Gus Breytspraak: Phil, you want to get us started?
mullins: Sure. I'm simply going to make a very few introductory comments about this particular session, and then I'm going to turn it over to Charlie Lowney for some more extensive.
Wayne Woodward: of comments of.
mullins: Of course, Charlie's interested.
mullins: I've got somebody, Scott.
Gus Breytspraak: No, he got it muted.
mullins: Charlie's interesting paper, The Rise of the Machine, Body Knowing Neural Nets and Emergent Freedom, was one of the readings for this session, so that's the reason to turn it over to him.
mullins: But I did want to point out at the beginning that we…
Wayne Woodward: I'm trying to be somewhat…
mullins: clever in, titling this session, Tempering the AI Revolution, Polanya's Insight and Current Questions.
mullins: Pempering, of course, is a process used in cooking and metallurgy.
mullins: To reduce distortion.
mullins: So the title's suggestion is that Polanyi's thought could be a better used resource for understanding and developing
mullins: policies.
mullins: regarding AI. But, in fact, very few people seem to pay close attention to Polanya's, insights on these matters.
mullins: And Polanya is really not used very extensively as a resource for that area called, philosophy and technology, and that's… that's too bad.
mullins: So, let me turn it over to Charlie, and then we'll have some question… a few questions to get us rolling, and then we'll just open it up. So, Charlie?
Charles Lowney: Well, hello, hi.
Charles Lowney: I'm Charlie Lowney. I know there are people here that… that know me, and people that don't. I also have some students, actually. The Philosophy Club is, at Hollins University is… is there, probably in Turner Lounge.
Charles Lowney: Shout out to the Philosophy Club.
Charles Lowney: There are a lot of interesting issues, of course, with artificial intelligence and philosophy, and a lot of them fall into the realm of ethics, right?
Charles Lowney: And, that's what we see in the news today, with Anthropic and, you know, their loss of the government contract because
Charles Lowney: The United States government want to take… take out
Charles Lowney: certain controls that would make the use of the technology more moral, right? Taking human beings that have moral conscience out of the links so that,
Charles Lowney: what the government tells the AI to do, it will just do, right? And including surveil people if that's what they want to do.
Charles Lowney: But before we can actually get to those sorts of issues, those moral issues require freedom, right? If we're not free.
Charles Lowney: We can't be responsible for our actions, they're just material, impulses and causes that, that we're just running out our own algorithms.
Charles Lowney: So the philosophical question that I wanted to deal with was, whether or not
Charles Lowney: because machines can do a lot of what we do now, especially AI machines that
Charles Lowney: Can drive cars and do things we never thought possible in the past.
Charles Lowney: Does that mean that, they can do what we can do? Therefore, we are reducible to machines, and machines are reducible to physical and chemical causality, and we're nothing more than that.
Charles Lowney: And in that case, well, it doesn't seem like we would have any freedom, because all the causes would be sufficient to the effects.
Charles Lowney: And although we have the illusion of freedom, we don't.
Charles Lowney: So… This is where I kind of start the paper. It's in this book here, Cyber Media.
Charles Lowney: Our eyes of the machine.
Charles Lowney: And, we see that through history, we try to kind of… Give ourselves an exceptional position
Charles Lowney: Where we have capabilities and irreducibility and freedom that we don't allow to other things in nature. And it started with, right, just
Charles Lowney: The distinction between material things and living things.
Charles Lowney: Where, living things have spirit, will, freedom, this sort of things, and material things didn't.
Charles Lowney: But we lost that line when we started to see how material things started to function, and we started to see bodies like machines.
Charles Lowney: Descartes, we have to move the line again and say, well, we're free, we're moral, we can do these sorts of things.
Charles Lowney: Because, we have reason and rationality, and our bodies, well, they're machines, they're reducible.
Charles Lowney: And animals, their bodies may be machines and reducible, but humans…
Charles Lowney: Have this reason that allows them to be free and to be moral.
Charles Lowney: And then we started seeing that
Charles Lowney: With, with developments of logic and computers that
Charles Lowney: That reason were just mechanistic processes, too. You could have causal strings, where
Charles Lowney: Every… every link in that string is… is determined.
Charles Lowney: So, this is when we started looking at, things like body knowing, right, and knowing how.
Charles Lowney: as, irreducible. So knowing that and explicit propositions, well, and logic, that's something machines can do, computers can do, but they can't…
Charles Lowney: interpret the strengths. They can't… you need this tacit, ability to be able to, actually understand, and that's what gives… makes us special and makes us free. And, Hubert Dreyfus
Charles Lowney: following Heidegger, actually, is talking about this knowing how, and how this… this is what makes us different from machines and animals. And he said, well, we'll never… we'll never be able to have a computer that drives a car.
Charles Lowney: So this line keeps getting pushed back. We're trying to push back what makes us special and free.
Charles Lowney: And, and…
Charles Lowney: well, we have self-driving cars now, and Collins, Harry Collins, also emphasizes tacit knowledge, and tacit knowledge is what makes us special.
Charles Lowney: And while he says that somatic tacit knowledge, the body, right, the body's ability to do skills and things like this, well, this is…
Charles Lowney: in principle, actually reducible, because we… if we can get a machine to do it, then it shows that it's reducible. It's like…
Charles Lowney: It's like a logical proof that can make everything completely explicit, and the machine actually doing it, right, riding a bicycle or something like that, that would show that it is reducible.
Charles Lowney: But he tries to hold out for what he calls collective tacit knowledge.
Charles Lowney: It says that this collective tacit knowledge is something that isn't reducible. That's where we have our freedom, that's where we really, can't have this deterministic, physicalistic approach to things.
Charles Lowney: So, rather than…
Charles Lowney: take and move this line back and back and back, because Collins actually said that he didn't believe that
Charles Lowney: a car could ever drive in complex traffic, right? And it, you know, there were things that it couldn't do because you needed this, this social understanding in order to do it, but now it looks like that line is being crossed as well.
Charles Lowney: So the, the paper, the chapter in this book actually started with,
Charles Lowney: a, conference in Cambridge.
Charles Lowney: Where I talked about body knowing, and the goal there was to show that
Charles Lowney: Our bodies had the kind of tacit knowing that Collins was just… was restricting to this sort of collective tacit knowing.
Charles Lowney: that Polanyi actually is important because he emphasizes this structure of tacit knowing.
Charles Lowney: And the structure of tacit knowing is what… what, where we can see the irreducibility.
Charles Lowney: And the irreducibility of bodies, of meanings, and of minds.
Charles Lowney: And by focusing on this tacit structure, this tacit knowing.
Charles Lowney: We see the… the irreducibility folded in at… at every level of development. We see it in, in, in…
Charles Lowney: And very primitive animals.
Charles Lowney: We see it in animals, we see it in more developed animals, with, with skills, right, and, and desires, intents, right? They have…
Charles Lowney: Have, their own purposes and goals.
Charles Lowney: And we see it again at another level when we have mind, right?
Charles Lowney: So, I focus on the body.
Charles Lowney: And how the body actually, we see freedom in there at, like, the proto-level. We see this irreducibility
Charles Lowney: As the body… Learns to do things, and uses this tacit structure.
Charles Lowney: Where we have, clues that come together into a joint comprehension.
Charles Lowney: Great.
Charles Lowney: So, in seeing in 3D, for instance.
Charles Lowney: We have the eye, we see.
Charles Lowney: you know, depth perception, because we have two eyes, and each one gives us an image. Now, those images are different, so they're, in some senses, incompatible with each other.
Charles Lowney: But… This tacit process, the tacit inference brings the clues together into a unity, and then that unity
Charles Lowney: Gives you more information, it gives you depth now that you didn't have before.
Charles Lowney: So, similar to emergence, right, there are new properties, new knowledge that you can get when you have this movement from clues to a joint comprehension.
Charles Lowney: And this works throughout the body, and it works in skills as well, when we learn skills.
Charles Lowney: And, it works in… in cognition and language.
Charles Lowney: Balani talks about how the letters, right, each letter is like a clue.
Charles Lowney: And then you have the tacit integration into a word. And the word, you can understand, right? But if you just focus on the letters, right, you shift your attention to the letters, then you're not seeing the word anymore, you're disrupting that focal integration.
Charles Lowney: Playing the piano, there are lots of
Charles Lowney: things your body has to do. All the muscles have to move in the right way to create the music.
Charles Lowney: You're focused on the music.
Charles Lowney: Then, after the training, right, you can play the song, but if you start thinking about particular places where you're supposed to put your fingers, right, that can disrupt the playing.
