NVIDIA’s Nemotron-4 340B models, Safe Superintelligence Inc. and AI agents
Are we just waiting for these, like, agents to get better and better? And humans will have to do less and less in the loop. Or, you know, is it less dependent on the model's capability and more dependent on kind of the use case, like where is it headed? Is there a limit to how much we can abstract away? Hello and good morning. From my hotel room in San Francisco, you're listening to Mixture of Experts, and I'm your host, Tim Huang. Each week, mixture of experts brings together a stellar group from research, engineering, product sales, and more to tackle and discuss the biggest trends in artificial intelligence.
So this week on the show, we've got three stories. First, Nvidia announced the launch of Nemotron-4 340B an LLM, specifically designed to aid in the creation of synthetic data. How big of a deal is it, and what does it say about the next stage of AI training? Second, we'll talk about recent developments for agents in the enterprise. Our agents reality now. And what can we expect the biggest impacts to be. And third, and finally, just earlier this week, former OpenAI chief scientist Ilya Sutskever launches a company, Safe Superintelligence Incorporated.
What is it? And does it have a chance of becoming a new contender in the space? As always, I'm joined by an incredible group of panelists that will help us navigate what has been another action packed week in AI. So joining us for the first time today, I'm Maya Murad, product manager for AI incubation. Maya, welcome to the show. Thanks for having me.
And then we've got two veterans who are joining that we haven't seen for some time, but I'm very excited to have both of them back. Kate Souleis a program director for generative AI research. Kate, Welcome back. Hey, everybody. Great to be here. And finally, Kush Varshney, who was part of the very first episode.
So he's the OG, for a mixture of experts. He is an IBM fellow working on issues surrounding AI governance. Yeah, O.G., I guess it's a great designation. Yes, it's some kind of distinction. So, Kush, welcome back. Well, great. So I think the first, story I really wanted to dive into was,
you know, I caught in the kind of constant, you know, wave of papers and releases coming out. a launch that happened from Nvidia late last week on Friday. And because it was a Friday launch, I think it kind of got lost in the news cycle. But I did want us to kind of focus on it. Nvidia released a model, a class of models, called Nemotron-4 340B and it's a set of models that are specifically designed for synthetic data generation.
and I think it's so interesting because if you're not familiar with the background here, right, the way we train LLMs, get them to do their magic. A lot of it relies on data. and in the past, the way people have done this is literally getting lots and lots of real world text, to train and improve their models. And so, you know, the dream of synthetic data, I think is very fascinating.
It's kind of like the idea that in the future, we actually won't even need text from the real world. It'll just be like stuff that another AI model generates. and I guess, Kate, I want to throw it to you first, cause I know you've thought a lot about this area and been kind of doing some work in the space. I'm kind of curious, just as a first cut.
Like, why is Nvidia getting into this area? I'm really kind of curious about why this kind of hardware company is saying what we're going to be launching these models. And one of the models we're launching is, is a synthetic data model. So I guess the first question I'll throw to you is like, do you have any thoughts on that? Conspiracy theories. Maybe not conspiracy theories, but just like why it is that they're investing in this space at all. So, I mean, I think there's, some more straightforward answers and then maybe some side answers on why they're working in this space.
I mean, one example of what you might think of why Nvidia is working, and released the 340B model into the open source, is for synthetic data specifically is because they're recognizing that no one wants to run inference on a 340 billion parameter model for real tasks like the value of models the size. I think originally there was a lot of excitement. Everyone wanted to build as big a model as possible and see how far they could push the field, but the reality is like no one's actually going to go and deploy this in production and hit a 340 billion parameter model every single time you want to run. Inference is just too costly. But there is a lot of value in running inference on this model.
Once using the data that you create to train a much smaller model and then deploying that out in production. And so I think part of this might be just the field is starting to find new ways to add great value to these really big models they invested on early on. because they do take a while to train, but it actually makes a ton of business sense for Nvidia to, if you think about it, because customers need their customers need to get models hosted on their compute running as soon as possible. And what we're seeing is the easiest, not the easiest, but one of the most exciting. And, most powerful ways to start to take models, improve them, use them for their use case and get them out into the world and deploy them in production is to align them using synthetic data. So more and more customers are using and consumers are using synthetic data in order to actually take models and tune them for your use cases and for your task.
And so if Nvidia can help customers with that cycle and help get models out into production faster, you know, ultimately that's going to create some some good drag for their their products. Yeah, it's such an interesting dynamic. I think particularly in that first point that you're making is, you know, Nvidia like the company that I identify with like really big compute, right.
