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How is AI impacting science?

freetonik 3y ago
Anecdotal, but I’ve talked to some physicists working in the field of quantum computing, and some of them think that it’s possible that advancements in AI will provide somewhat efficient solutions to some computational problems (namely in the NP class), and the solutions will be “good enough” for actual businesses (e.g. in logistics) and researchers (e.g. in chemistry), to a degree that it might negatively affect future funding for quantum computing research. And the pace of advancement in AI will continue to accelerate, while the pace of advancements in quantum computing is notoriously slow.
- ysofunny 3y ago
  I think AI and quantum computing are really the same thing, the difference being the same as girltalk in contrast with boytalk. what I'm saying is that there's no quantum computing; but I actually mean to say I simply do not understand whatever 'quantum' means in the context of computing given my own opinions of what computing really is (I have a philosophical opinion). in my view (as distorted and twisted as it is), what quantum computing devices really do is sensing or measuring. computing is classical in nature and no amount of hand waving will change this. whatever happens to logic in a qubit? if both A and B are anything between 0 and 1, what's the negation of such a thing?
  - tux3 3y ago
    >whatever happens to logic in a qubit? if both A and B are anything between 0 and 1, what's the negation of such a thing? The problem is the pop sci explanation where qbits are everything between 0 and 1 at the same time is super wrong and misleading. Your qbits are in states that have "amplitudes". You do things that change the amplitudes, and that happens to do computation. From the amplitudes of your qbits, you can figure out what the probabilities of observing different outcomes is. It's not that the qbit is anything between 0 and 1. It's in a precise state, that results in percentage probabilities for different outcomes. The negation of true 80% of the time is false 80% of the time.
    - firtoz 3y ago
      To be fair, in some quantum computers, it does work. For example if you count a beam of photons following the same path with (almost) all possible polarization combinations as a qubit, that is indeed closer to "everything between", however it's got limited utility indeed when compared to the broader view.
  - gcr 3y ago
    Apologies in advance—I don't know how to say this in a loving way, but I also don’t mean to pass judgment—couldn’t this just be a skill issue? Why not look into how quantum computing works and resolve that once and for all? https://quantum.country/ https://quantum.country/ is a pretty approachable starting point if you’re curious. The math is no more intimidating than any other skilled discipline.
    - ysofunny 3y ago
      you can just say it, it's not worse than getting downvoted for having off colored opinions "aren't you just too stupid to understand?" maybe I am... maybe I just think differently with another brand of depth what I wonder now, given another comment about amplitudes, is what happens to combinatorics? (and alphabets, and languages as sequences of strings from finite alphabets) but it's just simpler to dismiss me as foolish idiot.
      - wegfawefgawefg 3y ago
        People responded with civility which, in retrospect, you may not have deserved.
  - firtoz 3y ago
    Regarding the negation, you can consider it in a few different ways, depending on your use case. If your qubit is "all values", the negation is the lack of any value, e.g. "a measurement that results to 0". However, in most cases, you will have a segment within the possibilities. E.g. one qubit can hold "vertically polarised light at frequencies between X and Y at phase P", then the negation could be one of "horizontally polarised light at that frequency and phase", or, more common, same polarization and frequency but the phase is 180 degrees off P. That way, if you add them together, you get the cancellation. Another negation is "all other possible combinations". However I must note that when you call a qubit "all possible combinations" that's not a typical qubit value. Think of it like this, "binary" is "0 or 1", but a "binary value" is only one of either 0 or 1. A qubit value is, depending on your interpretation, photons of some polarisation, frequency and phase, or electrons of a particular spin, or some other combination of those. Some of those can have multiple combinations, for example a qubit value could be the combination of a bunch of photons at different values. Or, an electron with an "unknown spin". Or an electron with an unknown spin that's the complement of another electron at another unknown spin, but when you measure either you will reveal the other, and so on. So, only some qubit values will have "all possible combinations" i.e. "when observed, it could literally be anything". The internal of "what was the value before you observe it" is, well, controversial, and to most people, almost irrelevant, and even, "not good to consider".
  - wegfawefgawefg 3y ago
    Im just as ignorant as you about it. The quantum part to me doesnt seem to matter. I don't see why its any fundamentally different from making a computer out of any other substrate. Light, sand, chemicals, at the end of the day you compute a function by following a set of steps which may or may not have constraints of seriality. I think von neumann cant handle purely parralel algorithms efficiently. Even gpus. I think thats whats where the "all qbits interacting simultaniously for n steps after being put in initial conditions" is about. Its purely parralel. It could be the case that information cant be extracted before it is computed, and that for some algorithms the peak efficiency serial version is equal to the best efficiency purely parralel version. It could also be the case thats not true and that classes of quantum algorithms could be better. At the end if the day the way i see it substrate is just an engineered means of applied ops per time.