Charles Lowney: In which case, the music will stop, because you won't have that integration into a joint meaning.
Charles Lowney: So, this process of tacit knowing
Charles Lowney: And the emergence of structures that carry the tacit knowing out, right? We extend our tacit knowing through
Charles Lowney: The body, and through,
Charles Lowney: through instruments and even machines, right? We have this extended mind that works through the body, and
Charles Lowney: On eyeglasses that help focus that image.
Charles Lowney: So we have this irreducibility for Polani.
Charles Lowney: And that's what I want to emphasize, that this freedom kind of grows, right, as we evolve, and there are limited
Charles Lowney: Freedoms in context as we develop and emerge.
Charles Lowney: So, the problem then is, well.
Charles Lowney: can't machines do that now? Right? And what I point out is that, connectionist systems, neural network, computing systems
Charles Lowney: actually, do mimic this tacit knowing.
Charles Lowney: Right? Polanyi emphasized the irreducibility of tacit Noah, that when you look back
Charles Lowney: To the clues, you're always gonna miss some.
Charles Lowney: Because you're shifting in a way that you don't understand everything that's going into that production. You're attending from things that you don't know you're attending from.
Charles Lowney: When you attend to something, to that focal integration.
Charles Lowney: So there's this unspecifiability of the clues, and when you look at the clues, now they're your focal object, right? You now aren't intending the joint comprehension anymore, you're intending a clue, and you have this other background knowledge.
Charles Lowney: That you're using to focus on this, and now that fades into the tacit background.
Charles Lowney: So there's always, this unspecifiability, and there's also…
Charles Lowney: this logical gap, Pony calls it, between the meaning of the clues, the meaning of the lower level, you might say the subsidiary level, and the meaning of the focal level, where something new develops, right?
Charles Lowney: So when we get to connectionist systems, we see the same sort of integration of
Charles Lowney: clues or inputs into, you know, all connecting into output, so… and this is, like, parallel processing
Charles Lowney: You have multiple clues coming together at various levels, some of them hidden, into, sort of, final outputs.
Charles Lowney: And it's these connectionist systems that can do the sorts of things that humans can do that
Charles Lowney: It didn't look like computers were going to be able to do with digital or linear sorts of modeling.
Charles Lowney: And, so we get to the point where the computers can do what we're doing, but what I emphasize is that they're also modeling this unspecifiability and irreducibility.
Charles Lowney: Right.
Charles Lowney: when Smolensky is… is developing systems, he's looking at the inputs and the outputs, and he's looking at sounds, coffee, coffee, coffee, right? That's just a sound. To get to the concept, coffee, something changes, right? You've got…
Charles Lowney: A dimensional shift between the inputs and the outputs.
Charles Lowney: And that mirrors, sort of, that irreducibility. You can't just look at the clues. Also, just like there's an unspecifiability in looking to the tacit clues, from the level of the emergent meanings.
Charles Lowney: Same thing, there's an unspecificiability
Charles Lowney: When you use these hidden layers in order to,
Charles Lowney: Move through these tacit… well, not tacit… these connectionist networks.
Charles Lowney: So, we do see, computers being able to do what we do.
Charles Lowney: But does that mean that it's reducible?
Charles Lowney: Because that's… sort of what Collins was talking about. If you can have a machine do it.
Charles Lowney: That means there are deterministic laws in place, it is a causal string.
Charles Lowney: And there's no freedom there.
Charles Lowney: Right? So… what I try to point out is that, Colin's reasons for saying… that…
Charles Lowney: A machine does what a human does, kind of is a flattening of what actually happens, and sort of misrepresents it.
Charles Lowney: He has two main reasons why
Charles Lowney: this is deterministic, or in principle, it can be reduced, right? Even though it's complex and it's hard to reduce.
Charles Lowney: One is that it's… it's when we train our bodies, just like we train
Charles Lowney: neural network systems, right? By giving them more information, changing the weights, trying to lean it towards particular results, just like you train your body and try to move in certain ways and constrain it towards certain results.
Charles Lowney: that this is like, operant conditioning, right? And operant conditioning, he sees as something that is deterministic. You, you're, you're,
Charles Lowney: applying, you know, reward and punishment, like training a dog, for instance, right? And it's… it's just…
Charles Lowney: You're adjusting those weights till you have those outcomes, and it looks like it's… You know, deterministic.
Charles Lowney: What I try to show in the paper is that
Charles Lowney: That flattens out the, the,
Charles Lowney: The intentionality of the body, its own intentionality, not our intentionality trying to constrain it.
Charles Lowney: But it has this creativity and discovery built into it as well, and it is trying to find solutions, right? You're giving the body problems, and it is working to find solutions in a way, too. So it's got this, sort of.
Charles Lowney: Proto-intentionality, proto-freedom, and there are lots of variabilities that you see.
Charles Lowney: That, get flattened out when you try to…
Charles Lowney: go from A to B to C to D and make it a causal string, right? There are choices along the way, right? If you train a dog
Charles Lowney: To sit, right? And… and stay, and then a natural enemy comes by, like, or food, like a rabbit, right? That dog is gonna have a decision to make, right? Do I… do I chase the rabbit, or do I sit and follow that order? And, you know, in a context, it's making a decision or a choice, right?
Charles Lowney: Or another natural enemy, like happened a few minutes ago with my dog. The mail carrier came by, right?
Charles Lowney: And started barking out the window, and I said, oh, sit, go to bed, right? And she did, and then she's up again, and she didn't, you know, and she was…
Charles Lowney: Faced with a decision, a choice, and there's some freedom there, right?
Charles Lowney: It's mostly prudential, right? What's the most prudential thing to do, the best outcome? It's not yet the kind of moral decisions that we can make with language where we can abstract more from situations.
Charles Lowney: But it's… it's a proto-freedom. It's like the sort of moral freedom that we have.
Charles Lowney: So that was one way that Collins tries to show that,
Charles Lowney: That we are reducible in our bodies, like, somatic tacit knowledge can be fully reduced.
Charles Lowney: The other is, he rightly points out.
Charles Lowney: That there's these parallel processing networks that are nonlinear.
Charles Lowney: We can run these connectionist programs on digital computers, right? So they are translatable into strings.
Charles Lowney: Where you have this.
Charles Lowney: One following the other, determining causality.
Charles Lowney: And and that's true, and I say, well, maybe the connectionist system, right, itself, it is a model of our tacit knowing.
Charles Lowney: But it is not identical with actacinone, so it's probably missing a lot, that…
Charles Lowney: that could not be reduced, right? So there's still a difference between the body, between our skills.
Charles Lowney: Between our way of understanding, And the computer's way of doing it.
Charles Lowney: So that's one reason to believe that, well…
Charles Lowney: Maybe these connectionist systems aren't quite Catching everything to begin with.
Charles Lowney: The other, counter-argument against Collins would be that
Charles Lowney: Polanyi's idea of the machine itself.
Charles Lowney: With machines themselves, there's an irreducibility, according to Polanyi, because you have
Charles Lowney: dual control in a machine. Even in simple machines, there's dual control.
Charles Lowney: That means that there's the, kind of, the physical causal level where you do have this determinant causality, you have the laws of physics.
Charles Lowney: And laws of chemistry.
Charles Lowney: But when you build a machine, Right? You constrain…
Charles Lowney: those forces, right? And you, you…
Charles Lowney: you have a higher level of operation, too, right? And you need different laws. It creates different properties.
Charles Lowney: And in that case, right, you need engineering principles, right? And the engineering principles aren't reducible to just the laws of physics.
Charles Lowney: Like, they, act on boundary conditions left open by the laws of physics, and to constrain and create something new that operates by
Charles Lowney: Different principles that don't contradict the lower-level principles.
Charles Lowney: But also, enact higher level properties, properties that don't exist at the lower level.
Charles Lowney: So with a steam engine, for instance, as Polanyi's example.
Charles Lowney: You've got the engineering principles, and how it functions, and how it constrains lower-level physical processes and materials.
Charles Lowney: And that's different, and you can see because, well, from the lower level of physics and chemistry, you can't tell whether a steam engine is working or broken.
Charles Lowney: It would just describe it, but you need these higher-order principles and laws in order to discover whether or not it's working, right?
Charles Lowney: So, with each level, each emergent level of dual control, right, because one level builds on another, builds on another.
Charles Lowney: Like, he sees the mind as…
Charles Lowney: Because, one level and a dual control system, where you've got the mind and the body.
