6d ago

It's just aging /s

You're viewing a single thread.

130 comments

Aging should be studied a lot more. I believe once the AI bubble pops, the computing power and models should be applied to biology. How do ageless atoms become old meat? I want to know, as an old meat myself, and if we can treat, stop, or even reverse the process.
- How do ageless atoms become old meat? I want to know, as an old meat myself, and if we can treat, stop, or even reverse the process.
  Atoms must arrange themselves in a particular way to become a cell. A cell knows how to make copies of itself, but sometimes mistakes can happen. Like a game of telephone, the cell at the end of the line only knows how to make a copy of itself, not how to make a copy of the original cell it came from. The mistakes gradually accumulate over time, which causes improperly formed cells to accumulate over time and give the appearance of "aging".
  In theory, aging is a condition that is surmountable. There are jellyfish that are swimming in the ocean right now that are functionally immortal. They create perfect copies of their DNA every single time, and can repair damage to cells without leaving a trace of the original injury. If we could figure out the processes that allow them to do this, it could be applied to the human genome as well.
  
  Part of the problem is telomerase being lost. The downside is that it's a cancer prevention mechanism, so messing with it (by adding more) is bad news.
  
  And yet everyone seems to age the same. Funny how those "mistakes" never turn me into a whale or a plant, I surmise it's a bit more complex than that.
  
  That’s…not how it works. If I kept copying a car, using the previous one as a stencil, I’d eventually end up with something that mostly resembles a car but ultimately doesn’t work properly. Eventually, it would fail to function at all but at a glance it would look more or less the same. At no point would it ever resemble a motorcycle and by the time such a mistake would happen I would have stopped even trying long before that or, to go back to cells, the body would have died because too many things weren’t working correctly to live long enough to turn into a different animal entirely.
  
  I think that's accounted for under mutations and cancer and such. You theoretically could mutate into a whale but the probability of your cells making specific enough mistakes for that to happen is so astronomically small that it's essentially zero.
  
  Of course it's more complex than my overly simplistic explanation, but I don't want to bore you with details when you could achieve the same result by cracking open a biology textbook. I wouldn't really wish that on anybody right now, honestly. Not how I would want to kick off twenty-twenty-six.
  To circle around back to the main point, I agree with you wholeheartedly. Aging should be studied more. There are breakthroughs in medicine just waiting to be discovered that could not just extend our lives, but also extend the portions of our lives where we are healthy and fit enough to enjoy doing things, rather than wasting away in nursing homes and hospice beds.
- AI/ML has already been used to study protein folding and I'm sure it'll be used to study other facets of biology too. There's great use cases for the tech once you look past the tell-mentally-ill-people-to-kill-their-families-bots.
  I may be wrong but I think one hard part is identifying the places where ML makes sense to use. Need people who understand biology AND ML for that.
  
  Exactly. AI/ML absolutely has useful use cases, it's just not a complete solution for literally anything.
  
  Most of the stuff known as AI in the current environment is really, really powerful inference engines. And understanding the limits of inference (see for example Hume's Problem of Induction) is an important part of understanding the appropriate scope of where these tools are actually useful and where they're actively misleading or dangerous.
  So, take the example of filling in unknown details in a low resolution image. We might be able to double the number of pixels and try to fill in our best guesses of what belongs in the in-between pixels that weren't in the original image. That's probably a pretty good use of inference.
  But guessing what's off the edge of the picture is built on a less stable and predictable process, less grounded in what is probably true.
  When we use these technologies, we need domain-specific expertise to be able to define which problems are the interstitial type where inferential engines are good at filling things in, and which are trying to venture beyond the frontier of what is known/proven and susceptible to "hallucination."
  That's why there's likely going to be a combination of things that are improved and worsened by the explosion of AI capabilities, and it'll be up to us to know which is which.
- There's a number of different reasons but the hardest to overcome is the fact that we evolved to grow old and die. Having an upper limit on our reproductive age positively benefits our ability to keep evolving and having an upper limit on total age balances the benefits of age (wisdom and experience) with the need to not deny the younger generations of resources.
- Much of the modern science was discovered by pattern finding, which is where AI actually excels.
  EDIT: By AI, I do not mean language models.
- AI is going to be applied to biology.
  They'll develop all sorts of bioweapons. They've all ready made huge strides in parasymoethetic nerve agents.

130 comments