    - RandomLensman 3y ago
      The quantum part does matter in the sense that certain kinds of "maths" are not directly accessible in a classical computer. You can sort of simulate Shor's algorithm on a classical computer but you cannot get the same low complexity. Of course, whether the quantum computer uses substrate A or B doesn't matter (other than practically). The only way I ever found an access to understanding quantum computing is by doing the math, as other pop-sci explanations don't really reflect what is happening (at least for me).
- moffkalast 3y ago
  Well that sounds like good news, are there any practical applications for quantum computing other than breaking commonly used encryption that would push the modern world even further towards a complete surveillance state?
  - firtoz 3y ago
    I'm not in the field but in my understanding it's going to make it easier to do parallel competitions and also will allow us to find the inputs that can lead to a particular output in some types of equations. Factoring numbers is only one of the "practical" equations. Materials science and chemistry (e.g. predicting how drugs may behave in our bodies) will benefit, or other computationally heavy "try out many permutations and see which ones fall though" tasks may become a bit easier. Climate modelling, supply chain logistics, financial modelling, and of course AI.
  - dragontamer 3y ago
    The #1 proposed use of quantum computers is... simulating quantum effects. Modern quantum simulators running on supercomputers is an exponential (O(k^n)) kind of operation. But a quantum computer can simulate any quantum effect in just one step, O(1) time. Because ya know... a quantum computer is just a computer that has isolated quantum effects and allows a programmer to control them easily.
    - blharr 3y ago
      Isn't O(1) an oversimplification? In comparison wouldn't electrical computers then also be able to simulate all electrical effects in O(1) Time?
      - dragontamer 3y ago
        > In comparison wouldn't electrical computers then also be able to simulate all electrical effects in O(1) Time? Yes and OpAmps / analog electrical circuits were way faster than digital computers for decades because of this. When you know that the current of a diode is related to the exponent of the diode's voltage, you can do silly things like calculate logarithms using OpAmps and diodes in a mere nanosecond or so. Most 1980s synths used OpAmps as the basis of the math / calculations for signal processing, because digital computers just weren't fast enough to compete yet. Those circuits still work today. Alas, digital computers are too cheap, accurate, and fast these days so nearly everything is digital now. ---------- I guess your point though is that maybe a quantum computer (or OpAmp / electrical computer) can only physically simulate the effects that the hardware contains. Ex: an electrical computer cannot simulate BJTs unless it has a BJT somewhere. So it would still come down to the computers physical load out. Similarly, a Quantum simulation on a quantum simulation computer would only have some subset of primitives implemented.
- canjobear 3y ago
  Quantum doesn’t help for NP problems. In the case of codebreaking, where QC may have an advantage, AI is unlikely to provide an alternative because modern AI is all based on finding probabilistic patterns and cryptography is explicitly designed to be resistant to that kind of attack.
  - firtoz 3y ago
    Can you please elaborate?
    - canjobear 3y ago
      The class of this problems where quantum computing gives an advantage is called BQP and it includes things like factoring but it is not known to include NP complete problems like the traveling salesman.
      - firtoz 3y ago
        Thank you!
  - wegfawefgawefg 3y ago
    I have not programmed a quantum computer before. My current state of ignorance, and so im relying only on intuition here, is that it is massively parralel in the same way that the surface of water in your cup is massively parralel. To program a serial algorithm in a quantum computer would be to miss the point. You could encode the interactions between clumps of qbits to function as nodes in a wave simulation. There would be few enough nodes in the wave that it would be not equivalent to a true wave simulation. It would be a discretization, but now it functions as an analog asic like proxy for the real thing. If the groups of qbits are smaller than the true nodes in a wave surface your computation would be faster than the real thing, but lower precision. In a classical computer this would have to be done with buffers, or a very narrow set of parralel deterministic programs, otherwise impossible. (examples: a subset of cellular automata rules, gravity sort) Is any of that right or am i completely off base.
- sanderjd 3y ago
  Yep, I'm very interested in "good enough" optimization techniques using ML, to massively speed up optimization problems that require a lot of computation that doesn't parallelize easily. I'm not an expert in it, but I work adjacent to it, and it seems like a promising direction to me.
- nickpsecurity 3y ago
