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Current AI methods are great for classification. That said, I’m not convinced AI is making discoveries. What I mean by that, is that AI is often non-explainable

Current AI methods are great for classification. That said, I’m not convinced AI is making discoveries. What I mean by that, is that AI is often non-explainable, can be non-deterministic and only as good as it’s training data. For instance, asteroid discovery. Yes AI can be used to discover asteroids. BUT every asteroid needs to be checked by a human and some will undoubtably be false positives. We also miss some unknown true positives. We often (depending on method) can’t explain why [in-fact] why something is a true positive vs false positive from the network itself. Which is why I say AI doesn’t make discoveries. It’s not identifying something knew, it’s identifying something that looks like what it’s seen before. It’s error rates in the scientific frontier is also going to be unknown; as we often lack data. Using AI could potentially limit the scope of new discoveries - depending how heavily it’s relied on by hyper optimizing on the wrong items. Anyway, sorry for the rambling, as a research in deep learning I find this trend something to monitor.

sumy23 4y ago
I disagree. Yes, results need to be validated, but that doesn't mean ML is not making discoveries. Even for basic classification tasks, discoveries are still being made. For instance, ML can reliably detect the race of a person from an x-ray. There is clearly some anatomical difference that we're not aware of that's detectable in an x-ray. Most of human inference, in general, is "connecting the dots" between related phenomena. ML is a great tool for doing this kind of correlative work at scale.
- omgwtfbyobbq 4y ago
  I think we need to be cautious about what ML is picking up. Finding patterns is great, but we need to be fairly sure about what those patterns are so we don't misapply them in some different context we don't understand. As for whether finding patterns is discovery, I feel very whatever about it.
- andi999 4y ago
  I remember the paper and the conclusion you say is doubtful. Even when the data was heavily distorted and resolution reduced to insanely big pixels it still could do this. So, since magic does not exist, there is probably a hidden cofactor (like xrays done on different machines, one in a hospital in a white neighbourhood, the other in a black one, and the machine giving a technical artifact like colorrange). This is just an example of course, I dont know the cofactor. Here is the study:https://www.thelancet.com/journals/landig/article/PIIS2589-7500(22)00063-2/fulltext https://www.thelancet.com/journals/landig/article/PIIS2589-7... When i said insanely i didnt remember correctly, just saw even if reduce to 4x4 pixels they claim a statistical difference. So mind blowingly ludicrous resolution might have been a better term.
  - walnutclosefarm 4y ago
    I agree that there is something odd going on in the race study. However, I've seen analogous "discovery" results that don't have this problem. Scientists at my former employer built a model, e.g., that diagnosed asymptomatic heart failure from ECGs. No electrophysiologist in that world-class cardiac medicine unit can do this, but the algorithm clearly perceives the signal. And this was in fact a true "discovery" in that they didn't set out to diagnose heart failure, but rather to "score" ventricular ejection fraction using ECGs. Training the algorithm revealed a signal (a so far unexplained cofactor to reduced ejection fraction) that predicts reduced ejection fraction, and thus heart failure, prior to grossly measurable reduced ejection fraction.
    - andi999 4y ago
      Nice. I think this is very interesting, is it published?
      - walnutclosefarm 4y ago
        https://www.nature.com/articles/s41591-021-01335-4 https://www.nature.com/articles/s41591-021-01335-4
        andi999 4y ago
        Thanks, will read it. The data unavailability is a bit of a bummer.
    - coliveira 4y ago
      All these "discoveries" suffer from the problem of inconclusive data. As mentioned in another comment, this can be the result of data that is skewed somehow, for example by neighborhood, by the selection of patients, by the technology used, etc. Validating these results is not an easy task, especially when one talks about healthcare data.
    - YeGoblynQueenne 4y ago
      And did this discovery generalise to datasets from other hospitals?
- idontpost 4y ago
