This post is based on some conversations I have been having with friends, plus reading things written by child psychologist Alison Gopnik and listening to her interview with Timothy.  Links to the relevant Gopnik materials at the end of the post. More generally, my thoughts on AI are deeply influenced by Henry Farrell and his ongoing discussion of it on his substack blog Programmable Mutter.

It’s easy to see why the whole AI question is obsessing all the philosophers because it opens all the big questions.  It all seems connected—agency, individuality, other minds, consciousness, reason and reasoning, learning, novelty (creativity)—the whole enchilada.  Figuring out what AI is doing—and what is can and cannot do—challenges us to have coherent notions of consciousness, agency, reason, and all the rest.  And we, in fact, don’t really have coherent concepts of those biggies.  But thinking about them from the specific angle of AI might actually help bring conceptual clarity.  At least that’s one hope—what we might call the meta-hope in all the conversation around AI. 

I have just about finished The Philosophical Baby by Alison Gopnik [The Philosophical Baby: What Children’s Minds Tell Us About Truth, Love, and the Meaning of Life; Picador, 2010] and find it somewhat useful in trying to think about some of these things.  So, to just address one issue: when it comes to Gopnik’s views on AI, the argument is about learning as contrasted to brute force.  Or maybe more accurately, how learning is different when brute force is the path taken instead of the experimental method Gopnik ascribes to babies.  AI needs huge data centers because it learns by crunching its way through all the data as its way to a conclusion about what is the most probable “correct” prediction.  This is the basic Bayesian model that is thought to underlie both a human’s predictions and a computer’s.  It’s just that the computer reaches its prediction by a different path. Humans can’t access such massive amounts of data, so need to make inferences from a smaller sample.

Gopnik believes all babies are good Bayesians.  That is, babies assess a situation in terms of expectations formed from what has been learned in prior experiences.  They then revise their expectations in light of new information—or because this new situation has not disrupted (in some way) their expectation.  One key point is that expectations (predictions) are probabilistic; the baby has a hypothesis about what is most likely to happen.  But the baby may also have some notions of less likely (but still possible) outcomes.  It’s not all or nothing; it’s about what is likely to happen, not what must or necessarily will happen.  The current vogue for considering how minds process their relation to the world as Bayesian processing fits with 1) the current received wisdom that perception and cognition are active predictions of external states, not passive receptions of those states and 2) the salience of algorithms, that is, a conception of thinking as mathematical.  Bayesian predictions are statistical (literally so for large language model AI) and intuitively so for human processors.  Here’s Wikipedia’s pocket definition of Bayesian inference (leaving out the fairly complicated math that comes with a fully statistical account of Bayesian reasoning: Bayesian inference is a method of statistical inference in which Bayes’ theorem is used to calculate a probability of a hypothesis, given prior evidence, and update it as more information becomes available.

Of course, from where I stand, all this looks very Peircean to me.  Doubt generates inquiry when the expected outcome of a situation fails to materialize.  We update predictions in light of non-confirming experiences, developing new theories or hypotheses or expectations, after a process of inquiry.  And those new theories are fallible because only probable—and thus will also be revised the next time we encounter non-confirming evidence.  Necessity is downgraded.  There are not scientific laws, only probabilities, even if some predictions are more stable than others.  Ideally, persons are open to non-confirming evidence, which is how learning occurs and more supple expectations are formed.  In reality, various forms of closed-mindedness (conformation bias etc.) means expectation/hypothesis revision is less frequent than circumstances would warrant.  

Presumably, however, the computer (like the child) can learn something when its prediction proves false.  That experience leads to a revision of one’s picture of the world, of one’s expectations.  And that revision doesn’t seem, either in principle or in fact, different for the computer than it is for the child.  The feedback loop works the same in both cases. The only differences are 1) the sample size and 2) maybe something about how the data is “felt” or “processed.” In Peirce, the upsetting of expectation generates doubt, which is an uneasy feeling, one we try to dispel through our process of inquiry. Presumably, the computer is not experiencing doubt. It’s drive to find the most plausible explanation or next step derives from other sources.

But Gopnik is saying (see her conversation with Timothy) that the computer is still not “reasoning.”  The feedback it receives is just more data—and is taken to be authoritative, at least until another iteration justifies another revision.  This is the puzzle.  What is the nature of “reasoning” that makes it different from revising one’s views based on feedback offered by the data.  The answer seems to be something like “assessment” or “judgment,” which then gets connected to “logic.”  But trying to build an AI that works by being “logical” didn’t pan out.  A computer cannot get to predictions that way; it needs the brute force method.  (Or, at least, up to now brute force has proved much more successful than logic for AI.)

The attempt to base AI on logic occurred in the 1980s and 1990s when “cognitive science” tried to uncover the laws of thought and the laws of language use.  Chomsky’s project was central  If Chomsky could identify the fundamental grammar from which all languages were generated, then the computer that was given those fundamental structures would be able to talk.  Similarly, if the basic elements of logic were correctly identified, a computer with those elements installed would be able to think.  That project proved a failure.  No program that successfully produced thought or speech was developed. Current AI works by brute force; it surveys a massive number of instances and then “guesses” (via Bayesian inference) what the most probable response (next step) would be.

Human judgment, human reasoning, is notoriously fallible.  And despite computer hallucinations, AI is, in many instances and in many ways, less fallible than humans.  So it’s not clear what hinges on insisting on a difference between reasoning and brute force.  Two ways to skin the cat—and it is far from clear that reasoning is the better way to do the job.