  They’ve always gotten lots of funding in quantum for techniques with big promises that haven’t delivered yet. I’m not even saying they’re misleading us so much as it has had basically no payoff compared to HPC, FPGA’s, and even analog computing. The bigger concern of companies like that is if someone bankrolls dirt-cheap FPGA’s or Adapteva-style cores with open architectures targeted by a toolchain like Synflow’s or Cray’s Chapel. From there, domain-specific applications (esp optimized kernels) targeting those tools. Then, MPP-style hardware as cheap as commodity servers to scale it up and out. I’m talking engineering the thing by combining proven strategies, not doing new research. Even $100 million put into such systems would deliver more value in more areas than $1 billion put into quantum computing. If not, we’d at least get huge speed-ups with the parallel architectures for a while and then the QC folks eventually deliver something better in some areas. I’d be happy both ways so long as one is in my building or it’s in the cloud for $2.30/hr. :)
t_mann 3y ago
It's from May, pity, it would have been really interesting if the author had discussed DeepMind's recent FunSearch paper as well: https://www.nature.com/articles/s41586-023-06924-6 https://www.nature.com/articles/s41586-023-06924-6 But it goes to show just how fast we are currently progressing.
- nickpsecurity 3y ago
  Someone needs to host an event where those people, others doing genetic programming, and those doing executable synthesis are all in the same place. Let them bounce ideas off each other. All their brainstorming is recorded to be published into the public domain. We might see some interesting combos. EDIT: Maybe use a sample from the Humies as benchmarks for the techniques, including new LLM’s. Let people try every approach in parallel mixing the best of each. https://human-competitive.org/awards https://human-competitive.org/awards
vouaobrasil 3y ago
> how to predict the 3-dimensional structure of a protein from the sequence of amino acids making up that protein Keep in mind that proteins and deducing the structure of them (which in turn would help deduce their function) may not be a good thing. Proteins can also be poisons, prions, etc. But beyond that, we should not have such easy to access knowledge. It's just too much power for our current greedy, short-term thinking.
- JackFr 3y ago
  Chipping flints into sharpened edges may not be a good thing. Sharpened flints can also be stone axes, spear heads, etc. But beyond that, we should not have such easy to access cutting technology. It’s just too much power for our current greedy, short-term thinking.
  - vouaobrasil 3y ago
    Glib analogy, but there's got to be a limit to everything. Beyond resorting to superficial analogies, we should give serious thought into what level of technology is simply too much. Moreover, I actually agree with you in some ways, even though you meant to be sarcastic. Modern cutting technology, which certainly should include deep-well oil drilling, IS too much. In fact, all technology has a limit beyond which we should not go. We just have to apply deep thinking to figure out what it is, or at least do our best to try. You
    - _factor 3y ago
      You’re more likely to get cut with a dull knife. It can cause it to slip. Technology is fine. It’s this artificial cookie-cutter society we’re forcing on everyone. We have a mental health and education problem. The tech isn’t the issue, it’s the people who feel the need to use it for negative purposes. Limiting tech is just a bandaid.
      - vouaobrasil 3y ago
        People will always use technology for negative purposes as long as it can help them get ahead in the short term and they aren't held accountable for the long-term consequences. Technology is not fine. Not all of it is bad, but to say all of it is fine makes no sense. Yes, we have a mental and health education problem, but it's reasonable to have rules for all societies. Limiting technology that makes getting ahead too easy makes sense because even in the most utopian and good societies, if gaining something is TOO EASY, then it WILL be taken. It's much too simplistic to frame technology as fine. If all technology is fine, perhaps every corner store should sell automatic weapons? Can you imagine a society, ANY society, in which this makes sense?
BiasRegularizer 3y ago
Although the article focused primarily on AlphaFold, many other ML approaches are making impactful contributions in the general scientific field. One example is the diffusion model and its use of stochastic differential equations (SDEs). Microsoft has an initiative called AI4Sciencie (https://www.microsoft.com/en-us/research/lab/microsoft-research-ai4science/publications/ https://www.microsoft.com/en-us/research/lab/microsoft-resea...) which published a fair amount of SDE/diffusion-based method to solve scientific problems
justinl33 3y ago
Are there any molecular biologists here? I'm curious: what happens to society if/when we figure out protein folding? Hypothetically, how does the world change if we had a 100% accurate way to model quaternary structure from primary structure?
- namibj 3y ago
  We could do computer-only search for enzymes that catalyze desired reactions in enzyme-friendly reaction environments. Assuming that the way that makes the above 100% accurate also yields us the tooling to simulate the candidates in operation accurate enough to let the search/optimizer learn from the simulation feedback.
- alevskaya 3y ago
  The static shape of a protein doesn't automatically give you a prediction of its functional properties. There's a hell of a lot more biophysics going on that we have no predictive models for that are needed to understand catalysis, allostery, assembly, etc etc etc. We don't even have good comprehensive data for any of that (compared to sequences or structure) to model with. Fold prediction is an incredibly useful tool for scientists and genetic engineers to help design new proteins, but it doesn't magically solve molecular or cell biology. Designing new functions and mechanisms is still going to involve a huge amount of labor and brute-force experimentation.
