[HN Gopher] Generative A.I. arrives in the gene editing world of...
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Generative A.I. arrives in the gene editing world of CRISPR
Author : msmanek
Score : 73 points
Date : 2024-04-23 16:25 UTC (6 hours ago)
(HTM) web link (www.nytimes.com)
(TXT) w3m dump (www.nytimes.com)
| pointlessone wrote:
| What can possibly go wrong if we let ChatGPT edit our DNA?
| genghisjahn wrote:
| Have you seen DNA? Mistakes and dupes and hallucinations all
| over the place. Ever since Sherlock Crick and Doctor Watson
| started meddling with it.
| miraculixx wrote:
| It's one thing to analyze it. It's an entirely different
| thing to let a machine of dubious abilities create new DNA.
| Wissenschafter wrote:
| Isn't DNA in itself a machine of dubious abilities? It's
| only functional because what functions is what survives,
| imagine the amount of 'unsurvived' because of how shit the
| code is.
| yieldcrv wrote:
| machines that undergo accelerate evolution, I would trust
| them more under rigorous guidelines
|
| I just need to be able to test the guidelines and results.
| Clinical trial process to make an objective decision from
| that point.
| arcticfox wrote:
| This model has nothing to do with ChatGPT other than
| transformers. And as someone that could desperately use some
| advances in gene editing, this lowbrow dismissal is
| frustrating.
| m3kw9 wrote:
| And how does ChatGPT edit your dna
| Mindless2112 wrote:
| Have you seen how generative AI thinks hands work? Just a few
| edits and reality can catch up.
| mxwsn wrote:
| Gift article (no paywall):
| https://www.nytimes.com/2024/04/22/technology/generative-ai-...
|
| Preprint:
| https://www.biorxiv.org/content/10.1101/2024.04.22.590591v1
| byearthithatius wrote:
| Hey thanks:) That was nice of you
| neuronexmachina wrote:
| And their repo: https://github.com/Profluent-AI/OpenCRISPR
| JoeH2 wrote:
| Very kind of you! Thank you!
| miraculixx wrote:
| Reading their blog post I wonder if an LLMs is really the best
| way to do this. If I got it right, they used the LLM to enumerate
| potential protein DNA sequences. Does that really need an LLM?
| Enumeration is not novel, nor are LLMs particularily good at it.
| If you want to computationally parallelize the search in a large
| enumeration space it would be much easier to simply, well, do
| that instead of taking a detour via a statistical parrot.
|
| In a nutshell this sounds more like a case of "we wanted
| something with AI in the title".
| mxwsn wrote:
| It's not an English LLM, but a "protein" language model, where
| tokens represent amino acids or nucleotides. Learning a
| transformer language model on such data simply learns a
| distribution over sequences of tokens. It's a fine approach
| conceptually that in many ways is the "right" way or most
| elegant method, and not a stretch at all.
| devindotcom wrote:
| I enjoyed the feeling when I made this connection talking
| with a startup doing this a while back. It's just a different
| "language" and although it's not a given that LLMs can
| operate in it, it's a reasonable thing to try, and it turns
| out they can.
| dekhn wrote:
| Personally I think it was obvious that LLMs were going to
| be useful for protein modelling since the previous
| generation used HMMs _very_ successfully. Pfam (a library
| of HMMs for classifying proteins into preexisting known
| families) is one of the most important resources we have
| because of the power of HMMs to model sequential language.
|
| I suspect we will need to move from sequential modelling to
| graphical modelling to level-up again, though.
| echelon wrote:
| > Learning a transformer language model on such data simply
| learns a distribution over sequences of tokens.
|
| If statistical distributions can model higher level
| polypeptide structure, then it could be useful.
| swamp40 wrote:
| Well hopefully it's trained on genetic DNA sequences and not
| Reddit threads. If so, it should do pretty well predicting the
| next sequence given previous sequences. There are probably all
| sorts of undiscovered patterns.
| changoplatanero wrote:
| I think your intuition is off here. The number of sequences to
| enumerate is much greater than the number of atoms in the
| universe. You need a smart way to enumerate these and that's
| what the LLM is for. The statistical parrot is not a detour its
| a shortcut.
| luckman212 wrote:
| To be fair, having AI in the title landed it on the front page
| of HN, so...
| meowkit wrote:
| The real power of LLMs is they can model anything as a
| "language" given the right sequence training data.
|
| Warning: the following is my opinion.
|
| In the same way that MLP "neurons" are universal approximators,
| it seems that LLMs are universal mappers.
|
| They have the potential to help us organize and translate the
| immense quantity of data being generated by modern methods in
| all respective disciplines. We might create a model that
| translates english to protein synthesis, and vice versa, which
| would be pretty useful given my lay understanding of biochem.
|
| To your point - this probably is NOT the best way to do this in
| an objective sense. But to my mind we are hitting upper limits
| as finite beings and need things like this, which utilize
| native language constructs, to move forward.
| dekhn wrote:
| I don't have a direct answer to your question. My guess is that
| LLMs are too limited to make truly great solutions in biology
| but sequential modelling is a key component that will not be
| replaced any time soon. For example, transformers were key to
| AlphaFold's success, but they still needed many other steps to
| make accurate predictions.
|
| I worked on a predecessor to LLMs - HMMs for protein modelling.
