Comments URL: https://news.ycombinator.com/item?id=47172754

Points: 5

# Comments: 0

For me, deep research tools have been essential for getting caught up with a quick lit review about research ideas I have now that I'm transitioning fields. They have also been quite helpful with some routine math that I'm not as familiar with but is relatively established (like standard random matrix theory results from ~5 years ago).

It does feel like the spectrum of utility is pretty aligned with what you might expect: routine programming > applied ML research > stats/applied math research > pure math research.

I will say ~1 year ago they were still useless for my math research area, but things have been changing quickly.

But the real thing I wanted to mention here was the Poulsen Simplex [1]. This is the unique Choquet simplex [2] for which the extreme points are dense. This means that it's like an uncountably infinite dimensional triangle where no matter where you are inside the triangle, you're arbitrarily close to a corner. It's my favorite shape and absolutely wild and impossible to conceptualize (even though I worked with it daily for years!)

[0] https://en.wikipedia.org/wiki/Invariant_measure

[1] https://eudml.org/doc/74350

[2] https://en.wikipedia.org/wiki/Choquet_theory

Although the proofs are in the world of (L2-SP) ridge regression, experiments were run using an MLP on MNIST and CNN on CIFAR-10 and suggest the SNR-regularization relationship persists in non-linear networks.

Comments URL: https://news.ycombinator.com/item?id=45916020

Points: 1

# Comments: 1

That being said, oh for sure this will lead to more incidental fraud (and deliberate fraud) and I’m sure it already has. Would be curious to see the prevalence of em-dash’s in 10k’s over the years.

Establishes that accountants who certify financials are liable if they are incorrect. In particular, if they have a reason to believe they might not be accurate and they certify anyway they are liable. And at this stage of development it’s pretty clear that you need to double check LLM generated numbers.

Obviously no clue if this would hold up with today’s court, but I also wasn’t making a legal statement before. I’m not a lawyer and I’m not trying to pretend to be one.

[0] https://scholarship.law.stjohns.edu/cgi/viewcontent.cgi?arti...

On the other hand, presenting truthful data to investors is distinctly not fraud, and this again does not depend on the generation method.

I guess you’re right though, defining “mastery” is the key missing point here.

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At least in my view it's still inherently a next-token predictor, just with really good conditional probability understandings.

This means it's adding about 0.012% additional energy consumption to those users energy consumption.

From another angle: Average US house energy consumption is around 30kWh per day. 0.012% of that is 3.75 watt hours of energy per day. This is the equivalent amount of energy as streaming HD video to your iPhone on a 4G network for 1.5 seconds. [0]

So in other words, a 15s youtube ad you are forced to watch on your phone before watching the video you were going to watch anyway takes an order of magnitude more energy than the average AI user according to this article.

[0] https://www.statista.com/statistics/1109623/electricity-cons...

Additionally it's quite useful to have the background to understand the differences between Pearson correlation and Spearman rank, or why you might want to use Welch's t-test vs students, etc.

Not that you should know all of these things off the top of your head necessarily, but you should have the foundation to be able to quickly learn them, and you should know what assumptions the tests you're using actually make.

This format is common in heavily regulated industries and frequently a regulatory requirement since it’s fully unambiguous. I (American) worked in clinical research/pharma for a bit and still write my dates like 23Jan2024.

Sure I might want some slight changes to the notation I found skimming through on my phone, but everything they define and the notation they choose feels pretty familiar and I understand why they did what they did.

Mirroring what someone else said, this is exactly the kind of intro I’ve been looking for for deep learning.

In my experience it's at least marginally better, but one of the really nice features that Kagi has (and probably the main reason I subscribe) is you can extremely easily block domains. So whenever I hit a SEO garbage site, I just go back, block it, and I never worry about it again. In the areas you regularly search, this quickly gets you to a result page that is substantially higher quality than google.