smokel

This article focuses mostly on point forecasting. There are many other things that are interesting to forecast.

Consider for example the use case of forecasting the average speed on a road segment with a maximum speed of 70mph. Forecasting whether that will be 69.8 or 70.3 is not very relevant. What is relevant is forecasting when the speed drops below a traffic jam threshold. But the exact timing of that might be impossible to forecast due to the inherently chaotic behavior of traffic. Forecasting the probability of a traffic jam occurring may be more interesting to practitioners.

I'm looking at GoPro packaging here that says "Made in Thailand".

Lean is the Mizar here. For those who have no clue what this is about, Mizar [1] was an early automated theorem prover. Can't wait for HN to add AI features to explain concepts in the sideline, and autovoting.

[1] https://en.wikipedia.org/wiki/Mizar_system

I’d buy these. Say $100 for a frontier class model. Maybe more.

Sure you would. Running frontier class models on current hardware costs in the order of tens of thousands of dollars. It is more likely that these custom ASICs will be priced competitively with that, and not with Super Mario Bros.

Oh, and energy consumption will be in the same order.

There's an interesting easter egg in semantic.cpp, line 77:

  if (CheckWord(words, "OXio")) {

Some things do indeed improve, but some other things require a massive effort that no hobbyist or small company can make happen.

This is most obvious in places where a lot of coordination is required, for example in supporting proper color correction throughout all applications, or decent support for advanced printer functions.

There are many incremental changes, but we often get stuck in local minima for years.

Still, I personally like that one can (relatively) easily watch what happens under the hood. It's not entirely clear to me why Windows and MacOS must remain closed source.

It's probably helpful in this discussion to make a difference between two definitions of reasoning:

1. phenomenal reasoning, requiring consciousness and subjective experience

2. functional reasoning, transforming premises into conclusions using logic

I think you are attacking this using definition 1, whereas the article is obviously aiming at a different type of reasoning, and trying to formalize what is actually going on. It seems to be a genuine effort.

There are very few sites such as Hacker News around. Looking for one that is similar in quality, but with a slightly different focus is going to be disappointing.

Perhaps it helps to consider why you'd exactly want this? You may be better off not visiting any such site, and instead focus on real human-in-the-loop activities with a group of local hackers.

  for x in 1611.02731 1511.06391 1811.06965 1512.03385 1511.07122 1704.01212 1409.2329 1512.02595 1706.01427 1410.5401 1806.01822 1706.03762 1409.0473 1506.03134 2001.08361 1405.6903 1603.05027 math/0406077; do curl -fL https://arxiv.org/pdf/$x -o ${x##*/}.pdf; done
  for u in https://www.cs.toronto.edu/~hinton/absps/colt93.pdf https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf https://www.vetta.org/documents/Machine_Super_Intelligence.pdf https://www.lirmm.fr/~ashen/kolmbook-eng-scan.pdf https://scottaaronson.blog/?p=762 https://karpathy.github.io/2015/05/21/rnn-effectiveness/ https://colah.github.io/posts/2015-08-Understanding-LSTMs/ https://nlp.seas.harvard.edu/annotated-transformer/ https://cs231n.github.io/; do curl -fLO "$u"; done

The GitHub page clearly states that it is reverse engineered.

Even if you rewrite an article in your own words, you cannot claim copyright for that. At least, not to my knowledge.

You cannot claim authorship of something that you were not even allowed to take in the first place. How would you then allow rights to somebody else?

Seems pretty obvious to me.

It does make a difference. If this text was generated by the LLM, then by those same terms you are allowed to publish the data. But given that it is reverse engineered, you are not allowed to do so.

Note that even in the normal case, there are restrictions. You'd have to validate that the generated output is not copyrighted. Which in this case may not be trivial.

Can't have a discussion on art and cats without mentioning Louis Wain [1].

[1] https://en.wikipedia.org/wiki/Louis_Wain

If this is regular output of the LLM, I'm not sure, but given that the author proclaims that this is reverse engineered, then they are not allowed to redistribute it under their own license terms. The terms of service are also pretty clear on this not being allowed, which makes it extra hard to defend (section 3.3):

You may not access or use, or help another person to access or use, our Services in the following ways:

...

