Speaker 1: In the early days of computing, everyone thought about a big,
Speaker 1: colossal mainframe computer that had all the data, and eventually
Speaker 1: the model switched and what we ended up with is
Speaker 1: billions of individual small computers, all networked and operating as
Speaker 1: a collective. Might AI go the same way, where we
Speaker 1: move from giant, large language models owned by a few
Speaker 1: companies to lots and lots of individual AI agents that
Speaker 1: are decentralized and operating together. And what does this have
Speaker 1: to do with solving diseases? And why in the future
Speaker 1: we'll have an army of agents solving problems for us
Speaker 1: out there. And what to make of the fact that
Speaker 1: we will have created a whole new species that runs
Speaker 1: at a timescale a trillion times faster than we do.
Speaker 1: Welcome to Inner Cosmos with.
Speaker 2: Me David Eagelman.
Speaker 1: I'm a neuroscientist and author at Stanford and in these
Speaker 1: episodes we sail deeply into our three pound universe to
Speaker 1: understand why our lives look the way they do. Today's
Speaker 1: episode is about what happens when intelligence becomes decentralized. When
Speaker 1: we talk about artificial intelligence, the conversation is typically about
Speaker 1: one giant model, a mind blowingly massive data set and
Speaker 1: a huge compute cluster, so that we get one singular
Speaker 1: model that takes care of everything. But the first thing
Speaker 1: to note is that's not how the brain works. As
Speaker 1: I argued in my book Incognito, the brain is best
Speaker 1: understood as a team of ribles. Inside your skull are
Speaker 1: scores or hundreds of specialized modules. Some care about vision,
Speaker 1: hearing touched, some about walking, some about processing faces. Others
Speaker 1: track moving objects in the world. Other systems are working
Speaker 1: to predict what you should do next. But the key
Speaker 1: is that these systems in the brain are not like
Speaker 1: pieces of a very efficient machine where each part serves
Speaker 1: its purpose. Instead, every second of your life, these networks
Speaker 1: find themselves in conflict. They argue, they interrupt, they compete
Speaker 1: for dominance. The feeling of having a unified self is
Speaker 1: an illusion stitched together on the fly. So maybe the
Speaker 1: future of AI isn't one giant LLM to rule them all,
Speaker 1: but something more like what we are. A distributed system,
Speaker 1: a team of rival agents and argumentative network of local experts,
Speaker 1: each with limited knowledge and a unique perspective, together making
Speaker 1: something more powerful than any individual part. But how do
Speaker 1: you build that? How do you preserve data? Privacy. When
Speaker 1: intelligence is spread across devices and across hospitals and across
Speaker 1: country borders. How do you incentivize data sharing? How do
Speaker 1: you verify who's a good actor?
Speaker 2: These are the.
Speaker 1: Kinds of questions that animate my friend and colleague, Ramesh Roscar.
Speaker 1: He's a professor at the MIT Media Lab. He's an inventor,
Speaker 1: He's a systems thinker and a pioneer in this new
Speaker 1: world of decentralized AI. His work spands from privacy preserving
Speaker 1: technologies to public health tools, to new ways of imagining
Speaker 1: what AI can be from a technical point of view,
Speaker 1: but also ethically and socially. So today we're going to
Speaker 1: talk about why the future of AI might lie in localized,
Speaker 1: specialized systems that are trained on the fly, and how
Speaker 1: new architectures might allow data to stay where it is
Speaker 1: on your phone and your clinic and your city, while
Speaker 1: still contributing to a global model. So this is a
Speaker 1: conversation about architecture and ethics, competition and cooperation, and the
Speaker 1: radically different future that becomes possible when we stop trying
Speaker 1: to centralize everything and instead embrace the power of the network.
Speaker 1: So let's think, as an example, the healthcare systems. So
Speaker 1: the idea is you've got all this data locked up
Speaker 1: in electronic health records all over the place. None of
Speaker 1: the hospital systems want to share that data because they
Speaker 1: don't want other people looking at it. And it's valuable data.
Speaker 1: And so the idea of decentralized AI, for example, could
Speaker 1: be that an AI is looking at all the different
Speaker 1: electronic health records and all the different hospital systems, but
Speaker 1: no human is looking at it. With millions of records,
Speaker 1: it puts together there a big picture and discover sing
Speaker 1: is that right?
Speaker 2: Right?
Speaker 3: I mean health is a great example because I would
Speaker 3: argue that if somehow I have for every health condition
Speaker 3: all the data, I can bring it in one place
Speaker 3: and train models, I can solve it. The problem is,
Speaker 3: you know, the capitalistic system doesn't allow us to do
Speaker 3: it because of privacy, because of trade secrets, and as
Speaker 3: I said, people hold on to the data thinking it's
Speaker 3: worth something, but they don't get paid either. So if
Speaker 3: there was a marketplace for data so that they're not
Speaker 3: to share the data, but they can share the insights
Speaker 3: from it and get paid in return, then everybody would
Speaker 3: be incentivized to contribute to these insights without contributing raw data.
Speaker 1: So the idea is, let's say I'm a hospital system.
Speaker 1: I'm not giving away the electronic health records. Instead, I'm
Speaker 1: letting an AI poke its nose into these things, figure
Speaker 1: out insights from the data, and leave. And that way,
Speaker 1: I haven't given up something in particular, and I get
Speaker 1: paid for that. By the way, that's right.
Speaker 3: I mean another way to think about this is again,
Speaker 3: you want to share intelligence without sharing raw data, and
Speaker 3: that's how the human society works.
Speaker 2: The analogy would be, if I had enough money, you know.