- LeonardoTolstoy 3y ago
  My brother worked in protein folding (although his statement about specifically AlphaFold was years after he left that field) but I showed him the AlphaFold results from like 5 years ago and his reaction was "oh ... Yeah they solved protein folding" So at least according to him we've lived in that world for the last 5 years. As a person working with / tangentially to people in the same field I would say that it's made things faster and more scalable, but protein structure isn't the be all end all of things. Researchers use AlphaFold a lot for filtering potential candidates, but that is only one step in a lot of steps. A SNP mutation -> protein structure change -> functional change is already difficult without then considering that the vast majority of mutations that create function change in humans are not in exons, so something like AlphaFold (in the form I'm familiar with) would be useless for those as well. Eventually though an AI system that can go mutation->function change is entirely possible, although it is much much further in the future. In that case though I think you'll be quite close to a future where combined with things like CRISPR therapeutic treatment for all heritable disease would be possible.
Erratic6576 3y ago
I’m not a scientist but I enjoy reading the literary genre they write (the headlines). Most of the time, the articles themselves are hard or impossible to understand. ChatGPT can lend a helping hand
amelius 3y ago
Anyone here who knows a good project that attempts to solve large sparse linear systems using ML, preferably in Python?
- ivancho 3y ago
  Why would ML be any good at that?
Balgair 3y ago
I know the article focused on the deeper/code-ier aspects of AI. However, I think that it's mundane aspects are going to be huge. I once had a co-researcher that asked for help with his Matlab code. We were in a smell-lab together working on mice. His code was about controlling some scent valves in response to a mouse putting it's nose in a hole and breaking a laser beam. Importantly, this code was running his thesis, essentially, and he had no coding experience prior to this experiment. I said sure, I'd love to help, but you owe me a six-pack. So we sat down one day and started going through his Matlab code. After the 18th nested 'if' statement, I had to up the payment to a case of beer. LLMs would have helped my co-researcher out a lot. He might have actually gotten the code working at least. Most HNers don't appreciate, I think, how difficult coding is most people. AIs are already helping bridge that gap. Another more mundane area is in communication. Research papers are notorious for being obtuse and jargon filled, to the point where many fields are nearly speaking another language entirely. AIs can help with that as well. Not only in the writing side, but also in the reading side. Can can put a paper into them and ask for summaries, you can put multiple papers in and ask for review papers. You must be very careful, of course, but the speed increase is just amazing.
- boredtofears 3y ago
  How would they know if the code produced by ChatGPT had a side effect that effected their research? Isn't it risky to not fully understand the behavior of your experiment?
  - kian 3y ago
    I think in this case that problem even exists in their own code that they don't fully understand.
  - _giorgio_ 3y ago
    Breaking down code in cells and asking chatGPT for help is quite easy and mostly foolproof, if you have enough will to learn things.
    - dpflan 3y ago
CJefferson 3y ago
I can tell you one impact -- there are huge amounts of ChatGPT helped, or entirely written, junk papers appearing at conferences, which is causing serious problems getting reviewers. Some large events have introduced a pre-review phase to filter out junk, but it's getting harder, as LLMs are good at producing plausable looking nonsense. Honestly, I'm not sure of the long term solution here. Conferences and journals may have to introduce a system where you need an existing member of the community to "vouch" for your work to be allowed to submit (they don't have to say it should be published, just worth reviewing).
- fastneutron 3y ago
  LLM-written content has a certain gestalt to it that’s pretty easy to sniff out once you’ve seen it a few times, just look at Quora and LinkedIn these days. While I think it’s probably fair for people with English as a second language to use LLMs as a writing assistant (for example), there definitely needs to be some kind of author disclosure statement at the very least. This could be similar to how more papers are now including contribution statements when there are many coauthors.
  - corethree 3y ago
    That gestalt can easily be smoothed out. You simply need to prompt LLM to do so in your query. Tell the LLM to depart from its default style. Say make your response more brief, well written and add a witty conclusion at the end Then you say rewrite the final paragraph to be slightly shorter and incorporate a certain example. Yada yada. Likely you can even do this "write your response in a style that is not typical for an LLM"
    - throwup238 3y ago
      It’s easier to illustrate with prose: ask it to write in the style of Terry Pratchett or Hunter S Thompson and the style changes drastically. You can even ask it to rewrite your scientific paper in the style of Shakespeare: https://chat.openai.com/share/498804da-6d59-4a0c-91d1-da4cea38c261 https://chat.openai.com/share/498804da-6d59-4a0c-91d1-da4cea...
deleted 3y ago
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LabMechanic 3y ago
[dead]

