S1: A $6 R1 competitor?

> If you believe that AI development is a prime national security advantage, then you absolutely should want even more money poured into AI development, to make it go even faster.

This, this is the problem for me with people deep in AI. They think it’s the end all be all for everything. They have the vision of the ‘AI’ they’ve seen in movies in mind, see the current ‘AI’ being used and to them it’s basically almost the same, their brain is mental bridging the concepts and saying it’s only a matter of time.

To me, that’s stupid. I observe the more populist and socially appealing CEOs of these VC startups (Sam Altman being the biggest, of course.) just straight up lying to the masses, for financial gain, of course.

Real AI, artificial intelligence, is a fever dream. This is machine learning except the machines are bigger than ever before. There is no intellect.

and the enthusiasm of these people that are into it feeds into those who aren’t aware of it in the slightest, they see you can chat with a ‘robot’, they hear all this hype from their peers and they buy into it. We are social creatures after all.

I think using any of this in a national security setting is stupid, wasteful and very, very insecure.

Hell, if you really care about being ahead, pour 500 billion dollars into quantum computing so u can try to break current encryption. That’ll get you so much further than this nonsensical bs.

You can choose to be somewhat ignorant of the current state in AI, about which I could also agree that at certain moments it appears totally overhyped, but the reality is that there hasn't been a bigger technology breakthrough probably in the last ~30 years.

This is not "just" machine learning because we have never been able to do things which we are today and this is not only the result of better hardware. Better hardware is actually a byproduct. Why build a PFLOPS GPU when there is nothing that can utilize it?

If you spare yourself some time and read through the actual (scientific) papers of multiple generations of LLM models, the first one being from Google ~~not DeepMind~~ in 2017, you might get to understand that this is no fluff.

And I'm speaking this from a position of a software engineer, without bias.

The reason why all this really took off with so much hi-speed is because of the not quite expected results - early LLM experiments have shown that "knowledge" with current transformers architecture can linearly scale with regards to the amount of compute and training time etc. That was very unexpected and to this day scientists do not have an answer why this even works.

So, after reading bunch of material I am inclined to think that this is something different. The future of loading the codebase into the model and asking the model to explain me the code or fix bugs has never been so close and realistic. For the better or worse.

> the first one being from DeepMind in 2017

? what paper are you talking about