[Andy_G11]

I wonder if we could get AI to code biological outcomes using biomolecular objects (as in object oriented programming), and what level of computing technology / how comprehensive a database of biochemical reactions would be needed to do this. Could this be something that is achievable in 20 yrs, perhaps speeded up with the aid of quantum computing?

[junon]

Disclaimer, not a biologist. Ex did a lot of work in this area though.

If I understand your query (it's hard to parse), then no, AI is nothing that would help. This is an insanely hard problem to understand let alone solve. You're asking for a cartesian of every possible interaction of every possible enzyme, protein, molecule, etc. which, if it were possible to do with existing tech, it would have been done already.

ML (AI) is, at least right now, fancy pattern matching. Nothing more.

Further, Quantum computers can only run certain classes of programs, at least for now. Also not an expert there but if these two fields have been married in any way it's certainly not been done with any amount of clarity.

Hopefully that's a somewhat sufficient, serious answer. The question itself is very.... uh, r/futurism, if we're being honest. You can't just throw AI and Quantum at hard problems expecting them to just somehow solve them.

[jjcon]

> ML (AI) is, at least right now, fancy pattern matching. Nothing more.

I mean, every problem can be boiled down to some sort of 'fancy pattern matching', the question is really how fancy/sophisticated the solver and how large the problem space the problem. I'm not sure why AI couldn't be helpful here even if the convergence of the solver/problem space are still many years off.

[bawolff]

That's basically equivalent to saying, by the church-turring thesis computers can solve any solvable problem, therefore it can probably solve the problem at hand.

Which is technically true, but as a pragmatic matter doesn't really tell us much about if, when, or how the problem will be solved.

[jjcon]

Exactly - I’m not claiming a specific timeframe for AI to be helpful in this area - just pointing out that the claim that it is, ‘just fancy pattern matching’ isn’t limiting to its utility and that in theory it should be able to contribute here.

[taneq]

s/computers/engineers/g and your post still holds true. Predicting when we’ll solve unsolved problems is just hard.

[Andy_G11]

Thanks for your response. I did chemistry and physics and uni (almost 30 years ago now) and I remember how complex some of the computer modelling that was done at the time was (even for very simple things - I think we looked at a model of what happened when a proton and a hydrogen atom came into close proximity).

Since then things have advanced hugely - both in biochem and in computing - and I was curious to see what might have been done. Also, hard science is fundamentally pattern recognition, isn't it: it requires that given the same inputs, the same output is consistently delivered.

[narrator]

Biology is not a serial process though. Everything is interacting with everything all at once. Some of those processes take exponential time complexity to simulate in computers, though deep learning is getting us better approximations of those processes in a shorter amount of time. The point being, biological systems don't have the certainty and exactness to program them like a computer. Everything does works out roughly at the macro scale.

[dgowte]

What is this nonsensical buzzword soup?

[jjcon]

Buzzword soup aside I think we all understand what they are asking and it is an interesting question. Will we be able to model (through any computation via any computing means) biological processes at a deeper level to accurately determine outcomes someday in the future?

[bawolff]

I mean, if you divorce it from the buzzwords like that, the question becomes trivial:

* will we at some point in the future be able to model biological processes on a computer better (even if only slightly) than we currently can, at some point in the future? Obviously yes

* will we fully solve biological systems so that we can model them in their entirety with 100% accuracy? Not in this lifetime and probably not in the next generation.

The question when phrased this way is basically asking (depending on interpretation) either: will we make any progress ever? or will we make all the progress?

[Andy_G11]

What don't you understand?

[messe]

How each word in your paragraph connects to the next. Your question is something that could be described as “not-even-wrong”[1].

[1]: https://en.m.wikipedia.org/wiki/Not_even_wrong

[Andy_G11]

Seems pretty straightforward to me: 1) programming objects have properties and methods; 2) within cells it is probably possible to have analagous entities (perhaps various types such as molecules, organelles, etc) which have defined properties and predictable behaviours; 3) could we soon have a computer and a sufficiently comprehensive database of these objects and their behaviours for an AI to start correlating how they are combined and how they would need to act to produce a cellular effect (e.g. regenerate a damaged cell); 4) could this be speeded up with the advent of quantum computing?

[thaumaturgy]

No, because biology's developer didn't use OOP.

Less flippantly: biological processes don't behave similarly to a big network of discrete objects with specific traits (methods and properties in OOP parlance). The domain of biology is composed of lots of molecules that combine to form bigger molecules that in turn get classified into hormones and proteins and amino acids and other organic compounds, and these all interact in super complex ways that are very difficult to model. For example, protein folding is a big area of research that is attempting to model the behaviors of just one set of molecules [1], and it is proving to be a really difficult problem to solve despite throwing enormous amounts of computing power at it [2].

And, we don't even know what we don't know yet in broader biological terms. It's not like we have a pretty good model for biology at macroscopic scales and we're just working out details -- this isn't civil engineering. The details that we're still missing matter a lot in how biological systems behave.

Quantum computing likewise is not a magic pill that will suddenly make all of this easier. Quantum computing is good at solving certain kinds of problems a little bit faster, but expectations for quantum computing have so far greatly outpaced its actual development.

As a side note, "systems thinking" in programmers often leads down dark dead-end alleys full of misunderstandings and wrong questions. Modern science is pretty darn advanced, and today's PhD candidates are introduced to programming as part of their education. It's usually safe to assume that if an advancement in a given field were possible through rudimentary programming, then someone would be working on it; programmers who are curious about specific fields should first start at the basics in those fields and put the time in to become familiar with them. That process will eventually lead to the right questions to ask in those fields.

[1]: "What is protein folding? A brief explanation", https://news.ycombinator.com/item?id=25261591

[2]: "Protein folding: Much more intricate than we though", https://news.ycombinator.com/item?id=25284998

[Andy_G11]

Thanks for your response - I was curious if AI and tech might be able to bridge from a suitably detailed statistical picture to (at least some) cases of underlying deterministic behaviour, perhaps in a way (or ways) that might surprise us.

[herval]

The only word you forgot in that buzzword bingo was blockchain :-)