Anecdotal, but I’ve talked to some physicists working in the field of quantum computing, and some of them think that it’s possible that advancements in AI will provide somewhat efficient solutions to some computational problems (namely in the NP class), and the solutions will be “good enough” for actual businesses (e.g. in logistics) and researchers (e.g. in chemistry), to a degree that it might negatively affect future funding for quantum computing research. And the pace of advancement in AI will continue to accelerate, while the pace of advancements in quantum computing is notoriously slow.

ysofunny 3y ago
I think AI and quantum computing are really the same thing, the difference being the same as girltalk in contrast with boytalk. what I'm saying is that there's no quantum computing; but I actually mean to say I simply do not understand whatever 'quantum' means in the context of computing given my own opinions of what computing really is (I have a philosophical opinion). in my view (as distorted and twisted as it is), what quantum computing devices really do is sensing or measuring. computing is classical in nature and no amount of hand waving will change this. whatever happens to logic in a qubit? if both A and B are anything between 0 and 1, what's the negation of such a thing?
- tux3 3y ago
  >whatever happens to logic in a qubit? if both A and B are anything between 0 and 1, what's the negation of such a thing? The problem is the pop sci explanation where qbits are everything between 0 and 1 at the same time is super wrong and misleading. Your qbits are in states that have "amplitudes". You do things that change the amplitudes, and that happens to do computation. From the amplitudes of your qbits, you can figure out what the probabilities of observing different outcomes is. It's not that the qbit is anything between 0 and 1. It's in a precise state, that results in percentage probabilities for different outcomes. The negation of true 80% of the time is false 80% of the time.
  - firtoz 3y ago
    To be fair, in some quantum computers, it does work. For example if you count a beam of photons following the same path with (almost) all possible polarization combinations as a qubit, that is indeed closer to "everything between", however it's got limited utility indeed when compared to the broader view.
- gcr 3y ago
  Apologies in advance—I don't know how to say this in a loving way, but I also don’t mean to pass judgment—couldn’t this just be a skill issue? Why not look into how quantum computing works and resolve that once and for all? https://quantum.country/ https://quantum.country/ is a pretty approachable starting point if you’re curious. The math is no more intimidating than any other skilled discipline.
  - ysofunny 3y ago
    you can just say it, it's not worse than getting downvoted for having off colored opinions "aren't you just too stupid to understand?" maybe I am... maybe I just think differently with another brand of depth what I wonder now, given another comment about amplitudes, is what happens to combinatorics? (and alphabets, and languages as sequences of strings from finite alphabets) but it's just simpler to dismiss me as foolish idiot.
    - wegfawefgawefg 3y ago
      People responded with civility which, in retrospect, you may not have deserved.
- firtoz 3y ago
  Regarding the negation, you can consider it in a few different ways, depending on your use case. If your qubit is "all values", the negation is the lack of any value, e.g. "a measurement that results to 0". However, in most cases, you will have a segment within the possibilities. E.g. one qubit can hold "vertically polarised light at frequencies between X and Y at phase P", then the negation could be one of "horizontally polarised light at that frequency and phase", or, more common, same polarization and frequency but the phase is 180 degrees off P. That way, if you add them together, you get the cancellation. Another negation is "all other possible combinations". However I must note that when you call a qubit "all possible combinations" that's not a typical qubit value. Think of it like this, "binary" is "0 or 1", but a "binary value" is only one of either 0 or 1. A qubit value is, depending on your interpretation, photons of some polarisation, frequency and phase, or electrons of a particular spin, or some other combination of those. Some of those can have multiple combinations, for example a qubit value could be the combination of a bunch of photons at different values. Or, an electron with an "unknown spin". Or an electron with an unknown spin that's the complement of another electron at another unknown spin, but when you measure either you will reveal the other, and so on. So, only some qubit values will have "all possible combinations" i.e. "when observed, it could literally be anything". The internal of "what was the value before you observe it" is, well, controversial, and to most people, almost irrelevant, and even, "not good to consider".
- wegfawefgawefg 3y ago
  Im just as ignorant as you about it. The quantum part to me doesnt seem to matter. I don't see why its any fundamentally different from making a computer out of any other substrate. Light, sand, chemicals, at the end of the day you compute a function by following a set of steps which may or may not have constraints of seriality. I think von neumann cant handle purely parralel algorithms efficiently. Even gpus. I think thats whats where the "all qbits interacting simultaniously for n steps after being put in initial conditions" is about. Its purely parralel. It could be the case that information cant be extracted before it is computed, and that for some algorithms the peak efficiency serial version is equal to the best efficiency purely parralel version. It could also be the case thats not true and that classes of quantum algorithms could be better. At the end if the day the way i see it substrate is just an engineered means of applied ops per time.
  - RandomLensman 3y ago