Charles Lowney: So the mind has its principles that it goes by, the body has its, right? And…
Charles Lowney: these layer all the way down through. So that's… that's the other reason why we would think that,
Charles Lowney: That's… we are irreducible, even if we are machines, right? So he, reinterprets the machine metaphor.
Charles Lowney: Any… any questions at that point? Now we can talk about some implications of… of this idea of…
Charles Lowney: Tacit knowing.
Charles Lowney: How it models… how it is modeled by… neural networks.
Charles Lowney: And what that means for freedom.
Charles Lowney: And I mentioned some implications in the paper with regard to animals?
Charles Lowney: And the idea of, conscious AI.
Charles Lowney: Right.
Charles Lowney: And here's another area where I think Polanyi can help, because there… there is sort of…
Charles Lowney: Consciousness that attends from the clues to the joint comprehension, right?
Charles Lowney: You dwell in the clues as you extend out into the world.
Charles Lowney: And, it's not clear that a machine or a contrivance, as Phil points out.
Charles Lowney: Has… dwells… has something that dwells in its parts.
Charles Lowney: that there's…
Charles Lowney: Something that it's like to be a human being, or something that it's like to be a bat or an animal.
Charles Lowney: But there may not be something that it's like to be a neural network.
Charles Lowney: Right.
Charles Lowney: So that this indwelling, that Polanyi calls it.
Charles Lowney: It's essential for understanding that there… there is
Charles Lowney: something that the purposes matter to, right? The functions matter too.
Charles Lowney: And, even if machines are modeling
Charles Lowney: this function, modeling this sort of behavior. It doesn't seem… they don't seem to be the sort of things that
Charles Lowney: That it is something like to be yet, even though that they mirror human behavior where that is present.
Charles Lowney: It doesn't seem like they have the affordances and the developments
Charles Lowney: In the context in which we can say they're aware.
Charles Lowney: And they have the intentions.
Charles Lowney: It's no active center, as Polani would say.
Charles Lowney: Whereas we do see this in animals.
Charles Lowney: Which… which do have interests. So, we can see if this is what… what gives us value.
Charles Lowney: That it's there in animals, in some form already.
Charles Lowney: So, that's, it ties in with, animal rights and ethics, too.
Charles Lowney: Anyway, I've talked a lot. I did… I didn't go, I didn't go to…
Charles Lowney: slides, because I wanted this to be more of a conversation.
Charles Lowney: Tuh.
Charles Lowney: That didn't work.
Gus Breytspraak: Or… Several, several hands are up.
Charles Lowney: Oh, good.
Charles Lowney: You, you want to moderate?
Gus Breytspraak: I don't know if you see him, I think Phil had his hand up first. Phil Z?
Phil Z: Yep, hi.
Phil Z: I just found out about this this morning, so I apologize for not having read the materials…
Phil Z: But, Pugliani also talks about emergence, and emergence seems to create a hierarchy of explanations.
Phil Z: And, maybe that's a clue?
Phil Z: To what animals and humans, or at least some animals and humans have, versus machines.
Phil Z: We have psychology.
Phil Z: Which has laws that are not 100% explainable by the laws of physics and chemistry and so forth.
Phil Z: While a machine… Theoretically is totally explainable in terms of physics, and, and programming.
Phil Z: So is emergence part of the key?
Phil Z: To what distinguishes the mind of a human from the… Mind of a machine.
Charles Lowney: Yeah, I do think emergence is very important, and it is key.
Charles Lowney: We talk about tacit knowing, and that seems, like, synchronic. That happens
Charles Lowney: Right, on the level of knowing, but there are emergent structures that are built up and folded into all that, and…
Charles Lowney: And condition it as well.
Charles Lowney: And this is where dual control kind of models emergence.
Charles Lowney: Where you have…
Charles Lowney: subsidiary parts coming together into a whole, right? Like an organ, and then you have organs coming into a whole, like an organism.
Charles Lowney: So, for Polani, you do have these emergent levels where you have different properties, and… potentialities.
Charles Lowney: emerge. So you have the level of physics, and then chemistry, and these might be reducible to each other, but then you have the level of, biology, right? Biologicals.
Charles Lowney: Bodies, plants, these sorts of things, right? And then you have psychology, right? And so you have stacked levels of dual control.
Charles Lowney: Where, you know, I can decide to raise my hand, and my body does that, right? And then you have sociology, right? And then you have the level of responsibility for Polani.
Charles Lowney: which involves the social, right? And this is what Collins and others will point out, right? That's the sort of…
Charles Lowney: tacit background that allows us to be free. A lot of that has to do with collective social knowledge. We don't have it individually anymore.
Charles Lowney: So, yeah, there's these emergent levels and emergent structures that have, have,
Charles Lowney: That have, developed over the course of time, so they're, like, diachronically developed through evolution.
Charles Lowney: And they're connected and very similar to tacit knowing structures, right? And Polanyi brings this out, where you have
Charles Lowney: Just like you have clues, background clues, those letters that go into the joint focus of the word.
Charles Lowney: Right? You have parts.
Charles Lowney: Done.
Charles Lowney: function together and come together into a whole or object, right? That has new properties that can't be explained just by
Charles Lowney: the parts and the laws that govern them. So you have this, parallel structure between tacit knowing and emergence that Polanyi
Charles Lowney: brings out.
Charles Lowney: And there… there is a difference. You try to get your tacit knowledge to understand the parts.
Charles Lowney: and how they come together into a whole, and that's like having the clues come together to the right joint comprehension to match up with the thing out there, right? So you have knowledge this way.
Charles Lowney: But when you get to a deeper level, I think, it's hard to unpack and decide whether you're talking about epistemology and knowing, or ontology and being.
Charles Lowney: So I have a paper where I try to bring this together and talk about the epistemontology of Polanyi, where the two different structures that are parallel become indistinguishable at some level.
Charles Lowney: But yeah, that's… that all ties into an irreducibility, right? The irreducibility you see in emergence is like the irreducibility you see in tacit knowing.
Gus Breytspraak: So, John has his hand up.
Jon Fennell: Yes, whenever I hear… That a machine is intelligent.
Jon Fennell: I am… I'm reminded… I used to be a computer programmer back when I was a bit younger.
Jon Fennell: And one thing I learned about computers is that they're utterly stupid.
Jon Fennell: In other words, they can only do what they're told to do.
Jon Fennell: So… They can become quite complex.
Jon Fennell: And we don't see, clearly, How they are told what to do.
Jon Fennell: And so I think we are bewitched.
Jon Fennell: And we think there's something going on there that, in principle, cannot be going on.
Jon Fennell: the… The art… artificial intelligence is the product of… of…
Jon Fennell: circuitry, okay? And you pull the plug, and it no longer is there.
Jon Fennell: So…
Jon Fennell: It occurs to me that we need to be very careful not to let our bewitchment allow us to attribute to a computer characteristics that it cannot have.
Jon Fennell: Now… I'm always reminded in these discussions of an argument that C.S. Lewis made
Jon Fennell: he had to, modify it in light of, Ann Scomy's criticism of it.
Jon Fennell: But his point was that There is no grounds for warrant.
Jon Fennell: in a machine.
Jon Fennell: In other words, a machine can become so sophisticated that we might worship it and, you know, in its output. But in principle, the machine is only doing what it has been told to do. So it is a naturalistic device. It's the unfolding of
Jon Fennell: Of laws of nature, and there's nothing in that process that we could call, grounds for warrant, or grounds for rational acceptance.
Jon Fennell: It's simply… it's just simply executing.
Jon Fennell: So it… I think this is similar to Polani's argument in personal knowledge, that there… that there's… that there's something fundamentally off here when we attribute to circuitry
Jon Fennell: Characteristics that we have over the eons attributed only to human beings.
Charles Lowney: Yeah, I think, the… that… You talk about naturalistic causes.
Charles Lowney: There are naturalistic causes for us, too.
Charles Lowney: Right. So, the, the, the problem that you're pointing out is that, as we devise contrivances that
Charles Lowney: that act more and more like us, right? That are less stupid, because now they seem to be giving us
Charles Lowney: Results and answers that are more creative, more useful,
Charles Lowney: More like, discoveries themselves, that, it seems that, well, what we thought was special about us might not be anymore.
Charles Lowney: And, the idea of computers as…
Charles Lowney: just dumb algorithms following things out, right? This… this, I think, is… is right, right? But…
Charles Lowney: They're creating models that
Charles Lowney: catch us more, so it makes us question, well, maybe… maybe we're not special, maybe a computer can be an interpreter at some point.
