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AlphaCode as a dog speaking mediocre English

scottaaronson.blog

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Re: AlphaCode as a dog speaking mediocre English

#251

Post by qualudeheart »

trabant00 wrote: post #15

People think more of what we already have is going to go farther. 1 horse to the carriage gets you to the market. 2 horses to the next village. 4 to town and 6 cross states. Given enough horses we should reach the moon, right? 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…

You can launch a fast enough horse off a ramp and it’ll accelerate to escape velocity.

Re: AlphaCode as a dog speaking mediocre English

#252

Post by qualudeheart »

gspr wrote: post #235
alex-2k1 wrote: post #57

Earlier quoted context omitted.

Scientists in 50s expected to get language translation in 10 years as soon as computers will get enough computation power. They were real scientists, not "data AI scientists" who has little mathematics culture and not aware of any brain studies and problems in this field. But yeah, all aboard is hype train, we have a dog who speak English! Not a state machine that just do similar to what it was programmed on using st…

While I echo some of these sentiments, I don't think they apply in the slightest to the author of the blog post this thread discusses. Certainly a "real scientist" in every sense.

Aaronson doesn’t seem to believe in the scaling hypothesis like much of openAI and the Machine Intelligence Research Institute do.

That’s the only hypothesis that predicts something like AGI from emerging from even bigger language models.

Aaronson clearly isn’t expecting this tech to lead to AGI but that doesn’t make it not important.

Re: AlphaCode as a dog speaking mediocre English

#253

Post by qualudeheart »

indicisive wrote: post #113

Frankly asking a computer to write code is like asking a dog to speak facts in straight zfc. What else can I say, ai research has conspired to take over all other areas of computer science. I wonder who exactly started this hype. Also what else will come after this the stupid ai craze.

If we hit another AI winter the next big thing would be new/better energy sources and space travel. 3d printing will also enable a return to local scale manufacturing. Crypto will devour more industry.

We’ll have mostly automated local factories producing whatever you need and be able to sell it for untraceable crypto.

I want that to happen because it will enable resistance movements against the misaligned AI that will inevitably emerge.

I model misaligned AI as like an imperialist state. It’s impossible for a single power to conquer the world because every other state allies against it. That will be how AI alignment is solved.

Re: AlphaCode as a dog speaking mediocre English

#254

Post by dnautics »

nradov wrote: post #160
axg11 wrote: post #107

Earlier quoted context omitted.

When you're on that curve, it's indistinguishable until you hit the plateau. We're in an era where AI is continuing to improve and has already surpassed a level that many people doubted was achievable. Nobody knows when that progress will plateau. It's entirely possible that we plateau _after_ surpassing human-level intelligence.

The AI technology today has practical value for some use cases but it's basically just clever parlor tricks. There has been near zero discernable progress toward artificial general intelligence. We don't yet have a computer that can learn and make optimal resource usage decisions in an open world environment as well as a mouse. In most respects we're not even at the insect level yet.

> The AI technology today has practical value for some use cases but it's basically just clever parlor tricks.

Come on, you could have said the same thing about excel 30 years ago.

Re: AlphaCode as a dog speaking mediocre English

#255

Post by ThrowawayR2 »

nlitened wrote: post #164
whatshisface wrote: post #152

Earlier quoted context omitted.

Who cares if they're correct? If they fail a test, you can fix them. If they turn out to be wrong in production you can isolate the example and add it as a test. Producing regexes that pass all current tests but contain a subtle bug satisfy 100% of what programmers are incentivized to do. Producing them very quickly will get you promoted.

I think we will very soon start seeing a clear separation between programmers ("co-pilot operators") and software engineers (those who do the thinking and understanding when there's someone "who cares").

We already have that bifurcation in the sense that the majority of programmers can use frameworks, libraries, etc. to get work done but don't have the deeper knowledge to build them well.

Re: AlphaCode as a dog speaking mediocre English

#256

Post by nradov »

dnautics wrote: post #254
nradov wrote: post #160

Earlier quoted context omitted.

