zackchase

That's not quite right. Salary bands tend to be guidelines, not constraints. And in ML there are plenty of people getting paid far out of band.

I assure you, people have pointed it out...

Thanks for sharing! Happy to discuss here if anyone wants to engage in some (pre)debate. Also happy to butt out :).

These are excellent questions. I often ask Google researchers, "How Deep is the Mind?" They don't seem to know. You see, it all comes down to democratizing the blockchain, and the only way to do that is with AI in each and everyone's hands. Together, we can make a more open AI, for everyone! And for you too! The only trust required for CryptoMinds is trust in proof of work, am I right?

Thanks for the interest in JnJ AGI Research! As inter-global head of research I'm happy to answer any questions the HN community may have!

Flag-planting is relevant because the practice of flag-planting (and other abuses of the pre-print) is what has prompted some of this over-reaction.

Author here: Grisha Perelman's proof of the Poincaré conjecture has never been (by him, to my knowledge) submitted to or published in any journal. He decided, as is his right, that he could not care less about the professional community of publishing mathematicians or their protocols. Does not invalidate his achievement.

I should note, since I am not and do not expect to be the level of mathematician that Perelman is, I have not actually read his proof. So I defer to other superior mathematicians for this assessment and come by it as hearsay. :)

It's problematic that the "experts in AGI" are generally self-designated, and have mostly produced little that is concrete, either in the form of theory or engineering. We should be concerned about long-term outcomes of technological progress, but I'm not convinced the current conversation centering on the term "AGI" is productive.

While I don't take exception with the word machine learning (it's reasonably well defined), I agree with most of the points here. Some are grossly exaggerated (e.g. needing petabytes of data to do machine learning). In my research on medical time series, we get strong results with a few thousand examples (megabytes). Same for natural language datasets which are modestly sized. But yes, absolutely agree that function approximation for supervised learning and some kind of artificial consciousness as portrayed in the media are very far apart.