Like, you know, every release is like bigger and bigger. And the models that you can theoretically run on them are bigger and bigger and bigger. But I guess sometimes you're kind of saying here Nvidia is like conceding the reality that like most people actually are not going to be doing that. It's just like such an expensive thing.
And so almost they have to win the synthetic data game, or in the very least kind of like support that use case just because it isn't. It isn't what most people are doing to create like the biggest, biggest, biggest models in the whole world. Yeah. I mean the model, if you look at the, supported intended uses, it's
not just intended for synthetic data generation. It's a perfectly reasonable chat model. It can be used for chat use cases. But if you look at how it's been marketed by Nvidia, every single press release and blog and paper is all about the synthetic data aspect.
And so I think they're recognizing that this really is the only viable way to try and get value out of models of size. And it's also a really exciting way. I mean, it's just where the field is headed and where people are seeing a lot of, a lot of use and value. Yeah, for sure, Maya I'm kind of curious, So I can bring you into the discussion.
So I think one of the reasons we want to bring you on is my understanding is that you've done a ton of work with kind of enterprises, right? And getting them to integrate new kinds of technology. I guess I'm curious about what you're sort of seeing out in the space as like synthetic data becoming, you know, a bigger part of the discussion. Do people want it? I'm just kind of curious about like, what the market demand for, for this is looking like over time. I think it's a very exciting space.
And it's a it's a need that existed prior to the rise of generative AI. So customers are they have their own data, but they're also limited to it. And it's costly to create task specific data. So there is a really strong premise of can I start with only a few examples and augment that data set to for various use cases to train my own model to evaluate on a use case to red team? so there's a lot of value to be drawn out of it, and it's one of the top customer inquiries we get.
yeah, that's really interesting. Yeah. I wonder if, like in the future, I just I'm curious if any of the panel is kind of view on this as like, you know, we've we've so identified and I think this has actually been certainly something on the policy side. People have been like, oh, you know, AI is so data hungry.
And so therefore it is kind of privacy invasive, right. Like it just needs all of this data to get working. Synthetic data for me has always been like, well, maybe in the future, like actually real world data is actually not going to be that big of a deal because you have a few examples and then you scale up with synthetic and there you go. I mean, is that a fantasy or are we headed to that world? I mean, I think in terms of using synthetic data as a tool to protect privacy, it certainly has a lot of value. I don't think we have quite, you know, there's a lot of work to be done in that space. in order to really take advantage of the promise that it offers.
But my I've mentioned that even before, like, LLMs became around, we've been using, synthetic data. And I think that's very much true. Like we've looked at, for example, a couple of years ago, using synthetic tabular data just to create privacy protected versions of sensitive data sets where you can mask information.
so there's a lot of different kind of really cool applications that I think are going to start to converge with synthetic data around privacy, how that impacts generative AI training. and maybe help drive the space forward more. I know, Kush, you do a lot of work in this space.
Maybe you can comment. Yeah. No, I mean, I think the the privacy aspects are an important part of it. I think the exciting thing is actually going back to the fundamentals of probability. I know in the first couple episodes we covered Kolmogorov and other friends, from that time and just the ability to sample like really high dimensional, spaces, with just a few examples is like mind blowing in some ways, even though it feels very normal.
Like with five data points, you can sample this like trillion dimensional space and cover it so well. I mean, I think that's pretty crazy. So, yeah, I mean, this is just changing how work is done. I think, in the whole generative AI space. Yeah, for sure. I think it's really, yeah, it's a really interesting way of sort of thinking about it.
And yeah, I think we want to do more collaborative type sections, like it feels like that was actually really good for us to go so, so wonky. but I think it's actually important, I think in terms of like, exposing, you know, what's actually really going on under the hood. I mean, I guess one question for you is, given that this has been a long term research objective, right. Do you see the Nemotron release this kind of largely sort of incremental like, is this a big deal this launch, or is it pretty much just like the most recent salvo in the race for synthetic, data? So honestly, I think what I what is the most exciting part about this release is the release of the model terms, where Nvidia is actually saying we want this model to be used to train other commercially viable models and make no claims over models trained with synthetic data created by Nemotron which is very different than almost any other model provider that's created, especially ones creating their own custom model terms. If you look at like the Llamalicense, you know, gamma license and others that are being created, they all prohibit that type of use.