  > For instance, ML can reliably detect the race of a person from an x-ray. There is clearly some anatomical difference that we're not aware of that's detectable in an x-ray. I'm pretty sure forensic anthropologists have been doing that for decades.
knicholes 4y ago
I think we've all seen what happens when we extrapolate.
cameronh90 4y ago
I think like anything it's a tool that can be used to great effect. However, I suppose there's a possibility we could become over-reliant on it and blind to the sorts of new ideas that you'd generate from manually exploring the data set yourself. For example, if the state-of-the-art AI asteroid discovering program has a blind spot where it never detects a certain type of asteroid, we might stop finding that type of asteroid altogether. Then when the training datasets themselves are mostly derived from asteroids that the AI had previously found, any blind spots and biases could end up self-reinforcing. Then again, is this really all that different to the many human-driven blind spots science has had in the past?
- visarga 4y ago
  > is this really all that different to the many human-driven blind spots No. Humans are fallible like AI. Most humans can't perfectly apply logical reasoning to common day situations.
  - mensetmanusman 4y ago
    They are both fallible because humans supply the data.
    - JoshCole 4y ago
      AI includes supervised learning, but supervised learning is a very narrow part of the field of AI. In some problems, humans don't supply the data. For example, in reinforcement learning, rewards are provided by the environment, but there is no point where humans give labeled examples of what constitutes good play. For an even more extreme example, in evolutionary learning there is no reward provided to the algorithm and there can be no training data visible to the algorithm. In effect, it manages to work as an optimization because sometimes there is a lack of data - something dies before reaching a point where it should produce another generation.
  - YeGoblynQueenne 4y ago
    Humans can fail, but humans can also succeed, in applying logical reasoning- not only to common situations, but also to scientific inquiry. That's how we have science, today. If we were only bumbling fools that stumble in the dark we'd still be stuck in dark caves without fire. But we can be -and we often are- much more than that. But not our machine learning systems. Those are stuck in making stupid mistakes and not being able to generalise away from them. For the time being. You can ask GPT-3 a question and it may come up with a smart answer or a dumb answer, and it won't know which is which. But we will. Because, yes, we can make dumb mistakes but we can also understand that they are mistakes, and that they are dumb, and why they are mistakes and dumb, and we can recover from them and stop ourselves from making the same mistakes. We have blind spots, sure, but we can see through them, eventually, with hard work, after a very long time, very haphazardly. But not our machine learning systems. For our machine learning systems the entire world is one big blind spot. That is how humans are different to machine learning systems.
scroot 4y ago
Neil Postman used to say that we live in an era that is awash in information overabundance but simultaneously starving for wisdom. One of the immediate and great potential use for this kind of AI (ie, Skinner Boxes for computation) is to help us focus on what matters when faced with overwhelming piles of chaff.
schaefer 4y ago
Counterpoint: AlphaGo discovered new moves in the Game of Go, to the extent that books have been written about those new moves and how they change modern opening theory[1]. In Go, there are hundreds of years of tradition studying the games of past master players, and analyzing how their innovations changed our cultural understanding of the game. Humanity can't have it both ways. If those contributions were valuable when made by a human, they should be equally valuable when made by an AI. [1] https://www.amazon.com/Using-AlphaGos-Enlarged-Corner-Enclosures/dp/1090423365/ https://www.amazon.com/Using-AlphaGos-Enlarged-Corner-Enclos...
- visarga 4y ago
  Another counterpoint - AlphaFold. It really discovers structure we could not previously discover.
  - dekhn 4y ago
    Not really- all of alphafold depends on having enough sequence alignment information to existing proteins with known structures. It really does not perform all that well on out-of-dataset prediction.
    - fsloth 4y ago
      Still having good combinatorical guestimates extrapolated from existing knowledge is not a useless trick in itself.
