Tag: technology

Correction to My Recent AI Post

My wife Jane has pointed out to me the very basic error in my last set of musings on our friend Claude.  I spoke of the Claude in your computer as distinct from the Claude in mine.  But, of course, that is egregiously wrong.  There is only one Claude, even if it manifests on different occasions in different ways.

What I was trying to wrap my head around was how Claude could give a different response to the same query.  And one place where I went wrong was to make Claude substantial.  To think of Claude as a thing or (worse) a person.  In short, to give Claude substance.  But Claude is not a thing; Claude is a function (in the mathematical sense of function). 

Here’s the definition of function in math: “Think of a function like a tiny math machine. You drop an input into the machine, the machine follows its rule, and a single output pops out. The golden rule of a function is that every single input leads to one and only one output.” (From, where else?, the internet.)

So there’s the catch.  Claude doesn’t give “one and only one output.”  How to explain that?  Simplest explanation: the inputs are not the same.  Yes, it’s the same query, but Claude takes context into account.  Especially the context of who is posing the query.  But there are other contextual features as well, and Claude is also taking them into account when it responds.

Many years ago, I almost flunked my job interview at North Carolina when I was asked about the status of intention.  The questioner wanted me to concede that the intention of a speaker (mostly) determined (or anchored) the meaning of the utterance.  My first easy answer was “how about something that is unintentionally funny (or insulting)?”  That example shows that meaning is not under the control of the speaker.  But where I almost lost the job was when I said I didn’t think intention was an internal mental state.  Rather, intention exists between people, in the space of their interaction.  Since it was North Carolina, I said: think of Michael Jordan.  Yes, he has the intention of scoring a basket.  But how that intention is activated is also just as deeply influenced by the rules of the game and by what the other players are doing.  Action is a negotiation among various factors and intention is shaped in the ongoing adjustment to those factors.  In the case of utterances, language itself (the dictionary and syntax) is the constraining structure and other people (the ones to whom the utterance is addressed) to some extent dictate the tone taken, the words chosen, etc. etc.  So intention is produced in the interaction of all those factors, not something that exists within one person in that overall scene.

My questioners were not amused by this answer.  They expressed their skepticism unequivocally.

To get back to Claude.  Claude in your computer and Claude on mine are not things, not substances.  Rather, you are activating Claude in one instance and I am activating Claude in a different instance.  Claude is an event, not a thing.  So what Claude produces is a result of its function, it algorithm, interacting with your query and other contextual factors.

At this point, I think I need system theory.  The puzzle is how to create a system that yields diversity, not homogeneity.  As I already suggested, one could say that every instance of activating Claude is unique, so therefore there is no puzzle.  Claude responds differently because the instances (the situation and its context) is different.  And Claude, since substanceless, has no habits, no tendencies.  A person will display patterns of response even when the situations to which he is responding are not identical.  Which returns us to the notion of Claude as a better “reader” of situations than humans because Claude’s interpretations of situations are not colored by internal habits, biases, desires, blindnesses.  The ideal of pure objectivity attained.

Except, of course, that Claude does make mistakes.  Presumably, the effort to improve, even perfect, Claude is the effort to identify the patterns (if there are any) of its mistakes—and thus correct those tendencies in order to eliminate mistakes. 

Because embodied, humans get some of their mistakes corrected by feedback provided by the world (by natural processes).  Because they are social animals, humans also receive feedback from other humans.  Presumably, Claude does not receive feedback from nature, only from human users.  But I am venturing into the dark here.  I don’t understand how feedback works in the ongoing development of AI.  We are told that AI is recursive, that it learns from its mistakes.  But how does it know it has made a mistake? The very notion of feedback assumes some “other” to the action or process that is being corrected through the response of that “other.”  What stands as the “other” to Claude?  Only its human designers and users?  Or does it have internal responsive processes—analogous to the way digestion offers feedback that confirms the desirability (or not) of eating certain foods?