Furthermore (and this was Timothy’s argument in his conversation with Gopnik), it is at least debatable to claim AI does no reasoning.  This is where the novelty point comes in.  The cultural technology people (the multi-authored essay cited below) are saying AI is only capable of giving us new combinations of the archive;  AI produces mash-ups.  Sophisticated mash-ups sensitive to context, audience, persona etc., but mash-ups just the same.  But Timothy says that’s just not true.  Give AI certain mathematical problems and it produces novel solutions, ones that no human mathematician has ever come up with, and those solutions prove, to the best of expert human opinion, correct.  So the machine is doing something beyond mash-up—even though we don’t know how it is doing that something more.  So that’s one thing that has people intrigued, worried, puzzled.  Something’s going on and we don’t know what it is.

I think once you grant that AI has the ability to produce novelty, the cat is out of the bag.  Arendt’s definition of agency is precisely the “introduction of something new into the world.”  Traditionally, that notion of agency is connected with consciousness.  A person thinks of (or imagines) something and then acts to make it come into being.  In The Philosophical Baby, Gopnik seems to be on both sides of this traditional view.  On the one hand, her emphasis on “counterfactuals” absolutely fits the traditional model of imagining alternatives in thought and then acting on one of them. For Gopnik, the very ability to act is based on being able to imagine multiple possible scenarios, to think not only of what is, but also of what might be.  And she identifies such counterfactual thinking as present very early in babies (as early as nine months).  But, on the other hand, when she gets to her chapter on consciousness, she is skeptical that our phenomenological experience of consciousness is an accurate picture of what is actually transpiring.  “In experiences of executive control, we often feel sure we are making a rational choice when, in fact, we are in the grip of some irrational unconscious bias.  And in all of these cases we experience a homunculus, the inner observer, biographer, and decider that we know just can’t exist. . . .The gap between the way the mind functions and the shape of conscious experience is even greater for children than for adults. . . . Consciousness isn’t a transparent and lucid Cartesian stream.  Instead it’s a turbulent, muddy mess” (161, 163).

It would seem, then, that human actions (creative productions of novelty or even simple effective procedures of daily activities like cooking food; activities that require adjustments in response to current conditions as we go along ) are as mysterious as the “black box” of generative AI.  In both instances, we do not have a good account of novelty.  In the human case, we fall back on notions of intuition, genius, unconsciousness, adaptation—or to material, bio-chemical processes that we say must be doing the job even though we still don’t know how those processes actually work, actually produce their magic. 

I’ve got plenty more to say about all these topics. But I am going to finish up today with just one more issue.  Individuality.  An account of action seems to cry out for a complimentary account of “an agent.”  Not only what happens in the world, but who has made it happen.  Which then opens up the questions of motivation (why did the agent want to make that happen?) and of effectiveness (was the action successful?; was it based on a good enough assessment of what the context afforded or on a grievously fallacious misreading of the context?).  And also the old Aristotelean question of the interrelation of action and character.  Does one’s actions shape one’s character or does one’s character determine what actions one finds desirable to undertake? 

A quick aside on action.  It is worthwhile to keep in mind that any theory of action must take into account how the agent interacts with (has to negotiate) three different spheres (for lack of a better term): the material external world; the people among whom she dwells; and internal desires, moods, emotions, thoughts, fantasies, physical capabilities and/or their lack.  Just how those relations are established and played out by a machine as contrasted to a human has to be part of the larger puzzle in trying to characterize AI. For example, what does it do to our conception of action if we have an agent (the computer) to which we ascribe no internal sphere (no thoughts, emotions, desires etc.)

Since, so far, most people want to resist anthropomorphizing our machines (i.e. to deny that they have “characters” and certainly to deny them individuality—which would mean my computer’s character was different from your computer’s), we now have a disconnect between action and agent.  The computer acts, but it isn’t an agent.  I think the general speculation is tangling with this dilemma.  People like Gopnik want to deny that the computer acts.  That way they can preserve their conviction that the computer is not an agent.  Some AI enthusiasts will go the other direction.  The computer acts and it is an agent.  And then those in the middle are agnostic for the moment, trying to figure it out.

All of which might just be a way to say that the AI debates are reproducing fairly faithfully the consciousness debates.  After all, much of the consciousness debate (not all of it, but much of it) is about agency.  What is the source of human action?  And does consciousness have any role to play in the production of action?  The positions taken on these questions range all the way from saying consciousness has no role to saying that consciousness is the determining factor.  But there are lots of people in the middle who hedge their bets, saying consciousness is one factor among others. 

So maybe what this means is that the AI debate is misguided from the start because contaminated by the debate/puzzles about human action.  Maybe we need a whole new paradigm to think productively about what machines are doing.  To stop comparing them to humans; to stop worrying whether they act or think like humans.  We need a whole new set of terms and questions.  Which would mean a way of thinking the non-human or the un-human.  An uncanniness that does not, in the long run, reference home.  That takes up its abode in an absolutely foreign place. 

Bibliography:

Gopnik’s web site can be found at https://alisongopnik.com/   You can access all her various essays and interviews from that site (although not the text of The Philosophical Baby).

Here’s a link to her conversation with Timothy: https://www.youtube.com/watch?v=JIFdeXPB1Pg

And a link to the cultural technologies essay:  https://alisongopnik.com/Papers_Alison/science.adt9819.pdf

Henry Farrell’s blog is on Substack, called Programmable Mutter, and is free to all: https://www.programmablemutter.com/?utm_campaign=profile_chips