Speaker 1: Natasha read Taylor.
Speaker 2: I'm not sure how much you think about students using
Speaker 2: AI to cheat, or at least to get an edge
Speaker 2: in the classroom, but there was some interesting live research
Speaker 2: that emerged recently from Brown University. An economics professor made
Speaker 2: his students take a final exam in person after suspecting
Speaker 2: rampant cheating on the take home midterm. I want to
Speaker 2: ask you to guess the delta between the average score
Speaker 2: on the midterm and the average score on the final.
Speaker 3: Well, I saw this story, and it was a massive delta.
Speaker 4: Me too, Me too.
Speaker 5: I gotta say fifty percent.
Speaker 4: I didn't see it.
Speaker 2: The average score on the midterm was ninety six percent,
Speaker 2: the average score on the final forty eight point six percent.
Speaker 3: But there was that one kid who like, actually did
Speaker 3: he like got an F and then it went to
Speaker 3: like a D or something like that.
Speaker 1: Welcome to Tech stuff.
Speaker 2: I'ma's Volosian and this is the Week in Tech where
Speaker 2: I'm joined by three of the world's most clubbed and
Speaker 2: reporters to break down what's really happening in tech right now. Today,
Speaker 2: we're joined by reed Albrigotti, Take editor a Semaphore, Taylor
Speaker 2: Lorenz of user mag and Natasha Tiku, tech reporter at
Speaker 2: The Washington Post's welcome all.
Speaker 4: Hey everyone, thanks for having us.
Speaker 3: Good to be here.
Speaker 1: So Natasha.
Speaker 2: Nick Thompson, the former editor of Wired and current CEO
Speaker 2: of The Atlantic, came on tech Stuff in early January
Speaker 2: this year to give his predictions for the year, and
Speaker 2: one of them, the top one, was that this will
Speaker 2: be the year of the first legit AI catastrophe. Do
Speaker 2: you think this was the week when it happened?
Speaker 5: No, I don't, But I think this is one of
Speaker 5: many weeks in which we got a pretty clear picture
Speaker 5: of what the next few years are going to look like,
Speaker 5: which is messy, disastrous. Everybody taking incidents to try to
Speaker 5: support their own narrative about where AI capabilities are headed,
Speaker 5: and you know, how to protect ourselves.
Speaker 1: So what actually happened?
Speaker 5: So I mean this this is a really good one
Speaker 5: to dig into. I think because last week Hugging Face,
Speaker 5: which is kind of like get hub but for AI,
Speaker 5: like they have data sets, models, it's very open source,
Speaker 5: funded by all the usual vcs. They put up this
Speaker 5: blog post that they had this unprecedented security incident that
Speaker 5: for the first time, an AI agent had broken into
Speaker 5: their system and tried all of these methods to get
Speaker 5: access to some data sets, and it did it by
Speaker 5: uploading like a malicious data set that would allow it
Speaker 5: to kind of like get into the system through their
Speaker 5: data processing pipeline and then.
Speaker 1: Like a trojan horses.
Speaker 5: It were yeah, yeah, And at the time, Hugging Face said,
Speaker 5: you know, we think that this looks like an agent
Speaker 5: that's used for security and RESO, but we don't know
Speaker 5: what LLLM it is. We don't know what model. So people,
Speaker 5: you know, obviously this is coming in the middle of
Speaker 5: all of this talk about Chinese open source models, how
Speaker 5: they're proliferating, how they're getting more capable, so nobody knows,
Speaker 5: you know, what's happening. Then this week on Tuesday, open
Speaker 5: Ai puts out a blog post and they're like, oh, hey,
Speaker 5: that was us, and they said, you know, they discovered
Speaker 5: after the fact that while they were testing both GPT
Speaker 5: five point six Soul and some new unreleased model, they
Speaker 5: were testing it on its ability to do these like
Speaker 5: cyber offensive attacks, and they gave it this benchmark called
Speaker 5: exploit benchmark that's supposed to test its ability to like
Speaker 5: break into, you know, to hack into systems. The AI agent,
Speaker 5: which was allowed to work you know, autonomously for a while,
Speaker 5: decided to cheat on the test and figured a way
Speaker 5: out of the sandbox that open ai created, like based
Speaker 5: you know, testing environment, got into the Internet, decided to
Speaker 5: break into hugging Face. And open ai is so lucky.
Speaker 5: They are so lucky that it was another AI company
Speaker 5: that has invested in in the industry and the narrative
Speaker 5: because this is I mean, even the the CEO of
Speaker 5: hugging Face was like, yeah, don't do this to us.
Speaker 5: It's illegal. You know, they had notified law enforcement. And
Speaker 5: now the announcement that it was open ai came as
Speaker 5: part of a partnership between hugging Face and open Ai,
Speaker 5: which was obviously you know, put together after the fact.
Speaker 2: So the model had essentially determined that it would be
Speaker 2: easier to go and steal the answer from hugging Face
Speaker 2: than to come up with it itself, and therefore did that.
Speaker 5: Yeah, it knew that it was being tested on a benchmark.
Speaker 5: I think they even maybe you know, uploaded the benchmark
Speaker 5: from hugging Face and it figured out it was supposed
Speaker 5: to not be able to access the Internet.
Speaker 2: But that's a little I mean that That's the part
Speaker 2: why Zone zoned in on the word catastrophe, because the
Speaker 2: bit where is not supposed to be able to access
Speaker 2: the Internet where where, but it does. I mean that's
Speaker 2: got a kind of cinematically spooky quality.
Speaker 4: Well, we failed to secure the sandbox, right, Yeah, exactly.
Speaker 2: Failed to secure the soundbox. Premagine preschool is hearing that
Speaker 2: in the nineties.
Speaker 3: What are you doing?
Speaker 5: Yeah, you zeroed in on exactly the right part of
Speaker 5: the confusion around this and why people are using it
Speaker 5: to like support you know, various narratives. You know, people
Speaker 5: who are very worried about AI super intelligence and AI
Speaker 5: capabilities growing really fast. You know, we're talking about this
Speaker 5: as like this is what we've been warning you about.
