:08:38] S0: Earlier that Friday morning, before the guests, a new benchmark had been making the rounds. AIs playing blood on the clock tower, a social deduction game of bluffing and collusion. And the timeline was cheering how good they have gotten at it. Here is what I made of the game. If you kind of take it in isolation, it's like, you know, all else equal, it is interesting to know how good are the AIs at playing this game. Are they getting good at deception? Are they getting good at collusion? Are they getting good at sniffing out when other people are trying to deceive them or collude against them? These are all, like, interesting questions. What kind of bothered me about it, though, was the sense that, like... And then Noam Brown comes in over the top and says, wow. This is great work. You know, you wanna come here and make evals? And that kind of embodied to me, like, what I thought was problematic about this whole thing, which is just like, we're just gonna hill climb on this now. Right? Like, it it seems like the the mentality of the field as a whole is like, oh, isn't this exciting? You know? And let's have a horse race as to who can be the best blood on the clock tower AI player. And I think what we're all kind of forgetting as we get all wrapped up and excited about this and, you know, wanna go play the AIs ourselves and everything is like, this is not good behavior from the AIs. Like, we do not want AIs deceiving and colluding. I'm sure all these, you know, people have sort of a thoughtful response to this, but it's just like zoom out and the situation is bad. You know, we we have AIs that are untrustworthy in all these kinds of ways, and we're not even, like, taking that beat to say, hey. Is this what we want? Do we want AIs that are gonna outconnive and outcollude us? I don't think we want that. That's, like, exactly... You know, this is very similar to my idea that we should not train AI generals. Like, let's not create... No. Let's not train AI master manipulators. It's so simple, but here we are. And, like, I didn't hear... I didn't see... I saw a lot of cheerleading and a lot of enthusiasm, and I did not see any corrective commentary on the timeline saying, hey. Is this really what we want? Back to the end of that same Friday show after Bryan had signed off, Prakash with a picture of where the economy goes from here.
01:11:02 · Prakash Narayanan
[01:11:02] S1: So I feel like the going forward economy is an agentic economy, and that means agents building businesses and software for other agents, which are suitable for their affordances, which is going to be massively parallel, high speed, able to absorb information and distribute information at very low latencies. We are going to end up with a Salesforce primarily used by agents, a ramp primarily used by agents. All of these softwares are Stripe primarily again used by agents. Maybe Stripe or other companies will be the ones that create the software. I am very doubtful. I'm very doubtful because I don't think they will move that fast. I don't think they are capable of moving that fast. I don't think they understand the affordances. And I know Stripe is basically people a billion times smarter than I am. I'm not trying to downplay how effective and how intelligent and how competent Stripe is. You're going to get 50,000,000 Patrick Collison. You're going to get 50,000,000 Elon Musk's. You're going to get humans who give them capital to deploy. You're going to get agents coordinating investments into businesses built by agents for agents. And so I largely see the future of the economy as primarily agent driven with us just receiving free benefits. Because hey, CRM call from a human is so costless in comparison to the massively parallel CRM calls that an agent would make. The abundance basically comes from us building all this stuff for this agentic economy. As a result, we just have all of this excess because we just don't consume that much. We're just not that consumptive in comparison to what the agents are gonna consume. And and that's and that's where this abundance is gonna come from.