So the model itself is exciting. And, you know, I think people will start experimenting with it and seeing how far they can take it in the next couple of weeks. But what they did that is really totally different from how anyone else is behaving is actually the permissions in which it was released. it's like the legal terms are the real innovation here.
I'm sure there's lots of other innovation too, but that's what I'm sure you're excited about it. yeah. Yeah, for sure. And I think it's kind of I mean, you know, Nvidia is kind of playing a dangerous game here though, right? Because there's a bunch of people who are making these models proprietary because they want to build businesses at the synthetic data layer. I guess Nvidia is kind of saying like, we sort of don't care. Like we would rather just have everybody have access to this.
And, you know, for us to sort of enable all the secondary commercial models rather than creating like a market around synthetic data, specifically. I mean, you got to use Nvidia compute to, to generate synthetic data on a 340 billion parameter model. Then you have to use compute to train it, and then you have to use compute to host it. So I think they're playing a good long game there. yeah I saw this great tweet recently, which is, you know, the adage of like, oh, when there's a gold rush, everybody should be selling shovels, but it's almost kind of like Nvidia is like when everybody's selling shovels, you need to be selling shovel making machines. And I was like, that's actually like such a good way of kind of capturing what it is that they're what they're doing in the space.
There's a very broad topic I think we want to cover, on this second agenda item, which is agents. Right. This is like the jargon in the space shift so quickly. It's like it feels like a few months ago, people were like, oh, agents are coming.
And then now it's kind of like agents are here and everybody's working on it. And, I guess, you know, Maya, I think part of the first thing I want to kind of bring you in on is just if you want to explain to our listeners what are agents exactly? And like, why is it a jump from what we've had in the past? because I think the definition and the distinctions have not always been so clear. And so I think even for me, I don't really know. but we're just like, it's a good place to start. Like, what are agents and why are they different? Yeah, absolutely. So the way that I like to explain what our agents is to first contextualize where we are in terms of building applications with AI.
So in 2022, all about foundation models, all about large language models. I think we've learned since that simply inputs and outputs from a model is not going to unlock those high impact, enterprise use cases. So if I want to understand what is my vacation policy or if I want to retrieve real data, I have to build a system around it. And actually, Berkeley came out with a really good paper this year talking about compound AI systems. and it's a reflection of the fact that you need to go back to plain old systems engineering to build AI applications.
So modular components that are fit to solve certain problems. So you have a retrievers, you have external tools, and then you have your model interacting with those to solve a problem. And we're still in this world and agents still operate within the space of an AI system. the way most of AI systems are built, for example, retrieval, augmented generation is one of the most popular kinds is me as a developer, I prescribe the flow.
So take the user query, run it through a search, retrieve the results, feed it a model, and then the model generates an answer. So this is a flow that has been pre described and it's fixed. If I give it something else it's not going to work because it was pre described to solve problems maybe related to a vacation policy.
and agentic approach in a system is that the LLM can reason through how to solve the problem and understand what is the path to chart to to answer a query. And this is done through two capabilities and that are building on top of large language models. So one, large language models have really great reasoning capabilities.
And they're improving as LLMs are getting bigger and stronger and are seeing more data. And it's operating the same way we as humans do it. So if I give you a complex question like how many times can the UK fit in the US if you if you, I ask you to give me a an answer on the top of your head, you're not going to get you're most likely won't get it right unless you're a really great geography buff.
but we as humans, the way we think about it is we break down the problem into smaller parts. So let me find the size of the UK. Let me find the size of the US.
What tools do I have at my disposal to find this? Maybe Wikipedia is a trusted source, and then let me do some math to divide the bigger by the smaller. And this is exactly how an agent would reason about it. And there's no magic behind it.
You're just literally prompting the model to say, think step by step, create a plan. And then for each part of the plan, you have access to tools. so that's the other part of it, the ability to act and to call on tools. So a tool could be an API that interfaces with Wikipedia. It could be a calculator, it could be a piece of program that can run a script for you. And when you combine all of those together, you're actually giving a lot more autonomy to the model, to how to solve the problem.
You're not scripting how the solution would be. The model takes care about how to solve it. And this is what we mean by an autonomous agent. So long answer. But I think this is helpful to contextualize that. It's a continuation of where we are with systems design.
