11–20 of 263 posts
The way Scott describes it, AlphaCode appears to be practicing Agile / TDD! All it needs to do now is write its own tests. Update: See my reply to dudeinjapan's response - I had not been completely clear here.
I still don't believe deep learning is going to take us to AGI. The susceptibility of these systems to completely failing on weird inputs shows that it's a kind of massive memorization going on here, rather than true understanding. Has anyone tried searching for new basic operations, below the level of neural networks? We've been using these methods for years, and I doubt the first major breakthrough in ML is the mos…
> The susceptibility of these systems to completely failing on weird inputs shows that it's a kind of massive memorization going on here, rather than true understanding. Isn’t that a truism? “Understanding” is equivalent to AGI. Nobody would argue that we have AGI yet, but the intelligence-sans-understanding is somewhat similar to animal intelligence, which was the precursor to human intelligence. What should scare u…
Do we know that? If that is not difficult, why is it so rare?
I still don't believe deep learning is going to take us to AGI. The susceptibility of these systems to completely failing on weird inputs shows that it's a kind of massive memorization going on here, rather than true understanding. Has anyone tried searching for new basic operations, below the level of neural networks? We've been using these methods for years, and I doubt the first major breakthrough in ML is the mos…
All of which is to say that memorization as you describe it seems like a plausible path to AGI, and humans don’t deal with weird inputs well either. It’s not like we are trying to make the ultimate single intelligence that rules humanity (although that may eventually come to pass). But something of roughly human competence certainly seems achievable.
I still don't believe deep learning is going to take us to AGI. The susceptibility of these systems to completely failing on weird inputs shows that it's a kind of massive memorization going on here, rather than true understanding. Has anyone tried searching for new basic operations, below the level of neural networks? We've been using these methods for years, and I doubt the first major breakthrough in ML is the mos…
With absolutely no evidence (as none can be had about the future) I believe that AI can be reached with computers and programming languages as different from the current ones as rockets are to horses.
The way Scott describes it, AlphaCode appears to be practicing Agile / TDD! All it needs to do now is write its own tests. Update: See my reply to dudeinjapan's response - I had not been completely clear here.
I still don't believe deep learning is going to take us to AGI. The susceptibility of these systems to completely failing on weird inputs shows that it's a kind of massive memorization going on here, rather than true understanding. Has anyone tried searching for new basic operations, below the level of neural networks? We've been using these methods for years, and I doubt the first major breakthrough in ML is the mos…
If you believe that people are "general intelligence" systems, then your comment doesn't imply that the existing artificial systems won't achieve general intelligence, because people fail massively on weird inputs.
Consider optical illusions:
https://newatlas.com/science/best-optical-illusions-year-202...
Consider audio illusions:
https://www.youtube.com/watch?v=yKR2pGwavqE
Consider tactile illusions:
https://www.youtube.com/watch?v=WdhnEq76_PA
* Machines fail on weird inputs, therefore they will never be intelligent;
* People fail on weird inputs, therefore they cannot be intelligent.
1. That we are unimpressed.
I'm gobsmacked.
2. That we don't think these are significant advances.
They're obviously huge advances.
3. That we don't think these models will have practical applications.
It's hard to imagine they won't.
4. That we think these systems are rubbish because they get things wrong.
I'm a programmer. I make mistakes all the time.
Having countered those views, the article then seems to imply that it follows that "we’ve now entered a world where 'programming' will look different." As someone who makes a living writing software I obviously have an interest in knowing whether that's true. I don't see much evidence of it yet.
These systems are certainly not (yet) capable of replacing a human programmer altogether, and whether they could ever do so is unknown. I'm interested in the implications of the technologies that have been developed so far - i.e. with the claim that "we've now entered a world..." So the question is about how useful these systems can be for human programmers, as tools.
The reason I'm skeptical of it is that the only model I've seen so far for such tooling is "have the machine generate code and have the human review it, select from candidate solutions and fix bugs". The problem is that doing so is, I expect, harder for the human than writing the code in the first place. I've mentioned this concern several times and not seen anybody even attempt to explain to me why I'm wrong about it. For example, at [1] I pointed out why some generated solutions for a particular problem would have only made my job harder and got accused of "screaming at a child for imperfect grammar."
Reviewing and fixing code is harder than writing it. Please explain why I'm wrong about that (it's certainly true for me, but maybe most people don't feel that way?), why it won't be a problem in practice or what planned applications there are for these technologies that would avoid the problem.
Please don't accuse me of cruelty to dogs or children.
As someone who is skeptical, but open minded, about the impact these technologies will have on practical programming I think I'm one of the "people" in "people are complaining..." The article makes some assumptions about what such people think that certainly aren't true for me: 1. That we are unimpressed. I'm gobsmacked. 2. That we don't think these are significant advances. They're obviously huge advances. 3. That w…
The way Scott describes it, AlphaCode appears to be practicing Agile / TDD! All it needs to do now is write its own tests. Update: See my reply to dudeinjapan's response - I had not been completely clear here.
It does write its own tests, i.e. to the extent it checks the generated programs work on the data provided and discards the ones that don't. I imagine many of the coding challenges it's trained on come with a few tests as well.
Ironically, I had just replaced 'test cases' with 'tests', because I thought that the former might seem too generic, and arguably satisfiable merely by rephrasing the problem statement as a test case to be satisfied.