TobyTheCamel

There's a fantastic website called Difford's Guide [1] that allows you to list the cocktail ingredients that you have and will then provide a long list of cocktails you can make (optionally allowing substitutions).

It is however a bit of a dangerous game for those who feel the urge to collect things, as it somewhat gamifies your drinks cabinet by telling you how many drinks you're missing out on unless you buy XYZ ingredient.

[1] https://www.diffordsguide.com

I think people are typically referring to the task-completion time horizon at a fixed success rate [1]. That has had pretty robust exponential scaling for many years now.

[1] https://metr.org/time-horizons/

Genuinely, thank you. This is very encouraging and makes me feel much better about commenting more going forwards.

The quality of discussion and prose on HN is just generally so high that it can feel quite a bit intimidating to jump in (in contrast to Reddit where I have no worries about commenting haha).

Looks pretty exponential to me [1]. From a fully independent, non-profit research group.

[1] https://metr.org/time-horizons/

You're talking quite statically though. I don't think anyone is worried about today's models being a serious threat, but next year's, three years' time? Just three years' ago these models were useful bumbling fools and it's hard to judge where on the S-curve we currently are.

I'd rather be thinking about these issues in advance rather than waiting until the problem becomes real.

You're talking as if this is a static thing though. It's the God of Gaps [1] but for humanity's special sauce.

Two years ago, I couldn't trust an LLM to do anything that wasn't straight forward boiler plate.

One year ago, I was pretty solid at writing algorithms that were combinations of existing ideas.

Now, Fable is outputting stuff that I would genuinely consider to be creative and original if a colleague had presented it to me.

Yes, maybe the code style still isn't great, but given the pattern of the last few years, it feels correct (a priori) to assume that this gap isn't going to keep closing.

[1] https://en.wikipedia.org/wiki/God_of_the_gaps

If Anthropic were not developing these models, one of many other companies would be. I think it's good that the CEO of the current world-leader is at least considering these discussions and platforming possible solutions.

The fact that he doesn't support more restrictive approaches that don't align with his incentives doesn't invalidate the points he is making.

Your first point is very reasonable, and I agree that that is something Dario would likely be more opposed to.

However, my point isn't that I think Dario is our saviour who we should follow the every word of. As with everyone, his opinions should be filtered through the lens of his incentives. That said, I don't understand the knee-jerk reaction by many commenters to completely disregard the many important points he's making.

As for the lack of my account use, I can't comment for others, but I'm just quite shy. I've opened up the comment box many times to write a reply but rarely commit to actually posting it, especially because I feel like I'm not on the side of the general HN consensus.

Sure, I have no doubt that AI progress will follow an S-curve. The question is, where are we on it and is the plateau at a level safe for humanity? That's a very difficult thing to estimate without a crystal ball and not a risk I want to take.

I feel completely baffled by the other responses on this thread. People viewing this purely as a marketing stunt, regulatory capture or attack on their freedoms, with seemingly no appreciation of the real threat that AI could pose to society and even humanity given its current rate of progress.

I'm not going to claim that the CEO of pre-IPO company has no incentive to bolster the claims of his tech, but to completely disregard everything he is saying based on that seems awfully binary.

I don't know whether people are just high on copium, spouting "it's just fancy autocomplete" or "only humans can really be creative" on every LLM-related thread, but it is impossible to deny that in a span of a few years we've gone from models that could barely put together a sentence, to something maybe not equivalent to a junior developer, but at least resembling it.

And sure, you can point out every flaw that current day LLMs have, just how everyone pointed out that Stable Diffusion couldn't generate accurate hands (until it could 6 months later!). But the gradient is pretty clear and I am yet to see a well-argued narrative from anyone why scaling laws should fail in the next year or two (by which point it feels like we're going to have a real problem, extrapolating the current trajectory).

I'm very glad this discussion is at least being had, and I wish everyone would get off their high-horse and take things a bit more seriously.

> However, the idea is that often a lot of the probability mass - an amount that is not small - will be concentrated around the maximum likelihood estimate, and so that's why it makes a good estimate, and worth using.

This may be true for low dimensions but doesn’t generalise to high dimensions. Consider a 100-dimensional standard normal distribution for example. The MLE will still be at the origin but most of the mass will live in a thin shell of distance roughly 7 units from the origin.