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Using a neural network for things that have clear cut rules is wrong. When you know the exact rules, implement them as such, instead of bruteforcing a guesstima

Using a neural network for things that have clear cut rules is wrong. When you know the exact rules, implement them as such, instead of bruteforcing a guesstimation. This is also why I'm sceptical of the usr of GPT-3 for all sorts of purposes where accuracy is important. Think of the code generation case. Bugs may be very subtle and may go unnoticed.

Grimm1 6y ago
Code generation only needs to generate code with n bugs where n is less than the number of bugs a human developer generates for it to have usefulness, and maybe some other factor of severity where they are generally less severe than human developers. I think it'll make neat autopilot functionality for developers but not replace the need to have someone look over and understand the code.
- perl4ever 6y ago
  This is a perfect satire of the logic people use to advocate self driving cars being rushed into production. Only every time I read something similar, I think "surely no programmer could think this". Are you a programmer?
  - Grimm1 6y ago
    I sure am, and if I can code gen 90% of the boiler plate away I'll do it happily. Besides attacking me, do you have any point you'd like to make?
    - bobthebuilders 6y ago
      Do you want to die when your self driving car crashes? Debug issues when your app des at 12am? Same concept.
      - Grimm1 6y ago
        I don't want to die when I crash my own car, and I already debug my own apps at 12am. If your argument is that things need to be perfect than my god you must never leave your home! I'd trust a machine to drive more accurately than most people I see on the highway. Humans aren't special, in fact more often than not we're sloppy, subject to fatigue, and a whole bunch of other negative things. That considered, I had a pretty strict qualifier in my above post which means the machine must perform better than the average human in the respective task and therefore I'd be more likely to die driving my own car than a machine meeting my prerequisites.
        Judgmentality 6y ago
        > I'd trust a machine to drive more accurately than most people I see on the highway. Humans aren't special, in fact more often than not we're sloppy, subject to fatigue, and a whole bunch of other negative things. Humans are much, much, much more capable than the absolute state-of-the-art robots when it comes to doing things in an uncontrolled environment. https://www.youtube.com/watch?v=g0TaYhjpOfo https://www.youtube.com/watch?v=g0TaYhjpOfo
        Hasnep 6y ago
        One of the advantages of an autonomous driver is that its superhuman reflexes, never driving while tired, never getting road rage, etc., will make it less likely to get into an uncontrolled environment. Would you prefer your pilots to fly your plane with no AI assistance?
        Judgmentality 6y ago
        > One of the advantages of an autonomous driver is that its superhuman reflexes, never driving while tired, never getting road rage, etc. First of all, when you actually understand a self-driving car stack, you'll realize those super-human reflexes are more human than you think. The stack is complicated and not only are there delays to be expected, some hardware syncing requirements guarantee certain delays in the perception pipeline. It's still better than a person, but it's nothing close to approaching instantaneous. Likewise, sensors can get dirty, and blah blah blah there are other weaknesses robots have that humans don't. My point is simple: robots aren't perfect. In fact, they are almost always much worse than most people realize. > will make it less likely to get into an uncontrolled environment You're misunderstanding me. I'm not saying less likely to get into an accident. I'm saying the world, where cars drive, is an uncontrolled environment - and the current state of robotics is such that humans are better for doing things in the real world. There is no "less likely to get into an uncontrolled environment" because by definition you are always putting it into that situation. > Would you prefer your pilots to fly your plane with no AI assistance? AI assistance is fine. AI replacement is not.
- kulig 6y ago
  Its not that simple. People are understanding when car crashes happen in busy roads amongst other cars. They are not understanding if a self-driving car swerves into the sidewalk and kills a group of children.
- ben_w 6y ago
  I disagree that that is enough to be useful. To give a deliberately extreme example: if it produces code which has half the number of bugs as a human, but it only outputs Malbolge source code, nobody else will be able to fix those bugs which remain.
- yuliyp 6y ago
  This is a very simplistic of what code is and the role it plays in a system. There are many implementations that can fulfill a set of requirements. Not all of them are created equal. The ways in which they behave as the system changes can be wildly different. Well-written code will be able to handle those changes gracefully. Poorly-written code may end up proving brittle and bug-prone. Generated code will be completely unpredictable. Imagine you're trying to build a street network for a city. Some designs are much more predictable than others. If you've played Factorio, the distinction between a spaghetti base and one that has some design is abundant. Even if they currently fulfill the same requirements now, the ability to improve upon and reason about how it will behave after changes is vastly different.
