kzz102's comments

I am criticizing how AI progress is reported and discussed -- given how important this development is, accurate communication is even more important for the discussion.

I think you inferring my motivation for the rant and creating a strawman yourself.

I do agree that directing my rant at the generic "fans" is not productive. The article Tao wrote was a good example of communicating the result. I should direct my criticism at specific instances of bad communication, but not the general "fans".

It's really tiring that LLM fans will claim every progress as breakthrough and go into fantasy mode on what they can do afterwards.

This is a really good example of how to use the current capabilities of LLM to help research. The gist is that they turned math problems into problems for coding agents. This uses the current capabilities of LLM very well and should find more uses in other fields. I suspect the Alpha evolve system probably also has improvements over existing agents as well. AI is making steady and impressive process every year. But it's not helpful for either the proponents or the skeptics to exaggerate their capabilities.

I was applying a unfair standard to them of course. Every field has a few classics that last a long time, but most old books are not read. But I think Bourbaki maybe had grand ambitions that were eventually unrealized. My theory is that the presentation of mathematics is not based on unifying principles, but rather on the collective taste of mathematicians. So what end up being the most popular books is based on how the collective taste evolve.

It's interesting that while Bourbaki had a large influence on modern mathematics, very few people read their books (at least among the people I know). In a sense, their project of producing a definitive exposition for a large part of mathematics has failed. I wonder whether it's because different branches of mathematics have their unique personalities, and therefore the attempt to provide a unified point of view are bound to fail.

This is one of the things that everyone gets the reference, but it won't be good to admit it publicly. This quote is known to almost everyone born in that area, and it's the first thing that come to mind when you hear the name.

Xiaomi can also mean millet. I think it's a reference to this Mao quote: https://en.wikipedia.org/wiki/Millet_plus_rifles?wprov=sfla1

I use to read about the power border agents have over foreigners and was amazed at how easily they can destroy me. The only reason this hasn't happened seems to be that they're mostly decent, professional people. And now that's gone.

In academic publishing, there is an implicit agreement between the authors and the journal to roughly match the importance of the paper to the prestige of the journal. Since there is no universal standard on either the prestige of the journal or the importance of the paper, mismatches happen regularly, and rejection is the natural result. In fact, the only way to avoid rejections is to submit a paper to a journal of lower prestige than your estimate, which is clearly not what authors want to do.

Even if we pretend nepotism doesn't exists, academia is still not a strict meritocracy. In addition to merit, at least two factors play an important role in success, which having a good mentor helps a lot.

1) Tacit knowledge. In many fields, there are important information only accessible from having a mentor like heuristics, insider information, in-lab techniques etc.

2) Investment opportunities. A good adviser is often good at spotting opportunities for their students. It's also common for an academic adviser to share their most valuable opportunities with their students.

It's clear to me that the ideal of meritocracy (talent and hard work leads to success) does not hold in academia, and maybe not anywhere. Having a good mentor gives you extremely valuable information that contribute to success. On the other hand, I am not sure this can be fixed or even needs fixing. I think it's healthy for academics to be partially siloed, so that they can develop their unique approaches and maintain a healthy diversity for the field.

One of the biggest problem of maths education is that they are taught by people who dislike it. They think of maths as eating bitter medicine or training of a complex, rigid skill. The way maths is taught by them is clumsy and authoritarian, and this makes the students either passive or rebellious.

On a side note, recently the government of Manitoba in Canada removed requirement for maths teachers to take university maths courses. This is being pushed strongly by the education departments of university, which shows how much these maths teachers hate maths.

I am wondering how much of the hate of LaTeX document is due to the default font. Computer Modern seems to be disliked by many users of LaTeX as well. I personally am very conflicted, as I have come to identify it as the "vanilla" option so that I have lost any aesthetic sensitivity to it.

> That's not right. The primary task of management is alignment.

Fair enough.. at least they think they can add value by improving efficiency.

I think of efficiency as one example where naive economic thinking has poisoned common sense. Economists view inefficiency as a problem. Because a healthy economy is efficient, therefore inefficiency is unhealthy. Any inefficient market is a "market failure". Efficiency is also the primary way a manager can add value. But the problem is, efficiency assumes existence of metrics, and indeed is counter productive if your metrics are wrong.

In my view, prosecuting the bad actors alone will not fix science. Science is by its own nature a community because only a small number of people have the expertise (and university positions) to participate. A healthy scientific discipline and a healthy community are the same thing. Just like the "tough on crime" initiative alone often does not help a problematic community, just punish scientific fraud harshly will not fix the problem. Because the community is small, to catch the bad actors, you will either have insiders policing themselves, or have an non-expert outsiders rendering judgements. It's easy for well-intention-ed policing effort to turn into power struggles.

This is why I think the most effective way is to empower good actors. Ensure open debate, limit the power of individuals, and prevent over concentration of power in a small group. These efforts are harder to implement than you think because they run against our desire to have scientific superstars and celebrities, but I think they will go a long way towards building a healthy community.

I want to distinguish two sources of "feeling of stupidity". One come from the challenge of grasping a difficult concept. The other is the smack on the head when you fail to see a simple but brilliant insight. In my view, you should not feel stupid in either situations, and the teacher should try to ward you against this feeling.

For the first type, I argue it's simply the resistance to a new mental model. The article's example of epsilon-delta language is a perfect example. It's a new way of thinking that takes time (and it did historically) to sink in. Competing on how fast you grasp this new concept is stupid. When the new mode of thinking becomes natural, it won't care how long you took to adapt to it.

For the second type, it's simply an impossible standard to reliably have eureka moments. Clearly, smarter people will have more of these than the average people, but no one can do this reliably. On the other hand, while it takes more work for us mortals to have these insights than a genius, there are plenty of ways to get there that don't require a super high IQ. Teachers should try to foster these moments because they are huge confidence builders, but try to minimise the impact of someone showing off their brilliance.

It's interesting that humans would also benefit from the "chain of thought" type reasoning. In fact, I would argue all students studying math will greatly increase their competence if they are required to recall all relevant definition and information before using it. We don't do this in practice (including teachers and mathematicians!) because recall is effortful, and we don't like to spent more effort than necessary to solve a problem. If recall fails, then we have to look up information which takes even more effort. This is why in practice, there is a tremendous incentive to just "wing it".

AI has no emotional barrier to wasted effort, which make them better reasoners than their innate ability would suggest.

I kept thinking that Anandtech could have survived if they had not been part of a corporate ownership. Because they were owned by a media conglomerate, the pressure is on to behave more like other media business under the same ownership. They could have diversified in terms of revenue if they were independent.

If one works in a field where there is already an issue of abundance, which is (nowadays) basically any field that produce information, it's better for the society to produce less, but higher quality, more meaningful work. Of course, it is hard to do so because the incentives are against it.

I buy the argument that work arrangements can significantly shape psychology. I am really annoyed by the culture to "sell" your findings using grandiose phrasing. It's a observation that confirms many people's prior, but it's very cool to have a definitive test for one specific manifestation of it. But breakthrough research it is not, and not really telling you anything about cultivating minds.

Interest and competence is a feedback loop that drive each other. I really had it with the reductionist views that "don't force kids to learn things they don't like" and "keep up the practice and the rest will come". On one hand, kids have fleeting interests and they are easily frustrated. On the other hand, there is nothing more demoralizing than being forced to do busy work you are not interested in.

Teachers need to sparkle and be protective of their students curiosity. Amazing things happen when a student is eager to learn. It is also true that deliberate practice and spaced repetition are very effective at building competence and then confidence. Confidence drives further interest.