    The quantum part does matter in the sense that certain kinds of "maths" are not directly accessible in a classical computer. You can sort of simulate Shor's algorithm on a classical computer but you cannot get the same low complexity. Of course, whether the quantum computer uses substrate A or B doesn't matter (other than practically). The only way I ever found an access to understanding quantum computing is by doing the math, as other pop-sci explanations don't really reflect what is happening (at least for me).
moffkalast 3y ago
Well that sounds like good news, are there any practical applications for quantum computing other than breaking commonly used encryption that would push the modern world even further towards a complete surveillance state?
- firtoz 3y ago
  I'm not in the field but in my understanding it's going to make it easier to do parallel competitions and also will allow us to find the inputs that can lead to a particular output in some types of equations. Factoring numbers is only one of the "practical" equations. Materials science and chemistry (e.g. predicting how drugs may behave in our bodies) will benefit, or other computationally heavy "try out many permutations and see which ones fall though" tasks may become a bit easier. Climate modelling, supply chain logistics, financial modelling, and of course AI.
- dragontamer 3y ago
  The #1 proposed use of quantum computers is... simulating quantum effects. Modern quantum simulators running on supercomputers is an exponential (O(k^n)) kind of operation. But a quantum computer can simulate any quantum effect in just one step, O(1) time. Because ya know... a quantum computer is just a computer that has isolated quantum effects and allows a programmer to control them easily.
  - blharr 3y ago
    Isn't O(1) an oversimplification? In comparison wouldn't electrical computers then also be able to simulate all electrical effects in O(1) Time?
    - dragontamer 3y ago
      > In comparison wouldn't electrical computers then also be able to simulate all electrical effects in O(1) Time? Yes and OpAmps / analog electrical circuits were way faster than digital computers for decades because of this. When you know that the current of a diode is related to the exponent of the diode's voltage, you can do silly things like calculate logarithms using OpAmps and diodes in a mere nanosecond or so. Most 1980s synths used OpAmps as the basis of the math / calculations for signal processing, because digital computers just weren't fast enough to compete yet. Those circuits still work today. Alas, digital computers are too cheap, accurate, and fast these days so nearly everything is digital now. ---------- I guess your point though is that maybe a quantum computer (or OpAmp / electrical computer) can only physically simulate the effects that the hardware contains. Ex: an electrical computer cannot simulate BJTs unless it has a BJT somewhere. So it would still come down to the computers physical load out. Similarly, a Quantum simulation on a quantum simulation computer would only have some subset of primitives implemented.
canjobear 3y ago
Quantum doesn’t help for NP problems. In the case of codebreaking, where QC may have an advantage, AI is unlikely to provide an alternative because modern AI is all based on finding probabilistic patterns and cryptography is explicitly designed to be resistant to that kind of attack.
- firtoz 3y ago
  Can you please elaborate?
  - canjobear 3y ago
    The class of this problems where quantum computing gives an advantage is called BQP and it includes things like factoring but it is not known to include NP complete problems like the traveling salesman.
    - firtoz 3y ago
      Thank you!
- wegfawefgawefg 3y ago
  I have not programmed a quantum computer before. My current state of ignorance, and so im relying only on intuition here, is that it is massively parralel in the same way that the surface of water in your cup is massively parralel. To program a serial algorithm in a quantum computer would be to miss the point. You could encode the interactions between clumps of qbits to function as nodes in a wave simulation. There would be few enough nodes in the wave that it would be not equivalent to a true wave simulation. It would be a discretization, but now it functions as an analog asic like proxy for the real thing. If the groups of qbits are smaller than the true nodes in a wave surface your computation would be faster than the real thing, but lower precision. In a classical computer this would have to be done with buffers, or a very narrow set of parralel deterministic programs, otherwise impossible. (examples: a subset of cellular automata rules, gravity sort) Is any of that right or am i completely off base.
sanderjd 3y ago
Yep, I'm very interested in "good enough" optimization techniques using ML, to massively speed up optimization problems that require a lot of computation that doesn't parallelize easily. I'm not an expert in it, but I work adjacent to it, and it seems like a promising direction to me.
nickpsecurity 3y ago
They’ve always gotten lots of funding in quantum for techniques with big promises that haven’t delivered yet. I’m not even saying they’re misleading us so much as it has had basically no payoff compared to HPC, FPGA’s, and even analog computing. The bigger concern of companies like that is if someone bankrolls dirt-cheap FPGA’s or Adapteva-style cores with open architectures targeted by a toolchain like Synflow’s or Cray’s Chapel. From there, domain-specific applications (esp optimized kernels) targeting those tools. Then, MPP-style hardware as cheap as commodity servers to scale it up and out. I’m talking engineering the thing by combining proven strategies, not doing new research. Even $100 million put into such systems would deliver more value in more areas than $1 billion put into quantum computing. If not, we’d at least get huge speed-ups with the parallel architectures for a while and then the QC folks eventually deliver something better in some areas. I’d be happy both ways so long as one is in my building or it’s in the cloud for $2.30/hr. :)