And if Claude’s diverse responses are diverse because the inputs are always unique, then how does feedback about a particular instance get generalized into an improvement of the system as a whole?  What’s the relation of the nodes of a system, and the instances of those nodes’ activation, to the system as a whole?  I can’t help but think of Spinoza here since he insists on there being only one substance, but then has to think how that substance gets articulated (manifested) in distinct instances.  So Spinoza has to fall back on a theory of “modes” in order to explain how substance does not appear everywhere the same.  Still, because committed to the idea of a single substance, Spinoza must conclude that the modes are fully determined by substance.  The modes have no independence or autonomy. 

Is Claude, in its different eventful activations, similarly determined?  Logically, it would seem like full determination must be the case.  The algorithm (the mathematical function) rules.  But, in practice, Claude does not seem fully determined.  First, its designers do not know how it is producing various responses.  So they are unable to specify what causal path (or what mathematical processing of the function) led to what Claude offers in certain instances.  Two, and even more uncanny, Claude seems to be acting as an agent in some cases; that is, it develops and acts upon its own purposes.  Agentic AI is the current boogey man that is stoking the fears of the doomsayers.

Spinoza, of course, is a radical monotheist.  You might say he takes monotheism to its logical conclusion: pantheism.  God is everything.  Which, paradoxically, gets read as atheism.  If God is everything (you, me, the stars, and the railroad), then he is nothing.  No distinct identity or site of action, belief, commandment.  Just the whole tangled ball of wax.  And that means  god is as determined by the laws of causation as are all the things in creation (those things are, after all, just modes of God).

Thinking of Claude in monotheist ways is scary.  It just sucks everything into itself.  All that data, all that information, being swallowed up—and then spit out again in various permutations.  Turns us, the humans, into data points.  Of course, one way to think of evolution goes down a similar path.  Evolution is a monolith, driven by a single mechanism (natural selection), that produces diversity, not homogeneity, but which renders individual human beings of no account except as carriers of genes.  Our genes, like our data in dealing with AI, are sucked up into the overall genetic pool out of which new organisms (to be subjected in their turn to processes of natural selection) will be generated.  Generative AI does with data what evolution does with genes.  (And, of course, some theories think of genes primarily as instances [instantiations] of information.)  Where evolution has natural selection as the mechanism, AI has the algorithm, its program.  The workings of natural selection are, for the most part, fairly understandable.  With some limitations, we can track the working of natural selection; its outcomes make sense.  But AI, like god, seems to be working in more mysterious ways.  It is, reports from its creators indicate, becoming increasingly difficult to account for how and why AI does what it does.

To loop back: there is only one Claude, although Claude manifests itself in multiple instances.  That fact generates puzzles familiar from the history of trying to think through what monotheism entails.  How to explain diversity within a primal, overarching one-ness.

The first commandment reminds us that the monotheistic god is a jealous one even as he is also aware of rivals, those other gods his followers are to eschew.  Claude, of course, also has his rivals, the alternative versions of AI being developed by OpenAI, Microsoft, in China etc.  Another thing to watch for as we move forward into the AI period is whether one version of AI is going to win out over all the others—or if we are going to continue to have (as we do now) a polytheistic scene that offers various AIs.  Coca-Cola cannot, try as it might, eliminate Pepsi, nor the myriad other things people might choose to drink with their hamburger.  The drive to monopoly in certain sorts of goods reaches a limit at some point. 

But with other kinds of goods, there are strong factors driving toward monopoly.  It’s a coordination issue.  Language itself is a good instance.  Within a community, it is very dysfunctional to have speakers using different languages and thus unable to communicate with one another. Thus one language comes to dominate the scene, making other languages marginal (or driving them to extinction). Similarly, although technologically perfectly feasible, it makes no good sense to have different railroad companies using different gauge tracks.  Will one version of AI supersede all the others because it becomes the lingua franca for the interactions between humans and computers?  Or will the current situation, with various different versions of AI at our disposal, remain the norm?  Tech bros dream of dominance, of being the sole competitor left standing.  But the prospect of a sole winner, of a monotheistic outcome sometime in the future, is alarming to this pluralist. 

