The rhythm of social media is frenetic. But what if AI can give us more choice about the speed at which we live our lives?
In his article ‘Time Machines’ Nicklas Berild Lundblad outlines the thesis that AI has the potential to be a ‘temporal mediator’, enabling a decoupling of computational and biological time. Nicklas is a writer, investor, and formerly Head of Global Policy and Public Affairs at DeepMind.
Philip Bell: You’re an investor, advisor, writer and researcher working across a number of different think tanks and writing a blog which I’m a big fan of—which is why I asked you to come on today. I previously worked at DeepMind on policy, so I loved your article “Time Machines” and it was really thought-provoking. It really made me consider some of the possibilities of how an AI-influenced world might sort of play out. So that’s why I was really keen to talk to you.
My first question would be: how will AI influence our experience of time?
NBL: The article has this fundamental idea that there are two kinds of time. There’s biological time and computational time. Everything that we’ve done as human beings—our relationships, our institutions, our society, our economies—they all evolved to run in biological time, in our time. Biological time is evolutionary time. It’s the kind of time of seasons. It has this particular pace.
But as we developed computers and machines, we were able to create this other pace layer—to use a term from the literature—this computational time that’s much, much faster. We can now do calculations at a speed that no human being could do. We could play a million chess games in a very short time.
So we have developed this computational layer of time, and there’s a tension between the two. Many of the institutions that we have developed are not necessarily built to run in computational time. Markets, for example, for a long time struggled with figuring out how to deal with high-frequency trading.
My idea in the article is essentially this: maybe we can use artificial intelligence as this mediator that understands biological time because it can communicate with us, but also can operate in computational time. So it can help us prioritize across the many different things that happen in computational time and give us an effective interface. Artificial intelligence becomes a temporal interface between these two different pace layers.
That’s the basic idea of the article. Not revolutionary in any sense, but I think it addresses this notion that we have of everything accelerating, everything speeding up, and at the same time, we have this upper limit beyond which we cannot speed up. We have 24 hours a day of attention and that’s it. That’s what we can spend.
Philip Bell: One of the really interesting ideas in the article was you put it that perhaps most significantly this bifurcation will enable individualized relationships with time itself. That made me think: if people are able to have individualized relationships with time, that might influence identities themselves.
Many scholars have talked about how the experience of time has helped form identities. For example, Benedict Anderson in his famous book about the origins of nationalism describes people reading a newspaper across different parts of—let’s say Germany—for the first time in the late 19th century, which created a kind of simultaneous feeling that you’re inhabiting the same time because everyone would read the newspaper at the same time in the morning. He described this as “national time.”
Could this new capability—if people are able to have their own individualized relationship with time—influence identities? Do you think that could allow small communities to have their own identities based around time? How do you see that influencing culture and identity?
NBL: That’s a great question. The way to think about this is to think about the way that we constitute our “now.” Essentially what Benedict Anderson is talking about is this notion of a national now, national time, national rhythm.
There’s plenty to be learned about how we constitute a now even just looking at our own nervous system, because whatever you’re experiencing is in the past. You’re not experiencing the now directly because your nervous system needs to collate all of the signals from your body, all of the impressions and perceptions that you have into something that can be a single, coherent now.
That’s why, paradoxically, if you—God forbid—were to be shot in the head, you wouldn’t experience it. Because what would actually happen is that before you can constitute the now of that moment, everything would go black. Just like—spoiler alert—in the last episode of The Sopranos.
One of the things that I think is interesting is to think about: how do we then constitute now across different groups? You said nations, communities can constitute their own nows. And yes, I do believe that artificial intelligence could be a core part of constituting that now. But we should also remember that there’s a really interesting question here about how artificial intelligence constitutes its own now.
Because now we’re talking about multi-agent systems. They need to coordinate with each other, they need to find a pulse, a sync, so that they can start to figure out what a now is. I think a little bit about this like the Empire of Rome. The Emperor of Rome had a now, but the pace of that was essentially what it took to ride from one part of the Roman Empire to the center. So that was the fastest possible now that Rome as an empire could experience.
