r00tanon

My first impressions and thoughts on the presentation speak to the need for human mathematicians to accept, absorb, and understand new AI-generated proofs—and to the fact that it takes time to do so, as well as the increasing burden of sorting through essentially unsolicited material, which robs that same group of the time to work. Software developers are seeing this same issue with AI-generated contributions of software changes in the open-source arena. What they are seeing is a very high number of trivial submissions, which Dr. Tao points out is a propensity of AI generation. The second problem is that submissions are often not good, forcing reviewers to read hundreds if not thousands of lines of intricate yet incorrect code—and they waste their productive time doing so, making them less amenable to future consideration of such contributions. This is a real problem. As to the issue of human mathematicians needing to accept work as useful before it can enter wider consideration—now that is an interesting problem if humans are indeed unable to understand work AI creates that is useful and correct, but not understood to be so (perhaps due to the above issues), and yet we have to consider a point in time when models become more capable. Let me put it another way: how long would you spend trying to get your dog to understand calculus? AI has inexhaustible patience (unlike humans), and it will keep trying to explain its ideas and theorems to us (or our dogs) far past the point humans would be willing to listen—though our dogs, at least, are amenable to treats and used to us talking a lot while handing them cookies.

Josh Collinsworth paints an accurate picture here, and he’s not wrong. People will be hurt by the displacement and disruption that new technology brings, not just AI. It has been happening since humans first picked up stones and sharpened sticks. No one says you have to be happy about it, or even optimistic, even if you happen to be part of a privileged class.

It's a tough game for Perplexity as long as they use Google's search, and with Google now providing "AI Summaries," and with the whole ad ecosystem (invented by Google) designed to turn us into predictable consuming robots, what's a pre-IPO company going to do? Fight the good fight and stick with a subscription model, or knuckle under and start sending us Google ads?

Sucks. I will say this though: as soon as I see ads in my Perplexity search results, I'm cancelling my paid subscription and simply going back to Google. Without any other differentiating feature set, why would I pay Perplexity?

RIP, Perplexity; it was good while it lasted.

Bullshit.

Reading these comments after falling asleep to SNL sketch re-runs. They all sound oddly sarcastic and ironic.

Reading down further I see this is a follow-up to "40 Algorithms Every Programmer Should Know". So, is this new book the same 40 algorithms from the prior book with 10 added?

If so, perhaps the real book could be called "The One Book Writing Algorithm You Will Ever Need."

The android app wants full access to the file system? With the "trust us, we'll only really access the folder you tell us to..."?

Am I reading that right?

It's like Game of Thrones - without the intelligent intrigue.

Wow. I see almost nothing controversial in the McKinsey article. In a nutshell it addresses hard metrics (DORA) gathered via the repo tooling (commits, MRs, etc) which is enhanced with some outcome metrics, and they combine with soft metrics (SPACE) which are inevitably fluffy on the numbers combined with some other measurements to fill out arguable gaps. But overall, it didn't read like a manifesto.

As far as the criticism from the "ex-finance developers" - yeah, some of whom have done a lot of other noteworthy things outside of "fintech". I don't think it was overly harsh. In fact, maybe just focusing in on certain things that inevitably leave a bad taste - such as assigning numbers to human being's activities or otherwise trying to distill human activity down to hard numbers can lead to abuse - no kidding - almost a tautological argument.

That said, happy people with a shared vision and supportive management delivering a product/idea that is contributing to the team's concept of "the greater good" will always be the sweet spot for productivity.

I would lean more towards the SPACE metrics as the best way to measure, but allow that hard metrics such as DORA can be helpful as alarm bells.

Pair programming can be a productive, even fun, activity in limited situations. Now that we have the option of our pairing partner being human or AI, it sort of highlights a lot of the situations where pair programming is NOT productive, or even fun.

I have had a nearly identical experience. Thanks for sharing.

We've reached the point just past the elbow on the Moore's Law graph where things are skyrocketing. Any projections we humans make about the future advancement of AI are hopelessly inaccurate.

For example, running a GPT-4 model on your laptop will likely be possible in the next five years*.

*It may even happen within six months. At that time, much of the concern surrounding private conversations with an LLM will evaporate, and attempts to regulate its use will likely be hopelessly ineffective.