      - visarga 4y ago
        If you can simulate you can check and throw out the garbage, or get more training data for the next iteration.
- JoshCole 4y ago
  Another counterpoint is that the same technology applied to chess rediscovered human opening theory - then extended it in some openings. In chess, you don't have to write a research paper to make a notable contribution. If people find your move beautiful, they put a little exclamation point next to it in the notation. AlphaZero gets plenty of those - enough for a book about its play [1]. This took multiple human generations for us to accomplish, with literal geniuses applied to the task and spending their whole lives on it. Rediscovery and expansion happened in a wall clock time that is better measured in minutes than generations. [1]: https://www.amazon.com/Game-Changer-AlphaZeros-Groundbreaking-Strategies/dp/9056918184 https://www.amazon.com/Game-Changer-AlphaZeros-Groundbreakin...
albertzeyer 4y ago
Current humans are great for classification. A human is often non-explainable, can be non-deterministic and only as good as it’s training data. Yes humans can be used to discover asteroids. BUT every asteroid needs to be checked by a human (or AI) and some will undoubtably be false positives. We often can’t explain from the brain itself. Humans don’t make discoveries. (I guess you get my point.)
- coding123 4y ago
  That's not a point, you just replaced words but his is true and yours is not. Unless you can explain why "Humans don't make discoveries" is true??? Also this is a VERY tired trope in Hacker News. Please stop doing this. Here's the gist: A human can show that 50 people are dying of cancer because they were recently exposed to toxic sludge. An AI can't say a bunch of people were exposed to toxic sludge - it can only raise your prediction of getting cancer because you live near such and such coordinates (the coordinates where toxic sludge exist - but that's not visible to the AI)
  - BobbyJo 4y ago
    He was making a counter argument. Parent used all the same logic to posit 'ai don't make discoveries'. If those are reasons ai don't make discoveries, and they all apply to humans as well, then it must be that humans also don't make discoveries. This is obviously false, which refutes the parent's point.
    - coding123 4y ago
      Multiple places to refute the core logic: The asteroid has to be checked by a human (or AI) part is incorrect because a computer AI that's checking just images or data can't (yet) call a team of astronomers to focus telescopes somewhere and double check it. That part would almost nearly require general AI. Yes, someone could write a single-use software that tells a bunch of astronomers to check something that AI has found, but that's NOT "the AI" doing that. Humans don't have "training data" they have something else. I don't think "training data" and that "something else" is the same (yet).
deleted 4y ago
[deleted]
dekhn 4y ago
I don't know that I agree. Historically, many good scientific models had generalizable predictive ability (relativity being a good example) that was used to make predcitions that led to discoveries). I would say that if a scientist can use a model (that's all an ML model is) to anticipate likely regions of phase space to explore (say, in making engineered glass or steel), it is in fact "discovering" things. Also, when you reach the point of needing a human to double check all your predictions and the humans experts disagree about the golden label as much as the model has uncertainty in the prediction, the model has reached human performance, and "human checking the results" doesn't even make sense since they would increase the false positive or negative rate of the system. Already we are seeing some image recognition systems whose classificaftion of hard examples exceed that of the best human experts (while being hundreds of times fasteR).
JoshCole 4y ago
> What I mean by that, is that AI is often non-explainable, can be non-deterministic and only as good as it’s training data. By your logic, humans can't discover things, but AI can. You think you made the opposite claim - you didn't. 1. Consider that you can freeze a random seed, making an algorithm deterministic, but you can't do the same for a human. 2. Secondly, I dare you to explain to me exactly, down to the very activation of cells and synapses in your mind, how you came to understand these words as having meaning. You can't do it, but an AI that did this would do this implicitly. So it will do a much better job of explaining how this happens than you will. So you haven't ruled out AI being able to discover things. You've claimed that humans can't, but AI can. It directly contradicts your thesis. 3. We can point to specific examples of novel discoveries produced by modern ML that were made without the use of good training data provided by humans - indeed, without any training data provided by humans. Moreover, these techniques weren't limited by their training data being bad - they were built around the idea that the training data they would generate was bad.