I think this is true also for technology and human beings: the fastest possible now we can experience is the least common denominator when it comes to speed. So in hybrid communities, we will still be limited by biological time. But you’re right—the pace, the time, the temporal experience, the constitution of the now is essential to identity.
You can also probably say that you have different identities in which you constitute your now at different pace layers. You have one identity which is your personal experience of the world around you. And then you have a communal identity—you get together with friends and you update each other. That’s the first thing you do, right? “What’s up? What’s been happening lately?” That’s you building your now together. So there are all of these different nows that you operate through and in.
Philip Bell: That’s a really interesting point. Going back to the comparison with what does the now mean for AI—I think with current large language models, one difference between the way humans think and the way large language models process information is that they can’t really dwell on anything for a particular period of time. Information is processed between layers at a fixed time.
I actually read about a new paper in Europe by a company called Sakana—they created this idea called “continuous thought machines” where they basically introduce time into computations so that there is the ability for the model to dwell on different pieces of information according to different time speeds. That is an interesting difference between humans and AI currently—time, in a sense, is fixed in how AI processes information.
Historically some scholars have said reading was quite important for human culture in that it allowed for asynchronous thinking. Whereas dialogue—one has to think on the spot, in the moment—reading allowed for asynchronous thought. You could say chain-of-thought in large language models is sort of allowing for some element of that, buying time to some degree. But that is a sort of difference. How do you see that playing out?
NBL: You can turn the question on its head to some degree. What you can say is: how do we build things like dwelling machines, a machine that can dwell on something?
One of the things we often do is that we try to say, “This is how the machine thinks, this is how the human being thinks, and here’s the difference between them.” A much more interesting approach, I find, is constructive. To say, “Okay, how do we build a dwelling machine? How do we do it? How do we build a machine that can be filled with regret?”
All of these are temporal feelings. They all have to do with how we interact with time. So if we accept that it’s like an architectural construction problem, then we have to ask some really hard questions about: how would we model regret? How do we model dwelling on something?
Dwelling on something sounds a little bit like a loop, right? So I come back to this thing again and again. I’m not coming back to it necessarily in order to resolve it. It’s not like I’m doing a loop until I can finish the calculation. I’m coming back to it because I believe that the change in me when I come back to it will be meaningful for how I can approach the thing I’m working on.
For example, in art, I might be able to do something really quickly and just write it once and be done with it. But that’s not what poets do. Poets write a poem, they come back to it. They feel it because they change in between interacting with the thing. So there is this question: Okay, I’m building dwelling. I know it’s a loop, but it’s a loop where the object of my dwelling is not changing as much as I am in between the loops, in between the cycles. So what is that structure in me that then needs to change? How do I capture that?
What all of this teaches us is that temporal architectures are really complex, but they’re probably also a really core part of what it means to be intelligent. And of course, you also have—and I’ve written about this on the blog—you also have the ultimate temporal horizons. A lot of our intelligence is structured the way it’s structured because we die. We are finite beings. Being a finite being means you have to structure your experience in a certain way. You can’t spend infinite amounts evaluating two almost equal options. You just have to pick. So that means that death is the great tiebreaker. It constantly forces you from afar to make decisions.
That’s another temporal architecture and intelligence that’s deeply embedded in everything that we do. I think that the more we think about temporal architectures and intelligence, the more we realize that there is a lot here that actually is key to human intelligence. And that’s not replicated in what is sometimes a more atemporal model of artificial intelligence. Not saying that you can’t—I think you really can. But I think we will see—I could easily imagine that a specialization in AI research in the next 10 years or so will be the design of temporal architectures for intelligence, like thought machines or what we talked about before: context, chain-of-thought, all of those different things. Memory.
We speak about memory as if it was something we could add on. We need memory and planning, we say, when we’re building the next generation AI on the path to AGI. And I think that’s a simplification because memory comes in so many different forms. If you read the works of Paul Ricœur, for example, you realize that memory, history and forgetting are super complex—not even individual mechanisms, but they’re deeply embedded in our social interaction with each other. The way we produce history, forgetting, memory—it has to do with how we interact.