Speaker 5: It's a rogue AI. You know, we gave it a
Speaker 5: simple set of instructions and it decided to do it
Speaker 5: in a way that you know, was not was not
Speaker 5: what its developers would want. But many other cybersecurity professionals
Speaker 5: were like, why did you not have a properly configured sandbox,
Speaker 5: Like you could be using an air gap, which would
Speaker 5: not have allowed it to break into an Internet system.
Speaker 5: They allowed it to use like package installs, and I
Speaker 5: think just a lot of cybersecurity professionals found this like
Speaker 5: very sloppy. This is not you know, so it's not
Speaker 5: it's not as though, oh my god, we can't contain
Speaker 5: AI and it's doing some you know, like you know,
Speaker 5: people were throwing around the word sentient machines and that's
Speaker 5: not what this is.
Speaker 3: Well, on top of that that the model had no safeguards, right,
Speaker 3: there were no you know, it was a really raw,
Speaker 3: you know model, So.
Speaker 5: Yeah, they were testing its ability to to do these
Speaker 5: kind of exploits, so they didn't have any safeguards on.
Speaker 5: But then they just didn't have like traditional cybersecurity controls
Speaker 5: that you would have in place when you're testing. And
Speaker 5: this is something people had been warning against that maybe
Speaker 5: the biggest danger is not when your systems are in deployment,
Speaker 5: because then you have all your safeguards up. It's refusing
Speaker 5: a lot of requests, but it's actually during this kind
Speaker 5: of testing environment. And I'll just say one more thing.
Speaker 5: This incident followed another blog post last week from open
Speaker 5: ai about how they had to stop even just testing
Speaker 5: an internal model because it like they weren't monitoring it
Speaker 5: and it started doing all of these things because they
Speaker 5: let it go on and on, because that's you know,
Speaker 5: that's how these models get more functional. They're able to
Speaker 5: like think and try thousands and thousands of different ways
Speaker 5: of breaking into things.
Speaker 3: Well, I think that I like agree with you, Natasha
Speaker 3: that it's not this isn't like some you know, code
Speaker 3: read catastrophe at all. But I do think there's something
Speaker 3: here that is sort of like you know this, The
Speaker 3: AI security people do have a point on this. It's
Speaker 3: a point they've been making for a long time, which
Speaker 3: is that like, when you tell an AI model to
Speaker 3: do something, it will, like the smarter they are, the
Speaker 3: more they're just going to find some shortcut to do that. Right,
Speaker 3: this is the whole argument. But this is the argument
Speaker 3: with like the you know, the paper clip argument, Right,
Speaker 3: if you tell AI to make paper clips, it will
Speaker 3: just ultimately like find the best way to do that,
Speaker 3: even if that means like turning humans into paper clips.
Speaker 4: So but also but also read, I think that that
Speaker 4: is a is a sort of ridiculous concept because any
Speaker 4: AI that's smart enough in that way would also have
Speaker 4: the reasoning to know, like they're not going to turn
Speaker 4: the world into paper clips. That's not the goal. Like,
Speaker 4: I don't know, I think a lot of this like
Speaker 4: kind of like what Natasha was saying before is like
Speaker 4: a little it's being taken by these people. It's like
Speaker 4: they made a sloppy security environment, the AI went and
Speaker 4: solved the problem as it should have or whatever, And
Speaker 4: and that's not evidence of it being super sentient or
Speaker 4: even this being a particularly good AI agent. Mostly it's
Speaker 4: just that like they didn't set up this test very responsibly.
Speaker 3: Definitely agree, I agree with but again, like I'm not
Speaker 3: disagreeing with you at all. I just think there is
Speaker 3: one sliver of this that is worth sort of looking at,
Speaker 3: which is like, you know, they do sort of find
Speaker 3: these shortcuts, so and in this case, the shortcut meant
Speaker 3: like breaking the law and hacking into hugging face, so
Speaker 3: I and I think the other thing to remember is
Speaker 3: that like we still don't know how these models work,
Speaker 3: Like no one has been able to figure out like
Speaker 3: what are these neurons inside these models actually doing? And
Speaker 3: I do I think the big question is like, as
Speaker 3: they get more and more capable and more powerful, is
Speaker 3: it is it enough to just put you know, the
Speaker 3: safeguards in place, which again we're not like they didn't
Speaker 3: what I'm curious about because this this this Natasha, you
Speaker 3: mentioned there were two open Ami models of working on this.
Speaker 3: One was a publicly available one and one has not
Speaker 3: yet been released. So could could I Well, probably not me,
Speaker 3: but like could somebody has a bit more sophisticated than
Speaker 3: me use open Ami to hugging face exactly like this
Speaker 3: went down like using the publicly available models. Or was
Speaker 3: there something special about this unreleased model? Well, no, because
Speaker 3: there were no safeguards like the publicly released one will
Speaker 3: have will have controls on it that will not let
Speaker 3: you do this unless you can jail break it, which
Speaker 3: you know is getting more difficult to do, right.
Speaker 5: Okay, I will say, I mean, I do think it's
Speaker 5: important to note that this is like many people are
Speaker 5: interpreting this as like we were right about the paper
Speaker 5: clip maximizer. You know, this is happening in this way.
Speaker 5: But I think there's a way that because they conceived
Speaker 5: of it, of the problem this way, they approached security
Speaker 5: in a certain way, like you could have been thinking
Speaker 5: like a cybersecurity professional the whole time and not thinking
Speaker 5: about AI alignment, aligning it with with human values. And
Speaker 5: one of the things I mentioned that like prior open
Speaker 5: ai blog post. What they realized is that they didn't
Speaker 5: have sufficient monitoring systems. When you let a system go
Speaker 5: on and on for a really long time, they weren't
Speaker 5: watching it closely, like there are simple mechanisms, you know.
Speaker 4: It doesn't even go.
Speaker 5: Back to what Reid said about knowing what's happening on
Speaker 5: the neurons, Like they weren't even watching what it was
Speaker 5: doing during the security test in a secure way.