3. To decompile, reverse engineer, disassemble, or otherwise reduce our Services to human-readable form, except when these restrictions are prohibited by applicable law. [1]

[1] https://www.anthropic.com/legal/consumer-terms

Open source, MIT licensed.

I don't think that is how copyright licensing works.

Most interesting things in software engineering are (laughably) subjective.

Just check out any conversation on dynamic vs static typing, talk to a Rust zealot, or ask a backend engineer if microservices were a mistake.

It's unfortunate, and it makes it hard to have proper discussions on these subjects. It would be worthwhile to figure out how we can have more constructive arguments.

It is a Python thing, in the sense that it is Python-inspired:

design-wise copy the Python standard library's APIs whenever possible [1]

[1] https://github.com/jpakkane/pystd

This may have been the case one year ago, but with contemporary models such as Opus, I run into this less often.

I don't buy the article's title "Why big AI labs are hiring so many philosophers". They probably just hire one or two, and hundreds of software engineers.

I always found it somewhat annoying that a philosophy study would present itself by stating that graduated philosophers have great job opportunities, implying that studying philosophy would not be a bad choice. It just attracts really smart people, and these tend to find a job more easily. This article seems to make the same kind of mistake.

Also, for all we know these imagined herds of philosophers at AI firms are just labelling pictures of dogs.

solve our hardest math problems

We're not quite there yet :)

https://en.wikipedia.org/wiki/List_of_unsolved_problems_in_m...

Being able to drive a car properly also depends on having the right exploration-exploitation balance. A three-year-old is likely to explore too much in a situation where mistakes can be dangerous.

This requires not only knowledge, but also the control systems that develop with the prefrontal cortex. LLMs don't do much control yet.

18446744073709551616 possible values and you can't spare 1 for null? :)

TIL that Rust has NonZeroU64 which you can combine with Optional to get the required behaviour with only 64 bits per entry. [1]

[1] https://doc.rust-lang.org/std/num/type.NonZeroU64.html

If humans did actual reasoning, then why is this particular discussion failing so hard?

I don't think that is what the parent said, but I'm afraid my comment was too snarky (apologies), and the audience in this thread is not eager to be changed in their beliefs. Thanks for taking the time to reply though.

LLMs are fed a lot of data, and there are many patterns in there, including reasoning and some logic. Adding a little domain specific data will not immediately learn that domain, but it will also not be limited to only that data in its reasoning.

What would happen if you also feed it a book or two on logic, and some books on mathematics and physics?

The path to AGI (Artificial General Intelligence) must never be enclosed by high walls. We have always believed that AGI should be the cornerstone for all of humanity to collaboratively explore the boundaries of intelligence and solve complex challenges, rather than a privilege monopolized by a few rules and subject to revocation at any moment.

This is not obvious to me. If everyone gets access to AGI, but only a few people have the means to do really bad things with it, then what is the difference? Might as well make clear from the start that AGI is a powerful tool (read: weapon), and not a solution (e.g. world peace).

For those too lazy to watch someone talk on video for ages to make a point:

The link is to a famous YouTuber called PewDiePie and he uses a local LLM to parse his email, to save time with that. They have an autoreply system and get notified about urgent matters.

Most kids that grew up during the timeline you described had no interest in computer architecture. The small minority that did care is probably the same size now.

The other 99% who were into yoyo-ing back then are now into TikTok, that's all.

Do you swap SIM cards all the time? This seems to be the biggest blocking issue for me.

I tried switching phones once a week, which was heavenly. Might try that again, it requires some discipline.

Thanks for pointing that out. I scanned the paper and found that in their main experiments, they use a shared GPU resource and do not copy LLMs to target machines. Apparently they did other experiments in the ablation study where they did copy LLMs.

So it's even worse than I expected. The intended worm can spread through my thermostat, and when it reaches a GPU host, it can spread even harder. Fun times ahead.