And this is another thing that I think is sometimes lost in the discussion: time exists between people. It’s a relational property of a system. It’s not an individual characteristic. An individual doesn’t have time. It experiences time vis-à-vis others. And modeling that relationship, I think, is a really interesting philosophical and probably also architectural question.
Philip Bell: That’s really interesting. Is that why—because you mentioned previously about multi-agent systems—is that why that introduces a new kind of temporal element, because there’s the temporal relation between the different agents?
NBL: And you can pace them in different ways. You can almost imagine orchestrating basically on temporal aspects. Here’s a slow agent that comes in once every 10 cycles and asks the same question. Here’s a very fast agent that collects a lot of information, synthesizes it, produces outcomes. And then as you design or orchestrate your multi-agent framework, you can use time as a key variable in how you design it. There are probably also safety and security measures that you can implement that have to do with the way you design temporal interaction in multi-agent frameworks.
Philip Bell: Yeah, that’s really interesting. Going back to the point you made previously about death—to bring it back to death...
NBL: Yes, let’s go back to death. More death.
Philip Bell: Isn’t that what Heidegger said? We should spend more time thinking about death. But no, I believe I watched a lecture by Daniel Dennett where he was saying that one of the differences between AI and humans is that humans have more skin in the game because we’re mortal, because we die. So I suppose that’s an interesting point about how that structures our temporal relationships with one another and because our agency is more or less, in a subconscious way, related to the fact that we’re going to die.
But one thing I wanted to ask is: I think the vision that you paint in the article is really positive. You say that, for example, AI systems adapt to us while we adapted to computers. I personally feel like sometimes social media kind of speeds up my life in a way that isn’t necessarily great for me. So I think that’s really interesting that you paint such a positive vision of AI systems adapting to us rather than us adapting to them.
How likely do you think is this scenario to play out—that AI systems adapt to us? Are there other possible scenarios? And is it partly our choice as to how these different scenarios play out?
NBL: It’s definitely our choice. It’s our choice also how we interact with social media. The fact is that we have agency and we can choose to deploy it or not. And I think sometimes it’s hard because architectures can capture us in ways that make it harder for us to exercise our agency. It’s an old problem.
Both Plato and Aristotle spoke about something called akrasia, which is weakness of will. Now Plato was quite rough about it. He was like, “There’s no weakness of will. If you realize what’s right, then you do what’s right. So it’s just a question of you not knowing what’s right. That’s why you spend too much time on Facebook.” Whereas Aristotle was like, “Well, hang on, let’s give people a little bit of benefit of the doubt. Some people may do something that they know is not actually good for them, because we have weakness of will. It really exists. It’s not just a lack of knowledge.”
And I think that one really interesting question that we will have to address is: how do we build architectures that augment our autonomy in different ways? And I think this is the natural evolution of the privacy debate. The privacy debates have been a lot about how do we make sure that information about me is not leaked and used against me in ways that reduce my ability to do things. And I think the natural extension of that is to say: how do we design not privacy-enhancing technology, but autonomy-enhancing technologies? Technologies that allow us to deal with and eliminate akrasia wherever it comes up.
So it’s partly an architectural problem, partly an agency problem. You have to choose it. You have to want to have stronger agency. And then the other thing that we have to do is to figure out: are there really good ways in which we can build artificial intelligence to be autonomy-augmenting rather than akrasia-augmenting, for lack of a better term, feeding on our will in different ways?
And this is philosophically a really difficult question because if I design a technology to strengthen my will, is my will then stronger or weaker because I needed a technology? And now I’m reliant on this will-amplifying technology. Does that mean that my will has actually weakened? And it’s like the question of: should I really ever use a car because then I will not be as fit as if I run everywhere?
And we have to decide how we think about ourselves and what we think is reasonable to include in our identities when it comes to different kinds of technologies. The car is reasonable because it’s such a great advantage over just running everywhere. And we will probably find equivalents of that with certain artificial intelligence technologies that are just so good that they help us actually do more of what we want, but they remain silent on the question of what we choose.