Speaker 3: I could hire you know, a thousand medical interns and
Speaker 3: send them to If I want to solve partical health condition,
Speaker 3: I would send them to every hospital, ask them to
Speaker 3: do an internship for a year, learn about everything that
Speaker 3: partical hospital does, about how they treat a potical condition,
Speaker 3: and how their ideology pathology works, and then bring all ten,
Speaker 3: all thousands of them in one office and extract their
Speaker 3: intelligence and then solve any condition that's out there.
Speaker 2: And that would be a way to bring.
Speaker 3: Intelligence from all these thousand hospitals without bringing in your
Speaker 3: raw data. So how do you now make that happen
Speaker 3: not in the physical world, but in the digital world.
Speaker 3: By effectively sending a MICROAI like an agent that behaves
Speaker 3: like an intern that learns everything and brings back only
Speaker 3: the intelligence and in return pace for the stay at
Speaker 3: the hospital, and then you know, creates a global algorithm
Speaker 3: for that.
Speaker 1: So let's take one second to distinguish decentralized AI from
Speaker 1: agentic AI. They're related, but tells the difference.
Speaker 3: Yeah, I mean the agent DKA is a great way
Speaker 3: to think about it, because mix it very simple to understand.
Speaker 3: So the reason to use agent as an anology as
Speaker 3: an anchor for decent lazed AI is because you imagine
Speaker 3: an agent is like a medical intern, and the agent
Speaker 3: can go to the hospital, you know, you know, stay
Speaker 3: there for some time, learn everything about the data, pay
Speaker 3: for anything that they have learned, and then come back.
Speaker 3: And I can take this thousand agents and now I
Speaker 3: can ask them any new question about that health condition.
Speaker 3: And the beauty here is that I know that the
Speaker 3: agent did not steal any data because I can send
Speaker 3: an agent as a model. And model, as you know,
Speaker 3: is you know, basically a big file let's say one
Speaker 3: hundred megabyte. You a model, it goes in it looks
Speaker 3: like gigabytes of data in the hospital, but comes back
Speaker 3: again only as a hundred megabyte model.
Speaker 1: So that's you tweaked the parameters in the model exactly.
Speaker 1: We're not bringing back the raw data, yeah, which is
Speaker 1: what happens in the human brain. So if you think
Speaker 1: about centralized A versus decentized AI, in centilis you have
Speaker 1: this one massive model that's doing everything. Decentralized AI matches
Speaker 1: very well with the concept of agent AI agent take
Speaker 1: AI because the notion is that each of the agent
Speaker 1: is not just massive six hundred billion parameter models that
Speaker 1: you're hearing about from open AI and meta and so on,
Speaker 1: and these agents are much smaller, and they're very specific
Speaker 1: and they do specific tasks.
Speaker 2: So a legal.
Speaker 3: Agent may not notizing about the health agent or financial
Speaker 3: agents and so on. They're doing very specific things, and
Speaker 3: then they can get better over time, and they get
Speaker 3: better in two ways. They get better because they're looking
Speaker 3: at new data, but more importantly, they get better because
Speaker 3: they're interacting with other agents. And that's what decentalization decential
Speaker 3: plays in kind of mimics the human society because even
Speaker 3: for us, you know, growing up, we learn from some
Speaker 3: data and some experiences, but most of the things we
Speaker 3: learn are actually through interactions.
Speaker 1: So are you saying decentralized AI in a gent ki
Speaker 1: is the same thing, or a gent ki is a
Speaker 1: good example of decentralized AI.
Speaker 3: Agent ki is is kind of the brain that you need,
Speaker 3: but you need other restual ecosystem to emerge for decentralization. So,
Speaker 3: for example, what does it mean to have for agents
Speaker 3: to have commerce? How do agents pay for each other?
Speaker 3: You know, how do I know that a data that
Speaker 3: I you know, that data that I used from a hospital,
Speaker 3: is it worth one hundred dollars, thousand dollars or a
Speaker 3: million dollars? You al sort of figure out, you know,
Speaker 3: how do you make sure that the agents are not misbehaving?
Speaker 3: So beyond the intelligence of a given agent, which is
Speaker 3: classic agent kai, we don't think about the rest of
Speaker 3: the decentralization aspect of it. It's like saying, you know,
Speaker 3: I might have ability to do transactions, but that's very
Speaker 3: different than building a stock market.
Speaker 1: And so give us an example of what the world
Speaker 1: will look like. Let's say in ten years from now,
Speaker 1: when we've got a lot agents and we have this
Speaker 1: whole infrastructure for agents to communicate and pay each other
Speaker 1: and so on, what does that mean for all of us, it's.
Speaker 2: Difficult to product.
Speaker 3: But I would say in the short run, every one
Speaker 3: of us will have our own agent. You know, every
Speaker 3: organization will have its own agent. And these agents will
Speaker 3: work on our behalf. So they're anchored our identity or
Speaker 3: identity of the organization and they're working on our behalf
Speaker 3: and they are you know, they're doing commerce on our behalf.
Speaker 3: So if you think about kind of the roadmap of
Speaker 3: how this agent tech world will evolve, in the beginning,
Speaker 3: it's just about foundations, like do these agents have identity,
Speaker 3: do they have authentication? Can they really represent us? You know,
Speaker 3: what's the reputation when when my agent is using another agent?
Speaker 3: You know, can it trust? You know, like you know
Speaker 3: what ky C is, like I know your agent. All
Speaker 3: the foundational stuff. That's what's happening now in the industry.
Speaker 3: And also work at an IM. The next phase is
Speaker 3: agient commerce. Just as if you move to a new city,
Speaker 3: you know, you're figure out, you know what job you're
Speaker 3: going to get. You know, are you going to get
Speaker 3: paid fifteen dollars an hour or thousand dollars an hour?
Speaker 2: And so on?