Yeah, that's really useful. It sort of feels like we've kind of moved through these three acts already. Right. And like lasts, you know, 24 months, right. Where like my the way you kind of phrased it was the first act was everybody thought we'd have one big model and do inputs, outputs and like problem solved, right. Like we're done. And then it feels like act two was, oh my God, that doesn't work at all.
What we need to do is kind of like prescribe all of these steps and then kind of like insert the model into the process. And then I think act three is kind of it sounds like a little bit of where we're going now with agents is, well, we kind of go back to that big model state. But the trick is, I guess, that we're allowing the model to like, develop these step by step instructions on their own and then I guess, enable them to kind of like reach out and interact with all of these systems. so it, it strikes me that the big challenge here is can the model actually touch all these outside services? because, you know, we've had stuff like chain of Thought for a very long time. That interface seems to have been the difficult one. Right?
Which is like, how do you actually get the model to go and interact with all these services? Because I suppose, in fact, it's kind of like get the agent to write an API on the fly. Is that kind of how people are approaching it? Yeah. So models are being trained to be better at generating a correct output to interface with an API or to run a piece of code. There's also, actually good privacy and security considerations here when you move to the agentic space. So prompt injections would be quite scary. And then I think I pass it on to Kush to maybe talk more about that.
But the other part, as well as code execution, we've heard from even like I think the founder of OpenDevin, said the first time we run it, like all the systems in the file system were deleted because of like the agent behaving. I have to be like really careful and how you architect that. And yeah, I don't know if you want to comment more on like some of the security privacy considerations with agents.
Yeah. so clearly when, these things are interacting with, various, cyber physical sort of systems, then there is a risk of, the different types of, prompt injection attacks and what we call indirect prompt injection attacks that can either get corrupted because something out in the real world has, problem or that is the models themselves are going out and, actually causing stuff, that happen in the real world. So I think, yeah, I mean, it is a danger. but I think the promise is also really, really great.
And I think we can build in some of the, this security privacy sort of guardrails into the way that these, decisions actually interact. And. Yeah, I mean, as Tim said, I mean, the code itself, like the API code is being generated on the fly, is like the, the most exciting part about this, because, code has traditionally been written, for like a fixed sort of thing, but here, if it's being generated, then, you can compose things, as in a creative way. And, I think that's, a unique thing. Yeah. All of these things seem to point to kind of the struggle of deploying AI in the real world, where, you know, the act, one that I described is almost like the AI person's dream, where it's like it's a complete vacuum, and we just have one model that does everything completely out of the box input output.
And we're, you know, we're done. and, you know, each step of this seems to have been like, actually, you know, there's actually all these legacy systems you need to deal with. my is it right from Kush's last comment that this is one reason why agents are potentially really good in the enterprise? Because I guess what I imagine here is, you know, your main worry about agents on like a consumer side is like they're going out into the world and doing all sorts of things, and, you know, they're subject to all of these inputs from the public that might be attempts to manipulate these systems.
But I guess in the enterprise, like, you control a lot more of the variables, right? You could have an agent that just like operates within a business and that feels like it might like, constrain the problem more in a way that makes agents more viable. do you agree that. Yeah. So I think in general, when you're taking a new piece of technology, especially one that has more autonomy in how it can operate, so starting with low risk areas, maybe back end services in a company, I think that would be a good place to start. And then gradually exposing it more as you architected systems to safeguard it from threats. Or it's just guardrail, it's internal behavior so it doesn't end up wiping your system.
So I think with all technology like you start with the the less risky place and then you gradually add more risk to it. And I think that's a sound approach. and then, in this world. So I talked about compound AI systems. Right.
And then there's kind of on the one hand side you have a programmatic way of doing things. And on the other side is the agentic like way. I don't think it's going to be one or the other.
The two are going to talk to each other. So for some problems, there's a especially for narrow problems where there's a very specific way of solving the problem that's repeatable. You're not going to get something out of left field, a query out of the field that doesn't like fit the solution you had in hand go for efficiency. You go for the programmatic approach, for problems that there's many ways of solving it. especially like for example, how to solve software engineering problems. this is where an agentic approach can be easy because there's multiple ways of going through a path to solving it.