  - Grimm1 6y ago
    I don't know what you're arguing against but it sure isn't what I wrote. "Code generation only needs to generate code with n bugs where n is less than the number of bugs a human developer generates for it to have usefulness, and maybe some other factor of severity where they are generally less severe than human developers." Point to the part you're arguing against because you way extrapolated what "have usefulness" means I think.
    - yuliyp 6y ago
      It's of limited usefulness if it just leads to a system that will end up performing worse / less predictably.
- danfang 6y ago
  This is naive. The point is that code is a well defined system with clear rules that can be expressed through logic and mathematics. GPT is suited to approximate systems where the rules are not well defined. Until AI can actually learn the principles of logic, it may not be useful for code generation on a meaningful scale, other than things just like simple auto-completions. Not only that, AI would also have to learn the principles of system design, performance, security, readability, maintainability. That's what makes "good" software. It's a far stretch to say that AI could achieve anything of the sort based on current abilities.
  - Grimm1 6y ago
    Good thing I didn't say it would, I'm really not sure what you think I wrote.
    - danfang 6y ago
      Your only metric of "good" code generation is # of bugs. My assertion was that this is not accurate. I also disagree with the premise that code generation by AI will be very useful for programmers, for the reasons stated above.
      - Grimm1 6y ago
        It's clearly not if you read the rest of that sentence, that's my metric for it to be useful. My metric for it being good is much deeper. Which is why I questioned what you responded with. We are already at the point of useful and context aware code generation anyway which is why I've found everyone on this thread questioning it to be kind of funny, Microsoft was demoing complex generations a year ago. So we're well on our way. https://www.pscp.tv/Microsoft/1OyKAYWPRrWKb?t=29m19s https://www.pscp.tv/Microsoft/1OyKAYWPRrWKb?t=29m19s https://www.infoworld.com/article/3518429/jetbrains-taps-machine-learning-for-full-line-code-completion.html https://www.infoworld.com/article/3518429/jetbrains-taps-mac...
dealforager 6y ago
For code, I could see it being super useful for a beefed up auto-complete. There are many times I find myself searching for things like "how do I do X in Y language" to copy a snippet that I'm sure has been written 10000x times before. I can review the code and verify its correctness by writing tests.
- deleted 6y ago
  [deleted]
- colejohnson66 6y ago
  You’re (sadly) assuming everyone would verify its correctness. Proper programming would mean one would write tests, but not everyone does. I’m guilty of it too.
  - taneq 6y ago
    Just have GPT-3 produce the tests too! :D
    - Something1234 6y ago
      Please don't give the MBAs ideas.
  - refulgentis 6y ago
    When banks got ATMs, they thought it'd drastically cut headcount - instead it went _up_: freeing up time from doing the basics meant you had more time to focus on more profitable activities I hope to live long enough to be mostly writing tests for a gloriously hacky code generator that gets it right 80% of the time
    - visarga 6y ago
      tests and specifications / prompt engineering
- nmfisher 6y ago
skybrian 6y ago
This is good reason to stick with use cases where a human can verify the results, like autocomplete or something like a search engine interface.
HotVector 6y ago
How is it that human brains can follow deterministic logic but neural networks can't? What's the missing piece? Is it just that people are a lot more complex than algorithms like GPT-3?
- cercatrova 6y ago
  This is a very interesting question that I've been thinking of as well. Why can't neural network based AI learn deterministic logic? I think because it doesn't have internal representations inside its "head" so to speak. It does not know that it can manipulate such structures as we can.
- tsimionescu 6y ago
  That's essentially like asking "how is it that human feet can walk up mountain slopes, but car wheels can't?" That is, there is no relation between human brains and artificial neural networks, other than them serving similar purposes in particular environments.
  - mpoteat 6y ago
    I respectfully disagree - I believe this is a philosophical viewpoint that shouldn't be presented as a straightforward truth. Unless you are a dualist, I would say that it's reasonable to view that it is in principle possible to produce an artificial network accurately emulating the function and behavior of a human brain. If you are a dualist, then there is no further discussion to be had as we are very unlikely to ever be able to prove anything like the existence of a soul. Apologies if you were speaking to some more subtle nuance that I was unable to pick up.
    - tsimionescu 6y ago
