3 ms·

Trying to solve the lack of progress in simplifying the complexity of standard development scenarios needs to be the next step in framework development. I just

by weego 4y ago

Trying to solve the lack of progress in simplifying the complexity of standard development scenarios needs to be the next step in framework development. I just don't understand how or why it needs to be polluted by the unnecessary added hidden complexity of ML. You're just shifting your unknowns into a black box and it's magic output.

generalizations 4y ago
What you said actually sounds pretty similar to what I can imagine was said 'back in the day' when compilers were first developed and languages like C were invented. > Trying to solve the lack of progress in unifying computer architectures needs to be the next step in computer science. I just don't understand how or why it needs to be polluted by the unnecessary added hidden complexity of GCC. You're just shifting your assembly language generation into a black box and its magic output. (I don't actually know how to write assembly, so pardon any technical faux pas. But the comparison seemed apt. ML as an intent->code compiler just seems like an inevitable step forward.)
- gugagore 4y ago
  GCC is less opaque (less of a black box) than these language models.
- mywittyname 4y ago
  I can see why you would make this analogy but I personally don't think of code generation and code compilation to be analogous.
lagrange77 4y ago
It's what they call 'democratization of code' imo. ML generated code won't be used for high tech software at first, but for the mass of not so critical software like web frontend scripting.
- Kaze404 4y ago
  I have zero hopes for ML getting web frontend right when real life humans have such a hard time doing so.
turns0ut 4y ago
ML is not a “black box”. It’s formalized statistics translated to code by community. Regex and grep are just as “black box magic” to those who read a blog post on how to scan some text files. ML is statistics on arrays, whereas Unix tools are stats for a Unix system. It’s the same abstraction at its core, new implementation.
- acoard 4y ago
  In a recently reported issue with an Apple Car almost running over a pedestrian, someone on HN asked why can't we simply add the code: if (personInFrontOfCar) { halt(); } The reason, of course, is the self-driving algorithms are all done with machine learning. The model is trained with a corpus of information and the result is a "black box." The programmer could not insert the above `if` statement if they wanted to, they could not modify the source code of the model as if it was normal business logic. Instead, they would have to re-train the model on new data. This is what people mean when they say it is a "black box." There is no source code to edit, only training data that outputs an as-is model. It's similar to getting binary blobs, for eg Linux drivers. In contrast, I could fork regex if I wanted and add an `if` statement in the middle of it, and re-compile it. It is pure source code. Same with grep.
  - MathYouF 4y ago
    From my admittedly limited knowledge of how self driving policy models are implemented, I believe the main black box AI is determining the part `if personInFrontOfCar:` (using object detection, masking, 3D pose estimation, et/or cetera) And actually defined policies on how to handle certain situations given that inference are possible, in the case specifically of self driving vehicles. My assumptions come from when I'd asked someone at NeurIPS in 2019 about what reinforcement learning methods they use as they said "I don't think any self driving companies are using reinforcement learning, it is too risky". I don't mean to imply this is hard to find information either, reading a few papers would be all it takes to clear up the degree to which most self driving policies are "controllable" in the way you describe, or at least to what degree. My main point is that I think it is the inferring of the environment (current state) rather than the chosen policy at each time step which is more of an error prone black box.

by weego 4y ago

generalizations 4y ago

What you said actually sounds pretty similar to what I can imagine was said 'back in the day' when compilers were first developed and languages like C were invented. > Trying to solve the lack of progress in unifying computer architectures needs to be the next step in computer science. I just don't understand how or why it needs to be polluted by the unnecessary added hidden complexity of GCC. You're just shifting your assembly language generation into a black box and its magic output. (I don't actually know how to write assembly, so pardon any technical faux pas. But the comparison seemed apt. ML as an intent->code compiler just seems like an inevitable step forward.)

gugagore 4y ago
GCC is less opaque (less of a black box) than these language models.
mywittyname 4y ago
I can see why you would make this analogy but I personally don't think of code generation and code compilation to be analogous.

lagrange77 4y ago

It's what they call 'democratization of code' imo. ML generated code won't be used for high tech software at first, but for the mass of not so critical software like web frontend scripting.

Kaze404 4y ago
I have zero hopes for ML getting web frontend right when real life humans have such a hard time doing so.

turns0ut 4y ago

ML is not a “black box”. It’s formalized statistics translated to code by community. Regex and grep are just as “black box magic” to those who read a blog post on how to scan some text files. ML is statistics on arrays, whereas Unix tools are stats for a Unix system. It’s the same abstraction at its core, new implementation.

acoard 4y ago
In a recently reported issue with an Apple Car almost running over a pedestrian, someone on HN asked why can't we simply add the code: if (personInFrontOfCar) { halt(); } The reason, of course, is the self-driving algorithms are all done with machine learning. The model is trained with a corpus of information and the result is a "black box." The programmer could not insert the above `if` statement if they wanted to, they could not modify the source code of the model as if it was normal business logic. Instead, they would have to re-train the model on new data. This is what people mean when they say it is a "black box." There is no source code to edit, only training data that outputs an as-is model. It's similar to getting binary blobs, for eg Linux drivers. In contrast, I could fork regex if I wanted and add an `if` statement in the middle of it, and re-compile it. It is pure source code. Same with grep.
- MathYouF 4y ago
  From my admittedly limited knowledge of how self driving policy models are implemented, I believe the main black box AI is determining the part `if personInFrontOfCar:` (using object detection, masking, 3D pose estimation, et/or cetera) And actually defined policies on how to handle certain situations given that inference are possible, in the case specifically of self driving vehicles. My assumptions come from when I'd asked someone at NeurIPS in 2019 about what reinforcement learning methods they use as they said "I don't think any self driving companies are using reinforcement learning, it is too risky". I don't mean to imply this is hard to find information either, reading a few papers would be all it takes to clear up the degree to which most self driving policies are "controllable" in the way you describe, or at least to what degree. My main point is that I think it is the inferring of the environment (current state) rather than the chosen policy at each time step which is more of an error prone black box.