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Comment by AlbertCory | Hacker News Reader

I think you missed the point. No one is dissing AI or this achievement, per se. At least I'm not.

The point is a great deal of drug development time did not go away when DNA sequencing and bioinformatics became available, and a great deal will not go away with Alphafold.

> We're headed towards a word where data gets put in a blender to decide what to try next. That entire pipeline can and will be automated.

No, we're not, and no, it will not. Alphabet has an entire large organization trying to bring the benefits of Big Data to traditional sciences. It's not a waste of time by any means. It's also not a steamroller ready to crush every problem in its path.

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> It's also not a steamroller ready to crush every problem in its path.

That is not particularly convincing, given that they've crushed multiple problems that were viewed as being decades away in just a few years.

> not particularly convincing

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.

Machine learning is already contributing to figuring out how to get energy from a fusion reactor. https://spectrum.ieee.org/can-ai-make-a-better-fusion-reacto... and https://www.deepmind.com/blog/accelerating-fusion-science-th...

You're changing the question. OP said "they've crushed multiple problems that were viewed as being decades away in just a few years."

"contributing to figuring out how" is not the same as "crushing the problem." It's helpful, of course.

Containment is the hardest problem with fusion reactors, and ML directly addresses it. (Obviously, ML doesn't directly solve fusion.) Now it's likely that there will be a commercially viable fusion reactor in the 2030s, whereas it used to be the case that a proof of concept was perpetually 30 years away.

We could have landed on Mars already if we had funding.

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.

So... AI can solve every problem, but not that one, because reasons.

Each major advancement does not imply that more major advancements are necessarily coming.

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.

> they've crushed multiple problems that were viewed as being decades away in just a few years.

What are these multiple problems you speak of?

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