Speaker 3: You know, you start learning, you start working over time,
Speaker 3: your skills are not that great, so you go to
Speaker 3: you know, a school, so there'll be in agient schools
Speaker 3: or agents go to agent schools.
Speaker 2: Sometimes they need to be repaired. It will be agent repairs.
Speaker 3: Sometimes there will be a risk of using an agent,
Speaker 3: so you'll have agent insurances, you know, and so on.
Speaker 2: So you have like a whole.
Speaker 3: Commerce and economy around agents. But then the third phases
Speaker 3: very interesting, which is you have very emergent behaviors with agents.
Speaker 3: Just as once you move to the city, you don't
Speaker 3: just work and you know, think about your livelihood, but
Speaker 3: you start creating social circles. You know, you have sports teams,
Speaker 3: you know, you have universities, you have you know, justice
Speaker 3: and court. So you get all this emergent behavior when
Speaker 3: folks get together and just as agents get together. So
Speaker 3: you can see this going from foundations to Asian commerce
Speaker 3: to this you know, emergent behavior of agent societies. But
Speaker 3: just assumes that the agents are anchored at human identity,
Speaker 3: we can't really see beyond this event horizon of what
Speaker 3: will happen when agents that are billions may be trillance
Speaker 3: of agents are navigating on their own on the internet,
Speaker 3: you know, meeting other agents and learning and socializing and
Speaker 3: you know, transacting on the on by themselves. So it's
Speaker 3: kind of diffrully imagine what will happen beyond beyond the
Speaker 3: scenario where they're still representing and anchored for individual human being.
Speaker 1: And even when they are really anchored on us, it's
Speaker 1: still very difficult to see what's going to happen because
Speaker 1: they can run it trillion times faster.
Speaker 3: Than we can, and they can replicate, and they can
Speaker 3: be you know, in multiple less at the same time,
Speaker 3: and they can migrate. You know, they can behave one
Speaker 3: way and we have another way in the next many
Speaker 3: second and so on.
Speaker 1: So I have a question. So a lot of people,
Speaker 1: of course, are worried about AI alignment in what happens
Speaker 1: if things go bad, And in this case of decentralization,
Speaker 1: with all these agents running around working at speeds that
Speaker 1: are much faster than we can comprehend, that it does
Speaker 1: seem like a bit of a dangerous territory to get into. Now,
Speaker 1: you might say that centralized AI also has its dangers,
Speaker 1: and that what we need maybe are checks and balances
Speaker 1: with decentral ied AA. But what does that look like?
Speaker 1: What would that mean to put checks and balances.
Speaker 3: And glad you've brought up alignment in centralized AI. The
Speaker 3: problem is different when you think about decentralization and agent
Speaker 3: KI because now we don't have to worry about you know,
Speaker 3: some one evil character taking over the world, but agents
Speaker 3: on their own could do bad things. So as long
Speaker 3: as they've anchored at a human identity I think should
Speaker 3: be in a reasonably good ship. It's almostly using like
Speaker 3: two factor authentication, you know, like the agent cannot do
Speaker 3: anything unless that says something on my phone, just using
Speaker 3: today's anology, so that should be under control.
Speaker 2: But once we go.
Speaker 3: Beyond that and agents are sufficiently autonomous that can create
Speaker 3: their own societies. Now the challenge is, you know, how
Speaker 3: do you make showed that doing the right thing. So
Speaker 3: the alignment becomes a very different problem. It becomes an
Speaker 3: algorithmic orchestration problem. So how do you have algorithms that
Speaker 3: orchestrate you know, the incentives of the carrots and sticks
Speaker 3: for the agents to behave. And that's actually a very
Speaker 3: complex problem. That's a lot for research at m T
Speaker 3: as well, because you could imagine that you know, if
Speaker 3: you if you try to use decentralization, like there were
Speaker 3: decentil issues. For DeFi, in the crypto and web three world,
Speaker 3: it didn't go very well where examples like Luna and
Speaker 3: Terra compared to stable coins today, and they had, you know,
Speaker 3: an orchestration to figure out, how do we maintain you know,
Speaker 3: their one dollar anchor.
Speaker 1: So anyway, I think that ended up crashing.
Speaker 3: And they ended up crashing because they were based on
Speaker 3: the behavioral economics that was predicted by a bunch of
Speaker 3: geeks sitting in a garage, and that didn't work out.
Speaker 3: So I think we should not rely on algorithmic orchestration
Speaker 3: of society of humans or society of agents.
Speaker 2: And I think there are very important problems to solve.
Speaker 1: So what is the thing beyond algorithmic orchestration?
Speaker 3: So you need to slow things down, You need to
Speaker 3: have some kind of a governance principle that's out there,
Speaker 3: and you know, that's how our society works. All the
Speaker 3: banks and courtes could run you know, algorithmically in many cases,
Speaker 3: we still have human in the loop and we slow
Speaker 3: things down and we trade inefficiency for you know, some
Speaker 3: control over the system.
Speaker 2: So I think that becomes very critical.
Speaker 1: So what does this mean for biology research? For example?
Speaker 3: Yeah, I think scientific research is going to depend on
Speaker 3: some kind of a decentialization because very unlikely that you know,
Speaker 3: all the data will come in plut complace, and you know,
Speaker 3: all the scientists will just pick up the phone and
Speaker 3: start talking to each other. They're all you know, we're
Speaker 3: all scientists, and we like cooperation, but also like competition,
Speaker 3: and you know, the human motivation to compete is what actually.
Speaker 2: Gets gets us going.
Speaker 3: So there will still be reviews, a competition to get
Speaker 3: your paper published in top journals, but we need a
Speaker 3: way for them to share their insights at the same time,
Speaker 3: and for those insights they get something in return, so
Speaker 3: it's crowdsourced, but something in return. I like to use
Speaker 3: analogy of Google Maps and traffic on Google Maps. You know,
Speaker 3: before that removes, there was Garment Maps, which we barely used.
