Summarize

"Could scale be all you need?

I have been hard on the language model, but undeniably, GPT-3 appears to occasionally make some sense, where previous language models have done less so. The secret sauce seems to be scale. It turns out that if you take the same fundamental task as my tweet bot, but rather than a few hundred parameters you use 175 billion parameters, and rather on than 200 tweets you train it on hundreds of billions of words, you do get something that produces intricate syntactically correct output, which sometimes seems to display a true mastery of language and reasoning, passing bar exams and whatnot.

Maybe scale is all you need. Right now GPT-3 adds together 175 billion numbers to come up with a single word, and admittedly it still produces some suboptimal output, but maybe at 175 trillion numbers per word we can have a computer that can write an acrostic, solve a quadratic equation, or turn that 50.3% bar exam score into a C+ or even higher. Maybe at 175 quadrillion we get a computer with a soul.

I’m a little bit doubtful.

“Scale is all you need” commits you to a very specific and unusual position on the nature of general intelligence: namely, that it can emerge from a very large language model. It says that Artificial General Intelligence can emerge from a careful accounting of the relative frequencies of words in the history of text. If GPT-3 isn’t generally intelligent yet, it’s not because language models simply can’t become general intelligence, but that we just haven’t built one big enough yet.

I personally find this position hard to justify, and I think that people tend to paper over just how strong it really is. I’m not arguing against the possibility that a computer program could achieve some notion of general intelligence, however you want to define that. But “scale is all you need” is a much stronger claim. It says that this computer program is strong enough for AGI to emerge, that general intelligence is somehow encoded in the joint word frequencies of large corpora of text, that through counts of the combinations of words that occur in novels, papers, blog posts, Reddit comments, slash fiction, tweets, and so on, things like propositional rules of inference, state, mathematics, or (some might even go so far as to argue) consciousness can emerge. The only missing piece is more money. (Note that this happens to be a convenient position to hold if you would be the recipient of that money.)

This strong claim is imaginable, and some people might even find it compellingly so. But people talk about it like it’s inevitably and obviously true (see the above t-shirt), and that’s just not the case. Out of all of the tasks that we could sic a 175 billion parameter neural network on, why would modeling joint word frequencies from this specific one collection of text be the magical one from which AGI emerges? What would be the nature of that emergence? What happens when relative word frequencies change over time — does that change the nature of the emerging intelligence? It would be one of the most surprising findings in the history of science. Accordingly, we should have a high bar of evidence for expecting it to be true.

A typical caricature of the current state of affairs goes something like this: OpenAI built a giant mechanical brain and loaded it up with all of the data in human history. And, yeah, if that were the case, maybe it would have general intelligence, or even sentience or consciousness. And if it didn’t, it would only be because the giant brain isn’t giant enough, and eventually when the brain is made giant enough, we will have a conscious AGI. The nomenclature associated with all of this technology perpetuates the big brain mythology: neural networks, machine learning, artificial intelligence, and so on. But OpenAI did not build a big brain; they built a statistical model of historical word frequencies. Maybe, if it’s big enough, a sufficiently complex statistical model of historical word frequencies can become a generally intelligent thing, but there’s no a priori reason to expect that to be the case, and we should not simply assume it.

As a side note, I think that people are slightly overestimating the novelty of the technology that we are looking at here, and the pace of its development. Many people are seeing this technology for the first time now, but Transformer Networks have been around since 2017, and OpenAI’s first publications about GPT were in 2018, almost 5 years ago as of this writing. That may seem like a short or a long amount of time to you, but just to put it into perspective, GPT has been in America longer than TikTok. Due to my work in tech, I’ve had opportunities to interact with non-public versions of these kinds of LLMs for a few years now, and while what I’m seeing from GPT-3 is different in some ways from what I’ve seen in the past, it’s not so different that my mind is completely blown.

I’m not saying this to deride the innovation of OpenAI engineers and scientists, but only to put a slight damper on perceptions about the pace of technological change that is occurring here. I think for a lot of people, it seems like a switch flipped at the end of November 2022 bringing the state of AI from the dark ages to the space race in a month — so imagine what might happen next month! But in reality, OpenAI and others have been steadily plugging along at this for many years. GPT-3 isn’t even the largest large language model. Google Brain’s Switch-C model has been around for years and comprises over five times more parameters than GPT-3. GPT-3 is just the largest large language model that’s hooked up to a public-facing API and chat interface."