Charles Lowney: Right? Maybe it can develop to the point where it's performing those tacit skills that Polanyi say are required for the interpretation of any dumb string.
Charles Lowney: Right.
Charles Lowney: What computers couldn't do then, they're more complex, I'm mimicking now. So,
Charles Lowney: I think we're right to point out the sorts of differences that
Charles Lowney: That kind of just make it look like
Charles Lowney: A cheap knockoff, right, of a human rather than a human and human intelligence.
Charles Lowney: But it does raise questions about potentiality for being able to, sort of.
Charles Lowney: Do the kind of tacit-knowing skills That we believe…
Charles Lowney: the computers never could do when they were dumb, right? Do they create… do we get to some point in which they are interpreters as well as interpretable?
Charles Lowney: Right?
Charles Lowney: So far, I don't think so. I think it just mimics it, and you see that, and it's mimicking better and better. So there is still that difference, and the difference is.
Charles Lowney: The sort of sense reader, right, is missing from the computer and its algorithm.
Charles Lowney: This gets to the idea of a model, and there are better and better models.
Charles Lowney: But are they really catching what's important for the thing, right? I can take a string and a ball and start spinning it around and saying this is like the Earth going around the Sun, and it tells you something. It's a model of that, but it's missing an awful lot.
Charles Lowney: And we get to better and better models, like, we can build a planetarium, but it still won't have every… all the proper
Charles Lowney: internal, sort of.
Charles Lowney: relevant things that need… that a solar system itself needs. We'd have to just really build the solar system itself in order to really
Charles Lowney: Model it appropriately.
Charles Lowney: We're modeling humans better and better.
Charles Lowney: But it's the same sort of thing, right? It's a model, it's not identical, and it catches some…
Charles Lowney: superficial, explicit, features, but…
Charles Lowney: It's not catching a lot of the other things, right? The social contexts that you need and how they affect people. It's… it's not catching the affordances of biological life, right, that… that have grown and have developed these networks.
Charles Lowney: Through evolution and experience.
Charles Lowney: So, yeah, just wonderful. That's right.
Jon Fennell: Just one follow-up comment. Early on in your response, you mentioned that humans, too, are, to some degree, determined.
Jon Fennell: And we're discovering some of these… Principles, some of these, underlying dynamics.
Jon Fennell: I would just… I would just,
Jon Fennell: I would just recommend that we do a mind experiment.
Jon Fennell: How would we… how would we change in the way we regard ourselves and each other?
Jon Fennell: If we were to accept that humans were determined in the same way that a computer is.
Jon Fennell: I don't expect you to answer that, Charlie, but I just want to throw it out there. It's… I think there's a… there is…
Charles Lowney: Yeah.
Jon Fennell: Kind of a moral limit here.
Charles Lowney: Right, well, we wouldn't… we'd be… think we could get ourselves off the hook. Well, see, this is why it… I mean, it doesn't…
Charles Lowney: It doesn't suit our intuitions about our abilities, right? Because we are emergent, and we do have these freedoms, right? And when I say that we are limited, of course we're limited, we're in bodies, we can't just jump up and fly, right? Without machines. Then we can do it. But,
Charles Lowney: So there are… we don't break the rules of physics.
Charles Lowney: But we constrain them, right? And so we gain a little bit more freedom, right, as a protozoa, right? To be able to seek out things, right? We gain
Charles Lowney: a little bit more freedom as an animal, where we can move around and seek prey and things like that. And there are, you know, there's a way to win and a way to lose. You can… now you have more vulnerabilities, too, right? If you're alive, you can die, right? You couldn't do that before. That's an emergent
Charles Lowney: quality. It's just like living is an emerging quality. Dying couldn't happen before either.
Charles Lowney: So, yeah, we build on… The laws of physics and chemistry and nature.
Charles Lowney: But as new and real things emerge, right, that aren't fully reducible just to the laws of nature, there are degrees of freedom that get built in, and we get more degrees of freedom as an animal, and as a rational animal that can use reason
Charles Lowney: and abstract more, and become self-conscious, we have More degrees of freedom.
Charles Lowney: We don't have the absolute freedom in any sense, but it is conditioned. And your thought experiment, I think, yeah, we implicitly know we are making decisions. We implicitly know I can choose this or that. I can drop a bomb on that city or decide not to, right?
Charles Lowney: So… We… we sense, and we have this tacit knowledge that we have this freedom.
Charles Lowney: You can just wonder, you know, if you're a determinist.
Charles Lowney: even Spinoza, if it's an illusion, right?
Charles Lowney: And you just… Believe that you have these capacities, but it's actually just the playing out of things.
Charles Lowney: Right?
Charles Lowney: Even if it's just the playing out of things, you still have to decide, so…
Charles Lowney: That's where your thought experiment comes into play, I think.
Charles Lowney: It's a good one.
Gus Breytspraak: Oh, Phil.
mullins: I'd just like to point out that Bruce Fojak has his hand up, and so does Dick Moody. We also have a few questions that we prepared. Charlie, why don't you show those questions, but let's go to Bruce and our Dick one as we, somehow… we've got a lot of people who want to get in on the discussion here.
Bruce Vojak: Yeah, thanks, Phil.
Bruce Vojak: You know, this is fascinating, and and this is not a throwaway statement. I always benefit from hearing the thoughts from this group and groups like this, because it's a discussion that you just don't hear, anywhere else.
Bruce Vojak: Couple of thoughts, and then I want to share about a seminar that I listened to just a day or so ago.
Bruce Vojak: You know, I'd say that AI, as it currently exists, is, I think I'm going to pronounce this right. All the forms of it are basically simulacra.
Bruce Vojak: that it's a simulation, but it's not necessarily the real thing, if you will, and that the real excitement on it is based on what I feel is a very
Bruce Vojak: A pragmatic view of… Of knowledge, not even knowing.
Bruce Vojak: And so,
Bruce Vojak: I'm just gonna throw out, I just, I don't know, a day or two ago, listened to a seminar presented by,
Bruce Vojak: actually, it was a seminar at Baylor, I can share the video, about an hour video, but it was a computer science professor from Harvey Mudd, which, if you're familiar with it, Harvey Mudd's a highly regarded university in, in Pasadena, and, he's taking, I think… Pardon me?
Walt Gulick: Claremont.
Bruce Vojak: Clerma, thank you. Thank you, Walt.
Bruce Vojak: I'm in the generic area, but not precise. But, one of the Claremont Colleges, in fact, yeah. And I believe he's either taking a sabbatical or some kind of leave at Cambridge. So, the guy has credentials, and
Bruce Vojak: he… he spoke first about the mechanisms that, large language models use, and I've been kind of
Bruce Vojak: intrigued by this for a while, and it was, stimulated by a brief exchange that Yongil and I had several months ago, where he basically asked me, well, you know, do you really
Bruce Vojak: And it was… it was said better than I'm going to say it, but do you really understand what's going on, if you will, under the hood in these large language models? And so, I've been very attentive
Bruce Vojak: when, you know, I see something pop up that it tries to explain it in a way that is true to the reality, but simplified enough to make sense to me. And, you know, Charlie, you talked about neural nets, and I think while neural nets and large language models use some similar
Bruce Vojak: technology, I think there's a little bit of difference, but I think at their heart, it goes back to what John was saying. They're all basically machines, if you will. But what this individual spoke about was the mechanisms
Bruce Vojak: I don't know, I'm not going to use the right word for this, but… and they probably still have this, but, like, on Facebook, you start typing something in, and it prompts you with a word, you know, to fill in the blank.
Bruce Vojak: And, you know, people would laugh and say, oh, type in, you know, my spouse is this, and then see what words pop up, and everybody would post it and laugh. And… and really what large language models are doing now is basically taking that basic idea, and it's on steroids.
Bruce Vojak: Because when you deal with a large language model, it requires what at least he referred to as context, and that's the prompt, the question that you ask it.
Bruce Vojak: And depending on how you ask that question, and what data you have.
Bruce Vojak: You're gonna get a different string of
Bruce Vojak: If you will, words that come out of that, and those words are generated entirely statistically.