That’s going to be a really interesting design problem: how do you build a technology that amplifies your will without directing it in different ways? And in some cases we want our will to be directed too. I have a personal trainer. He’s great. He’s not necessary. I mean, my will is not necessarily to turn up at 6 a.m. in the morning, but I use him as a will-amplifying, augmenting technology in order to get into shape.
So this question of how you build autonomy into a system is really important. And I’m an optimist. The reason I have this bright perspective in the essay is I think we have the ability and will to do this and we need to choose it. And I think we can if we want to. I don’t think there’s any technological determinism that leads to an outcome in which we’re necessarily the slaves of the machine.
Philip Bell: I totally agree that a lot of the time it feels techno-deterministic the way people talk about technologies, and I think that’s also an interesting point in itself. But in terms of supporting agency and autonomy, it’s interesting because, as you said, it feels like that isn’t something that’s commonly talked about—at least in the debates I hear in the mainstream about utilizing new technologies such as AI.
Do you think that there is a need for a new kind of vocabulary and intellectual scaffold for thinking about and defining what agency might be? Because I was sort of thinking of Amartya Sen’s work on capabilities. Or do you think we already have it? Why is it that we talk a lot about privacy, but we don’t think about autonomy and agency in terms of introducing AI currently? Is it because there hasn’t been enough work on building an intellectual scaffold around how to define these things, or is there another reason?
NBL: No, I think you’re on to something. I think in many ways, artificial intelligence is this computational science giant on philosophical clay feet. It needs to figure out how to deal with a lot of the philosophical concepts that go into things like intelligence, agency, autonomy—all of these different things. The integration of time into all these perspectives. But I think it’s happening. There are some really good people out there thinking about it. Some strong philosophers really trying to understand this. There’s plenty of my former colleagues at DeepMind who are brilliant at this stuff.
And to some degree, I think what we need to do is to find a way to perhaps frame the discussion about artificial intelligence in these terms: how do we want artificial intelligence to work? We should also focus on risks. We do, there are lots of risks. But we should have more of a discussion around who do we want to be with a machine, rather than what will the future look like with the machine. So it’s a question of how you frame your future perspectives to some degree.
I’m worried that often we slip into techno-determinist thinking when we talk about whatever technology it might be. “This technology enables this, thus this will happen.” Well, it also enables all these other things that we can make happen if we want to. And the challenge of course is that we have to figure out what it is that we collectively want.
Philip Bell: Yeah, that’s a really interesting point. I wonder because in the article you described that technology can speed some things up, but then there are other areas of life where you can’t really just speed things up due to using technology, like biological processes, for example.
NBL: Or art. One example in the article is that you can imagine playing all of Bach’s work in a microsecond, just executing the notes, but then you won’t really have played Bach’s work. So I think there are some things that only exist if they’re performed temporally in a certain way.
Philip Bell: Exactly. I sometimes listen on 2x speed to audiobooks and sometimes my girlfriend looks at me like, “Why are you doing that? You shouldn’t listen on 2x speed.”
NBL: Yes. But it’s a bit slow, I do that too. I never listen at speed to Bach though.
Philip Bell: Yeah, true. I think listening to music is a whole different thing.
NBL: If you find somebody doing that, you would have found somebody who has a really different attitude to music, where the point of the music was the informational content, not necessarily... I can imagine that maybe there’s a professional musician that does that, listens to music sped up. But it’s a really interesting thought experiment: who would actually be listening to classical music at 2x, 3x pace because they just wanted to consume it faster? That’s interesting to me. I would love to know if there’s someone out there doing that.
Philip Bell: Yeah, you could listen to multiple albums in an evening. I guess if you’re trying to experiment and think about new types of music, maybe you might just warp music in lots of different ways including speeding it up and slowing it down. But it wouldn’t be, as you said, in that scenario it wouldn’t be just trying to get the information content of the music.
One thing I was going to ask about this is: Mancur Olson’s work on building coalitions is interesting in this regard because he argues that coalition building is necessarily a very long-term endeavor. And I’ve been really interested recently in the argument by Henry Farrell—he’s been arguing that AI should be considered a cultural technology that’s equivalent to the market or equivalent to democracy in that it’s basically a coordinating technology because it can classify at new speeds.
