A classical case of "when all you have is a hammer, everything looks like a nail".
Or rather, you're blind to everything that doesn't require a hammer.
Even in most of the examples he listed (speech/voice recognition software, translation, games, and databases), the vast majority of "code"/logic is not a neural network.
You don't "train" your UI, neural nets won't directly consume audio or spit out encoded audio, don't do HTTP, aren't an operating system and just having a game AI is pretty far from having a finished game.
Neural nets approximate and guess, but for the vast majority of problems in computing we want the exactness of code.
And coincidentally that's what makes neural networks great: They let us solve problems where stating the exact precise steps to solve them is impossible.
> You don't "train" your UI, neural nets won't directly consume audio or spit out encoded audio
I'm not an expert, but I thought pictures and audio were one of the few places where you could feed raw inputs into a neural net and get good results? Or am I wrong and we instead feed in some pre-processed version?
My understanding is that they took in RGB picture data, yes, but that they were required to be square (s.t. matrix transforms work on them, I think?).
But that's still different from taking in the actual JPEG data, which is sort of what the parent gets at: something has to decode that, and that software isn't a neural net.
(Further, when I worked w/ ML that dealt w/ image data, we had a host of non-ML code written around it to support it, dealing with the various facets of running in the cloud, where to get the data, where to store the results, who to notify about the results, and a bunch of preprocessing on the image — such as removing pointless borders that humans put around images.)
That software can be generated and continuously optimized by a neutral net. But that would probably mean that we achieved AGI, so decoding JPEGs won’t be that exciting by itself.
Just FYI, non-square images should work fine. Arguably that's one of the selling points of convolutional neural nets. The early big-name training sets tended to be all square though, and if your design ends with a fully-connected layer (more common back then as well) then you're pretty locked-in for aspect ratio unless you retrain.
I think the point is that the OS needs to interface with the hardware and read out the data, which is then passed to a program which works with it, part of which is a neural network in this case. Usually there's some marshalling and unmarshalling in there too.
I think you might have missed the point of the article. In various different ways it says that machine learning is capable of solving many problems better than traditional software designed by a developer. It says that it's akin to a new software programming paradigm or language. From this context, right now we're still all working in Assembly or C, and higher level languages are being developed that will allow us more productivity if we continue to build out the tooling and infrastructure.
I don't think he's suggesting your OS is going to be an ML model, but we will be surprised at how many problems they solve better than trying to do everything in a more manual way. I think this is pretty intuitive. Our programs used to say "Move this byte to this register" but now they say "Toggle this button". Eventually they will just say "Learn to produce results like this" with the same reliance on the developers who build the infrastructure as we have today for our compilers and interpreters.
> It says that it's akin to a new software programming paradigm or language.
That's a great way of thinking about it.
It's worth trying to imagine what the programming experience would be like if writing a function amounted to simply defining input/output types, providing a collection of input/output pairs (i.e. train/test sets), and leaving the process of finding the solution / optimization to the "compiler".
This is an unedited output of a language model which was not trained to pass programming phone screens. The only thing it was trained to do is to predict the next word in a sentence. Just 10 years ago this would be pure sci-fi. Today it's "meh". More importantly, there's no sign of slowing down.
For algorithms that fit a particular form, you could always automatically derive an optimal solution (think of dynamic programming). The actual question is: When is a neural net a good idea? Personally, I think it will always be great when the output goes to a human. So speech synthesis is a really great example. The same holds for game AIs.
But things that go into a "software 1.0" system (anything that needs to be understood by humans)? It will be difficult. Imagine recognition for self-driving cars sounds great until you have the first deadly accident that you simply cannot explain. Your system might even be really, really good, but that won't reduce the liability in that one case where it doesn't work.
Interesting ideas. How long should it take to create one of these solutions? NP-completeness doesn't care whether the thinker is human or neural-net, and optimality can take exponentially long to find.
Also, what if there simply isn't an optimal solution? Matrix multiplication comes to mind; we think it's quadratic-time, given the asymptotic number of operations that ought to be needed, but all existing algorithms are cubic-time (rounded up), and possibly there isn't a quadratic-time algorithm.
