Meanwhile, the major tech companies first financed this build out using the cash flow from their existing revenue. Once this became more difficult to justify, they literally set up companies to keep this off their books directly, and these companies are ultimately designed to take a loss and fail. But before reaching this failure stage and defaulting on debt and obligations, they went to the criminals on Wall Street and designed a financial product structured not all that different from mortgage backed securities. Also, as the "AI" aspects of their existing businesses expand, it eats into the traditional revenue generated by their core business, which if we remember, is the original collateral being used to justify all the debt in the first place. Now these core tech companies have to win "AI", whatever that ultimately means, to not only pay the debt from the build out, but to also replace their existing collateral from their original products. Think in terms of Google Search being replaced by AI. Maybe a clean transition, maybe not. Really just depends on the technology living up to the hype. If not, the hyperscaler companies designed to take a loss a fail, will fail and with it, the circular financing will pop the very real bubble that exists.
Speaking of IPO valuations as well, it's not to say that LLMs are not a valuable technology. Just look at what they have done to computer science already. But there may be increasingly limited returns on investment if every valuable problem to be solved isn't as well defined as logical computer science problems. Without solving the hallucination problem (as a result of the next token prediction limitations), the wide application of LLMs as being sold will never happen.
I do hear this position quite a lot though so I’d be interested to hear more about what I might be missing.
Having done my master‘s thesis on Transformer LMs, I think this is a fundamental limitation of the architecture.
These models can solve math problems and then fail to count the number of problems they solved. Not being able to count to 68 is not intelligence.
"Not being able to quickly reference the sum corpus of human knowledge across all fields, or contextually learn new languages in moments, or grasp the overarching architecture and modules of a million+ line codebase is not intelligence."
This would be a pretty ridiculous statement don't you think?
You are operating on a narrow, human-centric view of intelligence here for some reason... while I bet you would say an Orca is intelligent. So what drives you to place an LLM so low on the intelligence totem pole? This is clearly bias.
The fact that it is programmed is irrelevant. The fact that at it's core it is an ingestion of human data and an output of a prediction of more data doesn't declassify LLM's as intelligent. Unless you are willing to put that same lens against how humans learn. (you aren't)
Not that these distinctions are meaningful in any way, by the way, in light of raw functionalism. I am just annoyed by the clear bias.
Einstein is not hold in such esteem because he was really could at spewing out text or formal proofs or theorems, but because he was able to invent something no-one else could. Some people say he advanced physics 100 years wuth his theories in relativity, whether it is true or not.
So even if you can predict what Albert is going to write next, you are still merely copying old Albert. They are amazing at processing large amounts of data, naturally, but creating something new and surprising we have yet to see evidence of (the solutions of the millennium problems seem to be brute-forcing a solution based on fuckton of data to me, but I am not a mathematician so I guess it is up in the air still).
Yeah, that's typically the case for people who have no clue about LLMs or AI.
I don't think the nature of 'predicting the next token' is the problem. One could argue that any organism works the same way. The problem is that we've had million years of evolution to stabilize the platform such that the important parts of it work without that many hallucinations -- and even then that sometimes fails and you get failure modes like e.g. schizophrenia, where the context window gets confusing and the 'next actions' don't make that much sense anymore, seen from the outside.
An LLM never experienced evolutionary pressure the same way. There's no fundamental difference between how it was incentivized to answer correctly when asked about how many Rs there are in strawberry vs whether it is safe to leave a house through the second story window, even though for any physical organism, those two would be _very_ differently weighted during training.
I love the age of LLMs because people will post stuff like “the fact that I absolutely, unqualifiedly, fundamentally do not understand two separate things indicates that I understand at least one and maybe even both of them more than you do“ and then do that cool move where they pretend they were gonna high five you but don’t
it is a golden age of Posting
However, insight born of knowledge ignorance is worthless. I really don't care for your great insight about nuclear fusion if you're not a nuclear engineer or physicist working on nuclear fusion, because your insight is just a hypothesis in the void that is your ignorance.
Ironically TFA falls victim to the same thing. It doesn't seem like the author is an economist or an expert of corporate finance. Their assessment of whether something is a bubble or not has absolutely no value, and carries with it the arrogant implication that the hundreds of thousands of people who are actually experts at these things never had the little 2AM epiphany that they had.
It's a smaller version of the same delusion that produces crackpots.
I don't think current valuations need AGI to work. They just need enterprise and personal spending on AI to keep rising exponentially for a while.
We could absolutely wind up in a case where these companies are fairly close to a trillion dollars each, but not multiple trillions, at a reasonable multiple, all without AGI.
A theoretically perfect next token prediction machine has limitless utility. Predict the weather, stock market, lottery number, the next step of groundbreaking cancer research, etc etc. That's not the argument against current LLMs being capable of being AGI.
The "prediction" has nothing to do with predicting the future, it just means choosing the most likely next token for existing human text
The best next chess move is ___
The best stock to buy now is ___
For the chess example to work you need the current board state in the context or the move history. How an LLM performs will depend on what it has learned across the transformer layers and in the residual stream, as well as how much chess data it has been trained on. -- NN models like Leela and Stockfish have been trained on thousands/millions of games and have a game-specific state space representation.
The stock prediction example is similar, but doesn't have a consistent state space in which to make that prediction. It will need information in its context (current news articles, stock history of all stocks, etc.) in order to be able to make a prediction. Its performance will depend on whether it has been able to learn factors that make it possible to make accurate predictions.
This also presumes that accurate predictions are possible.
The point is that next token prediction is potentially very powerful. Nobody's claiming that LLMs can predict the market or win a chess match.
But the argument against next token prediction as a powerful tool is void.
The key question is not whether this architecture is powerful. The key question is how well these models can represent things outside of their design.
With chess for example, are they actually learning the rules and strategies of the game (like AlphaZero/AlphaGo, Leela, etc.) or are they just relying on a large corpus of chess games? For example, a lot of early game moves will have been played out and the best moves will likely be the more frequent moves in the training set. So LLMs could do well early game based on that game corpus.
A lot of people have dismissed LLMs four years ago for this reason. "It cannot possibly do coding since it doesn't think, it just predicts the next token." On the other hand, Scott Alexander did say in 2019 that next token prediction is a brilliant idea and might cover a lot, including math proofs [1]. So I wouldn't say it is impossible for either 1) LLMs to be all the way to AGI or 2) LLMs to become creative enough to tell us what the AGI architecture is.
[1] https://slatestarcodex.com/2019/02/19/gpt-2-as-step-toward-g...
I do. And it’s not good.