Those don't have the property of being easily verified, which the problems proposed here do. I think that's a smart idea because it's a way to capture quick PR seeking AI-lab dollars for quite useful outcomes.

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pfisherman 7 hours ago | parent [-]

Being easily verifiable does not elevate something to the level of being a grand challenge. It really drives home the point that biology is not math or coding.

I guess to me the grandiosity here feels fake and unearned - like they vibe slopped it together without much input in the way of deep thought or expertise.

For example, synthesize arbitrary dna sequences > 3000 nt in length with error rate < 0.001 at > 95% purity. Easily verifiable, beyond the frontier, and generally useful.

	▲	phreeza 7 hours ago \| parent \| next [-]
		I agree with you there are other much grander challenges, but apart from that I don't think the list is horrible, because as I said it's incentive-aligned with the current moment we are in. Your suggestion would definitely be a good addition to the list for sure. I think some of the items on the list are also way less likely to be worked on (like cryogenics) than others, because they really require some heavy infrastructure to iterate. My money for which of these gets cracked with help of an AI would be rubisco, that can be quite effectively worked on in a closed loop lab fashion with a standard lab or CRO.
	▲	pixl97 7 hours ago \| parent \| prev [-]
		>that biology is not math or coding Biology is a really really big field. Many problems in biology are that of math and coding. Just as a simple example look at the golden ratio in living organisms. Biology follows a lot of different algorithms because they are energy efficient and come with massive secondary benefits.