Game AIs are terrible for neural networks. Game designers want the ability to micro-tune difficulty and decision making to pace the game experience and the curve, and often times what's engaging for players is a simple model they can reason about and understand.
I think there's probably some value in neural networks in game design, but I don't see it being prevalent at runtime, mostly just in the tooling.
Reminds me of those videos from the 1950s that predicted what life would look like in the year 2000.
He's right about cleaning datasets being an entire job itself. But that should have been a red flag for his conclusion even then. Scaling anything means scaling costs. So, the more you want your neurochip to do that it isn't capable of doing by design, you need to do before or after it's done its task(s). That's what's between the lines of what he mentioned of "silent fails." If you don't want bias in your output, and your design isn't capable of vetting bias, you need to do the work of vetting bias before you pass that dataset off. That means you need an entire model defining bias, predicting impact and constraints on outputs.
I've said it before and will continue saying it 'til I'm apparently blue in the face: The complexity of a solution is dependent on the complexity of the problem it solves. I get that simple things feel attractive because they require less effort. We don't really reduce effort so much as we shift it from wholly solutionizing to partly solutionizing and mostly trying to continue to partly solutionize.
Ideas discussed in his post might seem too far from the reality and controversial, but first don't forget they have been doing Tesla's self-driving ML models for a few years now - so, he definitely has some material to generalize. It's one of the most advanced ML models in production, and without reflecting on the process it would be hard to develop it, would be hard to maintain it, etc.
Also, from my own experience building DVC - when you do any ML project you do have code indeed, but not doubt that data can be considered as important element as code, we need to take it seriously - track, review, etc, etc.
That is indeed the scariest part.
Given the number of vehicular fatalities this guy is personally responsible for at Tesla, I don't think this is a smart or important approach.
The callous nature in which people apply Silicon Valley mindset to critical infrastructure problems is scary: perhaps he should get off his self-driving high horse.
I think it's orthogonal question, right? Even if there were some mistakes, how many more death there would have been if they were not thinking about rigorous process behind ML development?
I would not use Silicon Valley technology to A/B test death rates by 'trying' arbitrary 'cool' things inside a fast moving metal torpedo. He can play with toys as he pleases but using real cars seems absolutely bonkers.
> Across many applications areas, we’ll be left with a choice of using a 90% accurate model we understand, or 99% accurate model we don’t.
I remember a doctor telling me that about a condition my child had. He said that "generally" it wasn't dangerous. It was interesting because to him what was important - naturally - was to work with the numbers and get the best overall results - without knowing or understanding exactly why. It was better for him and his time use to just know that it was "generally" OK. From my point of view I wasn't interested in "generally" but only specifically whether it was going to be dangerous in this case.
I think when the shit hits the fan for one personally then 90% you understand is going to be vastly better than 99% you don't. In a world where everything runs on the 99% model I imagine most people will have it good, but sometimes insanely terrible things will happen. Maybe that's ok.
As someone (I forget) pointed out we already have a form of opaque AI in the form of huge human bureaucracies that produce outcomes without anyone understanding all the steps that produce them. So maybe we already partly live in that world.
Even if you assume the benefits here are all true and not overly optimistic, there is a significant downside. Software that is "better" by means of more complexity via AI will be significantly harder for a human to understand. Even if it's generally more accurate, software that is a black box is inherently frustrating to at least some of it's users. If the advantage of AI generated software would be in solving problems through greater complexity than a human would think of, then it won't be able to achieve the kind of simplicity that makes it much easier to understand what software is actually doing.
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[ 3.0 ms ] story [ 62.7 ms ] threadOr rather, you're blind to everything that doesn't require a hammer.
Even in most of the examples he listed (speech/voice recognition software, translation, games, and databases), the vast majority of "code"/logic is not a neural network.
You don't "train" your UI, neural nets won't directly consume audio or spit out encoded audio, don't do HTTP, aren't an operating system and just having a game AI is pretty far from having a finished game.
Neural nets approximate and guess, but for the vast majority of problems in computing we want the exactness of code.
And coincidentally that's what makes neural networks great: They let us solve problems where stating the exact precise steps to solve them is impossible.