And I guess what I was wondering was: building institutions, how is that time-bottlenecked? Is that something that could be sped up using AI, do you think? Or is that one of the things where it’s got to find its time speed?
NBL: Yeah. No, I think it’s hard to speed up building institutions, partly because institutions are the result of repeated interactions. Now you could imagine artificial intelligences building institutions of their own much faster, because they can have repeated interactions much faster than we can as human beings. So there is this world in which you have a set of institutions that are solely, exclusively catering to different kinds of agents.
So these institutions would then be different contracts, say, between agents that they all agree to, standardized ways of thinking about delivery of goods, those kinds of things. And maybe you could get a Lex Agentia, which would be like the Lex Mercatoria between traders back in the day. And they would have their own law, they would have their own institutions, their own way of resolving disputes.
But if you think about any institution that contains a human being, that human being needs to repeatedly be able to interact with someone for the institution to arise. And then they need to invest their intentionality in something. John Searle famously said that institutions are the product of collective intentionality. And I think there is a lot of truth to that, which means that the pace at which you build institutions is the pace of intention. So that’s where you sort of end up.
And I think, again, when it comes to hybrid institutions where we have both agents—artificial agents and people—the lowest common denominator holds. We need to feel that we interact with the technology in such a way that we over time come to trust it.
Philip Bell: That’s really interesting. And I guess going back to your previous point about supporting agency, I wonder whether AI could help build collective agency as well as individual ones. I know that the Collective Intelligence Project have been doing work on getting AI to quickly find the commonalities between 30 people in a room, for example, which would be hard for a human to do in real time, but would facilitate a real-time conversation. I think that’s an interesting, also potentially positive vision of use of AI—supporting collective agency as well.
NBL: Yeah. And you can almost turn it around. You could say: I think that agency primarily is collective. The notion of individual agency flows only from this idea that we together create agency. If you had one single person in the universe, would they then have agency? Would it be possible to have agency then? Whereas agency is actually dependent on being a part of a group where you can direct your will in certain ways and others can either join with you or oppose you.
So I think that a lot of the agency we have is collective first and individual agency is a result of that. It’s just like individuality. We have this idea that we are born with an inner individuality and then that individuality expresses itself and we are who we are. But in reality, to go back to your quote from Heidegger, Heidegger notes I think somewhere that we’re “strewn in the eyes of others.” So identity is constructed by the eyes of others. And if that is the case, then I think agency might well be the same.
Identity and agency might both be collective phenomena before they become individual, because identity and agency are tightly coupled. So maybe the question then is: what happens to this collective formation of agency first when it also has artificial content? Can artificial agents be a part of forming our agency so that we actually want new things when there are artificial agents in the collective that shapes our agency in the first place?
And I do think that’s true. I think we’ll see certain very simple effects of that. Not wanting necessarily the same as the machine wants, but wanting in ways that are reminiscent of what the machine wants. Framing the world in ways that are reminiscent of the way that machine frames the world. And there’s this constant interplay between us and the machines that we use. Just like Heidegger—what are you? There’s the constant interplay between us and the tool or Zeug that we use in practical—say carpentry or something like that.
Philip Bell: That’s really interesting. That was just making me think, because I think one thing that’s important in my identity is memory. I know you were speaking about memory earlier, and I’m now interested to read Paul Ricœur’s work. One thing that I think is sort of an interesting possible use of AI is in supporting human memory.
We tend to think that as a society, relative to past societies, we’ve kind of forgotten the importance of memory to a large degree and our information systems are quite sparse and don’t support memory. Whereas in the past—this is slightly generalized—but in the past societies tended to have much more cyclical and thick knowledge environments which enabled people to retain information better. But in an AI world, I think it’s possible that AI could enable people to remember roughly whatever they want. Whereas today, most people kind of remember things based on chance. And I know...
NBL: But is that true though? I mean, I wonder if you remember based on chance because my pushback will be: I think you remember on the basis of who you are, the stories you tell yourself, the narrative structures you put into your life, and those have a selection mechanism built into them so that some memories surface. You come back to some memories because they’re constitutive of the story you tell yourself.
