Yep, this—we aren’t as close to the equator, but there’s less atmosphere to filter UV. Not so much in terms of beach days, of course, so sunburns here tend to be less than full-body.
All that said: a daily mean of 13-14 is past what we’d see here—and yeah that is some serious burning.
I'm guessing that what they meant by "proper harmony" is just intonation: where thirds and fifths are expressed by small, integer ratios of frequencies (e.g., a fifth is 3:2 and a major third is 5:4).
A just intoned major third is about 14 cents flatter than a major third played on a 12 tone equal temperament tuned instrument (e.g., piano).
I'm not sure how much this matters in terms of having or not having perfect pitch though. Some people with perfect pitch can hear the difference between JI and 12TET and correctly their singing accordingly.
Ah yes, Jacob Collier. What I like is that he suggests an exercise that he used to practice microtonal singing: see how many intermediate pitches you can sing between two notes (could be a half step apart or you could start with a wider interval) and try to increase that number.
Of course if you sing Indian classical music (or several other non-Western musical traditions) then you will learn to sing quarter tones.
Came here to say the same thing. It does use TCP/IP but I didn't really understand why "no TCP/IP" is a hard requirement of the original article anyway.
Thanks for sharing that link. My GitHub ID is 484.
I had no idea that I joined so early. It says I joined in 20/2/2008. I guess I was following some of the founders' work in Rails when GitHub was announced and must have signed up shortly after it got started.
> When my partner goes to the store I get periodic text messages from them asking how much X we have and to check I look in the fridge or pantry in the kitchen and then go downstairs to the fridge or pantry in the basement.
We used to have a similar problem until we made a policy that if you use something up you add it to our shared shopping list, usually with a voice command to Siri. Whenever someone is at the store we just check the list, making sure we mark off things that are purchased.
Officially we have a similar policy except that it's a paper list next to the pantry. But with a half-dozen people in our household the likelihood that everyone has been 100% reliable in adding finished items to the list and there are no omissions is low, hence the text messages.
On your site you make the claim that: "Our thesis is that there is 100 years of physics and math research that has gone unnoticed by the CS/ML communities and we intend to rectify that."
Extraordinary claims require extraordinary evidence. Especially considering that a decent fraction of the CS/ML researchers that I know have solid physics and math backgrounds. Just of the top of my head, Marcus Hutter, David MacKay, Bernhard Scholkopf, Alex Smola, Max Welling, Christopher Bishop, etc. are/were prominent researchers with strong math and physics backgrounds. More recently Jared Kaplan and Dario Amodei at Anthropic also have physics backgrounds, as well as plenty of people at DeepMind.
To claim that you have noticed something in "100 years of physics and math research" that all of those people (and more) have missed and you didn't is pure hubris.
Cliche phrase is cliche. And yeah, no shit, we are working on it.
Re: your other points: cool, yeah there are people in ML that studied physics. Do you feel like much of physics has made it to ML? Do we have scalable energy-based models? If not, why not?
Is it concerning to anyone else that the "Simple & Reliable" and "Reliable on Longer Tasks" diagrams look kind of like the much maligned waterfall design process?
I am mostly worried that I am wrong, in my opinion, that "agents" is a bad paradigm for working with LLMs
I have been using LLMs since I got my first Open AI API key, I think "human in the loop" is what makes them special
I have massively increased my fun, and significantly increased my productivity using just the raw chat interface.
It seems to me that building agents to do work that I am responsible for is the opposite of fun and a productivity sink as I correct the rare, but must check for it, bananas mistakes these agents inevitably make
The thing is, the same agent that made the bananas mistake is also quite good at catching that mistake (if called again with fresh context). This results in convergence on working, non-bananas solutions.
Look up The Old Lady who Swallowed a Fly. Or The King, the Mice and his Cheese
What you propose makes things worse, not better
LLMs are magnificent tools, but there needs to be a human hand holding them.
Nothing I have seen anywhere, yet, challenges my view that "agents" will not be a good idea until we have better technology, that there is no sign of yet (?), than LLMs.
Just to be clear, I wasn't claiming that "communicating clearly" is a new idea in software engineering, I'm mainly commenting on how effective embracing it can be.
When doing math, pretty much every term is "load-bearing" in that arguments will make use of specific aspects of a concept and how it relates to other concepts.
If you look at most graduate-level math textbooks or papers, they typically start with a whole bunch of numbered definitions that reference each other, followed by some simple lemmas or propositions that establish simple relationships between them before diving into more complex theorems and proofs.
The best software projects I've seen follow a roughly similar pattern: there are several "core" functions or libraries with a streamlined API, good docs, and solid testing; on top of that there are more complex processes that treat these as black-boxes and can rely on their behavior being well-defined and consistent.
Probably the common thread between math and programming is both lean heavily on abstraction as a core principle.
https://www.bom.gov.au/climate/maps/averages/uv-index/?perio...