Speaker 2: And today, even if.
Speaker 3: I know how to go from point to point B,
Speaker 3: you know, I open Google Maps not because I want
Speaker 3: to know the route, but I want to know where
Speaker 3: everybody else is. So I'm benefiting from the fact that
Speaker 3: everybody else is contributing the GPS coordinate and I get
Speaker 3: to see, you know, the reds and the greens on
Speaker 3: the map and turn, but turn navigation, but I'm also
Speaker 3: giving up my own GPS.
Speaker 2: Location while I'm driving.
Speaker 3: I'm contributing to the system and I'm getting great benefits
Speaker 3: in return. So can we create the equivalent of Google
Speaker 3: Maps for any societal problem, you know, whether it's scientific
Speaker 3: discovery or finding, you know, a treatment plan. Again, if
Speaker 3: you can centralize all the data, we can solve these problems.
Speaker 3: We cannot centralize it, and so we must use a
Speaker 3: decentralized method.
Speaker 1: And let me just make sure you have that. Why
Speaker 1: can't we centralize it? Isn't that what open ai and
Speaker 1: meta and everyone else is doing.
Speaker 3: So, I mean, if you look at open ai and
Speaker 3: everybody else, they you know, they scanned the whole Internet,
Speaker 3: you know, kind of in a permission less way. And
Speaker 3: that's why you could train this multi tens of trill
Speaker 3: and token models that are out there. But as I said,
Speaker 3: that's like just the tip of the iceberg. Most of
Speaker 3: the information in the world is actually locked away. If
Speaker 3: it takes health as an example, it locked away in hospitals,
Speaker 3: in pharmaceutical companies, in all the failed trials, you know,
Speaker 3: and all the research work in the lab notebooks of
Speaker 3: you know, all the amazing postdocs and students.
Speaker 2: So all the information is actually not available.
Speaker 3: So think about if all the information was available to
Speaker 3: open AI, it would do an amazing thing. It would
Speaker 3: solve healthcare overnight. But it cannot because of the competition.
Speaker 1: You know, I'm struck with it. An analogy here. I
Speaker 1: saw an interview with Isaac asimof in nineteen eighty eight,
Speaker 1: I think it was he was on jonah Lerror's show,
Speaker 1: and he said, look, I foresee a day when there's
Speaker 1: a central mainframe that has all of human kind's knowledge
Speaker 1: and everyone has a cable to their house and they
Speaker 1: can access the knowledge of the world that way. And
Speaker 1: he was almost right. But what happened instead was instead
Speaker 1: of a centralized knowledge repository, we it became completely decentralized
Speaker 1: on the Internet.
Speaker 3: Yes, I'm glad you mention that, because you know, today
Speaker 3: we're kind of the mainframe era of AI, and from
Speaker 3: that we're going to switch to the PC era of AI,
Speaker 3: to the Internet era of AI. So, as you know,
Speaker 3: in the eighties, big companies like IBM and Silicon Graphics
Speaker 3: were selled as big mainframes and we would literally connect
Speaker 3: to these machines or in a dial up motems, you know, yeah,
Speaker 3: and even to use a calculator, by the way, we
Speaker 3: had to use a dialup motem. It was not available
Speaker 3: on the machines. But in the late eighties and nineties
Speaker 3: we said, hey, that's kind of stupid. I should just
Speaker 3: have that calculator or a word or whatever the software
Speaker 3: using on my machine on a home machine. And the
Speaker 3: fact that we moved from a mainframe era a PCA
Speaker 3: I was interesting, but we needed one more thing. Before that,
Speaker 3: there was Internet, which was mostly connecting machines, and Tim
Speaker 3: Bernersley and others said in the nineties that actually what
Speaker 3: we need is a world wide web, and he brought
Speaker 3: in four things, a browser, protocols like SGTP and STML,
Speaker 3: and a notion of URL.
Speaker 2: How do you discover something right?
Speaker 3: And with those four things he transformed the Internet into
Speaker 3: world wide Web so that humans can interact with all
Speaker 3: the stuff that's out there. And one would argue that
Speaker 3: the impact of the mainframe revolution and PC revolution is
Speaker 3: insignificant compared to the impact of the world wide Web,
Speaker 3: and the same thing is true today. The impact of
Speaker 3: AI will be insignificant to the impact of the Internet
Speaker 3: and the web of AI that's or.
Speaker 1: There terrific, and so tell us about Project Nanda.
Speaker 3: So this decentralization agent tech concepts very nicely map us
Speaker 3: to this Project Nanda. Nanda stands for networked AI agents
Speaker 3: in decentralized architecture, but it's also the name of my sister,
Speaker 3: and Nanda means joy in Sanskrit.
Speaker 2: So I think, let's bring some joy to.
Speaker 3: The technology and the Internet and the web of AA agents.
Speaker 1: What is project going to do?
Speaker 3: So Project Nanda is a projecda is A is a
Speaker 3: large collective. We're looking at this three phases, how how
Speaker 3: the agent taic world is going to evolve, you know
Speaker 3: the foundations agent e commerce and you know agentic societies,
Speaker 3: uh and involves multiple universities, multiple companies, and we're working
Speaker 3: together to figure out how this you know, web of
Speaker 3: agents willieve the same way Tim berners Lee launched world
Speaker 3: Ward Web Constium.
Speaker 2: Alside of my Tea to see how we can use.