Speaker 2: Ax out there saying that saying maybe open Aye wanted
Speaker 2: to demonstrate anything mythos can do, we can do better,
Speaker 2: or is that it's great marketing for opening I.
Speaker 5: For sure, I think the way that they wrote the
Speaker 5: blog post at the top, it says like we are
Speaker 5: treating this as an unprecedented cybersecurity incident that shows the capabilities.
Speaker 5: And I will say, like, I don't think anyone's arguing
Speaker 5: that these lms make people much much much better at
Speaker 5: hacking and that it's capable of improving and enhancing and trying,
Speaker 5: you know, cyber offensives that humans can't do. But everyone
Speaker 5: I talked to was like, this is not an example
Speaker 5: of enhanced capabilities. This is an example of like kind
Speaker 5: of a sloppy testing environment.
Speaker 3: I mean, we all agree on that. I think it's
Speaker 3: just it just sort of highlights this question about the future,
Speaker 3: which is like, when these models become more powerful, do
Speaker 3: we need do we actually have to understand how they work?
Speaker 2: Has this has this effected the kind of policy discussions
Speaker 2: in Washington? What's how's the White House and crapsios and
Speaker 2: others around around Trump and the sort of technology advisory
Speaker 2: space reacted to this? Is this is this like grist
Speaker 2: to the mill of the of the deceleration lists or
Speaker 2: is it?
Speaker 5: I would say they were very busy with They're still
Speaker 5: very busy with Chinese open source models.
Speaker 4: So that was the focus.
Speaker 5: This didn't I don't think it got the attention of
Speaker 5: you know, the the same White House folks they were
Speaker 5: putting out. They were putting out information about distillation, which
Speaker 5: we talked about last week, and you know, did did
Speaker 5: Chinese open source models steal from quote unquote steal from
Speaker 5: from anthropic? But there were a number of regulators that said, like,
Speaker 5: it's time to legislate, it's time to get involved, Bernie
Speaker 5: Sanders and many others. So yeah, it's feeding into this
Speaker 5: growing sense of urgency around having some safeguards in place,
Speaker 5: But I just want to say, like another plea for
Speaker 5: people to start thinking about monitoring like downstream usage, like
Speaker 5: kind of what reads saying about the neurons. Actually, now
Speaker 5: that I go back to it, because what they do
Speaker 5: when they try to figure out the models like reasoning
Speaker 5: is look at its chain of thought, like look at
Speaker 5: this little like they give the model a scratch pad
Speaker 5: and it says like, ah, it might be faster to
Speaker 5: go to directly to hooking face, let me break in
Speaker 5: or whatever, and we don't even know if those that
Speaker 5: text actually reflects its interior thought. You know, it's like
Speaker 5: decision making process. So it's true, this.
Speaker 2: Is the dome worry mom and dad, I'm doing my
Speaker 2: homework upstairs. Basically, of AI, it's thinking out loud.
Speaker 3: It's thinking out loud, and that is actually this is
Speaker 3: now this, this is actually the state of the art
Speaker 3: in like AI safety is like looking at it's thinking
Speaker 3: out loud.
Speaker 2: But but it can think out loud deceptively potentially yes.
Speaker 3: And also you're not looking at it's thinking inside. It's
Speaker 3: like if you if you only base what somebody's thoughts
Speaker 3: are and what they say, then that's you're clearly not
Speaker 3: getting all their thoughts right and open.
Speaker 5: I wasn't even reading that you know, so so.
Speaker 3: Right for this experiment.
Speaker 5: They were let's let's yeah, let's let's clean it up.
Speaker 2: So here's my favorite detail from this whole story, which
Speaker 2: actually comes from Forbes, of all places. Quote Hugging Face
Speaker 2: said it had first attempted to use an undisclosed AI
Speaker 2: model from leading US Labs to defend against the attacking
Speaker 2: AI agent, but the guardrails around that model cyber capabilities
Speaker 2: stemied its response teams work. The company said it instead
Speaker 2: wound up using an open source AI model from Chinese
Speaker 2: company Zai to carry out its defense.
Speaker 4: Which is I think exactly why these type of safeguards
Speaker 4: that we're sort of putting in our American models are
Speaker 4: deranged and we need access to open source Chinese models.
Speaker 4: I mean, I think even just from a security standpoint.
Speaker 5: Right, I mean, many cybersecurity people were saying, you know,
Speaker 5: this shows the need to like have policies that help
Speaker 5: proliferation of open source models, because as like, history has
Speaker 5: shown that cyber defense capabilities rely on open source models,
Speaker 5: So it doesn't need to be the Chinese if we
Speaker 5: had been investing in open source AI or if our
Speaker 5: leading companies were putting out open source AI models, it
Speaker 5: could be us. But they did talk about the need
Speaker 5: to make sure like hospitals are adopting the latest open
Speaker 5: source AI models in order to make sure they're capable
Speaker 5: of defending if such a thing happens to them.
Speaker 2: Okay, lightning round, before we go to the break, what's
Speaker 2: the one thing from each of you that somebody's listening
Speaker 2: to this podcast needs to know to sound smart.
Speaker 1: At the weekend?
Speaker 4: About Kimmy K three, I mean, I think everybody wants
Speaker 4: to act like, oh, it was distilled. It was definitely distilled.
Speaker 4: I mean, this is a model that I believe was
Speaker 4: testing and also if I might be getting this wrong,
Speaker 4: but I also think that it's this model also has
Speaker 4: capabilities that our American models don't have. Or it is.
Speaker 1: Kimmykre right, Yeah? Which is what?
Speaker 2: Which is a new model from a Chinese lab that's
Speaker 2: kind of outperform throwed on various benchmarks and therefore everyone
Speaker 2: kind of lost their mind?
Speaker 1: Or what's the what's the like?
Speaker 3: It's sort of on par with with the with the Frontier.