      TL; DR: I'm saying that there is no structural resemblance between the human brain and artificial neural networks specifically (even though there likely is a strucutral resemblance between the human mind/brain and a computer in the general sense). You can believe in AGI and not believe it will be achieved with ANNs. > I would say that it's reasonable to view that it is in principle possible to produce an artificial network accurately emulating the function and behavior of a human brain. I think that claim is far too strong. As a non-dualist, I do believe that it is possible to create an artificial "brain" that has the same cognition as a human brain. However, simply rejecting dualism does not tell you anything about what the artificial brain has to be. You can further say that a non-dualist who accepts the Church-Turing thesis must accept that there must exist a Turing machine which has the same cognition as the human brain. Since the PCs we use are Turing machines, it follows that we should be able to program one to behave like a human brain, in theory at least (disregarding hardware requirements, of course). Still, that does not mean that a brain Turing machine has to look anything like a neural network trained through gradient descent & back propagation. This was my point: artificial neural networks and the methods we use to train them have no resemblance to the human brain, and there is no reason to believe that they are the way to create an artificial general intelligence. So, there is no reason to be surprised that a neural network, especially one as small as any of the ones we have realized so far, doesn't exhibit complex properties of the human brain. Artificial neural networks are just a statistical model that was once inspired by a very, very simplistic idea of what biological neural networks are. As we have discovered more about biological neural networks, we've abandoned any notion of comparing ANNs with biological neural networks in terms of actual structure. This is all not to say that it's impossible for a complex enough ANN to actually be an AGI. It's just not going to be that surprising if it won't be, if an AGI program will look significantly different, and will be trained in completely different ways.
Eridrus 6y ago
I see where you're coming from, but given the (usual) flexibility and generality of neural neys, it's often very tempting to assume that you can just keep adding data, and I think these are good reminders that being able to do complicated things doesn't mean they can do simple things efficiently (which is a conversation that has come uo several times at work)
Dolores12 6y ago
This is a great way to find out if you are talking to a bot or not :)
Agentlien 6y ago
I once (2010) had an oral exam in neural networks during which I had to design a system to solve a particular task. My solution used two neural networks connected by a simple logic circuit. The professor, who was actually a neuro scientist with little understanding of the technical, asked why not a third neural network. This was a neural network course, after all. Thinking this was a trick question I excitedly explained how stupid it would be to build and train a network to approximate a function which could easily be precisely described with a tiny circuit or code statement. The professor was not amused. He said that's what he would have done. After a few similar incidents he concluded the exam giving me an 8/10 saying my answers were perfectly correct but he didn't like my attitude.
- wodenokoto 6y ago
  Yeah, there’s a better way of saying that and it sounds like that was an expensive lesson in communication. “Something something, it’s better to spend training time on the parts of the network where we don’t know the function beforehand than to train a subnetwork to do a function that we do know exactly at the outset.” and still you need to be ready to be asked about how to backpropogate through your hard coded function.
  - Dylan16807 6y ago
    It's a test, you shouldn't have to sugar-coat explanations to an instructor (unless you're acting out some other scenario as part of the test).
    - gchamonlive 6y ago
      In a test you have to prove your knowledge by transmitting symbols through language. If you don't "sugar coat" it, how do you expect that the right symbols will be interpreted by the receiver? It is part of the test to use the appropriate language to ensure the best understanding of what you are saying. Nonviolent communication tries to do exactly that and is essential to this end. You can argue that since the professor understood what was being said, the language shouldn't matter, but it does. If again you don't use the correct language you risk offending the listener so much he can't get past that. After all you are dealing with humans, not machines, and in either case you are responsible for clear communication.
visarga 6y ago
GPT-3 is a generator, that's just half the equation. It needs a discriminator (critic) to check out its outputs and in the case of program synthesis and realistic physics, a simulator. That's how humans do creative stuff - generate silly ideas then check them out.
- ZephyrBlu 6y ago
  Sounds like maybe a GAN-like ML algo could be a good choice for attempting to do code generation.
YetAnotherNick 6y ago
This is not like we are asking GPT what is 2 times 3. It is not even a bug. Just that the author is using quantized multiplication which takes subset of floating point space and just makes all the value closest to nearest point. So 2 can be approximated as say 2.5 if there are few points. And that is not deterministic. Also it is a known thing that something like that could occur. It is just that neural network still seem to learn.