- karpierz 4y ago
  > 2. Secondly, I dare you to explain to me exactly, down to the very activation of cells and synapses in your mind, how you came to understand these words as having meaning. You can't do it, but an AI that did this would do this implicitly. So it will do a much better job of explaining how this happens than you will. I think you're working with a very different definition of "explainable" than is commonly used in the machine learning literature.
  - JoshCole 4y ago
    They are trying to create a distinction, not between the explainability of machine learning approaches, but between human approaches and machine learning approaches. Whatever paper you think I should be using for my definition, just go to there citations. You'll trace it back eventually to things like the Imitation Game paper by Alan Turing. You'll trace it back to the debate about whether machines can think, discover, learn, know. The papers you reference are built atop a mountain of papers that came before them which largely reject the claim that machines aren't capable of discovery. You'll also find my term usage early on in most books about machine learning. Most authors like introducing machine learning by pointing out why we need machine learning. They point out that humans not being able to explain solutions to very hard problems means they can't build programs that solve those problems. If they could explain their solution, they could code it up and they would have a program. Since they can't, they reach to learning tools to figure it out. It really shouldn't surprise you that we have to go back to the early parts of the field; his argument restated is "machines can't make discoveries, only humans can" and this confused thinking is something that gets addressed in the early writings much moreso than the latter writings. Frankly, we've known algorithms as far back as Aristotle that were capable of discovery. Peter Norvig in AIMA cites Aristotle when talking about some search problems and path discovery is still discovery. Some of these algorithms literally include frontier in the algorithmic description; clearly a framing that implies thinking of them in terms of discovery. Honestly, what is really happening in my estimation is some people do this thing where they pretend AI is categorically reduced to the subset of things which they can criticize. It is a really weird tendency, but extremely common. You can see it in the persons post actually. Look at the contortions they go through to only focus on supervised learning - to only focus on a narrow subset of supervised learning restricted to deep learning - to only focus on a narrow problem domain of supervised learning - classification - and to only focus on a specific example of that problem domain (we can give good explanations for smaller models) - the ones that humans can't easily explain.
ethanwillis 4y ago
It's not that it's only as good as it's training data. But depending on the underlying implementation it's also only as good as the quality of the random number it uses in its implementation. Let me give a somewhat broad example. In cases where a state space to be explored is absolutely massive and we have no currently good way to 1. prune it 2. traverse it to land at the states we want(that have certain properties), and so on. Then the best we have right now is "sample the state space." But "sampling" requires very good random number generation so that we're 1. not biasing ourselves (this is clear to everyone) but deeper than that 2. Good random number generation reduces the problem of us only grabbing states from the state space that confirm our existing ideas rather than the ones that might challenge our existing ideas. How many scientists are developing their ideas on single workstations that are running out of entropy?
mensetmanusman 4y ago
AI is analogous to a new type of lens. Lenses can be thought of thermodynamically as channeling the information included in rays of light into a different form more appreciable by humans. AI is that for rays of data :)

