221–230 of 328 posts
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I was just disputing "X exists as a press-release generation system, not as a real technology creation system." Definitely agree the path to profitability will be tough.
If the technology is not in production, is the creation complete?
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General working conditions are not "internal info"; it is beneficial to society to discuss working conditions (which can be pretty detailed), and is thus a protected activity under various laws. Nothing in any contract can obviate this lawful right (and, to some, a basic duty of citizenship, since the country is more important than any single company within the country). At best contracts can highlight what is privil…
Now you have me curious. Which laws, specifically?
"Workplace conditions" is protected similarly to wages.
>PyTorch/Nvidia GPUs easily overtaking TensorFlow/Google TPUs. TF lost to PyTorch, and this is Google’s fault - TF APIs are both insane and badly documented. But nothing comes close to performance of Google’s TPU exaflop mega-clusters. Nvidia is not even in the same ballpark.
> Unfortunately, this early lead would be completely squandered within a few short years, with PyTorch/Nvidia GPUs easily overtaking TensorFlow/Google TPUs. ML was, and frankly is, still too nascent to have significant technical barriers to entry. The sustained eye-popping funding for AI companies generated a surge in supply, with the number of ML researchers growing ~25% YoY for the past decade. I taught myself enough ML to blend in with the researchers at Brain over a relatively short 2 years, and so have many others. Nobody, not even Google, can afford to throw money into a bottomless pit.
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As somebody who has crossed the line between ML and chemistry many times, I would love to see: more ML researchers who know chemistry, more chemistry researchers who know ML, and best of all, fully cross-disciplinary researchers who are both masters of chemistry and ML, as those are the ones who move the field farthest, fastest.
Society is not structured to encourage this. Getting a job sooner is more lucrative. Any breakthough you make having studied for a couple of decades is property of a corporation not you.
I wish more people understood this and the value that it would add to society. For some reason most people understand how roads and transit improve commerce, but fail to understand how education and social support for the individual does as well.
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I don't think they are behind just because they have released less stuff. They had LaMDA way before ChatGPT, and they had multimodal models (both by DeepMind and by Google Brain) well before OpenAI.
They are behind because what they have released sucks. Have you tried Bard? It's dumb. Like you're talking to some 20th century gimmick dumb. GPT4 is far from perfect, but when it makes mistakes and you point them out, it understands and tries to adapt. Bard just repeats itself saying the same stupid things like a casette-tape answering machine. If you ask a googler about this, they typically assume GPT is just as st…
> We’re releasing it initially with our lightweight model version of LaMDA. This much smaller model requires significantly less computing power, enabling us to scale to more users, allowing for more feedback. [1]
> Bard is powered by a research large language model (LLM), specifically a lightweight and optimized version of LaMDA, and will be updated with newer, more capable models over time. [2]
[1] https://blog.google/technology/ai/bard-google-ai-search-upda...
I work for Google Brain. I remember meeting Brian at a conference and I have nothing but good things to say about him. That said, I think Brian is underestimating the extent to which the Brain/DeepMind merger is happening because it's what researchers want. Many of us have a strong sense that the future of ML involves models built by large teams in industry environments. My impression is that the goal of the merger i…
The gradient of current moment is that whatever approach is optimized to use more data and more compute is much easier to invest in than something which can do more with less, but with a significant number of possible dead-ends.
At some point, this will have diminishing returns, but until that is hit, this makes sense as a purely return-on-investment for both research progress and business returns.
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I keep hearing this, but Bard sucks so badly when i've tried to use it like GPT-4 or compare results its like night and day. What makes you so confident they have "secret" LLMs that are superior?
Bard is in full-scale production to all U.S. users for free. GPT-4 costs $20/month. Rather a big difference in the economics of the situation. Also it's pretty clear that even the $20 is highly subsidized. Microsoft is willing to incinerate almost any amount of money to harm Google.
This isn't the case. There's a podcast (somewhere? I thought it was a Lex one but I can't find it) where someone from open AI went into some depth about the economics.
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TF in its first version was stellarly misdesigned. It was infuriatingly difficult to use, particularly if you were of the "I just want to write code and have it autodiffed + SGDed" school, I found it crazy to use Python to manually construct a computational graph...
TF1 was pretty rough to use, but beat the pants off Theano for usability, which was really the best thing going before it. Sure it was slow as dirt ("tensorslow") even though the awkward design was justified on being able to make it fast. But it was by far the best thing going for a long time. Google really killed TF with the transition to TF2. Backwards incompatible everything? This only makes sense if you live in a…
nitpick Cockroach was built by the team that built spanner. So your phrasing is off there.
nit: Cockroach was founded by a Xoogler but there's no public evidence that they were on the Spanner team at any point.
Lots of great insight. Here’s one: “Given the long timelines of a PhD program, the vast majority of early ML researchers were self-taught crossovers from other fields. This created the conditions for excellent interdisciplinary work to happen. This transitional anomaly is unfortunately mistaken by most people to be an inherent property of machine learning to upturn existing fields. It is not. Today, the vast majority…
Part of this is because public health as an undergraduate discipline is extremely new, so the field is used to having to teach Folks From Elsewhere about our field, rather than the fields built on the assumption of a large foundation of undergraduate coursework.