And these two will come together to solve problems in the enterprise. It's not going to be one or the other, and you're going to always going to apply a systems mindset of how can I solve the problem most cost efficiently with the right guardrails around it? Totally. Yeah, it kind of there's like basically a real big question here on, you know, for a given enterprise, if you if you broke down all the tasks they need to do on a given day, right? Like what is what is structured and what is like requires an agent is actually kind of a big question about how that market's going to separate. Like, I don't genuinely know, like if you took an average enterprise in America and you say, let's map all your business processes, you know, do they tend to be quite routine in the way that like, you know, does this sort of like we just need to do this in this kind of like very systematic, systematic programmatic way? Or does it require like, you know, like the agent to go out and kind of like exercise some innovation? Do you have any early signal on that, like for the clients that you work with, the people that you talk to? are enterprise is still mostly favoring kind of this very structured approach or is there like like are there particular kinds of businesses that are like, oh yeah, the agent, that's exactly what we need. Yeah, I would say most enterprises are still in this systematic approach.
RAGRis a very popular use case. Most enterprises have built programmatic RAG, there's degrees of agenticness you can build into it. So maybe you could have a self-reflection loop. so you could take the output, of what the RAG system, provided and have the model reason on is this actually solving the problem at hand and maybe loop one more time.
So you we're seeing some companies dabble, but not fully embracing the the full autonomy of an agent solving a problem and for multiple reasons. one is we're trying to understand which sets of problems are better suited for a fully agentic approach compared to the programmatic approach. I have a hypothesis of if you have a narrowly defined solution that can unlock a lot of business value, let's say it's like you're interacting with a database and there's a narrow set of commands you want to apply.
You can engineer all the fall back loops by hand. And if you solve this, you unlock $1 million for the company. Go for the programmatic way. And then if you have a system that's going to get so many different queries that there's so many, you need different trajectories to solve it, and you can't even think through all of the different ways this could fail.
And running a pilot program, you could uncover some, but there's a lot more to figure out. I think this is where having more autonomy in the system could be useful. But we're still early days and we're still trying to thread this tension between the two.
And and I think cost efficiency will come into this equation as well. For some problems, you're willing to spend more for 1% more accuracy, and then for other problems, you're going to be a lot more tighter on what's the right solution to implement. Well, and isn't there, a third dimension on all of this, which is like, what do you want to handle programmatically? What can you start to free up and allow more agentic approach? But then what what always needs a human in the loop, like humans and agents working together with just humans separately.
so I'm curious, Mayaif you have any thoughts on that, on that other layer of it, I think great agent systems that where we are right now might need a human in the loop. just and it, it would be great to have the system know when to plug in the human. And you could like with the genetic frameworks out there, you could prescribe for the model to call in a human for help.
so I think it depends on like the, the what is the risk of harm or the risk of failure and the availability of an expert. And I think that's what you would consider of how you would bake it in. I think most autonomous agents we've seen, have a strong element of human in the loop. So if you're looking at the, the GitHub workspace assistance, all of this require you to revise the plan of the agent before it executes it, and then you get to see every step implemented. Then you have so many ways to, to, for recourse or changing the, the how the problem is being solved. And I think right now we're leaning very strongly in having human the loop because we're early days and there's many ways this could go wrong.
And I don't know if this answer your questions. Well, well, I was just going to ask, are we just waiting for these, like, agents to get better and better. And humans will have to do less and less in the loop or, you know, is it less dependent on the model's capability and more dependent on kind of the use case, like where where is it headed? Is there a limit to how much we can abstract away? Yeah, I think these are great questions. I'm always in favor of me personally.
I'm always in favor to have people engaged and for safety. I it's an open question of, are these models going to get better at what they do and be able to implement what they need more autonomously? Where we are with the technology right now, you definitely need a human in the loop. Yeah, I think we're also I mean, the meta here is that it feels like we're talking a little bit about trust, right. there's a hypothesis I've been chasing after, which is whenever people talk about AI, they're almost like the AI is coming.
And like, what does the AI enterprise look like? but the fact of the matter is like this. This depends so much on, like, the culture of a given company, right? And like the degree to which they trust autonomy. and what you could actually anticipate is that like the, the implementation of, like the programmatic approach versus the agentic approach, like where you lie on that spectrum will almost be like totally defined by like how much management trust the technology and then what their general behavior is like. I'm curious, like if a company who already is like very structured with its employees and it's like, here's the big list of things you need to do.
Like, it will be no surprise that when they implement AI, it will be like programmatic in that way, right? Whereas you might see cultures of, you know, enterprises that are just a lot more like, well, we just like give people the goals and they sort of figure out what happens. And it will similarly not be a surprise that when they implement technology or AI that they'll say, yeah, actually working with agents, you know, so long as that there's enough kind of trust in the technology. Yeah, there's the risk appetite.