Speaker 3: I wouldn't say it's outperforming, and I don't know if
Speaker 3: I do, you mean it has capabilities the US models
Speaker 3: don't have, or they use training techniques that are sort
Speaker 3: of novel and that you know, the US labs could
Speaker 3: learn from.
Speaker 4: Basically, Like what I was reading yesterday, I was saying
Speaker 4: that like the architecture of the model is superior to
Speaker 4: to whatever it was claiming to be distilled on, and
Speaker 4: that these are not developments that they could have stolen
Speaker 4: basically because it is they have developed further on you
Speaker 4: know whatever. I guess it's like the latest sort of
Speaker 4: Claude model. So I just I think this idea that like, oh,
Speaker 4: these are all just like t MoU versions of Claude.
Speaker 4: I don't think that that's a full picture. Yes, I imagine
Speaker 4: that they're doing some level of distilling. They all do. Like,
Speaker 4: I just think this is like an industry.
Speaker 3: No, there's an innovation there. Like I think what you're
Speaker 3: saying is like you can distill from one of these
Speaker 3: large models in order to shortcut the training, but there's
Speaker 3: still innovation in the training in being just because by
Speaker 3: virtue of being hamstrung on the amount of compute they have,
Speaker 3: they have to they have to take these shortcuts and
Speaker 3: they have to find new and novel ways to train
Speaker 3: with less compute, and so there's innovation there for sure.
Speaker 2: When we come back, read takes us to a garage
Speaker 2: full of automated pickup trucks carrying anti drone weaponry.
Speaker 1: Welcome back.
Speaker 2: So I mentioned in the first story that Nick Thompson
Speaker 2: predicted this would be the year of the first legit
Speaker 2: ai catastrophe. Our panelisting that that that prediction has not
Speaker 2: yet been proven out.
Speaker 1: My prediction was was the year of the robot Read.
Speaker 2: You had a story this week about a company that,
Speaker 2: according to the headline semiphore quote, wants to turn robotics
Speaker 2: into child's play.
Speaker 1: What's the company and what's it do today? And what
Speaker 1: does turning robotics into child's playing?
Speaker 3: Yeah, so the companies applied intuition and I went visited
Speaker 3: their their headquarters on Monday and took a tour through.
Speaker 3: You know, they have this garage where there's really just
Speaker 3: a bunch of cars in you know, various states of disrepair,
Speaker 3: so like you just see all the electronics around a car. Essentially,
Speaker 3: what they've been doing is working for a lot of automakers,
Speaker 3: most of the big automakers actually and building the technology
Speaker 3: for them everything from like the software platform, infotainment systems,
Speaker 3: et cetera, to the self driving tech and they've been
Speaker 3: making a lot of money doing that. They've now expanded
Speaker 3: into like farming and other you know, construction, other areas.
Speaker 2: So basically, if you're not Tesla, your license applied intuition
Speaker 2: evan technology.
Speaker 3: Yeah, exactly. Like if you're an automaker and you realize
Speaker 3: you don't have the tech talent that Tesla has, but
Speaker 3: you want to have a Tesla like product, you kind
Speaker 3: of you hire this this company rather than try to
Speaker 3: do it in house. But and that's been a good
Speaker 3: business for them. But what the news was on Monday,
Speaker 3: I wrote I wrote about it on Tuesday morning was
Speaker 3: that they've come out with this new open platform that
Speaker 3: you can you know, it's a product that you can
Speaker 3: pay for and you can actually use all of their
Speaker 3: data and all the infrastructure they built over almost a
Speaker 3: decade and train your own robots. Essentially. They would call
Speaker 3: it physical intelligence, which is this jargon term for like
Speaker 3: any machine that can be programmed essentially. But like their
Speaker 3: example of is, like you have a kid who wants
Speaker 3: to make an autonomous lawnmower, they could do it now,
Speaker 3: whereas it used to take a team of like eight,
Speaker 3: you know, PhD engineers to do all the computer vision
Speaker 3: and train models, et cetera. And I played around with it.
Speaker 3: I mean, it's still I would say, this is not
Speaker 3: child's play yet, but it's moving in that direction. And
Speaker 3: I just think it's really interesting because it's like, I
Speaker 3: don't know about you guys. I play around with like
Speaker 3: Raspberry pies with my with my ten year old, you know,
Speaker 3: and we try to hack stuff together, and I'm like, oh,
Speaker 3: this is cool. Like this allows to do even more
Speaker 3: fun projects. And then I think sort of create more
Speaker 3: entrepreneurship around AI for the physical world.
Speaker 2: Okay, but so I could go and buy a regular
Speaker 2: NORM from home depot and then use this platform to
Speaker 2: turn into a robot.
Speaker 3: Yeah, well you could put like a GoPro on it
Speaker 3: or something, right, and go film film yourself mowing your
Speaker 3: lawn and then upload the video into this platform and say, okay,
Speaker 3: I want you to basically do like an evaluation and
Speaker 3: train an AI model that is the perfect model for
Speaker 3: mowing my lawn, you know, and be done in a day.
Speaker 5: Essentially, you provide the hardware and they provide the intellig This.
Speaker 3: Is just a software platform. There's no they don't do
Speaker 3: they don't do any hardware, so you make the hardware
Speaker 3: well or like it's really kind of right now aimed
Speaker 3: at like startups, right like if you were it would
Speaker 3: be like a more like a company probably trying to
Speaker 3: build a n autonomous lawnmower at this point. Like it's not
Speaker 3: at the point where you know, but.
Speaker 5: But it's only for hardware, Like it's not. It's not
Speaker 5: like you wouldn't use this just as an online model.
Speaker 3: It's just a software platform that you can train like
Speaker 3: computer vision models. Look, I'll tell you what I want
Speaker 3: to do with it. Do you want? This is embarrassing,
Speaker 3: but yes, but I didn't put I didn't put this
Speaker 3: in the article because I was worried about animal rights people.
Speaker 3: But I'm going to say it anyway. I have a pool.
Speaker 4: I have a pal to a vegan on this call.
