That is not particularly convincing, given that they've crushed multiple problems that were viewed as being decades away in just a few years.
That is not particularly convincing, given that they've crushed multiple problems that were viewed as being decades away in just a few years.
Deep learning did turn out to be a very major advancement! And it's still bearing new fruit. But it does not at all imply that future advancement is inevitable or that advancement must continue at its present rate.
It's also worth considering that the domains where deep learning now excels are the same domains where it excelled when it first became popular (AlphaGo and AlphaFold are perhaps exceptions) and that all this advancement comes from hammering away at the same problem spaces for so many years, with huge budgets. Maybe the next area for advancement is something like combining fast on-device deep learning inference with robotics, and maybe that opens up a whole new world of possibilities. But that's a maybe.
It wasn't meant to "convince." It's an assertion.
So, it's going to land humans on Mars in the 2020s, too? How about cheap nuclear fusion?
Some problems are amenable to AI approaches, including many that were thought not to be.
Some are not.
"contributing to figuring out how" is not the same as "crushing the problem." It's helpful, of course.
There’s no incentive for the state to fund it so Musk has to do it while still relying on government funding for other SpaceX projects. If we get the National Security elites on board we’ll be there so fast it’ll make your head spin. We could have AGI 2-5 years sooner than we otherwise will (2030) if the government wasn’t run by geriatric humanities majors. These problems are mosyly downsyream from money, lots of it, and you need Manhattan project scale budgets for them.
AGI and fusion also require some theoretical breakthroughs but Mars? Mars doesn’t. Making it livable is hard but just landing there just means scaling up the moon landing.
What are these multiple problems you speak of?