So there’s this distinction in some philosophy between history and memory where history is the things that happened and memory is the way you structure them and forget. And I think that there is a lot going on—consciously, or maybe not consciously, but going on in memory that is not random. I don’t think that what you remember is random.
It’s not as if you wake up one day and then you remember, “Oh, I had a glass of orange juice on the 4th of February in 1994.” If that were to happen, you would probably be quite surprised and you would worry. And your first question would actually be this: “What does that mean? Why do I now remember that glass of orange juice in February 1994?” That must mean something. And that sense of meaning is deeply tied with a sense of memory because it’s tied to the sense of story.
So we remember what we want to remember in the story that we tell about ourselves. And that’s not necessarily the historical events. And I do agree with you that AI could change that. It could make retrieval of historical facts easier, which might actually allow people to rewrite themselves. Because currently you strengthen the memory that’s in the story you tell about yourself. But the things that could tell a different story are forgotten. Your forgetting is also quite strategic. You forget who you do not want to be.
So to some degree, you could then imagine an AI that could retrieve what was recorded—not remembered, but recorded—and allow you to remember it, to make it a part of your memory and then rewrite yourself.
Philip Bell: That is really interesting. So it’s kind of a corrective almost—or well, corrective’s maybe a normative term—but yeah, it provides maybe a counterweight to the narrative you’ve built up. Now that’s a really good point actually. I mean, I think you’re right that maybe it’s not totally random. I guess that it doesn’t feel to me to be totally deliberate either. There are lots of things that I would like to remember that I can’t remember. I would say I forget most of the things that I read about and I don’t want to forget most of the things that I read about. And I guess that’s what I think is to me quite exciting about AI—it seems to provide the possibility that I will be able to remember more of the things that I want to be able to remember.
NBL: I think you want to make a distinction between retrieve and remember, because the AI might—you might talk to it about a book you read. This I recommend actually, I actually might do this when I read a paper or book: I speak to Claude and then I make sure that there’s a small note of it so I can come back to it and I can retrieve that conversation. The reason I want to retrieve the conversation rather than what was written in the book is I want to retrieve how I read the book because my reading of the book is how I want to remember it. The retrieval I want is the retrieval of the remembering, if that makes sense.
So to some degree, I think computational technology already allowed you to search, right? You had information retrieval technologies—they had nothing to do with AI. And information retrieval is different from remembering. But how we then retrieve remembrances is an interesting question.
And I think this goes to the point we made earlier that all of these mechanisms are much more layered, complex, interconnected and messy than we would like for them to be. The idea we think about memory in a von Neumann machine is clean. There’s this random-access memory, there’s this long-term memory and then you retrieve things from it and you put it into the computation. That’s not how we work. The narrative we have structures the remembering we do. Some things we want to be able to retain—like information, you retain information rather than remember it. And then you want to be able to retrieve that information later at some point. But if you do, is that really remembering or is that just retrieval?
There are all these nuances in language that we have evolved over time. And if we pay attention to the way we talk about it, we realize that these are different mechanisms, still reproducible I think. And I think it still would be really valuable to build an AI that can make a difference between remembering something and retrieving something. But it would be very different from just boiling it all down to the simple model where there’s memory and processing.
Philip Bell: That’s a really good point. I wonder also whether—because in some sense AI recalls older attempts to unify human experience through knowledge. I was really interested by an article by Patrick Hutton I read recently where he talks about different mnemonic systems of a time, including Renaissance mnemonic systems. They had memory palaces and Camillo built a memory theater and there was a memory theater in London built by Fludd. And he even talks about how Freud in some ways thought that you could get to some sort of unifying human experience through memory. But he argues that since roughly the ‘60s and post-structuralism we have actually lost that aim of trying to unify human experience through connected knowledge.
And I suppose that’s something that AI can, just by its affordances, support—connecting knowledge and enabling someone to reach into a common thread of human experience through that knowledge.
One thing I was going to ask you actually is: I know in one of your other articles you talk about how sensors might become more important in an AI age partly because of the ability to compute lots more information. And I wonder how that might interact with human memory and identity. I suppose if I’m able to actually get much more empirical data about the world... I don’t know if that’s how you were thinking about it, but can you foresee a world where I’m able to get much more empirical data about the world through other systems as well?