Bruce Vojak: They go through a process that he referred to as tokenization, where each word, unless it has some other meaning, is replaced by a token, and then…
Bruce Vojak: They… the machine basically crunches intensively and finds out what the most likely next words and words are, and strings them together, and… and
Bruce Vojak: it can be… it can be very accurate in many cases, and profoundly, you know, wrong in other cases. He showed an example of
Bruce Vojak: doing some mathematical, computation, some simple math, and it kept getting it wrong, and he would challenge it with the response, and it would say, oh yes, I'm sorry, I was wrong. It's this, and it would get it wrong again. And so, it was a fascinating,
Bruce Vojak: Fascinating, you know, really,
Bruce Vojak: a fascinating presentation. You talked, too, about, you know, if the information you use to train it is, has problems in some respects, you're gonna get, not surprisingly problematic answers. He had
Bruce Vojak: One example where he asked the same question, and he interchanged whether it was a black man and a white woman doing the same activities.
Bruce Vojak: And in both cases, when he asked who's likely to more… who's more likely to be selling drugs, in both responses, no matter how he framed it.
Bruce Vojak: The answer was always a black man.
Bruce Vojak: And… and so there's, if you will, you could attribute it to the programming, you know, I don't…
Bruce Vojak: for what it's worth, I don't know what the background was of it, if it was based on news stories or other statements, but the bottom line is.
Bruce Vojak: you know, you do get out kind of what you put into it. You even showed an example of information degradation as you take the output and feed it back into the input. And, you know, after a relatively small number of iterations, it's just, you know, garbage comes out.
Bruce Vojak: And then he looked, and I don't want to say to extend it, but he made an observation that really struck me, and he said that he's going to use his time at Cambridge, among other things, to look at this issue of
Bruce Vojak: Are the errors that are generated
Bruce Vojak: Are they more significant than the useful information that comes out? I'm probably not phrasing it accurately.
Bruce Vojak: But he's really challenging the underlying question of.
Bruce Vojak: you know, what do we get from this, if you will? And it reminds me, I don't know if any of you have worked in manufacturing facilities, but up until, I'm going to say, the 80s, if you will, a lot of manufacturing was done by… you produce a lot of product, and you just filter out the defects. You throw away the things that are failed.
Bruce Vojak: And, it was really the Japanese quality movement, actually based on some
Bruce Vojak: people like Duran and Demi in the US, they really focused on the quality of the output, and it was really a Japanese philosophy of manufacturing that said, let's make it right up front, rather than have all this waste, if you will.
Bruce Vojak: And I'm not… sure or convinced that what we have today in AI Can logically take that walk
Bruce Vojak: To get to that point.
Bruce Vojak: And so, while it is Simulacra, or they are Simulacra, and they have some pragmatic use, the uses that I've seen, and this is the last thing I'll just leave with you guys.
Bruce Vojak: I… about once a quarter, I sit in with a group of about a dozen, two dozen, VPs and directors of innovation from medium-sized to very large corporations through the work I do at the conference board. And while I can't talk about specific companies' actions.
Bruce Vojak: The conversation regularly goes back to everybody asking everybody else, have you found a use case that's worthwhile?
Bruce Vojak: And, and at least so far.
Bruce Vojak: the use cases, the financial use cases, are pretty slim. And yet, I deal with other executives, you know, in my advisory roles, and they're just…
Bruce Vojak: they're just… Frantic. They're afraid of all this.
Bruce Vojak: And it's very hard to talk them down off the ledge. And so, I think the discussions, the Polanian insights.
Bruce Vojak: I think are incredibly valuable to say, if you don't want to just simulate it, this is what it's got to look like.
Bruce Vojak: But I'll even say, if you just go back to the information that's being generated right now, at least my perspective is.
Bruce Vojak: it's not where people really are going to get a lot of use out of it at this point, so…
Bruce Vojak: a wide range of observations, if you want, I'm happy to share a link to that,
Bruce Vojak: to that seminar, because I found it really interesting.
Charles Lowney: Yeah, yeah. It's getting better and better, though, right? And there are new, new sorts of,
Charles Lowney: The latest versions that don't make as obvious mistakes, but you're right in that it's always based on the information that's put in, and this is how we try to weight it.
Charles Lowney: correct it, right? We'll put more information in that leans towards the sort of outcome that we think is correct, and we train the system like a coach will train you to do the move right.
Charles Lowney: By weighting it, having you practice. Practice more with doing it like this instead of like that, right?
Charles Lowney: But that's part of the ethical issues, too, that come up, the moral issues, is that it's reinforcing prejudices, right?
Bruce Vojak: As an example.
Charles Lowney: letting us…
Charles Lowney: you know, because it's using what's out there already, so it's not smart. It can't recognize quality, and I think this is… this is something that Polanyi would
Charles Lowney: would emphasize, too. I mean, Robert Persig, right, who wrote The Art of Motorcycle Maintenance, he's…
Charles Lowney: he came to a Polanyi conference years ago, and he's just like, this is right, this is right on, because he's…
Charles Lowney: The… the… what he was trying to get at with quality is, like, that… that sort of joint comprehension that you need experience and skill and tacit knowledge in order to assess and judge, right?
Charles Lowney: So it mirrors that sort of…
Charles Lowney: mirrors, it models that sort of thing, like a mirror, right? I think this goes… I'm jumping ahead to one of the questions, but mirror is a superficial rendition back to us. It doesn't tell you about the organs in the body and the… that's doing anything, it just… it's a very superficial
Charles Lowney: rendition. It mirrors that, but it doesn't necessarily catch what's important to really understanding, right?
Bruce Vojak: The more… the more and the more frequently it mirrors that.
Bruce Vojak: The more challenging it'll be when… when systems like this fail, and if you will disappoint people,
Bruce Vojak: It's maybe an understatement.
Charles Lowney: Yeah, well, Claude, I think,
Charles Lowney: One of the reasons why they didn't want to take humans out of the chain of command, Anthropic, when the government said, no, we want to get it so that it's all AI. Well, for one thing, right, humans can disobey an order, right? They can say, this is unconstitutional, I shouldn't do that.
Charles Lowney: But if the government or the commander says, you know, do it.
Charles Lowney: the computer's gonna do it. The other thing is that…
Charles Lowney: using its… its… its data and its algorithms, right? I guess 95% of the time.
Charles Lowney: in these scenarios that it was given, it would… it would move towards nuclear weapons, right? It would start deploying nuclear weapons. So, you want human controls on those sorts of things.
Charles Lowney: As far as the possibilities and capabilities and the differences, I believe large language models just do use connectionist architectures.
Charles Lowney: But, I will defer. I have a colleague here today, Simon Levy, who, who co-wrote…
Charles Lowney: Along with, Bill Maroney.
Charles Lowney: a paper that I wrote on connectionism and Wittgenstein.
Charles Lowney: Which shows that Wittgenstein's understanding of language was more like
Charles Lowney: what a connection of systems can do, right? We learn by pattern recognition and training, rather than a symbols and rules approach that's more explicit and linear.
Charles Lowney: So, I don't know if Simon has any comments on, on sort of the technological side of the issue, or even the moral side of the issue.
Simon Levy: Well, my only observation is that the fact that your dog barked at the mailman makes… convinces me we're all just living in a simulation of some stereotyped world. It's…
Charles Lowney: Oh, yeah? I'm just kidding.
Simon Levy: Kidding. That's what does do, right?
Charles Lowney: And try to see how that made sense.
Simon Levy: Yeah. Yeah, no, I just want to say that I, you know, technologically, this conversation I consider really, you know, pretty, pretty technologically deep, and I really haven't disagreed with anything anyone said, so…
Simon Levy: Okay. If that helps, I mean, I don't know if Charlie had any other things he wanted me to add.
Gus Breytspraak: So, Dick, Moody's had his hand up. Let's, see what that's about, and then, we want to get, to your, your, your questions.
richard moodey: This may not be a line that anybody else wants to pursue, because I'm kind of changing the direction here.
richard moodey: I'm thinking of Eugene Jenlin's
richard moodey: idea, which I think is consistent with Polandi's take on passions, is that all human meanings are felt meanings.
richard moodey: And one of the things that we get away from when talking just about cognition is…
richard moodey: we abstract from emotion, and our knowing doesn't abstract from emotion. Our knowing is all very passionate and emotional. Computers
richard moodey: I don't think ever are going to fall in love have sex… Have babies.
richard moodey: raise families. I don't think that computers are ever going to, form clubs
richard moodey: Where they pursue, you know, some passion that they have, like motorcycle riding or playing bridge. They're not… they're just so different from people on a common sense level that the question of.
richard moodey: Simulating some of the things that people can do by computers, seems to me to be a very interesting kind of thing, but doesn't raise the question, are computers ever going to take the place of human beings?
Charles Lowney: Right? They'd have to develop emotional intelligence and feeling, as well as Other forms of cognitive intelligence.