sumy23 4y ago

I disagree. Yes, results need to be validated, but that doesn't mean ML is not making discoveries. Even for basic classification tasks, discoveries are still being made. For instance, ML can reliably detect the race of a person from an x-ray. There is clearly some anatomical difference that we're not aware of that's detectable in an x-ray. Most of human inference, in general, is "connecting the dots" between related phenomena. ML is a great tool for doing this kind of correlative work at scale.

omgwtfbyobbq 4y ago
I think we need to be cautious about what ML is picking up. Finding patterns is great, but we need to be fairly sure about what those patterns are so we don't misapply them in some different context we don't understand. As for whether finding patterns is discovery, I feel very whatever about it.
andi999 4y ago
I remember the paper and the conclusion you say is doubtful. Even when the data was heavily distorted and resolution reduced to insanely big pixels it still could do this. So, since magic does not exist, there is probably a hidden cofactor (like xrays done on different machines, one in a hospital in a white neighbourhood, the other in a black one, and the machine giving a technical artifact like colorrange). This is just an example of course, I dont know the cofactor. Here is the study:https://www.thelancet.com/journals/landig/article/PIIS2589-7500(22)00063-2/fulltext https://www.thelancet.com/journals/landig/article/PIIS2589-7... When i said insanely i didnt remember correctly, just saw even if reduce to 4x4 pixels they claim a statistical difference. So mind blowingly ludicrous resolution might have been a better term.
- walnutclosefarm 4y ago
  I agree that there is something odd going on in the race study. However, I've seen analogous "discovery" results that don't have this problem. Scientists at my former employer built a model, e.g., that diagnosed asymptomatic heart failure from ECGs. No electrophysiologist in that world-class cardiac medicine unit can do this, but the algorithm clearly perceives the signal. And this was in fact a true "discovery" in that they didn't set out to diagnose heart failure, but rather to "score" ventricular ejection fraction using ECGs. Training the algorithm revealed a signal (a so far unexplained cofactor to reduced ejection fraction) that predicts reduced ejection fraction, and thus heart failure, prior to grossly measurable reduced ejection fraction.
  - andi999 4y ago
    Nice. I think this is very interesting, is it published?
    - walnutclosefarm 4y ago
      https://www.nature.com/articles/s41591-021-01335-4 https://www.nature.com/articles/s41591-021-01335-4
      - andi999 4y ago
        Thanks, will read it. The data unavailability is a bit of a bummer.
  - coliveira 4y ago
    All these "discoveries" suffer from the problem of inconclusive data. As mentioned in another comment, this can be the result of data that is skewed somehow, for example by neighborhood, by the selection of patients, by the technology used, etc. Validating these results is not an easy task, especially when one talks about healthcare data.
  - YeGoblynQueenne 4y ago
    And did this discovery generalise to datasets from other hospitals?
idontpost 4y ago
> For instance, ML can reliably detect the race of a person from an x-ray. There is clearly some anatomical difference that we're not aware of that's detectable in an x-ray. I'm pretty sure forensic anthropologists have been doing that for decades.

knicholes 4y ago

I think we've all seen what happens when we extrapolate.

cameronh90 4y ago

I think like anything it's a tool that can be used to great effect. However, I suppose there's a possibility we could become over-reliant on it and blind to the sorts of new ideas that you'd generate from manually exploring the data set yourself. For example, if the state-of-the-art AI asteroid discovering program has a blind spot where it never detects a certain type of asteroid, we might stop finding that type of asteroid altogether. Then when the training datasets themselves are mostly derived from asteroids that the AI had previously found, any blind spots and biases could end up self-reinforcing. Then again, is this really all that different to the many human-driven blind spots science has had in the past?

visarga 4y ago
> is this really all that different to the many human-driven blind spots No. Humans are fallible like AI. Most humans can't perfectly apply logical reasoning to common day situations.
- mensetmanusman 4y ago
  They are both fallible because humans supply the data.
  - JoshCole 4y ago
    AI includes supervised learning, but supervised learning is a very narrow part of the field of AI. In some problems, humans don't supply the data. For example, in reinforcement learning, rewards are provided by the environment, but there is no point where humans give labeled examples of what constitutes good play. For an even more extreme example, in evolutionary learning there is no reward provided to the algorithm and there can be no training data visible to the algorithm. In effect, it manages to work as an optimization because sometimes there is a lack of data - something dies before reaching a point where it should produce another generation.
- YeGoblynQueenne 4y ago
  Humans can fail, but humans can also succeed, in applying logical reasoning- not only to common situations, but also to scientific inquiry. That's how we have science, today. If we were only bumbling fools that stumble in the dark we'd still be stuck in dark caves without fire. But we can be -and we often are- much more than that. But not our machine learning systems. Those are stuck in making stupid mistakes and not being able to generalise away from them. For the time being. You can ask GPT-3 a question and it may come up with a smart answer or a dumb answer, and it won't know which is which. But we will. Because, yes, we can make dumb mistakes but we can also understand that they are mistakes, and that they are dumb, and why they are mistakes and dumb, and we can recover from them and stop ourselves from making the same mistakes. We have blind spots, sure, but we can see through them, eventually, with hard work, after a very long time, very haphazardly. But not our machine learning systems. For our machine learning systems the entire world is one big blind spot. That is how humans are different to machine learning systems.

scroot 4y ago

Neil Postman used to say that we live in an era that is awash in information overabundance but simultaneously starving for wisdom. One of the immediate and great potential use for this kind of AI (ie, Skinner Boxes for computation) is to help us focus on what matters when faced with overwhelming piles of chaff.