So I think that we're seeing with companies and larger enterprises, like we said earlier, you might start in low risk places. So like your HR systems, your back end systems. And then you would bring it more consumer facing.
and it's I think it will be interesting to see this year what would be the appetite of enterprises to adopt more agentic like behavior. Yeah. That'll be so fascinating to see, especially because I think some of those determinations at even what functions are low risk is going to depend a lot on like what else is happening in the world.
Like you can imagine like a very high profile failure, basically totally lowering people's appetite to like implementing agents for a long time. And there's almost kind of like a Google Glass effect where it's like, oh, you have one wearable, which is like an enormous failure in the market. And then it's like difficult to, like, convince people to put anything on their face for the next decade. and so there's, there's a little bit it's kind of like crossfade, which is like, all these companies are making their own decisions, but it's also in like a soup of like what they're seeing out there in the press and in the open and what their competitors are doing.
Well, great. So I think to bring us home, I want to go to our third topic. there is a company that was launched, just this week, I think like 48 hours ago, 24 hours ago, by the name of Safe Superintelligence Incorporated. and if you haven't been following this soap opera, the background is that Ilya Sutskever, who is formerly the chief scientist, one of the key players in OpenAI, and played a role in the board drama and kerfuffle. that happened not too long ago. was, left the company. The circumstances are still disputed.
and has reemerged with a new company that, promises to finally deliver on the dream of superintelligence. and, Kush, I think, you kind of threw yourself, kind of in front of the bus to kind of talk about this topic. and I guess I'm kind of curious, just as a starter, how big of a deal is this? You know, there's my point of view, which is it's becoming increasingly clear that, like, in order to play in the AI space, you need very, very, very deep pockets. And, you know, there's kind of a part of me that this sort of despair is at competitiveness in this space where I'm like, I don't agree with Ilya. I'm very skeptical of all the superintelligence stuff. But like, man, if I don't hope that like, a small startup can, like go up against the big folks, is that even a possibility, like, like, does this this company SSI have a chance to, like, become a player in the space? Yeah. I mean, I think this is just a round of like multiple rounds that are happening
where you start with idealism and you then like kind of face the reality. see the professionalism of its openAI started with an ideal sort of thing. Then anthropic started with some ideals sort of thing.
And now this is kind of the, the next one. and I think what's happening is. Yeah, I mean, the deep pockets are the, the important aspect of it. So,
because you need so much sort of investment to even get off the ground and, at some point the question becomes so for where the money came from, what's the return that they're getting? It's hard to stay, like, isolated and only be like, a research organization or like have like that single, like, oh, we only have one thing on our roadmap. We only have one sort of goal because, like, people want to, they want to get paid to. They do. I think that that's right.
And like there's this conflict between like scaling and caring. so you can like, scale, and that is what I mean, capitalism is all about, you can care just focus on like, one person, one sort of issue and then really go deep in that. And like, you can't really do both. And so, I think that's where where the conflict is. And
is this going to be successful? Maybe, but I think it'll be just one more round of this. So for a couple of years, 2 or 3 years, whatever it is, they'll kind of have their point of view, keep it. But then, yeah. I mean, someone will be at the table, asking to be fed. So, I think we'll, see we'll see what happens.
Two forces interact. yeah. I've a friend who's observing, like, you start out trying to deliver on AGI, and then you find yourself being like, we got to do B2B SaaS. Like, actually, it's like you're eventually kind of dragged towards this, just even if you want to fund the dream, like basically like where the money's going to come from is like these kind of very like day to day kind of, applications. Kush, I actually we haven't talked about this in the previous episodes. I mean, do you buy their mission? Like, is the goal of superintelligence like something we should even be chasing after? Does it is it a coherent goal? Right. Like, I think there's, like,
real kind of critiques that people have made in that space. But as someone who thinks about AI governance, who's like researching these issues, how do you size up, I guess this ideathat, like, we're going to do a company where the promises, Yeah, like to an investor even you put money in and we deliver on superintelligence, right. Like, it's like the old DeepMind mission, which is like we solve intelligence first, and then after that, we solve everything.
yeah. You know, is that something that you think is like, the right way of approaching some of these problems? Yeah. No, it's a great question. And, let me like, give somewhat of a historical perspective, at least for me, like the first time I heard about superintelligence at all, it was, December of 2015. so this was at the NEURIPS conference.