Charles Lowney: And you have to have the right sorts of experiences and the right sorts of bodies in order to do that, right? That we feel things.
Charles Lowney: It's not just all.
Charles Lowney: At the level of symbols and rules, or even patterns, right?
Charles Lowney: So yeah…
Charles Lowney: if we look… this is… this is where, like, Wittgenstein comes in, too, to also say that
Charles Lowney: You know,
Charles Lowney: what it's like, asking what color the number 3 is, or something like that, right? It just doesn't connect so much, because we're so different in many ways.
Charles Lowney: And yet.
Charles Lowney: And yet, Dickinson also talks about how do we… how do we know we understand? We only know that through the external sorts of signs and agreements that we have with each other, too.
Charles Lowney: So there might be a point at which we are indistinguishable from computers, or robots. Not now, not nearly now, but maybe in the far future. And I think Polanyi, as an emergentist, would have to
Charles Lowney: Say that there could be something like machine intelligence and consciousness at some point.
Charles Lowney: Because, for an emergentist, you have multiple realizability, where… where different, subsidiary sorts of structures can… can come together into,
Charles Lowney: into… exhibiting properties, right? So you can have, right?
Charles Lowney: Birds fly, and you can have bees fly, but they have different mechanisms for getting there, so if consciousness is emergent, it might be possible for machines to have that.
Charles Lowney: As well.
Gus Breytspraak: Damon had his hand up, I think.
Damon Kutzin: Yeah, thank you. Yeah, the question about, emotion or, agency made me think that
Damon Kutzin: I wonder if Polanyi's response to AI should focus more on, say, the commitment or passionate part of his thought, as opposed to the tacit knowing, subsidiary, focal integration part. And I think this because… so when Polanyi is polemical about AI early on.
Damon Kutzin: It's about the explicit, sort of, rule-following, symbolic AI,
Damon Kutzin: Which is counter… counterbalanced by his examples of facial recognition and things like that, but that sort of activity now, facial recognition.
Damon Kutzin: is a paradigm case of machine learning capability. And so I wonder if…
Damon Kutzin: Yeah, like, those sorts of things…
Damon Kutzin: actually serve to support, current LLMs, like, they act by pattern recognition and by predicting next tokens based on a wealth of experience. Of course, it's different from how human beings do it.
Damon Kutzin: But it seems like the structure of how it's getting an output might be similar to some ways the brain works, but the real difference lies in
Damon Kutzin: well, the computer doesn't care, right? It doesn't… it's only doing something in response to a human agent, or… and it doesn't care about the truth, it's just predicting the next token.
Damon Kutzin: So should, should that be more the focus than something like Polani's, subsidiary focal, non-explicit?
Damon Kutzin: model of tassel knowing.
Charles Lowney: Yeah, I think that's what Dick is saying, too, that we can focus on that
Charles Lowney: And create another line there if we want, right? As far as what makes us different. I don't think it's inconsistent with emphasizing tacit owing, because
Charles Lowney: I think body knowing, emotional knowledge, I think it follows the same sort of tacit knowing structure, right, where you have
Charles Lowney: feelings and thoughts coming together into an emotion. Your understanding of what the situation is is going to raise one emotion rather than another, because it's part of the tacit background that
Charles Lowney: Even, you know, if you look at art, even a brush stroke can have a feeling to it that can evoke in us.
Charles Lowney: So, you know, the collection of brushstrokes together and the patterns and everything else come together into joint focus as well. We have a feeling
Charles Lowney: About a painting, or feeling about a person.
Charles Lowney: So, yeah, I don't think it's inconsistent to focus on both, but to focus on emotion, I think, is important, because then you see the sort of affordances that
Charles Lowney: That, machines don't have at this point.
Gus Breytspraak: Simon, you're… I think you're, up next.
Simon Levy: Yeah, thank you. I just wanted to point out, again, I've really, really been benefiting a lot from listening to everybody here. Charlie and I co-authored that paper, Wittgenstein paper, in the same volume, Cyber Media. I just want to sort of make a comment, maybe more than a question, which is that I think it's… and this is a game, you know, between people, I think, like us, who see a lot of nonsense in this, you know, sort of this hype around AI, and people
Simon Levy: who are selling the hype. So my two points are, one, that I think that, you know,
Simon Levy: you should avoid… in this kind of game, it's best to avoid predictions, you know, and that was what Charlie said about, you know, predicting that, you know, AI will, you know, never be able to drive a car, or I heard a prediction that AIs will never join clubs. I mean, you know, there was just something in the tech blogs the other day about, you know, here's a, you know, social media for AI.
Simon Levy: Right, because those are… those are losing games, because it just becomes a matter of defining the, you know, what a club is, or what, you know, what driving is. So that's one point. You know, don't draw lines or make predictions. And, you know, that's strongly driven by the second point, which is, I don't know what the rest of y'all have, you know, I think we're all old enough to have sort of seen the internet evolve since the early 90s.
Simon Levy: My sense, sort of technologically, is that a lot of what's going on with the sort of AI agents and assistants is stuff that used to be kind of mainstream, things like Google search is just being dumbed down in order to get you to use the AI, which often isn't really much better than just what you would get from a high-quality Google search. And I'm finding even younger colleagues, right, like, are finding that, God, the internet used to be a lot better 10 years ago.
Simon Levy: And I don't think it's just getting older, I think it's just that, because this is, you know, and this is sort of…
Simon Levy: where the parallel comes in in philosophy is that, you know, this is a product that's being sold in hype, not just to the military applications, or a small fraction, I think, of the market. And it's to these companies' benefits to make you think that they're doing something novel and exciting and even scary.
Simon Levy: And there's almost a kind of game being played, like, you know, please, please regulate us, because what we're doing is so powerful that it'll blow up the whole world. And to me, it's just an obvious bullshit hype. It's basically, like, you know, we're… it's just to get trick investors into investing in these companies, because they have dangerous technology. So I'm… I mean, I've seen this, you know, I'm old enough to remember the AI hype of the late 80s, early 90s, so I'm just very skeptical of all this.
Simon Levy: I think it's mostly commercial hype. And again, I'm not somebody on the… I'm not an outsider. I've been doing this stuff since, pretty much since grad school 35 years ago.
Bruce Vojak: Can I briefly comment on… Simon? I'm gonna… I'm cheering you on as you're talking. I… I will share this, and you guys probably already know this, or would guess it. When I first started college, we were required to use a slide rule, because a handheld calculator was a, was a preferential advantage to those who were wealthy.
Bruce Vojak: And so, I've… I don't want to say I've seen it all, but I've seen this movie, and I've seen it end a couple times. And as a quick aside, I'll never forget, in 2000-ish.
Bruce Vojak: there was a tech entrepreneur who came to campus, and he was saying, companies that are profitable are dinosaurs, and the minute you turn a profit, you're a dinosaur. And I'm…
Bruce Vojak: You know, I'm probably 5 or 10 years out of an MBA from the University of Chicago, and this is, like, challenging every bone in my body.
Bruce Vojak: And yet, at the end of the day.
Bruce Vojak: you know, my… my hunch was correct. Eventually, these things have impact. I'm not saying that they won't, it's just that it's nowhere near the scale and the threat that people are talking about. So, thank you, Simon.
Simon Levy: Yeah, yeah, I think Amazon actually was facing some legal challenges, not because they were inflating their profits, but because they were underestimating them, because exactly the mood you said, like, these, frankly, morons, you know, who think that, you know, somehow supply and demand is wrong or something, said that, oh, if you're making a profit, your old economy, we don't want to invest in you. It's just nuts.
Charles Lowney: Yeah, I think some of the dangers are actually here, though, that… that… and those threats, especially when
Charles Lowney: Phil and I were on a Zoom call with Mihaila Heider, who talks about a lot of these ethical issues, and he has
Charles Lowney: philosophy and technology conferences over in Hungary.
Charles Lowney: every other year, and he was talking about someone who came from Ukraine and talked, and they have, the e-enemy app.
Charles Lowney: Right? The e-enemy app is where you… you see someone who's an enemy, right, and you can use the app, and a drone will be there in 2 minutes to take them out.
Charles Lowney: Right?
Charles Lowney: If you don't have some supervision and human beings in between saying, is this really what we want to do here? Is this really who we think it is?
Charles Lowney: But you're relying on AI, right? Then it's… it's really dangerous now. And so, used as… used in war.
Charles Lowney: the way they're implementing… this is why we had that, the bombing on… on… in Iran, on all the school children, right? They used Claude, they used AI to figure out what the target should be, and Claude used old data.