schaefer 4y ago

Counterpoint: AlphaGo discovered new moves in the Game of Go, to the extent that books have been written about those new moves and how they change modern opening theory[1]. In Go, there are hundreds of years of tradition studying the games of past master players, and analyzing how their innovations changed our cultural understanding of the game. Humanity can't have it both ways. If those contributions were valuable when made by a human, they should be equally valuable when made by an AI. [1] https://www.amazon.com/Using-AlphaGos-Enlarged-Corner-Enclosures/dp/1090423365/ https://www.amazon.com/Using-AlphaGos-Enlarged-Corner-Enclos...

visarga 4y ago
Another counterpoint - AlphaFold. It really discovers structure we could not previously discover.
- dekhn 4y ago
  Not really- all of alphafold depends on having enough sequence alignment information to existing proteins with known structures. It really does not perform all that well on out-of-dataset prediction.
  - fsloth 4y ago
    Still having good combinatorical guestimates extrapolated from existing knowledge is not a useless trick in itself.
    - visarga 4y ago
      If you can simulate you can check and throw out the garbage, or get more training data for the next iteration.
JoshCole 4y ago
Another counterpoint is that the same technology applied to chess rediscovered human opening theory - then extended it in some openings. In chess, you don't have to write a research paper to make a notable contribution. If people find your move beautiful, they put a little exclamation point next to it in the notation. AlphaZero gets plenty of those - enough for a book about its play [1]. This took multiple human generations for us to accomplish, with literal geniuses applied to the task and spending their whole lives on it. Rediscovery and expansion happened in a wall clock time that is better measured in minutes than generations. [1]: https://www.amazon.com/Game-Changer-AlphaZeros-Groundbreaking-Strategies/dp/9056918184 https://www.amazon.com/Game-Changer-AlphaZeros-Groundbreakin...

albertzeyer 4y ago

Current humans are great for classification. A human is often non-explainable, can be non-deterministic and only as good as it’s training data. Yes humans can be used to discover asteroids. BUT every asteroid needs to be checked by a human (or AI) and some will undoubtably be false positives. We often can’t explain from the brain itself. Humans don’t make discoveries. (I guess you get my point.)

coding123 4y ago
That's not a point, you just replaced words but his is true and yours is not. Unless you can explain why "Humans don't make discoveries" is true??? Also this is a VERY tired trope in Hacker News. Please stop doing this. Here's the gist: A human can show that 50 people are dying of cancer because they were recently exposed to toxic sludge. An AI can't say a bunch of people were exposed to toxic sludge - it can only raise your prediction of getting cancer because you live near such and such coordinates (the coordinates where toxic sludge exist - but that's not visible to the AI)
- BobbyJo 4y ago
  He was making a counter argument. Parent used all the same logic to posit 'ai don't make discoveries'. If those are reasons ai don't make discoveries, and they all apply to humans as well, then it must be that humans also don't make discoveries. This is obviously false, which refutes the parent's point.
  - coding123 4y ago
    Multiple places to refute the core logic: The asteroid has to be checked by a human (or AI) part is incorrect because a computer AI that's checking just images or data can't (yet) call a team of astronomers to focus telescopes somewhere and double check it. That part would almost nearly require general AI. Yes, someone could write a single-use software that tells a bunch of astronomers to check something that AI has found, but that's NOT "the AI" doing that. Humans don't have "training data" they have something else. I don't think "training data" and that "something else" is the same (yet).

deleted 4y ago

[deleted]

dekhn 4y ago

I don't know that I agree. Historically, many good scientific models had generalizable predictive ability (relativity being a good example) that was used to make predcitions that led to discoveries). I would say that if a scientist can use a model (that's all an ML model is) to anticipate likely regions of phase space to explore (say, in making engineered glass or steel), it is in fact "discovering" things. Also, when you reach the point of needing a human to double check all your predictions and the humans experts disagree about the golden label as much as the model has uncertainty in the prediction, the model has reached human performance, and "human checking the results" doesn't even make sense since they would increase the false positive or negative rate of the system. Already we are seeing some image recognition systems whose classificaftion of hard examples exceed that of the best human experts (while being hundreds of times fasteR).