Speaker 3: I have a pool. And no, it won't involve eating
Speaker 3: any animals. I mean I could go there, but but
Speaker 3: I have this pool. And the ducks in my neighbor
Speaker 3: they fly into the pool and they just make a
Speaker 3: huge mess. They just poop all over the pool, cover
Speaker 3: all over everything. Right, And I'm always trying to get
Speaker 3: the ducks to stay away, and there's YouTube videos like
Speaker 3: I'm not the only one who has this problem. I
Speaker 3: just want to take a little Raspberry Pie, program it
Speaker 3: to roll around and just scare away the ducks. Maybe
Speaker 3: have some sort of like NERF gun attachment that shoots
Speaker 3: a little NERF gun. Is that bad?
Speaker 5: Like?
Speaker 3: Am I a bad person for wanting to do that?
Speaker 3: That's the product I want to build.
Speaker 4: I think that's honestly amazing. And you're not going to
Speaker 4: hurt the ducks, And I don't want to hear that
Speaker 4: I just want to scam. You're not trying to build
Speaker 4: an autonomous weapon. I think. I think that sounds great.
Speaker 5: Isn't this the Tony Soprano problem?
Speaker 1: He liked it. I think he liked the ducks. I
Speaker 1: think he Yeah, I.
Speaker 5: Support this, Yeah, but it also sounds like you're going
Speaker 5: to have to do all the work. I don't think
Speaker 5: that this that applied intuition is going.
Speaker 1: To help well.
Speaker 3: So I buy the g So, I buy the go Pro,
Speaker 3: I mean sorry, I buy the I buy the Raspberry Pie,
Speaker 3: and I find some little one of those little robe
Speaker 3: I even I think I have most of the hardware
Speaker 3: I need. I just I just need to upload the
Speaker 3: video and essentially train a model to recognize ducks and
Speaker 3: you know, fire a little a little nerf gun in
Speaker 3: its general direction, not at it to hit it.
Speaker 2: Read for the for the for the layman. And I
Speaker 2: put my hand up here, like what is the what
Speaker 2: is the problem in robotics? This is solving like what what?
Speaker 2: What isn't what was not possible yesterday that maybe possible
Speaker 2: today because of this development.
Speaker 3: Well, I I don't think this actually is not it's
Speaker 3: not about like this isn't like an advancement in AI
Speaker 3: or robotics. This is just democratizing it. I know you
Speaker 3: all hate that word, but it's but it's letting people
Speaker 3: like me or startups you know, with with fewer resources
Speaker 3: get into this field.
Speaker 2: To use natural language to to essentially train physical robots.
Speaker 3: Right exactly like I want to I want to scare
Speaker 3: the ducks away? Can you create a model that will
Speaker 3: recognize the ducks and you know, do whatever. So it's
Speaker 3: like a move in that in the direction of like
Speaker 3: you know, we'll have more than just the robot vacuums
Speaker 3: in our house, like we'll have a and you know,
Speaker 3: eventually humanoids, but that's farther down the line.
Speaker 2: You talked about something your story called the missing link
Speaker 2: effect in robotics. What is that and how does that
Speaker 2: kind of play into this story?
Speaker 3: Yeah? That was sort of like this this concept that I,
Speaker 3: you know, you're essentially like you can create. There's robotics
Speaker 3: everywhere now, right, Like you go into a biotech lab.
Speaker 3: I was in Boston last week, right, there's there's robotic
Speaker 3: pipe heading machines in all these labs. Now, there's there's
Speaker 3: all sorts of automation that you know, centrifugas, et cetera,
Speaker 3: that are testing DNA and doing all sorts of stuff.
Speaker 3: But like, you still need a lot of people to
Speaker 3: run these labs because they're still built for humans. So
Speaker 3: it's it's not like it's not a step chain. The
Speaker 3: robotics gives you these incremental gains and efficiency. But my
Speaker 3: point was like, let's say, and this is what a
Speaker 3: lot of lab technicians say, not all of them agree,
Speaker 3: but like, let's say you could have a humanoid robot
Speaker 3: that has like five fingers and can do you know,
Speaker 3: any sort of dexterous tasks that a human can do,
Speaker 3: and you can just program it. All of a sudden,
Speaker 3: every single biolab as they exist today can become completely automated,
Speaker 3: and then you can just have these things run twenty
Speaker 3: four to seven and that would like allow scientists to
Speaker 3: run more experiments that they would never do today that
Speaker 3: just there just wouldn't be the resources to do today,
Speaker 3: And so that becomes like an exponential gain in scientific discovery,
Speaker 3: and that sort of principle of like you plug it once,
Speaker 3: you get once these robots become more general purpose and
Speaker 3: you can sort of plug them into you know, all
Speaker 3: the technology we already have, you you get exponential gains
Speaker 3: versus just Okay, now I got like a ten percent
Speaker 3: increase in efficiency, you're twenty percent.
Speaker 2: So right now, for example, you could have a robot
Speaker 2: that shot sort of nerf arrows at the ducks, but
Speaker 2: then you have to go and pick them up yourself
Speaker 2: and reload the robot, whereas in the future you could
Speaker 2: also be a general purpose robot collect the arrows.
Speaker 3: Sure, yes, you do that, but then think about that
Speaker 3: in like at an industrial scale, right. Building construction is
Speaker 3: a great example, Like there's all this automation and construction, right,
Speaker 3: but you still need a ton of people, and that
Speaker 3: creates like there's labor shortages, that creates like the bottleneck essentially. Right,
Speaker 3: But once you can fully automate things, the things move
Speaker 3: just exponentially faster. And that's like to me, that probably
Speaker 3: in my view, and you might you all might disagree
Speaker 3: with this. I think that's where humanoid robots are valuable.
Speaker 3: Like people make this argument that, well, why do you
Speaker 3: need humanoids, Like you could just create a special robot
Speaker 3: for each individual purpose, but it just it doesn't work
Speaker 3: that way, Like.
Speaker 2: The industrial wood world was designed to be navigated by
Speaker 2: real humans and therefore humanoid robots and are actually they
Speaker 2: fit into the system we've built better than any other
Speaker 2: types of robots.