| They were, and still are for most people the best way to model
| protein sequences. It's usually done as prediction, rather than
| generation (IE, you use the model to classify an unknown
| sequence into a known category, rather than asking the model to
| generate new instances of a category). HMMs for proteins are a
| bit stuffy, and they model local changes well, but struggle
| with long-range interactions that LLMs seem to excel at (for
| example, an HMM will do a good job of letting you stuff a few
| more residues into a protein in a localized region such as a
| hinge, but are not so great at modelling groups of residues
| that are located far-apart in sequence space but close in
| protein space).
|
| One detail of the bitter lesson is, imho, that statistical
| parrots are better than they "should" be, probably for the same
| reason that mathematics is unexpectedly proficient in modelling
| physics: to some degree, the models recapitulate the true
| latent space of the underlying system well enough to generalize
| outside the original observation space.
| bglazer wrote:
| First the search space is way too large for brute force
| enumeration. We're talking like 10^300 combinations. Also the
| hard part isn't just listing amino acid sequences, its finding
| ones that do what you want them to. The only way to figure that
| out is by testing them, which is difficult and expensive. So
| you need an algorithm that is good at only listing sequences
| that are likely to work. That's precisely what LLM's are good
| at: finding patterns and sequences that are correlated in a
| useful way
| sharpshadow wrote:
| I still consider biological life as the best 'robot' because it
| can create more of itself.
|
| As long as robots are incapable of recreation I don't see the
| threat.
|
| One could say all maschines today are infertile.
| jprete wrote:
| What about computer viruses?
| r2_pilot wrote:
| Computer viruses run on hardware they do not create but
| merely hijack. Unless a virus took control of a semiconductor
| fab, it's hard to argue that they are alive/reproducing in
| the context of this discussion.
| scarmig wrote:
| Parasites do the same thing: hijack a piece of hardware and
| use it to reproduce. Computer viruses have even formed a
| kind of symbiotic ecosystem with blackhats: the scammer
| provides resources to help the virus reproduce, and the
| virus provides access to the scammer in turn.
|
| In a way, all life hijacks hardware (the material world)
| that it doesn't create to reproduce itself.
| mewpmewp2 wrote:
| Computer viruses need to get an ability to mutate by
| themselves, improving the code overtime though.
| ben_w wrote:
| Simulated evolution is trivial to implement, but my guess
| is also a bit pointless from the point of view of most
| people writing viruses -- the viruses might mutate to
| _not_ give them money.
| scarmig wrote:
| Those mutations would probably be favored by evolution,
| if anything. They would attract less attention and allow
| more focus on reproduction.
| nyokodo wrote:
| > Parasites do the same thing
|
| Only analogously. The reality is that what we call
| computer viruses are merely instructions running on a
| computer and are not substantially distinct from the
| computer in the same way that parasites or physical
| viruses are distinct from biological tissue.
| ben_w wrote:
| > Unless a virus took control of a semiconductor fab, it's
| hard to argue that they are alive/reproducing in the
| context of this discussion.
|
| It does not seem implausible at this point to imagine a
| virus which gets to control some currency, uses it place an
| order for parts to be assembled, delivered to a location,
| connected to power, etc.
|
| It is, in a sense, taking control by pulling the levers
| supplied by our society. Is that alive?
|
| I would say no, it is not 'alive'... but I would also
| paraphrase Dijkstra: "The question of whether an AI or
| computer virus is 'alive' is no more interesting than the
| question of whether a submarine can swim."
| karaterobot wrote:
| I think of robots as being physically embodied somehow. I
| don't think of a software program like a virus as being a
| robot.
| swamp40 wrote:
| Imagine an AI learning from photos/videos of a person _and_ their
| DNA sequence? And also a list of diseases, health records, etc.
| Then asking it for predictions while giving it feedback
| afterwards so it can tune itself.
|
| You could even guarantee privacy. That would be some really
| useful data.
| paxys wrote:
| You mean exactly what 23andMe tried to do, and failed miserably
| at.
| swamp40 wrote:
| We are still early. Eventually you'll be able to change your
| race, gender, add reptile eyes, regrow limbs etc. Has to
| start somewhere. Need more data.
| pharmaz0ne wrote:
| Exactly. We should start building global database
| connecting DNA with medical history.
| swamp40 wrote:
| Not a big fan? It could be provably private. You could
| have a kit with a random username/password. It could be
| done. People just have a bad taste from 23andMe.
| dekhn wrote:
| You mean, like UKBB and All of Us already do, but less
| nationally focused? The approach seems fraught with
| complexity due to the complexity of medical ethics, the
| variation of national laws, and strongly-held nationalist
| positions.
| ronald_raygun wrote:
| I can already change my race. I just check a different box
| on government forms...
| dekhn wrote:
| I really, really wanted to see a new generation of tattoo
| technology based on fluorescence and squid chromatophores.
| However, for the time being, the vast majority of gene
| editing will be for well-understood medical conditions
| where all the alternatives have been excluded. Germline (or
| even somatic) modification for recreational purposes or for
| non-urgent medical reasons is definitely still considered
| highly suspect by society as a whole, and I don't see that
| changing overnight. Somethings still work better in scifi
| than reality.
| hanniabu wrote:
| There was no privacy there
| vessenes wrote:
| I like this a lot; you could have a multimodal setup with a DNA
| transformer, an image transformer and an LLM. Extremely
| fundable startup.
| a-r-t wrote:
| So the six finger hands were just a foreshadowing?
| Karellen wrote:
| Is this going to be as good as when AI arrived in the world of
| materials science?
|
| https://www.404media.co/google-says-it-discovered-millions-o...
|
| Or is it only just going to be as good at generating headlines?
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