But he was definitely manipulative in this regard.

LOL

Excellent work! All the best for the documentary launch!

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.

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.

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.

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.

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.

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.

Perhaps a parallel to this is what's happening with youth sports in the US. It is becoming increasingly inaccessible.

I think this is a refreshingly forward looking idea and I agree with it 100%, especially the the "rigorous defense" part. That is a good measure of how well the topic has been researched and understood by the researcher. This is where the humans can be "in the loop".

> How different would this look from current PhDs? I think students would still meet with an advisor, who might suggest a topic. That topic could be explored with AI assistance, or not, but the student would be responsible for understanding it; it might be much more open-ended and larger than the typical PhD is currently.

Interesting point about "more open-ended" and "...larger than the typical PhD". I think the author has a point. Earlier, the bottleneck was the candidate's/researcher's understanding and knowledge. Now with AI tools, it is so much easier to zero in to relevant knowledge, get your questions answered quickly which might lead to understanding more quickly.

For e.g., before the advent of public libraries and printing press, the knowledge was inaccessible and guarded. So that was the bottleneck.

Then books became ubiquitous and the bottleneck to knowledge and understanding was people's motivation AND knowledge of WHAT books and topics to research.

Then came the internet and free PDFs of books and research articles. Now, the bottleneck was still people's motivation and a mild version of what books and topics to research. I say "mild" because one can lookup articles and newsletters, and book reviews and come up with a list of reading.

Now comes AI and it looks like the only bottleneck is people's motivation.

I believe there was also a silent, yet potent, bottleneck all along which is also removed by AI: personal tutor/coach/teacher/professor etc. Let's say if I am reading a textbook on manifolds or some research paper and I have a question about a specific theorem or even a mathematical operator being used. Before AI my only way to get my questions answered was to read more books (PDFs or print), or ask on math exchange or math overflow and wait for someone to answer, or to ask a professor. This could take up to a week.

Now all of that has been cut down to 1 hour or less with an interactive chatting session.

!!!!!

So....the only bottleneck is people's motivation! QED

Exciting time!

But I see your point too. I too like to generate cute and funny images and share it with my friends and family.

But this being done at scale can lead to overall degradation of the online experience.

Anyway, pretty cool to see them in high definition and realize they aren't any different than today's business cards.

> Introduce me to these concepts layer by layer with a textbook style introductions.

> Use color coded boxes etc. just like in textbook to highlight important details.

> Along the way, give me exercises to do myself that would test my understanding so far.

> Put everything in a local HTML file that I can open in my browser.

> The entire textbook should be printable so format accordingly.

That's about it.

The HTML part is important. Earlier I used to document in markdown format, but I found claude/chatgpt does a far better job with formatting and colors in HTML instead. Plus, you can open the HTML file in a browser and refresh when it makes changes.

Another thing: the HTML is built gradually. I keep asking AI some follow up questions and I instruct it to keep the HTML up todate.

This is why in order to understand new codebase or to ramp up to new projects, I use AI to generate a textbook style reading material for me along with "verify yourself" types of exercises along the way. Then I print them on a paper and read it using a pencil/pen and take notes.

Because of my math training, I am in the habit of slowing down to read textbook style texts which helps.

I am sorry, but how is this new and why is this scary? The article doesn't make a case for this being genuinely scary.

The author may have a valid point or two, but at the end of the day, it's not that big of a deal. Everyone over promotes their products because it is in their interest to do so.

> Evangelism is considered harmful as an organizational technology.

Ok, I don't disagree there.

> Second, evangelism suppresses dissent

Good point.

But still, it's not a big deal.