Comment by pfisherman
7 hours ago
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.
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.
>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.
> Just as a simple example look at the golden ratio in living organisms
What is this example meant to illustrate?
The golden ratio is one of those things you can study for years and be a amazed by.
>The golden ratio (≈ 1.618) and its related golden angle (137.5°) show up in nature because they maximize space, exposure, and flow.
The golden ratio is an algorithm, but it is also a natural law that systems of many different scales follow. Wherever we look we find examples of it. Without this algorithm the world is a much harder place to explain.
What many scientists starting to look for is these algorithms we have not identified in natural systems as an explanatory means of their workings. Also because they are algorithms they are things we can put into computer systems to increase efficiency, or may naturally emerge from evolutionary learning systems like AI in what we call emegence.
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I think it illustrates that there are cool things to find beneath the lamp post of math and coding.
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