Sunday, June 18, 2023

AI seems to be back. What is it now?

In one of the earliest posts here, several years ago, I mused What was AI?  At the time, the term AI seemed to have fallen out of favor, at least in the public consciousness, even though there were applications, like phones with speech recognition, that were very much considered AI when they were in the research phase.  My conclusion was that some of this was fashion, but a lot of it was because the popular conception of AI was machines acting like humans.  After all, the examples we saw in pop culture, like, say, C-3PO in Star Wars, were written by humans and portrayed by humans.

There's a somewhat subtle distinction here: A phone with speech recognition is doing something a human can do, but it's not acting particularly like a human.  It's doing the same job as a human stenographer, whether well or badly, but most people aren't stenographers, and even stenographers don't spend most of their time taking dictation (or at least they shouldn't).

Recently, of course, there's been a new wave of interest in AI, and talk of things like "General Artificial Intelligence", which hadn't exactly been on most people's lips before ChatGPT-4 came out.  To avoid focusing too much on one particular example, I'll call things like ChatGPT-4 "LLM chatbots", LLM for Large Language Model.

By many measures, an LLM chatbot isn't a major advance.  As the "-4" part says, ChatGPT-4 is one in a series, and other LLM chatbots were developed incrementally as well.  Under the hood, an LLM chatbot is an application of neural net-based machine learning, which was a significant advance, to the particular problem of generating text in response to a prompt.

But goodness, do they produce plausible-sounding text.

A response from an LLM chatbot may contain completely made-up "facts", it may well break down on closer examination by followup questions or changing the particulars of the prompt, and it may have a disturbing tendency to echo widely-held preconceptions whether they're accurate or not, but if you just read through the response and give it the benefit of the doubt on anything you're not directly familiar with, something people are strongly inclined to do, then it sounds like the response of someone who knows what they're talking about.  The grammar is good, words are used like people would use them, the people and things mentioned are generally real and familiar, and so on.

In other words, when it comes to generating text, an LLM chatbot does a very good job of acting like a human.  If acting like a human is the standard for AI, then an LLM chatbot is definitely an AI, in a way that a speech-transcribing phone app or a model that can pick out supernovae from a mass of telescope images just isn't.

But our perception of whether something is acting intelligent in a human way is heavily tilted toward language use.  All kinds of animals can recognize images and many can respond to speech, but only we can produce large volumes of text in human languages in response to a prompt.  Until now, that is.

Since LLM chatbots are an obvious major advance in acting-like-a-human, it's natural to assume that they represent a major advance in understanding what intelligence is generally, but those are two very different things.  As far as I can tell, we're not really any closer to understanding what "general intelligence" might mean, or how to produce it, than we were before.

To be sure, LLMs have shown some interesting behaviors that may offer hints as to what intelligence might be.  Once the model gets big enough, it seems to get better at learning from small samples.  For example, if you train a model on a huge amount of English text and a little bit of Italian and a little bit of Python code, it seems that it can do a pretty good job of producing plausible Italian or plausible-looking Python.  The code might well not do anything useful, unless it's close to one of the examples that the model was trained on, and it might not even build, but it looks like Python someone wrote.  There's some question as to whether this ability arises suddenly once the model hits a certain size, or actually happens gradually, but it's definitely a thing.

It seems likely that, after enough training data and with enough depth to the underlying network, an LLM develops connections that in some way represent something general about the structure of languages, at least ones like Italian and Python that are reasonably close in structure to the English that makes up the bulk of the training data.  It would be interesting to explore that, because it may capture something about language structure that current theories of grammar miss.

It seems reasonable that a suitably deep model trained on larger amounts of language would be able to capture something useful about the structure of language.  Beyond that, though, is it possible that an LLM can capture something about the world that the body of text is talking about?  If the training data contains millions of words about water, and rivers, and thirst, and drinking water, and clouds, rain and so forth, will it in some sense know that water is wet and that rain falls from clouds and some of it drains into rivers that flow into the sea, and that living things need water and that if a person is thirsty they can drink water and not be thirsty any more?

From interacting with an LLM chatbot, it's certainly easy to conclude at first that the answer is yes.  For example, I just asked a chatbot "If I'm thirsty and I drink water, will I still be thirsty?" and got back

Generally, if you are thirsty and you drink water, you will no longer be thirsty. This is because thirst is a signal from your body that it needs water. When you drink water, your body's water levels will increase and the thirst signal will be turned off.

However, there are some cases where you might still be thirsty after drinking water ...

and so on.  That seems like case closed.  One good way to find out if someone knows something is to ask them questions about it and see if the answers make sense.  I did, and the answer made sense.

The problem is, LLM chatbots have a tendency to go down rabbit holes once you get into details.  Later, I asked "If I'm thirsty and I drink ambrosia, will I still be thirsty?"  The answer starts off reasonably:

In Greek mythology, ambrosia is the food of the gods. It is said to be a delicious and intoxicating beverage that grants immortality. If you were to drink ambrosia, you would no longer feel thirst, as it would quench your thirst permanently.

So far, so good.  Going on .. 