Grimm1 6y ago

Code generation only needs to generate code with n bugs where n is less than the number of bugs a human developer generates for it to have usefulness, and maybe some other factor of severity where they are generally less severe than human developers. I think it'll make neat autopilot functionality for developers but not replace the need to have someone look over and understand the code.

perl4ever 6y ago
This is a perfect satire of the logic people use to advocate self driving cars being rushed into production. Only every time I read something similar, I think "surely no programmer could think this". Are you a programmer?
- Grimm1 6y ago
  I sure am, and if I can code gen 90% of the boiler plate away I'll do it happily. Besides attacking me, do you have any point you'd like to make?
  - bobthebuilders 6y ago
    Do you want to die when your self driving car crashes? Debug issues when your app des at 12am? Same concept.
    - Grimm1 6y ago
      I don't want to die when I crash my own car, and I already debug my own apps at 12am. If your argument is that things need to be perfect than my god you must never leave your home! I'd trust a machine to drive more accurately than most people I see on the highway. Humans aren't special, in fact more often than not we're sloppy, subject to fatigue, and a whole bunch of other negative things. That considered, I had a pretty strict qualifier in my above post which means the machine must perform better than the average human in the respective task and therefore I'd be more likely to die driving my own car than a machine meeting my prerequisites.
      - Judgmentality 6y ago
        > I'd trust a machine to drive more accurately than most people I see on the highway. Humans aren't special, in fact more often than not we're sloppy, subject to fatigue, and a whole bunch of other negative things. Humans are much, much, much more capable than the absolute state-of-the-art robots when it comes to doing things in an uncontrolled environment. https://www.youtube.com/watch?v=g0TaYhjpOfo https://www.youtube.com/watch?v=g0TaYhjpOfo
        Hasnep 6y ago
        One of the advantages of an autonomous driver is that its superhuman reflexes, never driving while tired, never getting road rage, etc., will make it less likely to get into an uncontrolled environment. Would you prefer your pilots to fly your plane with no AI assistance?
        Judgmentality 6y ago
        > One of the advantages of an autonomous driver is that its superhuman reflexes, never driving while tired, never getting road rage, etc. First of all, when you actually understand a self-driving car stack, you'll realize those super-human reflexes are more human than you think. The stack is complicated and not only are there delays to be expected, some hardware syncing requirements guarantee certain delays in the perception pipeline. It's still better than a person, but it's nothing close to approaching instantaneous. Likewise, sensors can get dirty, and blah blah blah there are other weaknesses robots have that humans don't. My point is simple: robots aren't perfect. In fact, they are almost always much worse than most people realize. > will make it less likely to get into an uncontrolled environment You're misunderstanding me. I'm not saying less likely to get into an accident. I'm saying the world, where cars drive, is an uncontrolled environment - and the current state of robotics is such that humans are better for doing things in the real world. There is no "less likely to get into an uncontrolled environment" because by definition you are always putting it into that situation. > Would you prefer your pilots to fly your plane with no AI assistance? AI assistance is fine. AI replacement is not.
kulig 6y ago
Its not that simple. People are understanding when car crashes happen in busy roads amongst other cars. They are not understanding if a self-driving car swerves into the sidewalk and kills a group of children.
ben_w 6y ago
I disagree that that is enough to be useful. To give a deliberately extreme example: if it produces code which has half the number of bugs as a human, but it only outputs Malbolge source code, nobody else will be able to fix those bugs which remain.
yuliyp 6y ago
This is a very simplistic of what code is and the role it plays in a system. There are many implementations that can fulfill a set of requirements. Not all of them are created equal. The ways in which they behave as the system changes can be wildly different. Well-written code will be able to handle those changes gracefully. Poorly-written code may end up proving brittle and bug-prone. Generated code will be completely unpredictable. Imagine you're trying to build a street network for a city. Some designs are much more predictable than others. If you've played Factorio, the distinction between a spaghetti base and one that has some design is abundant. Even if they currently fulfill the same requirements now, the ability to improve upon and reason about how it will behave after changes is vastly different.
- Grimm1 6y ago
  I don't know what you're arguing against but it sure isn't what I wrote. "Code generation only needs to generate code with n bugs where n is less than the number of bugs a human developer generates for it to have usefulness, and maybe some other factor of severity where they are generally less severe than human developers." Point to the part you're arguing against because you way extrapolated what "have usefulness" means I think.
  - yuliyp 6y ago
    It's of limited usefulness if it just leads to a system that will end up performing worse / less predictably.
danfang 6y ago
This is naive. The point is that code is a well defined system with clear rules that can be expressed through logic and mathematics. GPT is suited to approximate systems where the rules are not well defined. Until AI can actually learn the principles of logic, it may not be useful for code generation on a meaningful scale, other than things just like simple auto-completions. Not only that, AI would also have to learn the principles of system design, performance, security, readability, maintainability. That's what makes "good" software. It's a far stretch to say that AI could achieve anything of the sort based on current abilities.
- Grimm1 6y ago
  Good thing I didn't say it would, I'm really not sure what you think I wrote.
  - danfang 6y ago
    Your only metric of "good" code generation is # of bugs. My assertion was that this is not accurate. I also disagree with the premise that code generation by AI will be very useful for programmers, for the reasons stated above.
    - Grimm1 6y ago
      It's clearly not if you read the rest of that sentence, that's my metric for it to be useful. My metric for it being good is much deeper. Which is why I questioned what you responded with. We are already at the point of useful and context aware code generation anyway which is why I've found everyone on this thread questioning it to be kind of funny, Microsoft was demoing complex generations a year ago. So we're well on our way. https://www.pscp.tv/Microsoft/1OyKAYWPRrWKb?t=29m19s https://www.pscp.tv/Microsoft/1OyKAYWPRrWKb?t=29m19s https://www.infoworld.com/article/3518429/jetbrains-taps-machine-learning-for-full-line-code-completion.html https://www.infoworld.com/article/3518429/jetbrains-taps-mac...