t_mann 3y ago

It's from May, pity, it would have been really interesting if the author had discussed DeepMind's recent FunSearch paper as well: https://www.nature.com/articles/s41586-023-06924-6 https://www.nature.com/articles/s41586-023-06924-6 But it goes to show just how fast we are currently progressing.

nickpsecurity 3y ago
Someone needs to host an event where those people, others doing genetic programming, and those doing executable synthesis are all in the same place. Let them bounce ideas off each other. All their brainstorming is recorded to be published into the public domain. We might see some interesting combos. EDIT: Maybe use a sample from the Humies as benchmarks for the techniques, including new LLM’s. Let people try every approach in parallel mixing the best of each. https://human-competitive.org/awards https://human-competitive.org/awards

vouaobrasil 3y ago

> how to predict the 3-dimensional structure of a protein from the sequence of amino acids making up that protein Keep in mind that proteins and deducing the structure of them (which in turn would help deduce their function) may not be a good thing. Proteins can also be poisons, prions, etc. But beyond that, we should not have such easy to access knowledge. It's just too much power for our current greedy, short-term thinking.

JackFr 3y ago
Chipping flints into sharpened edges may not be a good thing. Sharpened flints can also be stone axes, spear heads, etc. But beyond that, we should not have such easy to access cutting technology. It’s just too much power for our current greedy, short-term thinking.
- vouaobrasil 3y ago
  Glib analogy, but there's got to be a limit to everything. Beyond resorting to superficial analogies, we should give serious thought into what level of technology is simply too much. Moreover, I actually agree with you in some ways, even though you meant to be sarcastic. Modern cutting technology, which certainly should include deep-well oil drilling, IS too much. In fact, all technology has a limit beyond which we should not go. We just have to apply deep thinking to figure out what it is, or at least do our best to try. You
  - _factor 3y ago
    You’re more likely to get cut with a dull knife. It can cause it to slip. Technology is fine. It’s this artificial cookie-cutter society we’re forcing on everyone. We have a mental health and education problem. The tech isn’t the issue, it’s the people who feel the need to use it for negative purposes. Limiting tech is just a bandaid.
    - vouaobrasil 3y ago
      People will always use technology for negative purposes as long as it can help them get ahead in the short term and they aren't held accountable for the long-term consequences. Technology is not fine. Not all of it is bad, but to say all of it is fine makes no sense. Yes, we have a mental and health education problem, but it's reasonable to have rules for all societies. Limiting technology that makes getting ahead too easy makes sense because even in the most utopian and good societies, if gaining something is TOO EASY, then it WILL be taken. It's much too simplistic to frame technology as fine. If all technology is fine, perhaps every corner store should sell automatic weapons? Can you imagine a society, ANY society, in which this makes sense?

BiasRegularizer 3y ago

Although the article focused primarily on AlphaFold, many other ML approaches are making impactful contributions in the general scientific field. One example is the diffusion model and its use of stochastic differential equations (SDEs). Microsoft has an initiative called AI4Sciencie (https://www.microsoft.com/en-us/research/lab/microsoft-research-ai4science/publications/ https://www.microsoft.com/en-us/research/lab/microsoft-resea...) which published a fair amount of SDE/diffusion-based method to solve scientific problems

justinl33 3y ago

Are there any molecular biologists here? I'm curious: what happens to society if/when we figure out protein folding? Hypothetically, how does the world change if we had a 100% accurate way to model quaternary structure from primary structure?

