canjobear

In practice this already happens, unfortunately.

> Nor have they flailed wildly at LLMs as if complaining enough about it will make them go away.

If you look at the comments on the blog post, you'll find plenty of this.

> such a random corpus

I see what you did there

> Mao came quite close to abolishing Chinese characters entirely during the Cultural Revolution

This was indeed the Communists’ original plan, but the real story is more interesting. Before taking over the country the plan was to replace characters with a pinyin-like system Latinxua Sin Wenz[1] and a whole generation in the north was taught to read this. After 1949, Mao was talked out of Romanization by none other than Stalin, who told him the Chinese language deserves its own script. Pinyin was designed after that, officially as a teaching tool, but probably by people who secretly still believed in more radical writing reform.

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

A refutation of that view: https://www.complexsystemspodcast.com/episodes/credit-card-r...

What was his reputation before OpenAI?

The point of TFA is that there are regularities you can exploit in the actually existing weights of machine learning systems, not in some hypothetically maximally efficient weights. The maximally efficient weights would indeed have no structure, but that’s not what anyone is working with.

The weights aren’t compressed. So there are interpretable redundancies in practice.

It was accepted to NeurIPS.

I’ve encountered several people now who consult LLMs constantly for nearly every decision. Some proportion of people will absolutely cede their thinking to these things, and some already have.

> What about cooking food? We've become entirely dependent on cooking food.

In evolutionary time, we lost the ability to digest raw meat because we offloaded so much of process of the digestion to the cultural technology of cooking.

Do you like the looks of a future where that happens but for thinking?

It is a collaboration across many institutions including those from Europe, the US, and Japan

His arguments apply generally to a large family of interpretability methods. Skimming suffices to figure out that the linked article is in that family.

Compression is still prediction, the predictions just might not be good.

His videos on the people this article is about https://youtu.be/9cwcN-mm4xs https://youtu.be/XcDmr2_fvNo

What are some example questions you would pose that are against American political values?

It was standard procedure to do invasive surgeries on newborns without anesthesia until the 1980s, on the theory that they can’t feel pain. https://hms.harvard.edu/news/long-life-early-pain

Midwives don’t participate in C-sections

I was clicking around in response to this article and found this video that explains the inspection paradox nicely. https://youtu.be/Jd1wNizPjoE

You can evaluate the logic for the decipherment step by step and make sure all the claims are justified. But the best test is to try the proposed decipherment against some new text and see if it makes sense. In the case of Linear A and the other remaining undeciphered scripts, there's not a lot of held-out text to test against, so it's tricky.

> Tom's work is credible enough that it's being reviewed by linguistics experts at Rutgers and Cambridge

What does this mean? Like he e-mailed it to some people at Rutgers and Cambridge? Or it's under some kind of non-anonymous peer review?

Gratuitous destruction of books by librarians has been done for a while. See https://en.wikipedia.org/wiki/Double_Fold

Egyptian writing had phonetic elements (as does the modern Chinese writing system) but it was not an alphabet in the strict linguistic sense that individual symbols indicated individual phonemes.

Close. One approach is to conjecture that the universe only supports a limited about of information per unit surface area.

https://en.wikipedia.org/wiki/Entropic_gravity

Also very well explained by PBS Spacetime here https://youtu.be/UKxQTvqcpSg

It’s not typeset in math mode so you can’t expect the hyphen to correspond to minus.

A mapping of Chinese characters to integers (like a tokenizer) would not be a dictionary. You’d also need definitions. At best it’s an index to a hypothetical dictionary.

Your model of what AI is good at is wrong. Generative AI is not good at wandering off into novel esoteric abstract corners while maintaining correctness, it is good at things that are close to its training data. I suspect that humans will long outperform AI in the domain of "novel esoteric abstract useless math" whereas AI will outperform humans in the domains of (1) making connections between already-well-understood concepts, things that seem obvious in retrospect but which no human figured out just because of the accidents of what people happened to focus on, and (2) proving things that require long, tedious, intellectually unsatisfying calculations, which would cause a human mathematician to give up for boredom.

I have multiple friends now who used to quote me that factoid, then went to Florida and came back convinced that no, Florida man is real.

Google search already had a huge quality slide before 2022.