there was this whole day, like symposium, which they don't do anymore, but there was one. It was "the algorithms among us" was the, the title of it. And, there were a lot of different things. It was about like the societal benefits of AI. And I mean, things that I was thinking like, oh, I would be really interested in. And then I show up at this thing and, one of the, the presentations is, Nick Bostrom talking about superintelligence and, and, like in this whole, like, sort of day.
but the word safety kept coming up again and again and again, and no one was defining what even safety is, what they mean by it. And I think, like, I came home, like I tried to figure out what safety means to me. kind of wrote something about it as well, which was, minimizing the probability of harms and risks and minimizing the possibility of unexpected harms and, this sort of stuff, which then lends itself to, kind of more, like clear and present sort of harms clear and present things that affect society now and then. at the same time, in 2016, there was this, now famous paper coming came out, which was this concrete problems in AI safety, Dario Amodei I was the first author on that one. And,
that somehow, like, just caught the attention of people. it became kind of like this religious sort of thing that, this existential risk, this big sort of, sort of thing for like, 100 generations into the future, like, what is I going to do to to humanity? And I mean, to me, yes, we do need to think a little bit into the future. so there's this, concept called the seventh generation principle.
So this comes from the Haudenosaunee, tradition and, like. Yes. I mean, you can think 150, 200 years into the future and think about what might happen, the consequences. But, like, so for into the in advance is a little bit, pretentious in my opinion. So,
superintelligence. There are risks, of course. but I would much rather both from a personal, societal and enterprise perspective, focus on, kind of. What can we do? where do we take things and where do we protect things? protect things now? Yeah.
I think kind of what I'm trying to reconcile is, and it may just be that we end up, like, talking a little bit about kind of like how the broad universe of AI is going to continue to diversify and look very different at very different places. It does seem like I know we've spent like maybe the last 40 minutes kind of talking about trends that are like almost very opposite of, you know, superintelligence, right? Or like what Illya working on, which is like, turns out a lot of people don't really need huge, huge, huge, gigantic models, right? Like, ergo, you know, like what the synthetic data stuff is, is kind of pushing towards like similarly like a lot of the issues that businesses are dealing with there, like, you know, how do we query a database effectively. And so it kind of feels like maybe there's actually going to be this, like the these two worlds were very similar for a while, which was, oh, LLMs are going to deliver on superintelligence. And oh, by the way, we can also do B2B SaaS, but it kind of feels like maybe over time those technical agendas are going to go further apart. Yeah, I think at least from my perspective, from the agent space, the types of agents that are coming out are very narrowly defined to solve a specific task. And then you have instantiations of narrow agents.
So an agent that focuses on data analysis, that collaborates with an agent that can do reporting. And this is kind of the path forward. That's that is getting gaining adoption and traction. And for several reasons you're able it's more democratic. You can work with open source models, smaller models that you can self-post. So you have full control over the system that you're building.
And I think this is the opposite view of the big monoliths that are trying to take a stab at superintelligence. And I think we and IBM research, I think I know which camp we're in, and it's like betting on giving you as developer more autonomy, you being able to control which models you use, trying to use your own data. And the way to go about it is more narrow applications. I have an open question in my head of like, how broad and general purpose we can go, and there's a limit to what would be useful to society like Kush mentioned, just echoing what Maya said, I don't think that they are going to be very well aligned, incentivize moving forward. you just look at where like, we're going to be incentivized to develop from the types of tasks.
You don't need super general intelligence. but I do question and I question, you know, whether this should be developed in a kind of proprietary closed company versus in a more like academic consortium and other groups that might be better incentivized and, have maybe, better priorities as we talk about, like, what society could actually benefit from for developing this type of technology versus just like some man behind a curtain with some VC money going at it. Pay no attention to the man behind the curtain.
yeah. Yeah, it's it's so competitive. And I think in part like the this kind of divergence like for some of these companies. Right.
Like so OpenAI anthropic is another great example. Right. Where it's basically like very much instantiated by people who like really believe in want to work on massively a agentic systems. Right, that are incredibly general purpose. You know, they would call it superintelligence.
but in practice they've also had to kind of just deliver on like the day to day of being a business. And it's kind of like to what degree can these businesses kind of keep these two objectives in line or like even work on things that achieve both ends? And I think kind of the question we're asking is, well, maybe at some point the research or product development agendas here become quite different. it producer Han's actually just dropped that anthropic just this morning launched a new model in the space.
so they've just dropped, Claude 3.5 Sonnet, and, you know, one of the things that everybody's kind of observing or chattering about this morning is that it's actually very similar to what Google and OpenAI I have done over the last few weeks, which is speed. They want to launch models that are just very, very, very fast.