dealforager 6y ago

For code, I could see it being super useful for a beefed up auto-complete. There are many times I find myself searching for things like "how do I do X in Y language" to copy a snippet that I'm sure has been written 10000x times before. I can review the code and verify its correctness by writing tests.

deleted 6y ago
[deleted]
colejohnson66 6y ago
You’re (sadly) assuming everyone would verify its correctness. Proper programming would mean one would write tests, but not everyone does. I’m guilty of it too.
- taneq 6y ago
  Just have GPT-3 produce the tests too! :D
  - Something1234 6y ago
    Please don't give the MBAs ideas.
- refulgentis 6y ago
  When banks got ATMs, they thought it'd drastically cut headcount - instead it went _up_: freeing up time from doing the basics meant you had more time to focus on more profitable activities I hope to live long enough to be mostly writing tests for a gloriously hacky code generator that gets it right 80% of the time
  - visarga 6y ago
    tests and specifications / prompt engineering
nmfisher 6y ago

skybrian 6y ago

This is good reason to stick with use cases where a human can verify the results, like autocomplete or something like a search engine interface.

HotVector 6y ago

How is it that human brains can follow deterministic logic but neural networks can't? What's the missing piece? Is it just that people are a lot more complex than algorithms like GPT-3?

cercatrova 6y ago
This is a very interesting question that I've been thinking of as well. Why can't neural network based AI learn deterministic logic? I think because it doesn't have internal representations inside its "head" so to speak. It does not know that it can manipulate such structures as we can.
tsimionescu 6y ago
That's essentially like asking "how is it that human feet can walk up mountain slopes, but car wheels can't?" That is, there is no relation between human brains and artificial neural networks, other than them serving similar purposes in particular environments.
- mpoteat 6y ago
  I respectfully disagree - I believe this is a philosophical viewpoint that shouldn't be presented as a straightforward truth. Unless you are a dualist, I would say that it's reasonable to view that it is in principle possible to produce an artificial network accurately emulating the function and behavior of a human brain. If you are a dualist, then there is no further discussion to be had as we are very unlikely to ever be able to prove anything like the existence of a soul. Apologies if you were speaking to some more subtle nuance that I was unable to pick up.
  - tsimionescu 6y ago
    TL; DR: I'm saying that there is no structural resemblance between the human brain and artificial neural networks specifically (even though there likely is a strucutral resemblance between the human mind/brain and a computer in the general sense). You can believe in AGI and not believe it will be achieved with ANNs. > I would say that it's reasonable to view that it is in principle possible to produce an artificial network accurately emulating the function and behavior of a human brain. I think that claim is far too strong. As a non-dualist, I do believe that it is possible to create an artificial "brain" that has the same cognition as a human brain. However, simply rejecting dualism does not tell you anything about what the artificial brain has to be. You can further say that a non-dualist who accepts the Church-Turing thesis must accept that there must exist a Turing machine which has the same cognition as the human brain. Since the PCs we use are Turing machines, it follows that we should be able to program one to behave like a human brain, in theory at least (disregarding hardware requirements, of course). Still, that does not mean that a brain Turing machine has to look anything like a neural network trained through gradient descent & back propagation. This was my point: artificial neural networks and the methods we use to train them have no resemblance to the human brain, and there is no reason to believe that they are the way to create an artificial general intelligence. So, there is no reason to be surprised that a neural