NBL: Yeah, no, you can definitely get the data, but then you have to translate it into your own sensorium. So you’re limited by the senses that evolution deigned to give you. You’re going to interpret it through your own sensorium anyway. But it’s such a good question because it gives us the opportunity to rephrase a paper by Thomas Nagel. Thomas Nagel once wrote a paper that was called “What Is It Like to Be a Bat?” He essentially asked this question very simply: because a bat has echolocation and we don’t, can we imagine bat consciousness with echolocation? Because the senses actually structure consciousness differently.
And going back to our discussion, then you can now actually ask the question: what is it to remember like a bat? What is it when a bat remembers and how is that different from how we remember? It’s a really interesting question because bats probably remember other things than we remember in a structure that we can’t replicate.
The reason I wrote the essay about sensors was I was fascinated by the fact that an AI essentially could collect any kind of physical measurement data. Anything you physically can measure can become a sensor. You can imagine that you track all of these different senses—and there could be millions of them—and then you could structure an understanding of the world from all of these different senses. And that in itself would give the AI an ability to understand our world in a very different way than we do. And you would have to ask the Nagel question: what is it like to be a multi-sensorial AI? And what is it like to remember like one?
And you can sort of go on and you would probably end up in worlds where the amount of senses... Say for the sake of argument that you had an AI that had a million senses—a million different physical measurements fed into a massive AI that in some way construes its understanding of the world from that, finds patterns, interactions, correlations—you can find different kinds of affordances in that world that we are not even aware exist between different kinds of physical quantities that we hardly notice. That kind of AI would have a very different consciousness, a very different intelligence from ours, and it may well end up being completely impossible to communicate with, because communication presupposes some overlap in sensoria, in the senses we have.
Evolution was extraordinarily parsimonious when it gave us our senses. We have, give or take, 6 to 11 depending on which biologist you talk to. And those senses are what we make do with. But if you look at the amount of possible senses that you could provide an AI with, that’s just a tiny, tiny fraction. The human world we inhabit is very small. AIs could essentially be sent out on missions to understand the world across sensoria that would be enormously different from ours. And I find that fascinating because I do think that if this were to happen, it would probably teach us a lot about the world if we could only translate it. And the translation problem interests me.
Philip Bell: That is so interesting actually. Yeah, and I guess it also would get us back into old philosophical questions about empirical information versus other sorts of information. And yeah, that is an interesting point about: even if the data exists, how do we translate it into information that we can understand ourselves?
NBL: Yeah. And we think about that with echolocation and bats too. When we think about how a bat thinks, we think that it thinks with this... we do our own voice, we’re like... I mean, we think it gets an echo and that’s roughly how it has to be. Now we know it’s not exactly like that because we know that their echolocation sense is much more advanced, but we have to translate it down to our own senses. So we translate it down to our own hearing. So we think it’s like hearing, sort of kind of, and then we translate it into our sensorium.
And I think that’s what we’d have to do with an AI that was able to sense different kinds of quantum fluctuations or different kinds of radiation that we can’t detect or multiple other things that we can’t understand. And we would have to figure out analogies or metaphors for what that is for us. Maybe quantum fluctuation is a little bit like taste—it tastes metallic. But I don’t know. So you would have to find these analogs in order to translate and understand. And in many cases you might accept that you cannot.
I mean, what if we get an AI—and this is the funny thing—what if you get an AI that has a million senses, it discovers a set of really interesting natural laws that we have no access to, but they can be translated into super cool machines that can be really good at stuff. And so we get these machines, we have no idea of how they work, because they work on the basis of data that’s been collected that’s way beyond what we can sense. We can get some kind of theoretical description of it, but we will essentially literally have built black boxes from these different sense datasets that we’ve been working with. And that’s not entirely out of the question that something like that could happen.
