ksd482

> That the way we teach what we teach is often more important than the what.

Partially agreed.

Whatever we teach today will be solved or obsolete tomorrow anyway. In fact, in school we learn methods that are never used in real life anyway. So the "what" is not fully the point.

It's how we teach it and what the point of it. The point of it is to teach how to learn things, how to practice, how to become experts, how to struggle with them and persevere eventually. It is not a sort of an exercise for our minds, but it is exactly an exercise for our minds. That's the point.

The "what" matters in that it is a foundation of knowledge upon which future knowledge and understanding is built.

So, I agree with the spirit of the argument but it is a bit more nuanced.

To add to that, online comments.

People post vile comments because they don’t have to face the consequences which adds to the vicious cycle.

I can chime in about this. I’m a software engineer and about 7 years ago I decided to get a masters in pure mathematics. Because I was working full time, I could not attend any class so I self studied from textbooks and it worked well.

When I would get stuck, I would struggle with the problem, ask around, ask on Math Stackexchange and along the process I would end up learning a lot more.

With AI now, it’s like having your own tutor: you can ask personalized questions and it would answer it very well.

But that would also mean the amount of time I would spend struggling would go down considerably.

So you really have to be self disciplined and struggle with the problem a little bit before asking for help. You can also ask it to give hints only.

Do not mistake understanding the answer with learning something rigorously.

So if you are curious and doing an exposition, use it as you would normally and it will work wonderfully.

But if you want to learn it rigorously, our usual present habits with AI spitting out answers and us reading them will not work at all.

Why is it hard ? It's basic empathy.

What does your being software engineer has anything to do with feeling sorry for Mathematicians' job ?

It's a classic "I have it worse than them...everyone should feel sorry for ME and not them".

Before calling that comment asinine, did you consider that the commenter might be reflecting @flowerlad's comment back to him/her to prove a point ?

hmm..perhaps I was biased because I watched a couple of documentaries, a tv film and read the book by John Carreyrou "Bad Blood", so my eyes were primed to look for this pattern.

But he was definitely manipulative in this regard.

The reminders section is the second most entertaining bit after the messages. Look it up. It's hilarious!!!

Not that it matters or anyone cares, but it's so obvious Balwani was manipulating her emotionally. There are so many instances where he says "Love you", or "love" or "I am in love with your beauty", while she is more direct like "I love you", "I am in love with you" etc.

I finished the juice. Now what ?

LOL

Excellent work! All the best for the documentary launch!

I see what you are sayings. That's an interesting point of view.

Perhaps we should define what rigor means.

Your point is that code is executable and speaks for itself whereas a Math proof (non-lean) is just someone's writing on a piece of paper.

Now let's compare the "practice" of doing Math and software engineering. In Math, every step is very intentional, and getting to a point where a proof is complete and correct is a very long, laborious, difficult and intentional process. Not to mention, the work is also peer reviewed (for published stuff). This is what I mean by rigor.

In software engineering, the practice is quite different. We defined the problem (somewhat), come up with a design that we "think" would work, write programs that we think is correct and then execute it. Most of the time it doesn't work exactly as we would have predicted. So we take the signal and adjust. So it's a more iterative part and this gets us closer to reality (what we actually want it to be), step by step.

So comparing the two, the major difference I see is in one each and every step is very intentional and we can't guess it. While in the other one we have lots of liberties, but we are still making progress.

So to me the difference is just between the practices followed in the field when it comes to rigor.

Agreed.

Their field is extremely rigorous. As rigorous as it can possibly get in that they have to prove each and every line of their work beyond any doubt. The discipline they have cultivated in their culture shows in their response to AI as well.

As opposed to some other fields in which rigor was either not part of the culture or was not always possible. For e.g., software engineering in terms of code quality being produced. There were some indirect signals here and there but they are all subjective.

I always wondered why in movies they did this. After thinking for 2.3 seconds, I concluded it's because so that movie watchers can see their faces.

Is there any other reason ?

I mean, they don't have lights inside the helmet that shines on your own face, right? That would mess with your ability to see.

That's a bit harsh.

I don't agree with intelligent design either simply because it is based on no data or scientific theory.

I also expect Destin to know better than to suggest that.

But still, I wouldn't say he went down the drain.

His contributions far outweigh this.

There absolutely is a parallel in the software world. I have 16+ years of experience in software and I have enough coding under my belt to guide and inform coding agents to solve the problems.

This is the reason why hiring is down for fresh graduates. We don't have low hanging fruits types of problems to give them anymore.

So until the industry figures out the workflow, the hiring for fresh graduates is going to be tricky.

I am not implying that this is a new idea and I don't believe the author did either. If you read the full article, the context is clear.

The author is re-emphasizing the importance of thesis defense old school style and highlighting that being familiar with one's own material rigorously should still be a requirement.

Everything that leads up to the thesis defense perhaps changes signficantly.