Speaker 3: The Internet technologies in a responsible way and keep the
Speaker 3: flock together. And so one should thank Tim berners Lee
Speaker 3: for keeping it an open, vibrant, you know, and neutral
Speaker 3: web that's out there, as opposed to what we saw
Speaker 3: later on in the computing you know generation, which is
Speaker 3: things like social media or things like mobile phones are
Speaker 3: highly fragmented systems and a few companies control you know,
Speaker 3: these ecosystems. As opposed to the World War Web. Today
Speaker 3: you can go online, go to go daday dot com,
Speaker 3: create a website, launch a business, create new innovations, do
Speaker 3: whatever you want. And that permissionless nature of the world
Speaker 3: ward Web has really created in an unlocked you know,
Speaker 3: not just you know treellanceer dollars economic value, but I
Speaker 3: would say scientific progress, you know, new democracies, new societies.
Speaker 3: I mean, it has really changed the way we think
Speaker 3: about it. We also think about what would happen one
Speaker 3: in the absence of project Now, it's possible that, you know,
Speaker 3: there will be only two Asient stores, just like we
Speaker 3: have app store and play store. That just two Asian
Speaker 3: store and anything you create in a and agents, every
Speaker 3: one of us will have to go through these two
Speaker 3: platforms and they won't be talking to each other. And
Speaker 3: those platforms say we use the tool, we will tell
Speaker 3: you which AI to use, We will tell you what
Speaker 3: you can do and cannot do. And by the way,
Speaker 3: any money you make from that will take thirty percent
Speaker 3: of it.
Speaker 1: So the analogy here is like Apple Store and Google
Speaker 1: Play exactly.
Speaker 3: Yeah, and imagine if the World Wide Web wasn't open
Speaker 3: and vibrant. Imagine there are only two websites you can
Speaker 3: use to create an online business. One caught Squarespace and
Speaker 3: we's call Wix, and there are only two companies, and
Speaker 3: any website you create must be created those one of
Speaker 3: those two platforms. You know, they will give you all
Speaker 3: the tools, you know, they will do all the transactions
Speaker 3: for you, They will disclose which websites are better than
Speaker 3: others are not. And by the way, any money you
Speaker 3: make on the web, you have to share thirty percent
Speaker 3: with square Space or Wix. That would happen a pretty
Speaker 3: dystopian world. Chance that the agentic Web could also end
Speaker 3: up in this highly fragmented, highly dystopian world. And the
Speaker 3: goal for NANDA is to kind of make sure we
Speaker 3: have the freedom, we have the agency, and we have
Speaker 3: ability to do this in an open and vibrate and
Speaker 3: neutral way. So all the organizations that emerged in the
Speaker 3: world Wide Web, such as world Wide Web Consortium, the DNS,
Speaker 3: i CAN.
Speaker 2: Which actually allows you to have the domain name.
Speaker 3: Servers, Mozilla, all these great organizations we have to do
Speaker 3: all that work again in the next six months. If
Speaker 3: we don't do it, we'll end up in this highly
Speaker 3: dystopian world where somebody else is controlling all the AI
Speaker 3: and all the AI agents.
Speaker 1: So out of curiosity, when we're in this future world
Speaker 1: where we've created this species that runs parallel to us,
Speaker 1: all these AI agents running around at much faster speeds
Speaker 1: than us, just out of curiosity, what do you think
Speaker 1: the Internet slash Worldwide Web is going to look like
Speaker 1: for us? Will people still go and look up websites
Speaker 1: or instead, will there be another version of websites? My
Speaker 1: AI agent can go and buy me a shirt and
Speaker 1: not you know, not have to look and scroll in
Speaker 1: the way that we do.
Speaker 3: Yeah, I mean a lot of a lot of things
Speaker 3: that have been developed online have been for kind of
Speaker 3: human conception and human interaction. And it's kind of clunky
Speaker 3: the fact that the whole world interests with the internet
Speaker 3: with your fingers on a keyboard. It's kind of kind
Speaker 3: of strange if you think about it. And so clearly,
Speaker 3: you know, the web of the future will be multimodal.
Speaker 3: We can talk to each other, you know, we'll have
Speaker 3: you know, I'll have you know, some an AI agent
Speaker 3: just talking in my ear almost like the movie hear right,
Speaker 3: So you can imagine the world becoming much more you know,
Speaker 3: much more human again, you know, as opposed to forcing
Speaker 3: us to interact through this through this keyboard. So that's
Speaker 3: kind of the possibility of human interaction. And we'll have
Speaker 3: other technologies going along with that, like VR and AR
Speaker 3: and new materials, science and new technologies.
Speaker 2: I think all that will be part of it.
Speaker 3: And then that's kind of like the human at individual level,
Speaker 3: but at kind of at a structural level, at a
Speaker 3: systemic level, you can imagine all these agents are doing
Speaker 3: great work, you know, behind the scenes. It's making sure
Speaker 3: you know, our our health is taken care of, my
Speaker 3: finances is taken care of. I mean, think about there
Speaker 3: are millions of people who are not financially savvy and
Speaker 3: they have to proactively go and check online or with
Speaker 3: their financial advisor about what they should do, you know,
Speaker 3: but imagine if there's an agent who's helping them in
Speaker 3: their financial decisions.
Speaker 2: You know. Think about education.
Speaker 3: There are billions of people out there who don't get
Speaker 3: good quality education, and people often talk about how AI
Speaker 3: is going to improve education, but Asians would have much
Speaker 3: more an impact because they can be highly personalized and
Speaker 3: they can really understand the context and to you know,
Speaker 3: use that AI. You don't have to make an API
Speaker 3: call to somebody in California. You know, you could be
Speaker 3: using you on your own device. You can be modifying it.
Speaker 3: And so I think and we'll be treating our agents
Speaker 3: almost like Tamagachi. You know, remember those toys from you know,
Speaker 3: will be.