And it is kind of interesting is like, why do you actually work on speed? Well, it's not necessarily because you think the superintelligence needs to be really fast, right? Like, you know, what you're doing for speed is like, oh, it actually opens up all of these other interesting consumer applications, like, you know, talking on the phone with your AI, why you like, walk along the street. and yeah, and it's a I think a really good example is like, if anything, the recent set of announcements this summer have all been kind of pointed towards the more practical than the more sort of speculative. but yeah, I don't know if the panel's got kind of thoughts on that or if they saw the recent Claude release.
I think it was literally like 90 minutes ago. So no, I haven't seen the Claude released. But I think on that point maybe being counter what I said before, which could be interesting.
You know, you think about the types of models that we're working with today, the class of technology with transformers. It's not they're not efficient at all. They require huge amounts of energy. Like there's a lot of question whether this is really the type of technology that will actually drive superintelligence. So if we are being more incentivized to focus on things like efficiency and speed, will that unlock and maybe help us discover new types of models, new types of architectures that could serve as a much better basis for potential in general intelligence systems? Yeah. That's right. So we actually kind of unexpectedly like in chasing through B2B, SaaS, we actually end up like coming out the other end and being like, I guess we have superintelligence now.
It's sort of like, sounds like what you're saying. Well, I, you know, maybe it it just helps us get a little bit more diverse in where we're, we're investing. But. Yeah. Right. And to go back to something we said earlier about like we used to have all this promise and like monolithic models will solve all your problems and will achieve superintelligence. And then I spoke a little bit about how it's actually like systems that are bringing a practical angle.
There's some really interesting papers and studies coming out that if you compare like the best in class models like GPT4o and and system approach, whether it's a genetic or not, using smaller models, you're actually on like a burrito efficient curve, you're actually achieving better accuracy than GPT4 and a much more cost efficient way. And I think when you like, if you're talking to enterprise customers, when like, this is the selling point of you're able to do more, more accurately and more cheaply. And I wonder if it's like in the future, are we going to get monolithic models that are going to be better and out of the box, or it's going to the power is going to come from a systems approach.
Well, and even the definition of like a monolithic model is, is different. Like we're seeing like, okay, this model is actually, you know, a mixture of experts of like eight smaller 8 billion parameter models either fuzed together, but sometimes they can be more independent, you know. So what is a model or a monolithic model versus what is a system model? I think those lines continue to blur.
Yeah, I think we're I think we're very much past the monolithic model. Like I think we all can safely say, like GPT4 is much closer to a system than it is to a monolithic model. Yeah, like the era of the big model is actually already over. Like we're not actually in that world anymore.
I it's it's also true to fact. I mean, if you're one of those people who uses, like, the evolution of the human mind as kind of like a projector or forecasting for like where AI is going. Yeah, it's kind of a view that like the human brain was kind of like clues together over many, many like centuries and millennia, which is like one piece being bolted onto another piece, bolted onto another piece.
And so in some ways, it's kind of a surprise that if we're working on general intelligence as something that resembles a human mind, that you'd actually end up with a model, ultimately that is like a bunch of pieces running around. It's like a bunch of kids in a trench coat, actually, is like how we achieve general intelligence. Yeah. And even the safety work that, Ilya, I mean, was leading on the super alignment, where it's, it's a smaller model that's kind of controlling to the bigger model and making sure it doesn't go haywire is, again, I mean, this sort of architecture, the sort of view that, I mean, you're going to have a bunch of things working together. So, I mean, the way they described it, was, like this weak to strong generalization.
so you have a weak model that's controlling the strong model, but I think a better way to think about it is like a wise model that's controlling the strong model. So there's some aspect of wisdom that's coming in. You like different properties of different components. can actually, keep things under control as well.
So just like, our wise, host Tim here keeps all of us under control. I think, the there's rules. I mean, there's reasons for, for all of us to exist and, kind of work together. So, that's great.
Well, thanks all the listeners for joining us again. please join us next week. And as always, if you enjoyed what you heard, you can get us on Apple Podcasts, Spotify, and, better podcast platforms everywhere. so, Maya, Kate Kush, thanks for joining us and hope to have you back on the show. at some point in the future. Thank you. Thanks, everyone. Thank you.
That's great.
2024-06-23 00:23