network, especially one as small as any of the ones we have realized so far, doesn't exhibit complex properties of the human brain. Artificial neural networks are just a statistical model that was once inspired by a very, very simplistic idea of what biological neural networks are. As we have discovered more about biological neural networks, we've abandoned any notion of comparing ANNs with biological neural networks in terms of actual structure. This is all not to say that it's impossible for a complex enough ANN to actually be an AGI. It's just not going to be that surprising if it won't be, if an AGI program will look significantly different, and will be trained in completely different ways.

Eridrus 6y ago

I see where you're coming from, but given the (usual) flexibility and generality of neural neys, it's often very tempting to assume that you can just keep adding data, and I think these are good reminders that being able to do complicated things doesn't mean they can do simple things efficiently (which is a conversation that has come uo several times at work)

Dolores12 6y ago

This is a great way to find out if you are talking to a bot or not :)

Agentlien 6y ago

I once (2010) had an oral exam in neural networks during which I had to design a system to solve a particular task. My solution used two neural networks connected by a simple logic circuit. The professor, who was actually a neuro scientist with little understanding of the technical, asked why not a third neural network. This was a neural network course, after all. Thinking this was a trick question I excitedly explained how stupid it would be to build and train a network to approximate a function which could easily be precisely described with a tiny circuit or code statement. The professor was not amused. He said that's what he would have done. After a few similar incidents he concluded the exam giving me an 8/10 saying my answers were perfectly correct but he didn't like my attitude.

wodenokoto 6y ago
Yeah, there’s a better way of saying that and it sounds like that was an expensive lesson in communication. “Something something, it’s better to spend training time on the parts of the network where we don’t know the function beforehand than to train a subnetwork to do a function that we do know exactly at the outset.” and still you need to be ready to be asked about how to backpropogate through your hard coded function.
- Dylan16807 6y ago
  It's a test, you shouldn't have to sugar-coat explanations to an instructor (unless you're acting out some other scenario as part of the test).
  - gchamonlive 6y ago
    In a test you have to prove your knowledge by transmitting symbols through language. If you don't "sugar coat" it, how do you expect that the right symbols will be interpreted by the receiver? It is part of the test to use the appropriate language to ensure the best understanding of what you are saying. Nonviolent communication tries to do exactly that and is essential to this end. You can argue that since the professor understood what was being said, the language shouldn't matter, but it does. If again you don't use the correct language you risk offending the listener so much he can't get past that. After all you are dealing with humans, not machines, and in either case you are responsible for clear communication.

visarga 6y ago

GPT-3 is a generator, that's just half the equation. It needs a discriminator (critic) to check out its outputs and in the case of program synthesis and realistic physics, a simulator. That's how humans do creative stuff - generate silly ideas then check them out.

ZephyrBlu 6y ago
Sounds like maybe a GAN-like ML algo could be a good choice for attempting to do code generation.

YetAnotherNick 6y ago

This is not like we are asking GPT what is 2 times 3. It is not even a bug. Just that the author is using quantized multiplication which takes subset of floating point space and just makes all the value closest to nearest point. So 2 can be approximated as say 2.5 if there are few points. And that is not deterministic. Also it is a known thing that something like that could occur. It is just that neural network still seem to learn.