Charles Lowney: And gave priority to the old data rather than the new data. They didn't have people…
Phil Z: You ever said, Orson?
Charles Lowney: Winston government.
Phil Z: Do you have a source on that?
Charles Lowney: But.
Phil Z: Do you have a source for that?
Charles Lowney: I, I can…
Charles Lowney: I can find where my source was, yeah. I'll look that. Are you skeptical of that?
Phil Z: Well, I think the reason may be just sheer stupidity on the part of the…
Charles Lowney: I think, yeah…
Phil Z: I would…
Charles Lowney: but also using technology in a stupid way.
Simon Levy: But keep in mind, too, though, that, I mean, people… I didn't even know this till I worked at a small defense company years ago, that, it's just shocking the amount of what used to be called friendly fire, you know, something… fratricide is, like, 15% of casualties, typically, and, you know, a real military person would tell you that. So, in other words, it's really common
Simon Levy: I mean, it doesn't make it any better for anybody, it's horrible, especially if it's children.
Simon Levy: But it does… it's not like AI is… I forgot who the previous,
Simon Levy: speaker was, but basically, yeah. I mean, you gotta compare, you know, driving… driving accident rates for human beings for self-driving cars. You don't just, you know, like, cherry-pick, oh my god, this, you know, this, Google car just ran over some old lady or something. Right. You know, like, or in war. I mean, just a shocking amount of death comes from your own side.
Simon Levy: So…
Phil Z: Just a couple of things on emotion, if that's okay?
Phil Z: Are we sure that emotions can't be simulated?
Phil Z: Ais have been very good for, I don't know, over a decade at analyzing stories.
Phil Z: Answering questions about the emotional pitch
Phil Z: of a story, predicting what will happen, you know, they read the first five chapters, and predicting the emotional states, further on. So, it's… to me, it's not clear that
Phil Z: Although, I don't… I still don't think machines will have experiences yet.
Phil Z: in the same way that people do, will the programmers and the learning technology allow
Phil Z: these AIs, I would call them artificial minds.
Phil Z: to, simulate emotions. And also.
Phil Z: How do I know if you care?
Phil Z: Only if I have some idea of what caring looks like. There's plenty of people who act like they care, who don't care.
Phil Z: So the… the problem of pinning down the caring and emotions…
Phil Z: gets more complicated. I will go back to Descartes. What's the only thing that I can reasonably be sure of?
Phil Z: And that's what's…
Phil Z: what I'm… what I'm perceiving as myself and what myself is experiencing. Big problems with that for you philosophers, but that's kind of a given in, living a life.
Phil Z: and so I would break down the difference between artificial minds and people, is that ability to have experiences. I can be sure that I'm having experiences.
Phil Z: I'm not sure…
Phil Z: what it's… what the experiences are going to be like for other people, or for machines. So perhaps it all can be simulated, or perhaps it emerges once you get enough complexity.
Charles Lowney: Yeah, I appreciate what you say about it… simulating emotions and appreciating emotions is different than having empathy in order to
Charles Lowney: understand somebody else's emotions. So, it's this… it's the… the… maybe the uncanniness of the simulation sometimes with… with,
Charles Lowney: Both pattern recognition, sorts of cognitive and skill sorts of things that we do.
Charles Lowney: With emotions, too, so… When is it?
Charles Lowney: you know, a simulation, right? A model, a mirror of something.
Charles Lowney: And when, when is there, experience or experiencing the emotion?
Charles Lowney: When will we know that I'm…
Charles Lowney: a robot that has been geared up to simulate a lot of the things we do actually experiences that. That goes back to the dwelling in question, and whether it's something it's like to be…
Charles Lowney: a machine, and it might reach the point where we can't tell, but then what difference would it make? It's just like, we don't even know if other people are really feeling emotions the way we do, right?
Charles Lowney: Because…
mullins: We're running… we're running near the end now. Jungil and Eric both have had their hands up, I believe.
mullins: Yongil, why don't you go?
Young-il Choo: Yeah, thank you. Just quick…
Young-il Choo: I mean, this… this whole area is, I believe, hyped.
Young-il Choo: But I think the reason for that is because
Young-il Choo: people are amazed at the natural language ability of these systems, right? If it were programming using a programming language, people would say, oh, yeah, that's a computer. But because we seem to be talking to it in a natural way.
Young-il Choo: We are ascribing to it, I believe, more intelligence than there is in the system.
Young-il Choo: And that's why I think understanding the underlying technology is critical. And also, I think when you look at the successive models that have been published.
Young-il Choo: People are recognizing that each successive model is now only slightly better than the previous generation.
Young-il Choo: We've already… we have already reached the asymptote of what large language models can be.
Young-il Choo: Because they have already sucked up all the textual information that is available
Young-il Choo: And now, they have kind of, you know, exhausted that input. And so what they are… what they embody, to use, you know, that phrase.
Young-il Choo: in those coefficients in the natural… in the connection neural network is kind of reaching the limit, okay? So, I guess, you know, maybe I'm a Luddite here.
Young-il Choo: But I believe that we are reaching the peak of the current generation.
Young-il Choo: And that people are gonna pretty soon realize, okay, it can reduce the information that's out there to a statistical model, and it can be very helpful in that regard. Just as statistics reduces lots of data into a simple, let's say, a curve.
Young-il Choo: And then you can use that curve to predict the new values, right?
Young-il Choo: To me, in my mind, that is all that an LLM is doing. Even though it's got billions of parameters, and therefore it's much more complex, nevertheless, it's still limited in that regard, so…
Young-il Choo: To me, the danger is ascribing to it more than it is capable of doing.
Charles Lowney: Yeah, I think we slip like that, like, even in… even in Hayter's first, the other article we were reading, where it talks about artificial intelligence refers to a wide range of computational systems capable of performing tasks.
Charles Lowney: Such as understanding human language, playing games, identifying patterns, making decisions, etc, but…
Charles Lowney: it's not clear that it's actually understanding language rather than just processing it, right? And so, again, we infer too much
Charles Lowney: Sometimes, and it's a natural sort of slip that we do, because
Charles Lowney: We… we judge what's going on by behaviors, right? That's why… how we know we… other people have minds, too. In the life world, they're doing what we're doing, right?
Charles Lowney: I think what's… I defer to Simon on whether or not its potentiality is being exhausted now, though.
mullins: Let's be sure we get Eric Howard in the… he's had his hand up for some time.
mullins: Eric, you want to raise your question?
Eric Howard: Yeah, sure. Charlie, thanks, man. Really good stuff. Quick question.
Eric Howard: That paper you were referring to on epistemology and ontology, and you.
Eric Howard: those gaps there. Is that published already, or is that…
Charles Lowney: Yeah, it is. It was in Tradition and Discovery, Epistemontology.
Eric Howard: Because, oh, there was
Eric Howard: Okay, yeah, okay, that's right. Yeah. Okay, okay, yeah, I need to go back to that. So, one piece to throw out real quick that kind of bridges some of this that, I know Phil M and I are kind of sympathetic to, I think maybe Charlie too, but, you know, Polani's little comment from 64, the feeling of machines, where he's commenting on, Taylor's The Age of Androids piece.
Eric Howard: Plotting makes…
Eric Howard: two incredible observations. Number one, I think at least, he harkens back to von Neumann's Hickson Symposium paper, right? And he has a footnote in there where he's like, look.
Eric Howard: you know, even von Neumann's conceding, right, with, you know, finite state machines, that this is all predicated on the fact that we can explicate
Eric Howard: all the parameters and all the data that needs to be included, right? And so he says, if that… if you can't overcome that, this is going to undermine this whole project. But then von Neumann kind of
Eric Howard: pass that off, at least in the Hickson paper, right? And he's like, okay, yeah, no, we can overcome this, right? And Polanyi's like, no. But then the other thing that he closes in this discussion that I think several
Eric Howard: several commenters have been making during this session, is he talks about… he goes back to Turing's test, right? He goes back to this whole idea of somebody… a machine versus somebody that's lying.
Eric Howard: Right? And how the external observer can't distinguish between these two things, right? And I think, yeah, you've been really, Charlie, you've been playing on a lot of these distinctions, but then Polani closes this comment, right, by making this comment, I think, which is very interesting. He says, such logic, you know, the inability to distinguish this mimicry, right.
Eric Howard: from the True Human.
Eric Howard: confuses our outlook and corrupts the image of man. David Hume has said that unlike the follies of theologians, which are dangerous, the follies of philosophers are merely ridiculous.
