tmnvdb

11 ms·

61.
by tmnvdb 2y ago
You have again ignored the trade-off in your word-play.
62.
by tmnvdb 2y ago
It seems easy to worry about somewhat far-fetched scenarios like this when one is not paralyzed and thus not making a trade-off between risks and a fully paralyzed life.
63.
by tmnvdb 2y ago
This seems like a bizarre claim on the surface, see also my other message above. https://epoch.ai/data/ai-benchmarking-dashboard
64.
by tmnvdb 2y ago
Really now. I think that deserves a bit more explaination, given the cost per token has dropped by several orders of magnitude, we have seen large changes on all benchmarks (including entirely new capabilities), multimodality is now a fact
65.
by tmnvdb 2y ago
Nobody has certainty about the future. We can only look at what seems most likely given the data.
66.
by tmnvdb 2y ago
Why do you think this is a fundamental hurdle, rather than just one more problem that can be solved? I dont have strong evidence either way, but I've seen a lot of 'fundamental unsurmountable problems' fall by the wayside ove
67.
by tmnvdb 2y ago
Compute power increases and algorithmic efficiency improvements have been rapid and regular. I'm not sure why you thought that Back to the Future was a documentary film.
68.
by tmnvdb 2y ago
People somehow have expectations that are both too high and too low at the same time. They expect (demand) current language models completely replace a human engineer in any field without making mistakes (this is obviously way too optimisti
69.
by tmnvdb 2y ago
Isn't this Musk offer just for the nonprofit part, i.e. not a comparable number?
70.
by tmnvdb 2y ago
From scrolling though his articles he is a guy who likes to write polemic articles declaring companies dead
71.
by tmnvdb 2y ago
Interesting stuff. As the authors note, using latent reasoning seems to be a way to sink more compute into the model and get better performance without increasing the model size, good news for those on a steady diet of 'scale pills&#x
72.
by tmnvdb 2y ago
Another sure sign of this is custom internal tooling for things that are not business specific like time tracking and report generation. A company I worked at had an IT budget of 5000 euro for 120 people (90% SWE). The logic was: if we buy
73.
by tmnvdb 2y ago
Clearly generative AI can currently only be used when verification is easy. A good example is software. Not sure why you think that I claimed otherwise.
74.
by tmnvdb 2y ago
You are correct to note 'real' costs of the leading labs are not public. It is surely true that the labs are operating at below cost (we are definitely not paying for the full R&D), but it seems unlikely that this fully explai
75.
by tmnvdb 2y ago
There was a paper a while back on AI usage at work among engineers and it was very strongly correlated to age. This is not surprising, technology adoption is always very dependent on age. (None of this tells you if the technology is a net g
76.
by tmnvdb 2y ago
There are different criteria in use for that. But sycophantic behavior is not the goal. It's something model builders actively try to prevent.
77.
by tmnvdb 2y ago
Regarding the apple paper: https://andrewmayne.com/2024/10/18/can-you-dramatically-impr...
78.
by tmnvdb 2y ago
Craving "authenticity" is somewhere very high up the hierarchy of needs, i.e. a luxury. That a lot of people do not care much for it is not a sign of moral failing but of having bigger fish to fry.
79.
by tmnvdb 2y ago
https://a16z.com/llmflation-llm-inference-cost/
80.
by tmnvdb 2y ago
> So, a living parrot might be more intelligent than these stochastic parrots. Or the other way around.
81.
by tmnvdb 2y ago
The real costs are dropping because of real efficiency gains and compute cost reductions. The inference costs are dropping at a similar rate.
82.
by tmnvdb 2y ago
This is incorrect, there are real effiency gains. Slightly old by the standards of this field, but a good overview: https://arxiv.org/abs/2403.05812
83.
by tmnvdb 2y ago
The costs are a problem. We don't have hard evidence that this will be solved, but with algorithmic efficiency and raw compute costs both changing rapidly, the cost per token has gone down by about a factor of 10 per year for the last
84.
by tmnvdb 2y ago
It's clearly true that the LLMS are 'stochastic parrots', but for all we know that might be the key to intelligence. It is in itself not a deep observation any more than calling your fellow humans 'microbial meatbags&#x2
85.
by tmnvdb 2y ago
> And if there are facts or code involved, both require manual confirmation. The hidden assumption here seems to be that the model needs to be perfect before it has utility.
86.
by tmnvdb 2y ago
Having a ground truth doesn't mean it does not make huge and glaring mistakes.
87.
by tmnvdb 2y ago
I think the first part of that statement requires more evidence or argumentation, especially since models have shown the ability to practice deception. (you are right that they don't _always_ know what they know)
88.
by tmnvdb 2y ago
Death, taxes, and insoluble and exhausting debates around machine thinking and its value.
89.
by tmnvdb 2y ago
Is it good for one person (the writer) to ask a loaded question just to save some time on making their reasoning explicit, ony for lots of other people (the readers) to have to do extra work to understand what the argument is?
90.
by tmnvdb 2y ago
This is patently false. They are trained to generate correct responses.

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