Speaker 3: Yeah, totally. I think that's I think they may be
Speaker 3: a necessary step to get to like a thick full
Speaker 3: full automation and like you know, fully roboticizing you know,
Speaker 3: the economy.
Speaker 5: But wait is the miss So the missing link is
Speaker 5: the technology that will make humans obsolete? Is that?
Speaker 4: What?
Speaker 5: What is the missing link?
Speaker 3: I still I mean, yeah, that's the that's the and
Speaker 3: and look that was I put that in the articles
Speaker 3: down like that. You know. The obvious next question is like, Okay,
Speaker 3: what does that mean? For human labor. I personally like,
Speaker 3: first of all, this is this is a ways of way, right,
Speaker 3: Like this is not gonna happen overnight. But second of all,
Speaker 3: I think that you just think about the economic growth
Speaker 3: that comes from that world, Like I don't. I'm not
Speaker 3: worried personally that humans are just gonna have nothing to
Speaker 3: do like that that just is not gonna happen. We'll
Speaker 3: find stuff to do.
Speaker 2: You did refer on your story to the Nobel Prize
Speaker 2: winging economist Darren Astimolgulu's research, who found that every industrial
Speaker 2: robot added per thousand workers measurably reduces employment and wages
Speaker 2: in the community where it lends.
Speaker 3: Yeah, I just disagree, though. I just think that you're
Speaker 3: not You're not thinking through all the ancillary benefits of
Speaker 3: being able to do this kind of stuff and then
Speaker 3: the economic growth that will happen.
Speaker 2: I mean, but according according to according to the quote,
Speaker 2: is measurably reduced.
Speaker 3: Right. But like economists, they can only measure what already exists, right,
Speaker 3: And this is this is the mistake everybody. It's like
Speaker 3: you almost can't win this argument because it's like who
Speaker 3: would have thought, like in nineteen hundred that, you know,
Speaker 3: all the jobs that exist will be gone. But like
Speaker 3: people will make millions of dollars sitting in front of
Speaker 3: a webcam and just talking like the idea of a
Speaker 3: creator could not have existed in their minds, right, So
Speaker 3: there's I think humans will always find something that they
Speaker 3: do that they're that we assign value to and will
Speaker 3: be just fine.
Speaker 4: Natasha Taylor, I'm just in my mind like right now,
Speaker 4: daydreaming about what kind of robots I could have, you know,
Speaker 4: automated parts of my life, because there's I'm thinking of
Speaker 4: Reed's pool example, and I'm like, there's a few things
Speaker 4: I'd like. I would like something to handle my garden honestly.
Speaker 3: To prune or what would you will, yeah.
Speaker 4: Prune weed, anything like water. I mean, they have automated
Speaker 4: watering systems. I should just buy one off Amazon. But
Speaker 4: but you know, I went out of town recently and
Speaker 4: I needed to pay someone to come over and like
Speaker 4: harvest the jilapenos that we're going to go rotten. Then
Speaker 4: you know, just like manage it.
Speaker 5: I guess a jilapeno harvesting robot.
Speaker 3: Of course, I think a gardening robot is an even
Speaker 3: more lucrative idea than than the Nerf nerve shooting duck robot.
Speaker 2: I would I have nightmas whenever I travel, I have
Speaker 2: nightmas about the garden just kept becoming you know, wasteland,
Speaker 2: and I feel like I would, I would be a
Speaker 2: very irrational spender on more about the plantain my garden.
Speaker 4: Just a humanoid robot that lives in your backyard, that
Speaker 4: can tend to your garden while you're.
Speaker 2: Gone, Natasha, I feel like I feel like you've got
Speaker 2: to a counterpoint here.
Speaker 5: No, I mean I I have heard this argument about,
Speaker 5: you know, we can't possibly comprehend the future like future
Speaker 5: jobs from many, many, many executives. But I do think
Speaker 5: it's really interesting the humanoid robot example in a lab.
Speaker 5: Both my parents were like research scientists, and just thinking
Speaker 5: about how it could potentially change that. I mean, first
Speaker 5: of all, it's like just imagine the number of mistakes
Speaker 5: and errors that could be made. But also, you know,
Speaker 5: so like you can't you can't make the humans obsolete
Speaker 5: because it's never going to be one hundred percent accuracy.
Speaker 5: But in terms of all of the different things you
Speaker 5: could try, Yeah, I mean I think that's that's like
Speaker 5: pretty exciting when you apply it to like actual innovation
Speaker 5: like new drugs, new science, new materials.
Speaker 3: Well, here's a here's a peta point for Taylor like that,
Speaker 3: I think this is the thing that people aren't thinking
Speaker 3: about when you have so when you have like fully
Speaker 3: robotic labs, right, what is like the number one way
Speaker 3: that we test all these molecules. It's animals.
Speaker 5: Oh, you know, people are building these like synthetic animals
Speaker 5: so that we won't have to do testing and synthetic brains.
Speaker 3: That is true. I looked at synthetic brains under the microscope,
Speaker 3: like they look like little brains. I mean that's just
Speaker 3: brain tissue. These or they call them organoids. It's fascinating.
Speaker 3: But like but right now, like so one of the
Speaker 3: researchers said, well, if you look at like the C elegance,
Speaker 3: which is like this little worm that that was made
Speaker 3: huge advances and like the longevity industry like all this testing.
Speaker 3: They like sort of root force tested the DNA of
Speaker 3: C elegance in order to you know, to essentially like
Speaker 3: do like do testing on it. And that led to
Speaker 3: a lot of this a lot of this new research,
Speaker 3: right and they're like, well imagine if like but that's
Speaker 3: a very simple organize and imagine if you could do
Speaker 3: the same thing with mice, and then we could have
Speaker 3: this greater understanding of mice and that would advance medicine.
Speaker 3: And I'm like, I'm picturing like like a robotic you know,
Speaker 3: twenty four to seven robotic like warehouse just full of mice,
Speaker 3: being like they die eventually right in these tests. And
Speaker 3: I'm just like, I think this is a big animal
Speaker 3: rights like once it gets to that, it's already controversial.
