arctic-true

This just pushes the main argument back one step. Let’s assume for the sake of argument that the answer is yes, a stochastic parrot is a living creature.

The next question is: does it matter? And for many people, the answer to that question depends on the extent to which the stochastic parrot is conscious. Which is an entirely different kettle of fish.

A lot of Harvey’s value prop is not in the underlying software - someone else came out with an “open source Harvey” (called “Mike”) months ago and it hasn’t really put a dent in uptake of Harvey or its main rival, Legora. Harvey’s value is in the infrastructure, the help integrating it into workflows, the ability to represent to other parties that you are using a trusted provider.

Mistral Large 4 has been spending too much time on r/atheism. Also, some of the takes labeled as “humans value belonging ahead of truth” are really closer to “humans value power ahead of truth.” All of which is a bit circular, because in many ways, the organs of power have a heavy hand in deciding what the truth is.

As someone who has long bought into the idea that the labs are not profitable on inference, I have a question. How is it simultaneously true that there is more demand for inference than supply (apparently by orders of magnitude), but also that inference is so cheap that labs can make healthy margins? It seems like the inference providers, not the model providers, should have the leverage. Why haven’t they said, “give me a bigger cut of each token or I won’t serve your model”?

Also, what happens when the inference providers suffocate on their own debt burdens? Who will serve inference?

I keep seeing this argument. But isn’t the inverse true as well - if you extrapolate from the past movement of the ball, you will see it going way farther than it will actually go because of friction, wind, someone catching it, etc.? It seems reasonable to look at where the tech is right now if it helps you understand fundamental constraints. A person witnessing the first flight as a child and the moon landing in adulthood might expect FTL travel for their children if they didn’t understand physics.

Writing is a very slippery thing. “Good” writing from 200 years ago reads as unbelievably dense and overwrought to most people today. So even if you made an LLM that was “good” at writing, how long would it stay that way? AI systems can put out more writing per day than all of humanity combined, meaning it can also generate and beat to death cliches much more efficiently.

A lot of the examples they show seem to be focused on common law countries. The US and UK are where most of the money is to be made in the legal industry, but I am curious to see what ripple effects we get from people attempting to use AI tools geared toward US/UK-style legal systems in other kinds of systems.

The web was a shared experience, by its very nature. The interconnection was the point. LLMs allow anyone to put themselves in a one-man bubble of their own creation. In a way, it is the antithesis of the internet - if the web is a global public square, the API is a digital cloister.

Hemingway also borrowed the penultimate line in For Whom The Bell Tolls. I also had to memorize this back in the day but I still stop to read it whenever I see it somewhere.

That tells me very little. What was the cost to OpenAI in dollars? What differentiates the high-cost problems from the low-cost problems? And that’s before you consider that OpenAI has strong incentives to downplay its costs while emphasizing its results. A one-liner in a write-up doesn’t change the fact that they have access to massive resources.

Not a doomer but I try not to be a denier, either. These are hugely impressive results. I do not see the technology plateauing at the current level (though I am dubious about an infinite exponential growth).

Before I cope, I’ll note that there are plenty of “doom” scenarios that do not require any improvement in capabilities from what we had before this latest unreleased model. We’re at the point where a determined bad actor with enough compute could compromise critical infrastructure in a way that results in casualties, where this actor would not have been capable of such without LLMs. This may not sound like Skynet, but I don’t see why it makes a difference if I’m one of the casualties.

With that in mind, here is the cope: first, mathematics is an inherently verifiable domain. An LLM can use tools to determine with absolute certainty whether it is correct, and an independent third-party could review and confirm. All of this can be done without any interaction with the physical world or with other minds.

Second, OpenAI is able to marshal compute at a scale that an individual mathematician can only dream of. It’s possible that these problems were lower-hanging fruit (in relative terms), such that they could be resolved simply by throwing a ton of compute at the problem guided by an intelligence that is not itself remarkable in comparison to a human.

Third, none of these problems are solved in a vacuum - the reason OpenAI chose these problems is that they are widely discussed and many people are working on them. It’s possible that someone else was close, and OpenAI only contributed the finishing touches. (This wouldn’t need to be plagiarism, to be clear - people publish their work!)

For antitrust purposes below-cost pricing is not expressly illegal. It is only illegal if there is a “dangerous probability” that the firm in question will be able to recoup its losses after eliminating its competitors by taking away all their demand and then jacking up prices when they’re the only game in town. In the case of your Claude tokens, even if they’re being sold below cost, they’re being sold for more than anyone else is selling tokens. So it’s hard to argue that they’re going to drive everyone else out of the market with their pricing.

They spend 95% of the money, perhaps, but burning compute is not the same as doing the work.

Yes, I do. They’ve definitely gotten better, I wouldn’t dispute that. But we haven’t gone from useless toys to Skynet in the last 6 months, progress is slower and steadier than that.

Most of the impressive accomplishments we’ve seen in the last few months have been the result of huge agent swarms working together and brute-forcing solutions, not massive leaps in intelligence from standalone models. That is still an improvement in the usefulness and power of the technology, but it is NOT evidence that model intelligence is increasing faster than before.