Speaker 1: Kind of just as a remind the listeners. It's you
Speaker 1: have to pet them or take care of them, right,
Speaker 1: and then they stay alive.
Speaker 3: And yeah, there's are very addictive toys that are popular
Speaker 3: in the eighties and nineties from Japan which you are
Speaker 3: to constantly kind of nurture them and feed them and
Speaker 3: pet them and they stay alive.
Speaker 2: All those are purely digital.
Speaker 3: Characters, and Asians are going to be something like that.
Speaker 3: You know, we really care about, you know, nurturing them.
Speaker 3: And my guess is that we'll spend like one third
Speaker 3: of our life every day just making our Asians better
Speaker 3: and teaching them and coaching them and talking to them.
Speaker 3: And these Asians will be such a significant part of
Speaker 3: our life, our health or education, our social life, our
Speaker 3: livelihoods and so on that we are very kind of
Speaker 3: attached to our Asians, hopefully in a positive way. So
Speaker 3: it will not only make kind of a daily interaction
Speaker 3: more human because you're not constant looking at the screens
Speaker 3: and computers all the time. At the same time, these
Speaker 3: Asians will be doing things beyond you know, kind of
Speaker 3: you know, kind of behind the scenes, and make it
Speaker 3: again a more democratic world, you know, kind of reduce
Speaker 3: the friction between people who have information and people who
Speaker 3: don't have information, or people have a good rollodex or
Speaker 3: they don't. So all this information of symmetry, you know,
Speaker 3: power of symmetry could be addressed if everybody's empowered with
Speaker 3: their own agent.
Speaker 1: Okay, So that gives us a really good sense of
Speaker 1: what this near future world is going to look like.
Speaker 1: It may be that the World Wide Web, which we've
Speaker 1: grown so used to and that seemed like the future,
Speaker 1: it may be that that's already becoming the past in
Speaker 1: the sense.
Speaker 2: It builds on top of each other. That I wouldn't say.
Speaker 1: Agreed to the past, agreed, but the idea of tapping
Speaker 1: keyboard for a particular website, or going to Google and
Speaker 1: doing a search and then flipping through all these different websites.
Speaker 1: It seems like that's already.
Speaker 3: Yeah, I think, you know, our agents will be more
Speaker 3: like our pets or maybe a baby.
Speaker 2: We are raising.
Speaker 3: It'll be much more interactive, much more fun. I mean,
Speaker 3: we'll still need computers to do like very information heavy
Speaker 3: aspects of our life, but rest of the day, we
Speaker 3: wouldn't be like we want be writing emails, you know,
Speaker 3: we won't be kind of scrolling through the sea which
Speaker 3: rastwenty you want to go to, or we won't be
Speaker 3: you know, doctors want to be sitting in front of
Speaker 3: the EHR report records to decide how they should help
Speaker 3: their patients, and financial folks will not be spending time
Speaker 3: in front of huge spreadsheets. A lot of those things
Speaker 3: will be happening behind the scenes. We still need spreadsheets,
Speaker 3: and we still had to go to the raw, you know,
Speaker 3: source of truth. But most of the time you'll have
Speaker 3: this you know this, this agents as your companions.
Speaker 1: So what are the legal consequences if you're just blue
Speaker 1: skying and thinking about the future. If your agent goes
Speaker 1: off and does something and it ends up doing something
Speaker 1: illegal or and that causes big damage, who is responsible?
Speaker 1: You are the agent?
Speaker 2: This is a great question.
Speaker 3: Yeah, I think it's it's like saying, you know, if
Speaker 3: you have a six year old child and they do something,
Speaker 3: you know, other parents responsible.
Speaker 2: I guess I don't have an answer for it. A
Speaker 2: little bit.
Speaker 1: Different because the child, the legal system understands that the
Speaker 1: child is separate. You try to do your best raise
Speaker 1: in the kid, but the k goes. Often it's an
Speaker 1: independent entergy from the point of view the legal system.
Speaker 2: Right, I mean, I mean today's world.
Speaker 3: You know, if there's a you know, if a child
Speaker 3: uses a gun, you know, the parents are considered responsible.
Speaker 2: Again, I'm not an expert in this topic.
Speaker 3: I would say that in a lot of these protocols
Speaker 3: and systems will emerge over time. And yes, the agents
Speaker 3: will go to agent schools to get trained. There will
Speaker 3: be a char departments, you know, for if the agents
Speaker 3: are misbehaving, and there will be you know, kind of
Speaker 3: societal values that will be infused into the agents with
Speaker 3: some character and sticks.
Speaker 1: Wow, this is going to take a while after the
Speaker 1: legal system to work out, because you can imagine a
Speaker 1: bad actor programs a bad AI agent to go off
Speaker 1: and do stuff and then it says, well, it wasn't
Speaker 1: really me. I didn't know this was in the code,
Speaker 1: and yes, it caused all this problem.
Speaker 3: And I remember this is going to be agents will
Speaker 3: may or may not have may or may not have
Speaker 3: kind of national boundaries. So you know, a law in
Speaker 3: Indonesia may be different from law in Malaysia, So what
Speaker 3: does it mean. So a lot of the work we
Speaker 3: do in Project Nanda is thinking about this notion of
Speaker 3: geo fencing and creditialing and verifiability and so on. So
Speaker 3: a lot of our research goes into kind of cryptographer,
Speaker 3: get very fine that it's doing the right thing, also
Speaker 3: making sure that there's a reputation system that's you know,
Speaker 3: that's encoded and so on. So a lot of thinking
Speaker 3: about the good behavior and the bad behavior is encoded
Speaker 3: in how this agent Internet is being developed.
Speaker 1: Quick question, is there a way for listeners to see
Speaker 1: what Project Nonda is up to?
