I think it has more to do with the fact that there has to be some term to describe the concept of "model which has weights that are openly accessible"
Like, that's just a logical thing to call it. I don't believe anyone is making a judgement on the intelligence of the reader to call it "open weight" when it refers to weights that are openly available.
"Open source" would be a more appropriate term to describe a model which also includes the training source.
> People have tried to make businesses out of this stuff but it has rarely worked
The only two startups I'm aware of who have managed to make compchem-as-a-service a viable business are Mat3ra and Materials Square. Both are a little rough in their own ways (to be expected from startups), but it's still interesting to see some of this move beyond university clusters.
> it appears computational chemistry is stuck in batch processing era
I wouldn't call it "stuck," since it's one of the few fields where batch processing makes sense.
You generally run your jobs on a supercomputer that's being shared by a few hundred other people. You need a scheduling system to allocate resources (e.g., 5 jobs requesting 10 nodes for 8 hours, 1000 jobs requesting 0.5 node for 5 minutes, etc).
It's hard to tighten the workflow when the average calculation takes 2-3 hours of time.
While they can, drones are still limited by physics.
Sure, the aircraft can pull a ton of G's to defeat a missile or achieve a firing solution. Maybe it even succeeded at that.
It still pulled 9+ Gs and has burned an incredible amount of kinetic energy doing do. And the harder it pulls, the quicker it bleeds speed. It might not be able to defeat the next missile or outmaneuver the other plane's buddies because it is now slow and very likely outside its optimal turning speed.
Even today, missiles can pull dozens of G's yet still are defeatable by humans who can only pull about 9.
They also succeeded in the monumental task of making VR look boring.
VR platforms are an escapist's dream: you can be anything you want doing whatever you want. And how did they show off their fantasy world machine? They did office meetings in avatars of their real life selves.
Just spend one night in VRChat and everything Meta did will look like Plato's cave shadows.
I'm the opposite personally; I love automating things (not my main job, but it makes things easier in the long run) but bash sucks my soul out.
What I do on occasion if it's a nontrivial script (or I don't feel like remembering how to handle non positional arguments) is have the AI write it up, and then I verify by myself it is correct. I can write the script and understand it over an afternoon, it's more that I just don't find it enjoyable.
Cheaper than automating with an AI, it's deterministic, and I don't end up eyeballing the clock every 5 minutes at 2pm because I hit one of the many rough parts of bash.
Any performance gains caused by the internal bandwidth of the card will evaporate once you spill into system RAM, because now your bottleneck is probably a slow PCI lane.
And if your jobs do fit onto a 24GB card, then you are not the target user for the "AI mini PC" niche that these guys are trying to carve out
Exapunks and TIS-100 were a huge influence on my career trajectory.
I was always scared of assembly and low level stuff as a kid / college student, who mostly was trying to learn from random sites that assumed a lot of CS background.
Even though they're not near the complexity of x86, these games made me realize that assembly isn't really that scary. I still don't daily drive x86, but they gave me the confidence to go through a few Advent of Code and Project Euler problems. Having a really stripped down assembler was a fantastic learning tool!
Without them, I'd probably still only be working in Python (which is a great language, but abstracts a lot)
It looks like git after 2.22 was dropped because it took an LLM commit. Same with ghc.
If I have to choose between this or git and the latest ghc, I think I'm going to just wait for someone to fork annex.
I don't even feel strongly one way or the other on AI stuff; pragmatically, I'm just not going to stop using the most widely used version controller, or Haskell, just for some guy's (forkable, AGPL licensed) hobby project.
UX work is often also a lot heavier and more subjective than the plumbing.
I might open a pull request to support some new video code, and that might only require a few dozen lines over a few files. That's easy to review, and it either works or it doesn't. Worst case they say "our convention is to register codecs as a subclass of X class, but you subclassed Y class" or something equally straightforward.
Let's say instead I wanted to change the workflow to register an account. Now I'm changing a bunch of JavaScript, CSS, templates, I'm adding pages, and I also need to update the backend. Even if someone is that into frontend work, it might take forever to even get reviewed by the maintainers because it's a massive PR.
Plus, now we've moved into subjectivity land: "I'm used to the old workflow," (because they designed it) "The last one was really easy" (for an engineer), "I think we should focus on the backend before we work on the UI," "I don't like this font because the license isn't free as in freedom" etc.
Even if you just mockup something on Figma or whatever, unless you're a maintainer it's probably going to just get ignored as a feature request. Because there's also the psychological aspect of basically being told that the UI you wrote is implicitly bad, if you're the maintainer reviewing the mockup.
Academics base their careers around citation numbers. You need publications and a high H-index to make it anywhere. Self-plagiarism reduces the effectiveness of that metric, which makes it harder to evaluate the actual impact of a researcher.
It should be no surprise that republishing in multiple journals was accepted in the pre-computer era, where citations were inherently harder to track (and thus less valuable as a metric).
Quoting Upton Sinclair, "It is difficult to get a man to understand something, when his salary depends on his not understanding it."
> I typed the project name into Google, and my repository appeared in the results. I entered the same query into Bing, and someone else’s repository appeared in the results
Side story, this kind of thing is what made me stop using Bing.
I had been using it as the default for searches (it sucks, but it's at least not Google), until I landed on a phishing page for my bank (I haven't committed it to memory yet). The page was a near perfect copy, and I would easily have gotten pwnd by it if they didn't have a modal asking me to run some code in my terminal for "security activation" that made me go "that's a little odd... Is this the right address OH SHIT that's a .ru domain"
I never see Google return phishing pages or typo squatters in the first page. Bing constantly returns that stuff in the first several results.
I would argue it's not even old people. Most people do not have any understanding of what's going on when you click a button. Website either acts as expected, or it doesn't "work"
If the button doesn't work, the average user is going to say "this most be broken" and then use a competitor (or contact your support). That's why it's really important to error-proof one's design (eg https://en.wikipedia.org/wiki/Poka-yoke).
So instead of the button failing because you didn't check a box, pop up with a message telling them "Please click $box before continuing". Or if you want to be fancy, feed them whatever form you're giving them piecemeal, so that they can't continue until they finish this small part (e.g., have them input a name, then the next page only has a spot for an address, then the next page only has a spot for card information, then the next only has a spot to select shipping). Simple bite sized chunks anyone (well, anyone you would ethically want to sell to) can understand.
Like, that's just a logical thing to call it. I don't believe anyone is making a judgement on the intelligence of the reader to call it "open weight" when it refers to weights that are openly available.
"Open source" would be a more appropriate term to describe a model which also includes the training source.