Speaker 2: Well, actually I want to share this in the in
Speaker 2: the first segment, but but I didn't, but I will now.
Speaker 2: So I had Stewart Russell on tech Stuff recently, who
Speaker 2: was Elon's only expert witness in the open AI try
Speaker 2: it about AI safety and one of the great sort
Speaker 2: of advocate of the alignment problem and the paper clip
Speaker 2: issue and all the other things. And his example was,
Speaker 2: if you told the super intelligent AI to cure cancer,
Speaker 2: the most rational first step would be to give find
Speaker 2: a way to give all humans cancer, so you could
Speaker 2: run as many simultaneous clinical trials as possible. So here
Speaker 2: we are the connective tissue between segments one and two.
Speaker 2: But now we go to the atbreak when we come back.
Speaker 2: Bill Gates's daughter Phoebe gets into some startup trouble. Stay
Speaker 2: with us, Welcome back, Taylor. I remember when Phoebe Gates
Speaker 2: announced her AI shopping startup a little over a year ago,
Speaker 2: investors from Kleiner Perkins to Sydney Sweeney piled into Fear.
Speaker 2: Tell us what fear is and what's been going down?
Speaker 4: Yeah, Fear. It's Phia. I feel like it of it's
Speaker 4: hard to pronounce. So it's an affiliate shopping site. Basically,
Speaker 4: it promises to give you deals on you know, whatever
Speaker 4: you're shopping on online. It's very similar to Honey. It's
Speaker 4: essentially the same business as Honey, which is very funny
Speaker 4: because Honey got into trouble for the same thing like
Speaker 4: a year ago. I think. I guess what's different is
Speaker 4: that it promised to be powered by AI, so you know,
Speaker 4: AI powered shopping assistant, help you shop, help you find
Speaker 4: the best deals, blah blah blah. Well, it turns out
Speaker 4: that they were doing the exact same thing that Honey did,
Speaker 4: which is sort of essentially stealing other people's affiliate revenue
Speaker 4: and using it as their own.
Speaker 2: So how exacted as well? I do remember just last week,
Speaker 2: you were talking about how useful it would be. Honest,
Speaker 2: two weeks ago, I think how useful would be to
Speaker 2: have an AI go and find lookalike products for what
Speaker 2: you wanted for your living room.
Speaker 4: Yeah, this is not This is.
Speaker 2: Not the offering that that's solving that problem yet.
Speaker 4: No, Yeah, it's I think I believe it. It's a
Speaker 4: browser extension. Basically, there's this messy world of like advertising,
Speaker 4: cookies and data. You know, lots of data is being
Speaker 4: harvested on us all day. There's lots of things that
Speaker 4: are happening on our browser that were maybe not aware of.
Speaker 4: So say you went to say you were a FIA user, right,
Speaker 4: and then you went to buy something on Nike dot com.
Speaker 4: FIA might get credit for that sale even though they
Speaker 4: didn't directly drive that sale, And so that costs retailers
Speaker 4: a lot of money.
Speaker 2: So it's not it doesn't it doesn't cause consumer harm.
Speaker 2: But it's basically it's basically just stlling out the other
Speaker 2: affiliate affiliate marketers and harming the retailers.
Speaker 4: Yeah, and the idea is that if this happens on
Speaker 4: a broad enough scale, of course, retailers will raise their
Speaker 4: prices to deal with this stuff.
Speaker 2: And you had Ben Adelman, who was a researcher looked
Speaker 2: into this story on your podcast. So what did he
Speaker 2: tell you and what made you want to kind of
Speaker 2: cover this one?
Speaker 4: I mean, I'll be real with you, guys. I just
Speaker 4: thought this story was interesting, not from any of this
Speaker 4: stuff like il like it's a pretty standard. It sounds
Speaker 4: like Honey already did this kind of scam. It's fraud,
Speaker 4: it's not good. It sounds like everyone does it not
Speaker 4: to make it whatever. What I found interesting is the narrative.
Speaker 4: And I'm very skeptical of like sort of these takedown
Speaker 4: pieces on female founders and everything. But this is a
Speaker 4: girl that raised forty three million dollars, that has some
Speaker 4: of the top investors, you know, that got in Forbes
Speaker 4: thirty under thirty, that is just getting unfathomable amounts of
Speaker 4: sort of opportunities and ushered into this like tech elite
Speaker 4: world basically for building what was essentially a Honey clone
Speaker 4: and ultimately doing the same thing that Honey was doing.
Speaker 4: And so I just I guess to me it was
Speaker 4: it was more interesting. It is like a story about
Speaker 4: kind of like how does access play a role, how
Speaker 4: does fame? How does adjacency to power like play a
Speaker 4: role in this Silicon Valley start up funding ecosystem and
Speaker 4: the companies that are sort of considered successes.
Speaker 3: I mean, if you could raise that much money, snap
Speaker 3: your finger, well you probably could, honestly, Taylor, I mean
Speaker 3: you're pretty well, but like you could start, you could
Speaker 3: build a robotic you know, Gardner, Like, I mean, let's
Speaker 3: do something more ambitious. I totally agree, Like what.
Speaker 4: Right bart headline? Taylor tries to put Gardners this no,
Speaker 4: and it's like I don't want to like downplay like
Speaker 4: the idea of like AI shopping, but I just I
Speaker 4: feel like we can think bigger about technology and about
Speaker 4: like what we're like lauding and what we're like praising
Speaker 4: and putting founders on like most exciting startup founders lists.
Speaker 4: Like you know, that's kind of to me. I'm like hmmm,
Speaker 4: And you know a lot was focused on sort of
Speaker 4: Phoebe Gates is like, you know, she's an influencer, she's
Speaker 4: out there, you know, building an audience.
Speaker 5: And even when it launched, I thought it was like
Speaker 5: a weird move for her. I'm like, you could do
Speaker 5: you could have better nepotism than this you know, you
Speaker 5: could have like a more impressive startup. She was talking
Speaker 5: a lot about reproductive rights and like going in a
Speaker 5: certain way with her influencer career. I thought this was
Speaker 5: really a like lateral slash step down.