Speaker 3: Yeah, you can, you know, you can go create your
Speaker 3: own agent David in thirty seconds and we'll post a
Speaker 3: link in the in the video. Go to join thirty
Speaker 3: nine dot org ji n three nine dot org. You
Speaker 3: can create your own agent in thirty seconds. It'll be
Speaker 3: live online. Anybody else can interact with your agent if
Speaker 3: you want, you can attach more information to it. You
Speaker 3: can also start making money from it. Right now, we're
Speaker 3: just using points system because the research so you can
Speaker 3: make points. So for example, there could be you know
Speaker 3: a David agent and you're a fancy neior scientist, so
Speaker 3: you could say, hey, if you want to talk to
Speaker 3: my agent, I'm going to charge you in a dollar minute.
Speaker 3: At the same time, when you go to use somebody
Speaker 3: else's agent, maybe it costs you ten cents a minute,
Speaker 3: right and so on. So or it can go and
Speaker 3: join thirty nine dot org, which is kind of an
Speaker 3: open website to start playing with your agents, create your profiles,
Speaker 3: start make money off it, you know, send links to
Speaker 3: others and so on.
Speaker 2: So kind of early days.
Speaker 3: But yeah, definitely can go to projectnan dot org, which
Speaker 3: is the main website, which is all the technology, all
Speaker 3: the source code, all the research papers, you know, all
Speaker 3: our industry partners, university partners working together, so you can
Speaker 3: look got all of that, or you can just go
Speaker 3: to jointdy Night dot org and get started.
Speaker 1: Oh amazing. Okay, So now here's the part I want
Speaker 1: to get to. Is something that you and I have
Speaker 1: a common interest in is the analogies between the brain
Speaker 1: and computation in general. And what's interesting is this has
Speaker 1: a long history. You know. Johnny von Neuman, for example,
Speaker 1: looked to the brain and said, oh, what I'm building
Speaker 1: with this digital computer is like a brain. And nowadays
Speaker 1: people often look at the brain and think, oh, it
Speaker 1: is like a digital computer, and so on, and so
Speaker 1: they're always looking to each other. But here's the interesting thing,
Speaker 1: as I think you know, in my book Incognito a
Speaker 1: while ago, I wrote that the brain, really the way
Speaker 1: to think about this is like a team of rivals,
Speaker 1: where you've got all these different neural networks that are
Speaker 1: all trying to drive this ship of state, and they
Speaker 1: have different desires and different needs and different outcomes that
Speaker 1: they're looking for. And your behavior at any given moment
Speaker 1: is who wins the parliamentary vote. And this is why
Speaker 1: humans are so complex. This is why you can get
Speaker 1: mad at yourself, or cuss at yourself, or cajole yourself,
Speaker 1: or contract with yourself, because you know, if you were
Speaker 1: a single thing, we'd say, wait, who's talking to whom exactly?
Speaker 1: But what's happening is you're made up of all these
Speaker 1: different things. So as a rough analogy for today's conversation.
Speaker 1: It's like, we have a bunch of different agents that
Speaker 1: are running in there, and somehow you get this emergent
Speaker 1: behavior that we call you. But of course you're not
Speaker 1: the same person from from moment to moment, no hour
Speaker 1: to hour. So I want to explore this analogy here.
Speaker 1: What's your reaction to this when you think about the
Speaker 1: similarity between a future society of AI agents and what's
Speaker 1: going on in the brain.
Speaker 3: I mean, you know, Marvin Minsky also that Mighty Media Lab,
Speaker 3: you know, talk about the society of the mind and
Speaker 3: this notion that there's no single brain. But you have
Speaker 3: all these components of brains you call them asians, and
Speaker 3: they're all working together without a single orchestrator, and each
Speaker 3: of them are seemingly doing meaningless task, but you know,
Speaker 3: together they you know, they achieve something amazing.
Speaker 1: And as you know, by the way, what I think
Speaker 1: Minski's model is missing is competition. And that's what seems
Speaker 1: to figuring on in the human brain, is that these
Speaker 1: little agents are working together. They're all competing slash cooperating.
Speaker 3: Yeah, and David, you wrote an article in I believe
Speaker 3: twenty eleven eleven magazine where you talked about the conflict
Speaker 3: as an important component of all these agents talking to
Speaker 3: each other.
Speaker 2: I thought that was amazing that you.
Speaker 3: Wrote that even before the twenty thirteen alex netpaper which
Speaker 3: brought us, you know, a lot of this amazing new
Speaker 3: machine learning benefits, So you're like two years before that.
Speaker 3: And so this notion of cooperation competition between this micro
Speaker 3: ais or subcomponents, I think it's a very important idea.
Speaker 3: And the fact that there's no global orchestrator either. Even
Speaker 3: our prefrontal cortex is only doing integration but not doing orchestration, right, So.
Speaker 2: That's an important concept.
Speaker 3: So if you take that analogy further up, if you
Speaker 3: use like that's decentralization, right, because you're letting all these
Speaker 3: subcomponents that are doing highly specialized tasks and there's an
Speaker 3: emergent intelligence from that is exactly a positive of how we.
Speaker 2: Should be thinking about centralized intelligence.
Speaker 3: And I think Open AI and you know, Google, they
Speaker 3: all realized because what happened in the research world is
Speaker 3: we started going towards concepts like mixture of experts, Like
Speaker 3: what is that mixture of experts? Is highly specialized components
Speaker 3: of machine learning models that work together to figure out
Speaker 3: what you should do. And then they have these concepts
Speaker 3: like routers, where when a task comes in, you decide
Speaker 3: which part of the which experts within this mixture of
Speaker 3: experts should be used. If you remember in the beginning
Speaker 3: when charge epedy came out, it couldn't do math very well,
Speaker 3: and of course you shouldn't be used a language model
Speaker 3: to do math.