namibj 3y ago
We could do computer-only search for enzymes that catalyze desired reactions in enzyme-friendly reaction environments. Assuming that the way that makes the above 100% accurate also yields us the tooling to simulate the candidates in operation accurate enough to let the search/optimizer learn from the simulation feedback.
alevskaya 3y ago
The static shape of a protein doesn't automatically give you a prediction of its functional properties. There's a hell of a lot more biophysics going on that we have no predictive models for that are needed to understand catalysis, allostery, assembly, etc etc etc. We don't even have good comprehensive data for any of that (compared to sequences or structure) to model with. Fold prediction is an incredibly useful tool for scientists and genetic engineers to help design new proteins, but it doesn't magically solve molecular or cell biology. Designing new functions and mechanisms is still going to involve a huge amount of labor and brute-force experimentation.
LeonardoTolstoy 3y ago
My brother worked in protein folding (although his statement about specifically AlphaFold was years after he left that field) but I showed him the AlphaFold results from like 5 years ago and his reaction was "oh ... Yeah they solved protein folding" So at least according to him we've lived in that world for the last 5 years. As a person working with / tangentially to people in the same field I would say that it's made things faster and more scalable, but protein structure isn't the be all end all of things. Researchers use AlphaFold a lot for filtering potential candidates, but that is only one step in a lot of steps. A SNP mutation -> protein structure change -> functional change is already difficult without then considering that the vast majority of mutations that create function change in humans are not in exons, so something like AlphaFold (in the form I'm familiar with) would be useless for those as well. Eventually though an AI system that can go mutation->function change is entirely possible, although it is much much further in the future. In that case though I think you'll be quite close to a future where combined with things like CRISPR therapeutic treatment for all heritable disease would be possible.

Erratic6576 3y ago

I’m not a scientist but I enjoy reading the literary genre they write (the headlines). Most of the time, the articles themselves are hard or impossible to understand. ChatGPT can lend a helping hand

amelius 3y ago

Anyone here who knows a good project that attempts to solve large sparse linear systems using ML, preferably in Python?

ivancho 3y ago
Why would ML be any good at that?

Balgair 3y ago

I know the article focused on the deeper/code-ier aspects of AI. However, I think that it's mundane aspects are going to be huge. I once had a co-researcher that asked for help with his Matlab code. We were in a smell-lab together working on mice. His code was about controlling some scent valves in response to a mouse putting it's nose in a hole and breaking a laser beam. Importantly, this code was running his thesis, essentially, and he had no coding experience prior to this experiment. I said sure, I'd love to help, but you owe me a six-pack. So we sat down one day and started going through his Matlab code. After the 18th nested 'if' statement, I had to up the payment to a case of beer. LLMs would have helped my co-researcher out a lot. He might have actually gotten the code working at least. Most HNers don't appreciate, I think, how difficult coding is most people. AIs are already helping bridge that gap. Another more mundane area is in communication. Research papers are notorious for being obtuse and jargon filled, to the point where many fields are nearly speaking another language entirely. AIs can help with that as well. Not only in the writing side, but also in the reading side. Can can put a paper into them and ask for summaries, you can put multiple papers in and ask for review papers. You must be very careful, of course, but the speed increase is just amazing.

boredtofears 3y ago
How would they know if the code produced by ChatGPT had a side effect that effected their research? Isn't it risky to not fully understand the behavior of your experiment?
- kian 3y ago
  I think in this case that problem even exists in their own code that they don't fully understand.
- _giorgio_ 3y ago
  Breaking down code in cells and asking chatGPT for help is quite easy and mostly foolproof, if you have enough will to learn things.
  - dpflan 3y ago

CJefferson 3y ago

I can tell you one impact -- there are huge amounts of ChatGPT helped, or entirely written, junk papers appearing at conferences, which is causing serious problems getting reviewers. Some large events have introduced a pre-review phase to filter out junk, but it's getting harder, as LLMs are good at producing plausable looking nonsense. Honestly, I'm not sure of the long term solution here. Conferences and journals may have to introduce a system where you need an existing member of the community to "vouch" for your work to be allowed to submit (they don't have to say it should be published, just worth reviewing).

fastneutron 3y ago
LLM-written content has a certain gestalt to it that’s pretty easy to sniff out once you’ve seen it a few times, just look at Quora and LinkedIn these days. While I think it’s probably fair for people with English as a second language to use LLMs as a writing assistant (for example), there definitely needs to be some kind of author disclosure statement at the very least. This could be similar to how more papers are now including contribution statements when there are many coauthors.
- corethree 3y ago
  That gestalt can easily be smoothed out. You simply need to prompt LLM to do so in your query. Tell the LLM to depart from its default style. Say make your response more brief, well written and add a witty conclusion at the end Then you say rewrite the final paragraph to be slightly shorter and incorporate a certain example. Yada yada. Likely you can even do this "write your response in a style that is not typical for an LLM"
  - throwup238 3y ago
    It’s easier to illustrate with prose: ask it to write in the style of Terry Pratchett or Hunter S Thompson and the style changes drastically. You can even ask it to rewrite your scientific paper in the style of Shakespeare: https://chat.openai.com/share/498804da-6d59-4a0c-91d1-da4cea38c261 https://chat.openai.com/share/498804da-6d59-4a0c-91d1-da4cea...

deleted 3y ago

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LabMechanic 3y ago

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