JoshCole 4y ago

> What I mean by that, is that AI is often non-explainable, can be non-deterministic and only as good as it’s training data. By your logic, humans can't discover things, but AI can. You think you made the opposite claim - you didn't. 1. Consider that you can freeze a random seed, making an algorithm deterministic, but you can't do the same for a human. 2. Secondly, I dare you to explain to me exactly, down to the very activation of cells and synapses in your mind, how you came to understand these words as having meaning. You can't do it, but an AI that did this would do this implicitly. So it will do a much better job of explaining how this happens than you will. So you haven't ruled out AI being able to discover things. You've claimed that humans can't, but AI can. It directly contradicts your thesis. 3. We can point to specific examples of novel discoveries produced by modern ML that were made without the use of good training data provided by humans - indeed, without any training data provided by humans. Moreover, these techniques weren't limited by their training data being bad - they were built around the idea that the training data they would generate was bad.

karpierz 4y ago
> 2. Secondly, I dare you to explain to me exactly, down to the very activation of cells and synapses in your mind, how you came to understand these words as having meaning. You can't do it, but an AI that did this would do this implicitly. So it will do a much better job of explaining how this happens than you will. I think you're working with a very different definition of "explainable" than is commonly used in the machine learning literature.
- JoshCole 4y ago
  They are trying to create a distinction, not between the explainability of machine learning approaches, but between human approaches and machine learning approaches. Whatever paper you think I should be using for my definition, just go to there citations. You'll trace it back eventually to things like the Imitation Game paper by Alan Turing. You'll trace it back to the debate about whether machines can think, discover, learn, know. The papers you reference are built atop a mountain of papers that came before them which largely reject the claim that machines aren't capable of discovery. You'll also find my term usage early on in most books about machine learning. Most authors like introducing machine learning by pointing out why we need machine learning. They point out that humans not being able to explain solutions to very hard problems means they can't build programs that solve those problems. If they could explain their solution, they could code it up and they would have a program. Since they can't, they reach to learning tools to figure it out. It really shouldn't surprise you that we have to go back to the early parts of the field; his argument restated is "machines can't make discoveries, only humans can" and this confused thinking is something that gets addressed in the early writings much moreso than the latter writings. Frankly, we've known algorithms as far back as Aristotle that were capable of discovery. Peter Norvig in AIMA cites Aristotle when talking about some search problems and path discovery is still discovery. Some of these algorithms literally include frontier in the algorithmic description; clearly a framing that implies thinking of them in terms of discovery. Honestly, what is really happening in my estimation is some people do this thing where they pretend AI is categorically reduced to the subset of things which they can criticize. It is a really weird tendency, but extremely common. You can see it in the persons post actually. Look at the contortions they go through to only focus on supervised learning - to only focus on a narrow subset of supervised learning restricted to deep learning - to only focus on a narrow problem domain of supervised learning - classification - and to only focus on a specific example of that problem domain (we can give good explanations for smaller models) - the ones that humans can't easily explain.

ethanwillis 4y ago

It's not that it's only as good as it's training data. But depending on the underlying implementation it's also only as good as the quality of the random number it uses in its implementation. Let me give a somewhat broad example. In cases where a state space to be explored is absolutely massive and we have no currently good way to 1. prune it 2. traverse it to land at the states we want(that have certain properties), and so on. Then the best we have right now is "sample the state space." But "sampling" requires very good random number generation so that we're 1. not biasing ourselves (this is clear to everyone) but deeper than that 2. Good random number generation reduces the problem of us only grabbing states from the state space that confirm our existing ideas rather than the ones that might challenge our existing ideas. How many scientists are developing their ideas on single workstations that are running out of entropy?

mensetmanusman 4y ago

AI is analogous to a new type of lens. Lenses can be thought of thermodynamically as channeling the information included in rays of light into a different form more appreciable by humans. AI is that for rays of data :)