However, ambrosia is not a real substance, so it is impossible to say for sure what would happen if you drank it.

This is a bit ... off.  If ambrosia is not a real substance, then you can't drink it, period.  It's not impossible to be sure what would happen, it's impossible to say, period.  The answer goes on to conclude:

However, it is an interesting question to ponder, and it is one that has been explored in many works of fiction.

OK, I'll bite.  "What are some works of fiction that explore the drinking of ambrosia?"  By "explore", I'd expect something like a storyline that features a person, or multiple people, drinking ambrosia in various amounts or circumstances and discusses the consequences of the various possibilities.  I wouldn't want something that just "features" or "includes" the drinking of ambrosia.

Nitpicking?  I mean, isn't it close enough just to mention some stories that include ambrosia and count that as exploring?

No.  If the chatbot has a true understanding of the concepts behind the words, it will catch this distinction.  If it has merely picked up from its training data that "explores" is a word often used when discussing fiction, it won't.  In my experience, LLM chatbots miss this sort of distinction over and over again.

So what works did it come up with?

  • The Odyssey by Homer: In this epic poem, the hero Odysseus drinks ambrosia and nectar while he is a guest of the gods on Mount Olympus. This gives him the strength and vitality to continue his journey home.
The first time through, I took this at face value.  It's been a while since I read the Odyssey.  But it doesn't look like this scene actually occurs.  Ambrosia is mentioned, briefly, in other contexts.  Even if this scene were in the poem, would it really be exploring the drinking of ambrosia?  Not really.  It just records the common understanding in his time and place of what ambrosia was and what it did.
  • The Bacchae by Euripides: In this tragedy, the god Dionysus gives ambrosia to his followers, which makes them immortal. However, the ambrosia also makes them insane, and they go on a rampage, killing people and destroying property.
That would count as an exploration in my book.  One problem, though: That's not the plot of The Bacchae.  From what I can make out, without carefully reading through the whole play, there's no mention at all of ambrosia in The Bacchae.
  • The Tempest by William Shakespeare: In this play, the magician Prospero uses ambrosia to create a magical elixir that can grant youth, beauty, and immortality.
Nope.  The word "ambrosia" is not even in the play.
  • The Last Olympian by Rick Riordan: In this book in the Percy Jackson series, the Greek gods give ambrosia to the demigod heroes to heal their wounds and give them strength.
That's probably about right -- I'm not going to take time to look, given the rest of the entries -- but again, not really exploring the question of what it would be like to drink ambrosia.
  • The Mortal Instruments by Cassandra Clare: In this series of fantasy novels, the main characters drink ambrosia to heal their wounds and give them strength.
As far as I can tell, this is not so.

OK, so maybe the chatbot understands water perfectly well, but it's not good with literature and finer points like what's in it?  Let's try something more direct.

"If I have a bell, a book, a candle and some water, how can I stack them?"

(I picked stacking things because I ran across a pretty impressive example in a news item, of a chatbot suggesting a solution the author hadn't thought of.  I have no doubt this happened, but I strongly suspect that the author took the impressive example as "proof" and didn't press on by, say, asking variants of the original question.)

  • There are a few ways to stack a bell, a book, a candle, and some water. Here are a few examples:
    • Bell, book, candle, water: This is the most straightforward way to stack the items. The bell would be on the bottom, followed by the book, the candle, and the water.
    • Water, bell, book, candle: This stack is a bit more challenging, as the water would need to be supported by the bell and the book [wait, what?]. However, it is possible to do this by placing the water in a small container, such as a cup or a bowl [so in the first stack, the water isn't in a container?].
    • ...
I think the easiest explanation for this one is that the chatbot doesn't really understand what physical objects and substances are and how they interact.

In some meaningful sense, the model understands something interesting about the structure of languages, or at least some languages, because it was trained on a mass of text.  It can also model associations among words, for example that "water" tends to be associated with words like "wet" and "drink" and "flow".

However, since its training had nothing to do with actual water, or anything else in the physical world, it's no surprise that it doesn't show any real understanding of how things interact in the physical world.  Since the training text doubtless included reviews and discussions of various works of fiction, it can imitate something talking about fiction, but it misses details like the meaning of "explore", or even that if you say a story explores something, that thing should actually appear in the story.

So, after that fairly long digression, how does all this fit together?
  • Except among people closely associated with the research, "AI" generally means "acting like a human" and "doing things that are particularly human", like using language
  • LLM chatbots are pretty good at acting human in that sense ...
  • ... including making up plausible-looking responses when they don't really know the answer
  • But language is just one part of the picture.
  • "General Intelligence" is not at all well-defined, but if it includes some sort of general understanding of the world and how to solve problems in it, then there's no real reason to think LLM chatbots have it, or are even close to acquiring it ...
  • ... even if they're sometimes good at looking that way

1 comment:

  1. Here are a couple: Invert the bell (supported by, say, a bucket of sand), fill with water, float a large candle on it, and on that set a small book (of matches?) or

    fill a pie pan with water, suspend a candle in it vertically while it freezes, invert the bell over the candle, and again with the book of matches

    Or the book could be quite large, say, the OED, if it were on a chip.