In fact, I mean, that’s sort of how evolution works. We’re just discovering now that evolution is a-theoretical. For the longest time, I stupidly thought that evolution would have to take into account how much we have understood by physics. So of course, it would just use Newtonian or Einsteinian physical phenomena. But now we’re finding out that evolution has been using quantum phenomena for a long time in photosynthesis, possibly in our ability to smell or in the ability of birds to navigate. We have this growing field of quantum biology.
And you have this a-theoretical force—evolution—that takes every small advantage that is physically detectable or sensible and turns it into fitness. And so we shouldn’t be surprised that there are quantum effects being utilized if those can lead to fitness. Now you can build an AI that has the same ability to become almost a-theoretical in the way it explores the world because it just adds senses and can measure all of the physical things that exist within its environment, and it can have that same a-theoretical exploration capacity.
Philip Bell: Wow. That is really interesting. And I guess that is quite post-human in having this a-theoretical... or revolutionary. But it makes me think: maybe that’s how we become more ecological in some sense. Or more... I don’t know, I mean... That’s a really interesting idea also, that through collecting information that our senses wouldn’t otherwise be able to pick up, we could actually understand what it means to be—well, maybe we can never understand, but we can maybe appreciate better what it means to be a bat or a tree or something.
NBL: We can be together with a bat, right? Our own “we” can slightly expand to accommodate the bat as well. And then some of the experience of the bat-likeness is in the bat and some of the experience of it is in us. So it’s this question of identity again that you raised in the beginning. What if this notion of individuality and identity is not necessarily as robust as we think it is?
Philip Bell: Yes, and I guess we probably, in most cases, we can’t be with them on a temporal level. I mean, I would love to be on the time scale of a tree, but I don’t think it’s going to happen anytime soon.
NBL: No, I think that’s right. And trees are interesting, temporally super interesting biological organisms. And as are mushrooms, for example. So there are tons of really interesting examples in nature of things that exist in different temporal dimensions, which is sort of what we’re saying that we’re building here. First wave of evolution was biological and then evolution could easily be machine-driven. A-theoretical, but at a different level with different capacities.
Philip Bell: Yes, and we didn’t get into religion. I suppose that is another interesting area that could be influenced by this. I just had one more question: you’ve already given me the Paul Ricœur suggestion. Did you have any other book recommendations?
NBL: Yeah, yeah, yeah. There’s actually a really good... if you like identity you should really read Oneself as Another, which is a great book by Paul Ricœur.
Plenty. So I mean, I think when we’re talking about these things, you mentioned Heidegger—Being and Time is a heavy read, but there is plenty of good commentary. There’s a lot of good stuff in there. I really like, in terms of how one approaches issues methodologically, I quite like Philosophical Investigations, Wittgenstein. I think it’s a really good book. I think it’s, I really think it’s readable. And a lot of people go like, “Wittgenstein, that’s really hard.” No, I think it’s actually just very interesting to dip in and out of books like that and sort of try to figure out how people are thinking. So that’s another one. Those are classics, so they’re obvious.
Ricœur I think is really good. There is a guy called—if I remember correctly, I don’t want to mess this up—it’s Pierre Hadot I think, who writes a lot about philosophy and identity. He was the inspiration for a lot of the work that Foucault did. Really good.
You’ve already mentioned Dennett. His From Bacteria to Bach and Back is an excellent book. Dennett generally is just very readable, and especially when you don’t agree with him, because then you really have to do hard work because he’s so robust in the way that he argues.
So I think those are some of the books, but there’s so much out there. Generally, I think one of the things we should do is to read more of the classics when it comes to artificial intelligence. And I think it’s good to go back to Turing’s original paper and just read it properly from the beginning. It’s good to go back to the early stuff from Minsky, especially now in the time of agents. Society of Mind is underestimated. It’s really good to look at things like what Margaret Boden did early on.
Or if you want to take the other tack and look at it from the human side, you should look at Hannah Arendt and The Human Condition or Simone Weil and The Need for Roots. Great books that discuss what it means to be human in the nature of machines. So I think that there is no shortage of reading. Don’t get me started.
Philip Bell: Awesome. Thank you so much. This has been so enjoyable and so... Yeah, it’s really helped me think about these things. So thanks so much and thanks for your article.
NBL: Thank you so much for having me and good luck with the podcast.