I'm not an expert, but I thought pictures and audio were one of the few places where you could feed raw inputs into a neural net and get good results? Or am I wrong and we instead feed in some pre-processed version?
But that's still different from taking in the actual JPEG data, which is sort of what the parent gets at: something has to decode that, and that software isn't a neural net.
(Further, when I worked w/ ML that dealt w/ image data, we had a host of non-ML code written around it to support it, dealing with the various facets of running in the cloud, where to get the data, where to store the results, who to notify about the results, and a bunch of preprocessing on the image — such as removing pointless borders that humans put around images.)
I don't think he's suggesting your OS is going to be an ML model, but we will be surprised at how many problems they solve better than trying to do everything in a more manual way. I think this is pretty intuitive. Our programs used to say "Move this byte to this register" but now they say "Toggle this button". Eventually they will just say "Learn to produce results like this" with the same reliance on the developers who build the infrastructure as we have today for our compilers and interpreters.
That's a great way of thinking about it. It's worth trying to imagine what the programming experience would be like if writing a function amounted to simply defining input/output types, providing a collection of input/output pairs (i.e. train/test sets), and leaving the process of finding the solution / optimization to the "compiler".
This is an unedited output of a language model which was not trained to pass programming phone screens. The only thing it was trained to do is to predict the next word in a sentence. Just 10 years ago this would be pure sci-fi. Today it's "meh". More importantly, there's no sign of slowing down.
Although, really, the more important question: Can we do studies on this without comparing expert computers to undergraduates who are just learning?
For algorithms that fit a particular form, you could always automatically derive an optimal solution (think of dynamic programming). The actual question is: When is a neural net a good idea? Personally, I think it will always be great when the output goes to a human. So speech synthesis is a really great example. The same holds for game AIs.
But things that go into a "software 1.0" system (anything that needs to be understood by humans)? It will be difficult. Imagine recognition for self-driving cars sounds great until you have the first deadly accident that you simply cannot explain. Your system might even be really, really good, but that won't reduce the liability in that one case where it doesn't work.
Also, what if there simply isn't an optimal solution? Matrix multiplication comes to mind; we think it's quadratic-time, given the asymptotic number of operations that ought to be needed, but all existing algorithms are cubic-time (rounded up), and possibly there isn't a quadratic-time algorithm.
I think there's probably some value in neural networks in game design, but I don't see it being prevalent at runtime, mostly just in the tooling.
He's right about cleaning datasets being an entire job itself. But that should have been a red flag for his conclusion even then. Scaling anything means scaling costs. So, the more you want your neurochip to do that it isn't capable of doing by design, you need to do before or after it's done its task(s). That's what's between the lines of what he mentioned of "silent fails." If you don't want bias in your output, and your design isn't capable of vetting bias, you need to do the work of vetting bias before you pass that dataset off. That means you need an entire model defining bias, predicting impact and constraints on outputs.
I've said it before and will continue saying it 'til I'm apparently blue in the face: The complexity of a solution is dependent on the complexity of the problem it solves. I get that simple things feel attractive because they require less effort. We don't really reduce effort so much as we shift it from wholly solutionizing to partly solutionizing and mostly trying to continue to partly solutionize.
Also, from my own experience building DVC - when you do any ML project you do have code indeed, but not doubt that data can be considered as important element as code, we need to take it seriously - track, review, etc, etc.
The callous nature in which people apply Silicon Valley mindset to critical infrastructure problems is scary: perhaps he should get off his self-driving high horse.
I remember a doctor telling me that about a condition my child had. He said that "generally" it wasn't dangerous. It was interesting because to him what was important - naturally - was to work with the numbers and get the best overall results - without knowing or understanding exactly why. It was better for him and his time use to just know that it was "generally" OK. From my point of view I wasn't interested in "generally" but only specifically whether it was going to be dangerous in this case.
I think when the shit hits the fan for one personally then 90% you understand is going to be vastly better than 99% you don't. In a world where everything runs on the 99% model I imagine most people will have it good, but sometimes insanely terrible things will happen. Maybe that's ok.
As someone (I forget) pointed out we already have a form of opaque AI in the form of huge human bureaucracies that produce outcomes without anyone understanding all the steps that produce them. So maybe we already partly live in that world.