Wednesday, March 1, 2023

The NYTimes is doing better on next-word prediction, much better [Here be dragons.]

After some setting the stage, including introducing Terry Sejnowski, Metz tells us this:

As it analyzes that sea of good and bad information from across the internet, an L.L.M. learns to do one particular thing: guess the next word in a sequence of words.

It operates like a giant version of the autocomplete technology that suggests the next word as you type out an email or an instant message on your smartphone. Given the sequence “Tom Cruise is a ____,” it might guess “actor.”

As you might imagine, my initial reaction was: No! No! No! A thousand times NO! and then some. After all, I’ve been objecting to this characterization for a while now. After I’d peeled myself from the ceiling I decided to click on that link, and found something interesting, indeed.

This a bit of animation, which I can’t show here. It is showing us next-word generation starting with this question: Who is LeBron James? The AI has already replied: LeBron James is an American professional... The animation steps through the addition of new words and, shows us the shifting probability distribution as it moves along. We get these short paragraphs as we move down the page:

When artificial intelligence software like ChatGPT writes, it considers many options for each word, taking into account the response it has written so far and the question being asked.

It assigns a score to each option on the list, which quantifies how likely the word is to come next, based on the vast amount of human-written text it has analyzed.

ChatGPT, which is built on what is known as a large language model, then chooses a word with a high score, and moves on to the next one.

The model’s output is often so sophisticated that it can seem like the chatbot understands what it is saying — but it does not.

Every choice it makes is determined by complex math and huge amounts of data. So much so that it often produces text that is both coherent and accurate.

Yes! This is just the kind of thing we need. And we need more of it. Much more. And it gets better as we go on. 

“This is terra incognita,” Dr. Sejnowski said. “Humans have never experienced this before.”

Let’s go back to Cade Metz. He continues on:

When you chat with a chatbot, the bot is not just drawing on everything it has learned from the internet. It is drawing on everything you have said to it and everything it has said back. It is not just guessing the next word in its sentence. It is guessing the next word in the long block of text that includes both your words and its words.

The longer the conversation becomes, the more influence a user unwittingly has on what the chatbot is saying. If you want it to get angry, it gets angry, Dr. Sejnowski said. If you coax it to get creepy, it gets creepy.

The alarmed reactions to the strange behavior of Microsoft’s chatbot overshadowed an important point: The chatbot does not have a personality. It is offering instant results spit out by an incredibly complex computer algorithm.

I suppose that makes sense even if you either knew about shifting probability distributions already or if you linked out to that wonderful animation. But the more you know, the better.

Metz goes on:

But there’s a caveat to this reassurance: Because chatbots are learning from so much material and putting it together in such a complex way, researchers aren’t entirely clear how chatbots are producing their final results. Researchers are watching to see what the bots do and learning to place limits on that behavior — often, after it happens.

Microsoft and OpenAI have decided that the only way they can find out what the chatbots will do in the real world is by letting them loose — and reeling them in when they stray. They believe their big, public experiment is worth the risk.

That’s how it is for now. And, if you’ve played with ChatGPT you know that have the opportunity to give a thumbs up or thumbs down to each response. OpenAI is, of course, logging that information and using it to further tune ChatGPT.

I have come to think of these large so-called Foundation Models as the digital wilderness. We need to explore it, stake out claims, clear the land, and start building on it. Ultimately, “building on it” is going to mean linking it to a world model. We’re not there yet, alas.

Here's the last words in the article:

“This is terra incognita,” Dr. Sejnowski said. “Humans have never experienced this before.”

YES! As I said in the title of a working paper I did two and a half years ago, Here be Dragons!

There’s more at the link.

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