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Patel takes anticommunism to such an extreme that he repeatedly brings up and speculates (despite being met with repudiation by even the staunchest anticommunist of guests) whether naziism is preferable, that Hitler should have the war against Soviets, that the US should have collaborated with Hitler to defeat communism, and that the enduring spread of naziism would have been a good tradeoff to make.
Where does he say this?
Earlier quoted context omitted.
They have a moat defined by being well known in the AI industry, so they have credibility and it wouldn't be hard for anything they make to gain traction. Some unknown player who replicates it, even if it was just as good as what SSI does, will struggle a lot more with gaining attention.
Being well known doesn’t qualify as a moat.
So is the translation endless scaling has stopped being as effective?
Scaling is not over, there's no wall. Oriol Vinyals VP of Gemini research https://x.com/OriolVinyalsML/status/1990854455802343680?t=oC...
Even as criticism targets major model providers, his inability to answer clearly about revenue & dismissing it as a future concern reveals a great deal about today's market. It's remarkable how effortlessly he, Mira, and others secure billions, confident they can thrive in such an intensely competitive field. Without a moat defined by massive user bases, computing resources, or data, any breakthrough your researchers…
He has no answer for it so the only thing he can do is deflect and turn on the $2T reality distortion field.
Do you think OpenAI could project their revenue in 2022, before ChatGPT came out?
My guess is we'll discover that biological intelligence is 'learning' not just from your experience, but that of thousands of ancestors.
There are a few weak pointers in that direction. Eg. A father who experiences a specific fear can pass that fear to grandchildren through sperm alone. [1].
I believe this is at least part of the reason humans appear to perform so well with so little training data compared to machines.
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> this implies higher intelligence Not necessarily. The problem is that we can't precisely define intelligence (or, at least, haven't so far), and we certainly can't (yet?) measure it directly. And so what we have are certain tests whose scores, we believe, are correlated with that vague thing we call intelligence in humans . Except these test scores can correlate with intelligence (whatever it is) in humans and at t…
Fair, I think it would be more appropriate to say higher capacity.
The whole mess surrounding Grok's ridiculous overestimation of Elon's abilities in comparison to other world stars, did not so much show Grok's sycophancy or bias towards Elon, as it showed that Grok fundamentally cannot compare (generalize) or has a deeper understanding of what the generated text is about. Calling for more research and less scaling is essentially saying; we don't know where to go from here. Seems reasonable.
I don’t think he meant scaling is done. It still helps, just not in the clean way it used to. You make the model bigger and the odd failures don’t really disappear. They drift, forget, lose the shape of what they’re doing. So “age of research” feels more like an admission that the next jump won’t come from size alone.
It still does help in the clean way it used to. The problem is that the physical world is providing more constraints like lack of power and chips and data. Three years ago there was scaling headroom created by the gaming industry, the existing power grid, untapped data artefacts on the internet, and other precursor activities.
The impactful innovations in AI these days aren't really from scaling models to be larger. It's more concrete to show higher benchmark scores, and this implies higher intelligence, but this higher intelligence doesn't necessarily translate to all users feeling like the model has significantly improved for their use case. Models sometimes still struggle with simple questions like counting letters in a word, and most p…
Models aren't intelligent, the intelligence is latent in the text (etc) that the model ingests. There is no concrete definition of intelligence, only that humans have it (in varying degrees).
The best you can really state is that a model extracts/reveals/harnesses more intelligence from its training data.