https://savingjournalism.substack.com/p/i-talked-to-elon-mus...

This article has Clickhouse more-or-less spanking TimescaleDB, but the blog post it references[1] is basically the reverse. Are the use cases just that different?

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0. https://github.com/timescale/tsbs

1. https://blog.timescale.com/blog/what-is-clickhouse-how-does-...

Comments URL: https://news.ycombinator.com/item?id=27546552

Points: 2

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=27408967

Points: 2

# Comments: 0

I ran into this when doing some OCR experiments[1], finding acquiring data and pre-trained models to be the most time-consuming part of the enterprise. This ended up adding enough additional hassle that I didn't manage to get anything really interesting going, although figuring out how to containerize other peoples' code was educational. Personally, I think I'll be relying on some combination of institutional repositories + torrents/IPFS for any large datasets/models I end up releasing in the future.

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1. https://github.com/rldotai/ocr-experiments

Less tersely: this article is one in a long procession of journalists trying to exert control over tech. The opening example (Speech2Face, which they aver is transphobic) is inflammatory and utterly unrepresentative of the usual topics of AI conferences. The other references are far better, but the choice is revealing-- it's not so much an abstract concern about an unaccountable few exerting control from the shadows, but alarm that someone else might be muscling in on their territory.

For unfamiliar topics or when presented with uncommon insight, I believe rants, monologues, even diatribes are actually some of the best things to read.

It's often frustratingly difficult to go from the known to the proven; still, just because something's not proved doesn't mean that scientists are ignoring it.

There's tons of math and physics blogs that contain useful results that the author wanted to make available but didn't manage to incorporate into a paper. I wonder if there'd be any interest in a sort of GitHub for proofs? It could even use git, since (assuming consistency) isn't math just a DAG anyways (and therefore isomorphic to a neural net, as are all things).

That's a very nice way of putting it. Is there a particular book, lecture, or other resource you could recommend for this interpretation of theorizing?

There are other sites with similar features and layout to Reddit (Voat, Ruqqus, Saidit, Raddle, communities.win, etc.) but none are really in competition to be "the front page of the Internet". This is because Reddit has a huge community with varied interests which provides self-perpetuating advantages. For example: the activity level is generally higher; if you start a subreddit for a niche topic you have a chance at finding an audience; great comments/submissions will garner thousands of upvotes rather than a few dozen (smaller sites have a proportionately tinier vote ceiling, making it hard to differentiate between "good" and "amazing").

I can see increased retention/monetization maybe leading to a larger user-base in the short term, but if it makes good content less likely to crop up, or leads to less discerning users (ingenious effortposts lose out to low-effort pandering memes), or even if it just narrows the interests/hobbies represented on there, then over time it will destroy Reddit's main source of value.

Rich used to be very bullish on neural nets, then somewhat dismissive of them (due to the fragility/inadequacy of FCNs), and then increasingly enthusiastic as the renewed interest demonstrated that those problems could be overcome-- e.g., through better initialization, training, and (as you note) different architecture choices. His main concern was whether a method could keep working as more resources became available, as otherwise you would tautologically end up with something short of true artificial intelligence.

The important thing is that the technique can scale with increasing data or compute without hitting a hard or soft limit.

  One thing that should be learned from the bitter lesson is 
  the great power of general purpose methods, of methods that 
  continue to scale with increased computation even as the
  available computation becomes very great. 

I didn't enjoy it either when I was reading it, but later appreciated that it depicted a certain kind of angst— that of someone leaving childhood for adulthood, feeling anxious and threatened by this shift, and wishing to just remain as the most adult-like child (the "catcher in the rye" that gives the book its title) instead of having to go through that awkward period of being the most childlike adult.

Under that reading, it's much less irritating, because Holden isn't supposed to be valorized, just understood.

But if it's a time-sharing system, then it might not matter as much. The supercomputer at my university tends to run a lot of one-off jobs like an experiment repeated thousands of times with different parameters. On a desktop that might take weeks, but if run in parallel it's like a couple hours. Tightly optimized code might bring that down to an hour on the cluster (or a mere week on my home PC) but I wouldn't bother because making the code more efficient might itself take a week or more. So the fastest way to get the results I need would be to just run it on the supercomputer.