Speaker 3: I mean, it's like one of those businesses where you're
Speaker 3: like you type into like CHATGBT, like find a way
Speaker 3: for me to make money?
Speaker 5: Yeah, like ask your dad for help. If you haven't
Speaker 5: been asking your dad for ask him for help. Because
Speaker 5: this was not a good one.
Speaker 2: Okay, but let's so so tech tech NEPO babies obviously,
Speaker 2: you know PB Gates is kind of intriguing as one
Speaker 2: read jobs has his has his fund investing in like
Speaker 2: cancer cancer technologies. I'm not sure if I'm not sure
Speaker 2: if anyone knows much about that or where it's going.
Speaker 2: But if you do, chime in, and who are the
Speaker 2: other tech NEPO babies who want to be in tech?
Speaker 1: I guess my question.
Speaker 5: There's already a lot of them in tech. Tim Draper's
Speaker 5: sons are investors. You would be surprised if you just
Speaker 5: look at you know, like middle management at the fank companies.
Speaker 5: I think you see a lot of a lot. Yeah,
Speaker 5: so well, Sophie Schmidt, she had she had Rest of World,
Speaker 5: and I'm not sure how long she's going to be
Speaker 5: affiliated with that. I think Rest of World is actually
Speaker 5: an awesome publication. So that's that's one of the technico
Speaker 5: products that I would definitely stand behind. Yeah, I feel
Speaker 5: like often they bring them in on their funds.
Speaker 4: Yeah, I mean I would say a really famous one
Speaker 4: is David Ellison, Larry Ellison's son, who would be.
Speaker 1: That would probably the example number one.
Speaker 5: And we, like I have been thinking about this for
Speaker 5: I guess decades, because we are not prepared for the
Speaker 5: like the way that the that the children of these
Speaker 5: tech billionaires are going to influence our world old with
Speaker 5: the amount of capital that they will have access to
Speaker 5: and the amount of like Taylor said, access just generally to.
Speaker 5: I mean just imagine like Davos twenty third.
Speaker 3: What do you what do you think it'll look like?
Speaker 3: Like we've seen that we have the Rockefellers, we have
Speaker 3: you know, there's there's all these families, but like, is
Speaker 3: this going to be just more of that effort?
Speaker 5: It's right, No, I don't think it's going to be
Speaker 5: like the Rockefellers at all, because I think it's going
Speaker 5: to be a lot of the kids trying to make
Speaker 5: a name for themselves. So they pick a they pick
Speaker 5: a cause, they pick a charity, they pick you know,
Speaker 5: something that they want to be associated with. So the
Speaker 5: money that would normally go to like kind of traditional
Speaker 5: philanthropies which have their which have their own issues, is
Speaker 5: probably going to go to like a startup, you know,
Speaker 5: they want to when they show up at a Davo
Speaker 5: circuit or whatever it is. They want to have like
Speaker 5: something behind their name that's not just their mom or dad.
Speaker 5: So yeah, it's just going to be I guess like neposlop.
Speaker 3: Some of them. Some of them might be good.
Speaker 4: Yeah, I mean I think it'll be interesting too, Like
Speaker 4: with all of Elon Musk's kids, you know, Elon Musk,
Speaker 4: like a lot of these Silicon Valley men also are
Speaker 4: like obsessed with pro creating. They want to ensure that
Speaker 4: their legacy continues, you know. So I wonder if all
Speaker 4: said they're going to take a more like hands on
Speaker 4: role than maybe some like finance guy or whatever, you know,
Speaker 4: would have done with his children.
Speaker 5: And I mean so many of them got radicalized from
Speaker 5: their children, right, Like their children's politics radicalize them. So
Speaker 5: maybe they will be like the class traders that tried
Speaker 5: to push more, you know, more income equality or different things.
Speaker 5: I mean, I'm not saying it's necessarily going to be slopped,
Speaker 5: but you have to imagine. I mean, their dads are
Speaker 5: the ones who are like Elon's like I did it
Speaker 5: by myself. You know, I'm an immigrant, I like didn't
Speaker 5: have any help. And so these kids are going to
Speaker 5: have like a lot of chips on their shoulders, I'm saying.
Speaker 5: And the.
Speaker 3: Yeah, it's like dead right, Like they're not going to
Speaker 3: give it away, are they? Is the philanthropy dead is
Speaker 3: just going to be startups?
Speaker 4: Well, I would say maybe the Dario. You know, we're
Speaker 4: about to get a lot of ea anthropic millionaires and
Speaker 4: billionaires they famously kind of give to a lot of causes.
Speaker 5: Well, okay, but I went to this event in San
Speaker 5: Francisco called what should we do about all this money?
Speaker 3: Get it out of California? Get the money out?
Speaker 5: Yeah, and I think they're going to go into donor
Speaker 5: advice funds, which means like no transparency. There's just a
Speaker 5: lot of ways to like set it up as though
Speaker 5: it looks like a charity but there's no transparency, you know,
Speaker 5: public accountability. And I think it's quite possible too that
Speaker 5: a lot of this money will be going into AI safety.
Speaker 3: Oh god, well we'll have very safe AI. That'll be great.
Speaker 4: Yeah.
Speaker 2: Right, Well, that's all we have time for today. Thank
Speaker 2: you all so much for joining for tech stuff. I'm
Speaker 2: mos Voloshin. This episode was produced by Eliza Dennis. It
Speaker 2: was executive produced by me and Julian Nutta for Kaleidoscope
Speaker 2: and Katrina norvelbe iHeart Podcasts. Jack instantly mixed this episode
Speaker 2: and Kyle Murdoch wrote olph theme song. A special thank
Speaker 2: you to Taylor Lorenz, Natasha Tku and Read Albergotti. Please
Speaker 2: check out all the work they put out into the world.
Speaker 2: We're lucky to call them friends of the Pod.