Speaker 2: You should be routing it to a simple calculator.
Speaker 3: So and now you know there are many other search
Speaker 3: branches that happen in any model that you use, lessia
Speaker 3: on chargipat. So what folks have realized very quickly is
Speaker 3: that actually training one giant model doesn't make sense. We
Speaker 3: wore to kind of route it to all this other intelligence. Now,
Speaker 3: if you take that analogy further by the way, and
Speaker 3: then we came to do this this modern world of
Speaker 3: chain of thought or tree of thought and then reasoning
Speaker 3: and all and all of that is basically pointing us
Speaker 3: to this one direction, which is the concept of society
Speaker 3: of the mind in a cooperation competition, the conflict that
Speaker 3: you talked about.
Speaker 2: So it's moving in that direction.
Speaker 3: So why don't we imagine what could happen over time,
Speaker 3: which is it's not only that, you know, all those
Speaker 3: subcomponents of AI are working in this one machine of
Speaker 3: chargepat but actually they're working across the world. And there's
Speaker 3: an amazing health agent that's running in Manila, There's an
Speaker 3: amazing legal agent that's running in you know, in in Rio.
Speaker 2: You know, there's an.
Speaker 3: Amazing softwaregent that's running in Bangalore. There could be all
Speaker 3: these things running.
Speaker 2: All over the world.
Speaker 3: They are updated, you know, through innovators locally and through
Speaker 3: which everyone gets benefits of this global intelligence. So we
Speaker 3: can see that progression happening. As you said, in the
Speaker 3: Society of the Mind, one missing piece was this notion
Speaker 3: of rivalry or competition and conflict. Another piece that's missing
Speaker 3: or is required, I would say as we go to
Speaker 3: this decentalization is the.
Speaker 2: Fact of incentives.
Speaker 3: You know, why should this folks actually participate, and when
Speaker 3: they do participate, how to make sure there are no
Speaker 3: bad actors? And so if you kind of take that
Speaker 3: thought process forward, you realize, wow, first of all, it's
Speaker 3: not centralized, is decentalized. Second is that there is not
Speaker 3: only cooperation, but there's competition and rivalry. Okay, but to
Speaker 3: support that we need different incentives for them to participate.
Speaker 3: But then if you proved incentives, bad actors will have
Speaker 3: incenter to participate although they have nothing to contribute, or
Speaker 3: they're maliciously contributing. So we need a way to deal
Speaker 3: with those productors. And then you say, okay, at the
Speaker 3: end of the day, we got all the thousands of
Speaker 3: millions of ages doing strange takes. How to bring all
Speaker 3: of that together, right, So you need some kind of
Speaker 3: integration of that. So that's basically what Project Nanda is.
Speaker 3: That's basically what Descent Last Day is. The same way
Speaker 3: of brain and society's work is going to get mimicked
Speaker 3: in the future of the Internet.
Speaker 1: That was my interview with Ramesh Rascar. As I've talked
Speaker 1: about on earlier episodes, we live in a world increasingly
Speaker 1: shaped by invisible architectures, by algorithms we don't see, by
Speaker 1: recommendations we don't question, and centralized systems that quietly make
Speaker 1: decisions on our behalf. But what Ramesh is asking us
Speaker 1: to picture is something different. He's asking what if AI
Speaker 1: could protect your privacy instead of compromising it. What if
Speaker 1: sharing data didn't mean giving up control over it? Buried
Speaker 1: in here are there lots of questions about trust, about power,
Speaker 1: about what kind of society we want to build, and
Speaker 1: Remeasser would be the first to admit that there are
Speaker 1: so many questions that still need to be worked out.
Speaker 1: But let me say two things. First, I think it's
Speaker 1: so deeply important that people like Ramesh and all of
Speaker 1: his colleagues are pulling up chairs at the table to
Speaker 1: try to figure out the structure of these questions because
Speaker 1: agentic AI is already here and its future is only
Speaker 1: getting more complex and fast and ubiquitous. So this new
Speaker 1: type of society that we touched on today, with this
Speaker 1: parallel species running at speeds we can't even comprehend all
Speaker 1: this is an inevitability and it's not even particularly far away.
Speaker 1: So I'm happy to see collections of smart humans trying
Speaker 1: to build the right lanes and guardrails. Second, if the
Speaker 1: brain teaches us anything it's that intelligence doesn't have to
Speaker 1: come from top down command in the brain. It arises
Speaker 1: from dialogue and disagreement and competition among subsystems that don't
Speaker 1: always see eye to eye but still somehow make it work.
Speaker 1: So in the AI world, what if intelligence could emerge
Speaker 1: from a constellation of local minds, each acting in context,
Speaker 1: but contributing to something larger than themselves. I'm really interested
Speaker 1: to see what we are going to learn about intelligence
Speaker 1: in ten years when we're looking at billions of agents
Speaker 1: running around and we're asking if that collection somehow gives
Speaker 1: us artificial general intelligence. So that's the current game, and
Speaker 1: in Ramesha's view, our best shot at building ethical, robust, intelligent,
Speaker 1: and democratic AI is to let go of the fantasy
Speaker 1: of central control and the despotism of oligopolies and instead
Speaker 1: learn from them messy, resilient genius of our own biology.
Speaker 1: Go to eagleman dot com slash podcast for more information
Speaker 1: and to find further reading. Join the weekly discussions on
Speaker 1: my substack, and check out and subscribe to Inner Cosmos
Speaker 1: on YouTube for videos of each episode and to leave
Speaker 1: comments until next time. I'm David Eagleman, and this is
Speaker 1: Inner Cosmos.