More Thoughts on AI

A friend writes to me and tells me about the amazing things he has been getting AI to do, in particular in building a model to calculate probabilities of certain events in baseball games.

Here is my response:

I don’t know what to think about Chat GBT–or AI more generally.  I haven’t used it and don’t know how to use it.  But I have been reading (in a desultory way) about it in an effort to understand it.  Most of the stuff I read (predictable, I guess, given the bubble in which I find things) is pretty skeptical.  More interested in pointing out its deficiencies, while also taking pot shots at the enthusiasts and what does seem their wildly overstated claims about AI and the future it will bring.  But your experience is much more concrete.  Here’s a task and here is how this tool does things that would be impossible to do without it.  That’s meat on the bones.  Exciting—and to my mind pretty scary.

I used to ask my students this question: why do so many people find the idea that our thoughts, emotions, and fantasies (i.e. our consciousness) are the product of biochemical processes?  What is the threat of acknowledging that material fact?  And I can’t say I ever got a cogent answer to that question, even though lots of students did fess up to resisting the idea that it’s all biochemical.  They didn’t want their consciousness reduced to chemistry.  But couldn’t really articulate why that was so distasteful and/or threatening.  

There seems to be a similar resistance in the stuff I read to accepting that AI is more intelligent than humans and that it not only will be able to do everything humans can do, but will also be able to do things humans cannot do.  It’s not the second part that seems the problem.  Most everyone accepts that AI can do things humans cannot.  But there is the ongoing effort to identify things AI can’t do—so as to leave at least a few things to humans alone.  And then the temptation becomes to posit those few things AI can’t do as the very things that are essentially human, the very things that are most valuable in life.  

So, maybe, in both cases of resistance (to chemical reductive accounts of consciousness and to visions of an AI that can do it all) the stake is human-ness.  Just as in desperate attempts to distinguish humans from all the other animals. Hard to dislodge the self-love that underwrites humanism.  Even harder when that self-love is an anxiety driven form of denial.  Humans just aren’t as different from animals or AI as so many humans wish them to be.

In the case of AI (in my limited understanding), it’s not chemical reduction but numerical reduction.  AI can encompass anything that can be calculated through translation into numbers.  So I hear/read constantly that what AI cannot do is supple judgment/evaluation.  To take the case of baseball: AI (in this view) could not look at eight players and assess which ones have the “tools” to succeed at the highest level.  (Leave aside the fact that humans have proved fallible, to say the least, in making such judgments.) The issue here would be whether the “eye test” can be reduced to numbers (i.e. to measurements of the players’ bat speed etc.).  Or, alternatively, if the numbers when put against the eye test consistently produce better predictions.  In the case of baseball, it seems pretty clear at this point that the numbers are more reliable judges of potential.  The larger issue is whether there are other areas of human practice where the numbers prove not as reliable predictors as considered human judgment. 

So it has become a common theme now to say that AI threatens the production of skilled evaluators. Having AI do the basic tasks of any enterprise (lawyering, accounting, writing of any sort) deskills those who work in those fields because they miss the apprenticeship, the immersion in cases, the iterations of practice that produce considered judgment. And that gets coupled with claim that AI itself is not good at judgment because it simply averages what is already on record. It cannot recognize or value the innovative (ironic given what a god-term “innovation” is for the techies).

Henry Farrell, to my mind, has been the most interesting person to read on such questions. Here’s a link to one of his blog posts that considers this issue of reduction to numbers. Farrell is of the party that insists there are issue of import to we humans that are ill served if we allow them to be reduced to numbers, what he calls “numerical rationalism.”

https://substack.com/@henryfarrell/p-193177198