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sanxiyn
Karma: 683
Someone from South Korea is extremely skeptical and wrote a long thread going into paper’s details why it must be 100% false: https://twitter.com/AK2MARU/status/1684435312557314048. Sorry it’s in Korean, but we live in the age of miracle and serviceable machine translation.
But it wasn’t until the 1940s and the advent of the electronic computer that they actually built a machine that was used to construct mathematical tables. I’m confused...
You are confused because that is not the reality. As you can read on Wikipedia’s entry on difference engine, Scheutz built a difference engine derivative, sold it, and it was used to create logarithmic tables.
You must have read this while writing this article. It is prominent in the Wikipedia article in question and hard to miss. Why did you make this mistake? If it was a deliberate act of misleading for narrative convenience I am very disappointed. Yes, the reality is rarely narratively convenient, but you shouldn’t lie about it.
My median estimate has been 2028 (so 5 years). I first wrote down 2028 in 2016 (so 12 years after then), and during 7 years since, I barely moved the estimate. Things roughly happened when I expected them to.
I am curious how this fine-tuning for function calling was done, because it is user controllable. In the OpenAI API, if you pass
nonetofunction_callparameter, the model never calls a function. There seem to be one input bit and one output bit, for “you may want to call a function” and “I want to call a function”.
While I agree being led by someone who is aware of AI safety is a positive sign, I note that OpenAI is led by Sam Altman who similarly showed awareness of AI safety issues.
I did the obvious thing and it worked? I have a suspicion you haven’t tried hard enough, but indeed we all have comparative advantages.
Click the link, which is https://twitter.com/BAAIBeijing
Click the link on the first tweet, to the conference website, which is https://2023.baai.ac.cn/
The website is titled “2023 北京智源大会”, copy to the clipboard
Type https://www.bilibili.com/ on the address bar, everyone knows that’s where Chinese videos are
Paste “2023 北京智源大会” to the search box and press the enter key
Click and watch 麻省理工Max Tegmark教授: 把AI关在受控的笼子里 (2023北京智源大会开幕式主旨演讲)
Parallelization part (data parallelism, tensor parallelism, pipeline parallelism, ZeRO) is completely standard. See Efficient Training on Multiple GPUs by Hugging Face for a standard description. Failure recovery part is relatively unusual.
That is trivial to program? For example, you can have AutoGPT UI which lists pending tasks with icons next to them, where clicking a trashcan will completely erase it from the context. That doesn’t need any LLM-level help like LEACE.
What do you mean? Current LLMs are stateless. If unsuccessful attempts to solve the task are made, just reset the history and retry.
There is no problem with air gap. Public key cryptography is a wonderful thing. Let there be a license file, which is a signed statement of hardware ID and duration for which license is valid. You need private key to produce a license file, but public key can be used to verify it. Publish a license server which can verify license files and can be run inside air gapped networks. Done.
I note that this is how Falcon from Abu Dhabi was trained. To quote:
Falcon is a 40 billion parameters autoregressive decoder-only model trained on 1 trillion tokens. It was trained on 384 GPUs on AWS over the course of two months.
I think bow and arrow is powerful enough and gun is not necessary.
As an example of question specific enough to be answerable by science, there is Is Pornography Use Associated with Sexual Difficulties and Dysfunctions among Younger Heterosexual Men? (2015). It begins:
Recent epidemiological studies reported high prevalence rates of erectile dysfunction (ED) among younger heterosexual men (≤40). It has been suggested that this “epidemic” of ED is related to increased pornography use. However, empirical evidence for such association is currently lacking.
The answer is no. As far as I know, this was among the first study powerful enough to answer this question. Well done, science!
Of course, nobody listens to science. Compare the introduction above with another introduction written 4 years later, from Is Pornography Use Related to Erectile Functioning? (2019).
Despite evidence to the contrary, a number of advocacy and self-help groups persist in claiming that internet pornography use is driving an epidemic of erectile dysfunction (ED).
The shift in tone is palpable, and you can just feel the powerlessness researchers feel about the situation.
Since the topic of chess was brought up: I think the right intuition pump is endgame tablebase, not moves played by AlphaZero. A quote about KRNKNN mate-in-262 discovered by endgame tablebase from Wikipedia:
Playing over these moves is an eerie experience. They are not human; a grandmaster does not understand them any better than someone who has learned chess yesterday. The knights jump, the kings orbit, the sun goes down, and every move is the truth. It’s like being revealed the Meaning of Life, but it’s in Estonian.
I agree timescale is a good way to think about this. My intuition is if high school math problems are 1 then IMO math problems are 100(1e2) and typical research math problems are 10,000(1e4). So exactly half way! I don’t have first hand experience with hardest research math problems, but from what I heard about timescale they seem to reach 1,000,000(1e6). I’d rate typical practical R&D problems 1e3 and transformative R&D problems 1e5.
Edit: Using this scale, I rate GPT-3 at 1 and GPT-4 at 10. This suggests GPT-5 for IMO, which feels uncomfortable to me! Thinking about this, I think while there are lots of 1-data and 10-data, there are considerably less 100-data and above that most things are not written down. But maybe that is an excuse and it doesn’t matter.
I kind of disagree. (I was on South Korean IMO team.) I agree IMO problems are in similar category of tasks including research math than high school math, but since IMO problems are intended to be solvable within a time limit, there is (quite low, in absolute sense) upper limit to their difficulty. Basically, intended solution is not longer than a single page. Research math problems have no such limit and can be arbitrarily difficult, or have a solution arbitrarily long.
Edit: Apart from time limit, length limit, and difficulty limit, another important aspect is that IMO problems are already solved, so known to be solvable. IMO problems are “Prove X”. Research math problems, even if they are stated as “Prove X”, is really “Prove or disprove X”, and sometimes this matters.
Eh, there are not that many IMO problems, even including shortlisted problems. Since there are not that many, IMO contestants basically solve all previous IMO problems to practice. So it’s not like AI is having an unfair advantage.
I am of the opinion that adding the condition of “not trained on prior IMO/math contest problems” is ridiculous.
GPT-6 will probably be able to analyze all the neurons in itself with >0.5 scores
This seems to assume the task (writing explanations for all neurons with >0.5 scores) is possible at all, which is doubtful. Superposition and polysemanticity are certainly things that actually happen.
No. Traditionally, donors have no standing to sue charity. From https://www.thetaxadviser.com/issues/2021/sep/donor-no-standing-sue-donor-advised-fund.html