Simply explained: How does GPT work?

This is absolutely not viable because exponential growth absolutely kills the concept.

Such a system would already struggle with multiple-word inputs and it would be completely impossible to make it scale to even a paragraph of text, even if you had ALL of the observable universe at your disposal for encoding the entries.

Consider: If you just have simple sentences consisting of 3 words (subject, object, verb, with 1000 options each-- very conservative assumptions), then 9 sentences already give more options than you have atoms (!!) in the observable universe (~10^80)

See my problem with virtually every single example is that we talk about "I can't describe in any other way than it is thinking", "such complex solutions" but in the end we get a 50 lines "app" that you'd see in a computer science 101 class

It's very nice, it's very impressive, it will help people, but it doesn't align with the "you're just about to lose your job" "Skynet comes in the next 6 months" &c.

If these basic samples are a bottleneck in your day to day life as a developer I'm worried about the state of the industry

It’s a fallacy to describe what the machine does as “thinking” because that’s only process you know for achieving the same outcome.

When you initiate the model with some input where you expect some particular correct output, that means there exists some completed sequence of tokens that is correct—if that weren’t true then you either wouldn’t ask or else you wouldn’t blame the model for being wrong. Now imagine a machine that takes in your input and in one step produces the entire output of that correct answer. In all nontrivial cases there are many more _incorrect_ possible outputs than correct ones, so this appears to be a difficult task. But would you say such a machine is “thinking”? Would you still consider it thinking if we could describe the process mathematically as drawing a sample from the output space; that it draws the correct sample implies it has an accurate probability model of the output space conditioned on your input. Does this require “thought”?

GPT is just like this machine except that instead of one-step, the inference process is autoregressive so each token comes out one at a time instead of all at once. (Note that BERT-style transformers _do_ spit out the whole answer at once.)

It’s possible that this is all that humans do. Perhaps we are mistaken about “thinking” altogether—perhaps the machine thinks (like a human), or perhaps humans do not think (like the machine). In either case I do feel confident that human and machine are not applying the same mechanism; jury is still out whether we’re applying the same process.

Any overly simple "it's just predicting next word" explanation is really missing the point. It seems more accurate to regard that just as the way they are trained, rather than characterizing what they are learning and therefore what they are doing when they are generating.

There are two ways of looking at this.

1) In order to predict next word probabilities correctly, you need to learn something about the input, and the better you want to get, the more you need to learn. For example, if you just learned part-of-speech categories for words (noun vs verb vs adverb, etc), and what usually follows what, then you would be doing better than chance.. If you want to do better than that they you need to learn the grammar of the underlying language(s).. If you want to do better than that then you start to need to learn the meaning of what is being discussed, etc, etc.

If you want to correctly predict what comes next after "with a board position of ..., Magnus Carlson might play", then you better have learned a whole lot about the meaning of the input!

The "predict next word" training objective and feedback provided doesn't itself limit what can be learned - that's up to the power of the model that is being trained, and evidentially large multi-layer transformers are exceptionally capable. Calling these huge transformers "LLMs" (large language models) is deceptive since beyond a certain scale they are certainly learning a whole lot more than language/grammar.

2) In the words of one of the OpenAI developers (Sutskever), what these models have really learnt is some type of "world model" modelling the underlying generative processes that produced the training data. So, they are not just using surface level statistics to "predict next word", but rather are using the (often very lengthy/detailed) input prompt to "get into the head" of what generated that, and are predicting on that basis.

If we could create a sentient being, it would be the first evidence of it being possible at all. If this casts doubt in the mind of a believer, then it tells us more about what belief is than anything else.