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I'm surprised no one at Google realized that both OpenAI and Anthropic's naming schemes follow a fairly clear progression and that they should do something similar to reduce confusion. I would have assumed the opposite given that Argon has a higher periodic number

Ah okay that makes sense. Yeah ultimately I think Nick just gives hypothesis given what he's seen, and I'm not an expert in this field but it does seem to be driven by experimental results rather than mere description.

Personally from reading the article, I felt like it scratched the 'why' itch more than the 'what' itch which presumably is what you mean by explanation vs description but it is fairly dense and I can see how either interpretation would work.

I wouldn't mix up "neurons do one thing" with "each thing lives only in one neuron" since he doesn't claim that.

Re the first paragraph I'm not sure what you mean by "the assumption that conflates multiple concepts that to me are substantially non-overlapping are one and the same thing"

Nick's claim is that that's the fundamental misinterpretation. Each neuron eventually ends up mostly doing one main thing (and the rare case that some neurons do several related things). Neurons compose to embed ideas which is a starkly different interpretation than them being holograms and fractional encodings.

Nick isn't trying to re-claim progressive feature complexity. He's saying that many polysemantic examples were misread because we assumed neurons packed unrelated things together. But also I think what he's claiming if you read the full blog post isn't inconsistent with that paper, where that paper actually isn't itself claiming semantic condensing but geometric condensing. That paper doesn't analyze neuronal firing patterns whereas Nick's trying to analyze what fires together and what doesn't fire.

Article: "How Good Are Frontier Models at Physics? Expert Re-Grading Reveals Broken Evaluations and Near-Saturation of Leading Benchmarks"

John Sous from Yale posted a fairly solid study on how nearly all the physics benchmarks are broken, as they consistently mark correct answers as incorrect.

When hand grading instead, they found out that the models have actually already saturated the benchmarks which is a little bit scary.

You're mistakening Tristan Buckmaster for Sebastien Bubeck. Seb is the one where there's at least 2 (unless the personal friend is Dheeraj) allegations, not Tristan

Isn't that what's solved by this method? Your SSO provider (e.g. Okta) is now what gates each employee's resource access for different MCP resources.

the commenter never said they came up with nothing, they said o3 came up with something better.

On our apps we consistently see a p50 3-4x speed difference between iOS and Android (though there are more lower end android devices). Hard to fathom if it's all due to variability in android devices vs RN being less performant on Android.

Which parts of reasoning do you think is missing? I do feel like it covers a lot of 'reasoning' ground despite its on the surface simplicity

> React doesn't make you a better developer, it makes you a better React developer.

React's pure component functional style translates really well to nearly every other type of software development.

808 ELO was for GPT-4o.

I would suggest re-reading more carefully

A lot of the internet would break if YouTube removed/tweaked their embedded video player so I doubt he has to worry.

He's pointing out a motte/bailey meaning he's against the motte. If to him e/acc is the motte, then he's likely against

It's actually impressive that Weird Al has made it as far as he did now that I think about it

FYI, it was sarcasm (the emoji at the end is the giveaway)

He groups them together because ultimately the result is that the science can't be trusted. He doesn't go so far to claim that one was intentionally faked vs gross incompetence.

I don't think the statement reads that the 44% and the 26% should be additive. Especially given the zombie graphic where it looks like they overlap the 26 on top of the 44, where the orange bar is the 26% and the remaining yellow bar is the 44%

Thanks, didn't know. This article seems to have been written by a reader though so not the same authors of the sketchy lithium work

I'm impressed you were able to not get tangled up by the youtube/captcha/color hex/roman numeral mess! The youtube one is what screwed me over and over out of all my attempts

I don't think it's easy. Verification is much easier than generating correct solutions for this.

Looking at the JS, these rules use RNG such that you can have an inconsistent or impossible password. E.g. if the only youtube video URLs that work with your duration have roman numerals that multiply above 35 in it you are hard stuck. Your youtube URL can also hard stuck your atomic number summation to 200 if it happens to contain enough elements that adds above 200. Your color hex can hard stuck your 25 sum, etc. The code does not try to generate working passwords given all the rules, it simply adds checks and randomly generates the requirement per rule.

You'd have to have the RNG rules to align well in order to win i.e. youtube video with no roman numerals or numbers or elements, captcha with no numbers or roman numerals or elements, to minimize conflict.

Hmm I looked into it, and looked at papers/pdfs in google scholar's advanced search with her as an author that mentioned LLMs or GPT in the past 3 years. Every single one was a criticism about how they couldn't actually understand anything (e.g. "they're only trained on form" and "at best they can only understand things in a limited well scoped fashion") and that linguistic fundamentals for NLP was more important.

Good to know my hunch was correct

In her field doesn't mean that's what she researches, LLMs are loosely in her field but the methods are completely different. Computational linguistics != deep learning. Deep learning does not directly use concepts from linguistics, semantics, grammars or grammar engineering, which is what Emily was researcing for the past decades.

It's the same thing as saying a number theorist and a set theorist are in the same field cause they both work in the Math field.

Her field has also taken the largest hit from the success of LLMs and her research topics and her department are probably no longer prioritized by research grants. Given how many articles she's written that have criticized LLMs it's not surprising she has incentives.

Current topic aside, I feel like that stochastic parrots paper aged really poorly in its criticisms of LLMs, and reading it felt like political propaganda with its exaggerated rhetoric and its anemic amount of scientific substance e.g.

> Text generated by an LM is not grounded in communicative intent, any model of the world, or any model of the reader’s state of mind. It can’t have been, because the training data never included sharing thoughts with a listener, nor does the machine have the ability to do that.

I'm surprised its cited so much given how many of its claims fell flat 1.5 years later

oh my bad, totally misread that

It's trained on pre-2021 data. Looks like they tested on the most recent tests (i.e. 2022-2023) or practice exams. But yeah standardized tests are heavily weighed towards pattern matching, which is what GPT-4 is good at, as shown by its failure at the hindsight neglect inverse-scaling problem.

Curious since it does well on the LSAT, SAT, GRE Verbal.

For Vietnamese, ChatGPT has been much more effective for me. You can tell it very specific things to modify as well as give it additional context which you can't really do with Google Translate. Especially with pronouns, google will just translate everyone as 'uncle' or 'aunt' or 'grandma' or 'grandpa' and it'll get genders wrong all the time, which you can correct for with ChatGPT.