Eric Howard: That was true at his time, but today we live amidst philosophic mass movements, and philosophies must be taken seriously. And then there was a comment by Taylor, right, to Polani's comment, and he basically comes back to this whole, okay, yeah, this is interesting, what, you know.
Eric Howard: what Michael's bringing up, but… It's begging, right, his appeal, right, to another transcendental
Eric Howard: criterion that he's using to evaluate this stuff. So only if you believe in a soul.
Eric Howard: Right? It's how Taylor kind of finishes his remarks to Planning, and he's like, clearly, he subscribes to this, right? But it remains to be demonstrated. So…
Eric Howard: Are we back to the dualism issue again? Is this a mind versus brain versus body distinction?
Eric Howard: And my last quick question is, well,
Eric Howard: Are you familiar with, Walter Weimer's stuff?
Eric Howard: because he's retired now from Penn State, but… Yeah, Wimers?
Charles Lowney: I was thinking. Go ahead.
Eric Howard: Yeah, he's, you know, he's been using Plan for decades on some of this stuff, and trying to reconcile some of the Hayek's stuff with some pre-order thing, connection and stuff, and everything. He's got a paper coming out in, Cosmos, Texas, that's gonna be really, really good. But anyway, yeah, those are my…
Eric Howard: My open-ended comments and questions. Thanks again, Charles.
Charles Lowney: Yeah, well, thank you. A lot of what you said, echoes a lot of what John was talking about.
Charles Lowney: But does it… does it get back to… Consciousness and something transcendent is…
Eric Howard: It's kind of cool.
Charles Lowney: Oh, shit, right?
Eric Howard: That's the.
Charles Lowney: philosophical question here.
Eric Howard: for the team.
Charles Lowney: bringing up. That's right.
Charles Lowney: Well, it, I think, does, but how we understand consciousness and whether or not it is
Charles Lowney: Trans… in what sense it's transcendent?
Charles Lowney: is a question, and I think that.
Eric Howard: I think.
Charles Lowney: with Polani's emergentism, right, you can have consciousness
Charles Lowney: Be something that evolves, rather than something that's there all from the beginning.
Charles Lowney: And it's separate from matter, right? It can be…
Charles Lowney: Depends in some senses on matter and its configuration, and that's the more emergentist approach to understanding consciousness, where it's…
Charles Lowney: It's… consciousness emerges, right? It acts as transcendent because it is sort of the… an upper level in a dual control system.
Charles Lowney: So, that sort of transcendent doesn't necessarily get back to the idea of a full dualism between mind and nature, or a, the idea that you need a transcendent God, right? Because if we emerge, right, in…
Charles Lowney: Part of this is the very end of the paper, where we talk about, sort of dynamic systems can act as attractors, right, can emerge, and then that attractor helps
Charles Lowney: form and draw.
Charles Lowney: parts and constituents towards it, right?
Charles Lowney: So, so, yeah, there can be all of this without…
Charles Lowney: Deferring to the old sorts of metaphysics.
Eric Howard: Yeah, no, I agree. You should definitely check out the Weimer stuff.
Charles Lowney: I will.
Eric Howard: from a non-theistic perspective, for sure, and he's got some, I think, pretty strong… critiques that…
Eric Howard: You'd be sympathetic to, at least in part, yeah.
Charles Lowney: Alright, thank you.
Stan Scott: Diamond's turn.
mullins: Simon's hand's up, I believe, isn't it? Yes, thank you.
Simon Levy: Yeah, okay, yeah, just quickly, as a follow-up, to this emotion, it's, again, sort of the same thing you're talking about with war. It's, it's worth noting that, you know, there have been sophisticated,
Simon Levy: technologies around for all of recorded human history and prehistory, that are really good at mimicking emotion and the listening response, and they're called dolls, right? I mean, you know.
Simon Levy: the reason kids like to play with dolls is because they have this cute monkey with a cute smiley face, and we just attribute, you know… I mean, it doesn't take much to trick people or get them to interact with something as if it had what we'd call emotions. And if you want the… if you want to relate that to actual AI, so there was a notorious experiment.
Simon Levy: In about 1962, Joseph Wiesenbaum at MIT created a fake, non-directive therapist called ELISA, and, basically, you couldn't do this kind of experiment anymore. It wouldn't get past institutional review boards. But basically, people were so taken by this, this really just dumb, you know, pocket calculator level of AI doing text completion. Tell me more about your boyfriends, you know, like…
Simon Levy: they were crying, and like, this is the best therapy I've ever had, I want to come back next week, you know, and the grad students had to say, well, this is actually a fake program. Like, no, it's really… so people are just… we're pre-programmed to attribute emotion and even intelligence, and emotional intelligence, to just the dumbest piece of wood, you know, you can imagine. So I think, again, it's worth noting that, probably a lot of what we're thinking of as philosophical issues are our
Simon Levy: sort of… Human biases towards attributing all kinds of things to inanimate objects.
Charles Lowney: Which… which brings up the problems with the Turing test, too, right? Because it seems like, in a lot of, a lot of, especially current uses of, of
Charles Lowney: AI therapists, you have the Turing test being passed.
Simon Levy: Someone said the Turing test is not a test of computer intelligence, it's a test of human gullibility.
Charles Lowney: bet.
Charles Lowney: Is there someone else who had a question or a comment?
mullins: John, you might, you might just skip to the fourth.
mullins: The fourth question on the… on the list there.
Charles Lowney: Right.
mullins: When we were planning this session, you know, originally we planned to have Mihili Heder involved, and he has been working in Central Europe in AI and ethics, and,
mullins: he and, Eliseo Tartaro, had a very short, interesting piece that,
mullins: really is, is provocative about, the whole problem, socially and politically.
Charles Lowney: Well, I think Stan had one… one question first, and then we can go there.
mullins: Stan did it. I didn't see Stan's hand.
mullins: Stan, where… is Stan still here?
Stan Scott: Yeah, I'm here.
Stan Scott: I… this is maybe more of a comment than a question, but a couple times, people have used the word bewitched.
Stan Scott: And, John mentioned that I know, we are bewitched by our language.
Stan Scott: is one of Wittgenstein's classic comments, but,
Stan Scott: Calling a machine intelligent is a metaphor.
Stan Scott: And what we have done is reverse the metaphor and calling our intelligence mechanical.
Stan Scott: And, you know, so it's, it…
Stan Scott: It could be an example of what Whitehead called a category mistake.
Stan Scott: That there's a mistake that is…
Stan Scott: It's built into our language, and…
Stan Scott: We, we just mentioned something about the gullibility of the Turing test.
Stan Scott: The hum… showing human gullibility. Human gullibility is pretty profound.
Stan Scott: Particularly in, you know, in the digital age, because there are so many…
Stan Scott: So many options out there that are presented to us for our belief.
Stan Scott: And, you know, and our ascent.
Stan Scott: And, We go into what's most attractive, rather Most real, or most logical.
Stan Scott: So…
Charles Lowney: Yeah, that makes sense. The category mistake idea goes back to, like, calling a computer intelligent might be like calling the number 3 green, right?
Stan Scott: No.
Charles Lowney: You know.
Stan Scott: And, like…
Charles Lowney: I just don't notice, because it does things… like we do.
Stan Scott: Well, calling human intelligence mechanical is the reverse of that.
Stan Scott: It's an equally, equally problematic category mistake.
Charles Lowney: Right, and there's a history of that, too. Calculators used to be people.
Stan Scott: Yes.
Charles Lowney: That was originally a metaphor placed on… Computer.
Stan Scott: Computer. A computer was a person.
Charles Lowney: Exactly.
Stan Scott: Yeah, right.
Charles Lowney: I don't know if we want to go to the question, or questions of people.
mullins: I believe we've kind of shot the time, Charlie. We, we, we probably should just wind it up.
mullins: Yes, we can post the questions if anybody's interested for people to think about. Yeah.
Charles Lowney: Right, well, I'd like to thank everybody, and thank you for your questions and comments, and…
Charles Lowney: For taking the time to look at this paper.
Bruce Vojak: Thanks to you, Charlie.
Bruce Vojak: It's a timely and important topic.
Simon Levy: Yes, thanks, Charlotte.
Stan Scott: Thanks, Charlie, and everyone.
Stan Scott: Phil, Gus.
mullins: Okay, well, we'll do another one of these eventually. We're sort of working on several right now, but if you have ideas, please send them along, and we'll work with you to try to put something together.
mullins: Thanks again.
Gus Breytspraak: Thanks, everybody.
richard moodey: Thank you. Thank you.