atty

And physics

Factuality is orthogonal to political leaning generally. People can use the same set of facts and come to very different conclusions. That’s a separate issue from “are these facts correct” and what happens when an individual or entire party starts getting most of their news from highly partisan and unreliable sources.

In most cases it seems (at least to me and colleagues) to be turning out that picking best intelligence is a better option than picking better intelligence / dollar, assuming you can afford the cost. At least on interesting problems. If you’re doing generic web dev work, probably not the case.

The federal government and the executive branch has a well specified set of responsibilities to the people of this country, and they are massively failing at just about every metric. So yes, they are truly incompetent.

as long as the tests pass

To be pedantic, tests prove that the code passes the test suite, nothing else. They do not prove by themselves that the code is correct, secure, maintainable, efficient, etc. Those are much harder to measure and have a ton to do with organization, architecture, culture, shared knowledge of the maintainers, etc. All of which is lacking during and after this rewrite.

I do not work in the space at all, but it seems like Cloudflare has been having more network disruptions lately than they used to. To anyone who deals with this sort of thing, is that just recency bias?

I think both your statement and their statement are too strong. There is no reason to think LLMs can do everything a human can do, which seems to be your implication. On the other hand, the technology is still improving, so maybe it’ll get there.

Never ascribe to malice what can be sufficiently explained by incompetence. And i think it’s fair to say the best and brightest at Google aren’t turning their attention to YouTube lately. Except maybe to make training datasets for Gemini N+1 :)

The unspoken assertion that Rust and Python are interchangeable is pretty wild and needs significant defense, I think. I know a lot of scientists who would see their first borrow checker error and immediately move back to Python/C++/Matlab/Fortran/Julia and never consider rust again.

To be fair, triton is in active use, and this should be even more ergonomic for Python users than triton. I dont think it’s a sure thing, but I wouldn’t say it has zero chance either.

Which ICE are you referring to? This is an EU law.

It would be quite strange indeed for Mira Murati to have a say over their technical decisions, considering she does not work for OpenAI :)

The idea that a GPU is dedicated to a single inference task is just generally incorrect. Inputs are batched, and it’s not a single GPU handling a single request, it’s a handful of GPUs in various parallelism schemes processing a batch of requests at once. There’s a latency vs throughput trade off that operators make. The larger that batch size the greater the latency, but it improves overall cluster throughput.

I think they’re referring to hardware like TPUs and other ASICs. Which also exist, of course :)

I work on a team that has actually deployed NN based surrogate models into production in industry. We don’t use PINNs for the simple reason that many industrial scale solvers are solving significantly more complex systems than a single global PDE (at least in CFD, perhaps other areas are simpler). For instance, close to the boundaries, the solver our engineers use uses an approximation that does not satisfy conservation of mass and momentum. So when we try to use physical constraints, our accuracy goes down. Even in the cases where we could technically use PINNs we find they are underwhelming, and spending time on crafting better training data sets has always been a better option for us.

I do the same thing, and I keep doing it regardless of how many times I see it working properly.

That is what Intel 18A is, no? In some ways it’s worse than N2, and in some ways it’s better. Overall seems comparable to me, and apparently it’s still on track for next year.

Generally the game is created by external forces, not the players. If you’re an independently wealthy individual you can afford to work for pleasure. The rest of us? We like to eat and live in comfortable dwellings and take care of our families, so we play the game of pleasing our superiors in return for the money to live a comfortable existence.

Jax is, as far as an outsider can tell, Google research/DeepMind’s primary computation library now, not Tensorflow. So it’s safer than most Google projects, unless you think they’re secretly developing their third tensor computation library in 10 years (I suppose if anyone was going to do it, it would be Google).

Perhaps these groups could be regulated and insured by an even larger entity, perhaps even one with the authority to punish individuals and organizations for wrongdoing?

Rather cynical comments so far. I personally am very interested to see how this line of chips does, both in terms of performance (really efficiency, for this sort of chip), and market performance. Hopefully things like Lunar Lake, Arrow Lake, etc, and their 18a node all turn out to be as good as some of the early leaks and press releases make them would indicate, because Intel needs some big wins to get back on track.

It’s very reasonable to want Google and Bing to index your page for search, but not have your data collected for training models, i think. I’m not familiar with robots.txt to know if it has a whitelisting mechanism

Apples memory is on package, not on die.

Insanely fast whisper is mainly taking advantage of a GPU’s parallelization capabilities by increasing the batch size from 1 to N. I doubt it would meaningfully improve CPU performance unless you’re finding that running whisper sequentially is leaving a lot of your CPU cores idle/underutilized. It may be more complicated if you have a matrix co-processor available, I’m really not sure.

Our comparisons were a little while ago so I apologize I can’t remember if we used BS 1 or 5 - whichever we picked, we were consistent across models.

Insanely fast whisper (god I hate the name) is really a CLI around Transformers’ whisper pipeline, so you can just use that and use any of the settings Transformers exposes, which includes beam size.

We also deal with very poor audio, which is one of the reasons we went with faster whisper. However, we have identified failure modes in faster whisper that are only present because of the conditioning on the previous segment, so everything is really a trade off.

Yes I am not a fan of the naming either :)

To be fair, defense is an existential risk for the US and its allies. NATO can’t really afford to not have a reasonably up-to-date combat jet. They also need to continually feed money into the military industrial complex so that suppliers don’t go under/downsize too much/etc.

Not disagreeing with your sentiment, just think that certain fields like defense, healthcare, etc have slightly different priority lists.

I think this is using the OpenAI Whisper repo? If they want a real comparison, they should be comparing MLX to faster-whisper or insanely-fast-whisper on the 4090. Faster whisper runs sequentially, insanely fast whisper batches the audio in 30 second intervals.

We use whisper in production and this is our findings: We use faster whisper because we find the quality is better when you include the previous segment text. Just for comparison, we find that faster whisper is generally 4-5x faster than OpenAI/whisper, and insanely-fast-whisper can be another 3-4x faster than faster whisper.

As another commenter pointed out, you can give context for the decoder. So you can feed previous chunks into the model as the context. This is how we do it for streaming, at least.

I think the Ars writer here is very uninformed. Floc is completely different from Topics, and if you actually read the Topics spec, it seems to be significantly better than 3rd party cookies? At least to me. Maybe I’m missing something.