The AI technology today has practical value for some use cases but it's basically just clever parlor tricks. There has been near zero discernable progress toward artificial general intelligence. We don't yet have a computer that can learn and make optimal resource usage decisions in an open world environment as well as a mouse. In most respects we're not even at the insect level yet.

> The AI technology today has practical value for some use cases but it's basically just clever parlor tricks. Come on, you could have said the same thing about excel 30 years ago.

Yes Excel has more features today. So what? We're still not making any measurable progress towards true AGI. We don't even know what the development path or ultimate goal looks like except for some vague hand waving about passing the Turing Test.

Re: AlphaCode as a dog speaking mediocre English

#257

Post by dnautics »

nradov wrote: post #256
dnautics wrote: post #254

Earlier quoted context omitted.

> The AI technology today has practical value for some use cases but it's basically just clever parlor tricks. Come on, you could have said the same thing about excel 30 years ago.

Yes Excel has more features today. So what? We're still not making any measurable progress towards true AGI. We don't even know what the development path or ultimate goal looks like except for some vague hand waving about passing the Turing Test.

I work for a company that provides "ai" services in a "boring" domain. The Time saved and accuracy (for a legally audited result) we provide is quantifiable, and when I saw the numbers I was surprised. So while a lot of the "AI" hype is stupid marketing bs, some of us are actually doing real stuff, it's not just parlor tricks. You might also be surprised in how many domains a small improvement has huge yield in terms of raw money and/or quality of life improvements.

Re: AlphaCode as a dog speaking mediocre English

#258

Post by moyix »

ahgamut wrote: post #242
moyix wrote: post #240

Earlier quoted context omitted.

AlphaCode does some analysis of training data copying in their paper (Sections 6.1 and Appendix F): https://storage.googleapis.com/deepmind-media/AlphaCode/comp... It does not seem to be copying from the training data in any meaningful way.

> It does not seem to be copying from the training data in any meaningful way. My point is, I would like to verify this claim with different metrics, because we probably have different interpretations of the word "meaningful". AlphaCode measures similarity between programs via longest common substrings . That's better than nothing, but that would mean two programs that differ only in variable naming would not be cons…

Sure, I think this would be a really interesting study to do! I have been meaning to scrape a big chunk of GitHub in order to do this kind of analysis. I think I would be surprised (just based on my own use of Codex and Copilot) if they were copying at the level of "same code but renamed variables" either. Past that I think comparisons get pretty difficult, and to some degree I would actually be more impressed if it understood enough about programs to be able to mimic higher-level structure without just memorizing text.

Why do you think Python isn't good for comparing program structure? Certainly you could compare ASTs and bytecode pretty easy; I think it's actually much easier to do so than in C/C++ since you don't have to deal with preprocessor junk and compile flags influencing the meaning of the code. There's less available for classical analyses like data and control flow analysis, in part because those are much harder in dynamic languages, but there are some tools out there like the ones used in PyPy for getting SSA CFGs [1].

I feel like many people are equivocating a bit on what they mean by "regurgitating" here. It seems clear that the models have at best a shaky grasp on code semantics (e.g., they are bad at things like predicting what the output of some code will be: https://arxiv.org/abs/2112.00114), and struggle with problems that are very different from anything they've seen before.

[1] https://foss.heptapod.net/pypy/pypy/-/tree/branch/default/rp...

Re: AlphaCode as a dog speaking mediocre English

#259

Post by killerstorm »

runarberg wrote: post #231
killerstorm wrote: post #213

A lot of people who are skeptical about AI progress call it "statistical modeling" and point to large data sets involved and large amounts of hardware thrown at it. Sort of implying it's some sort of a brute-force trick. I'm afraid they do not understand the size of problem/solution set. Suppose problem and solution are 1000 characters long and there's a set of 32 characters. Then a model is function F: X -> X where…

We are still inferring a best fit based on available data, so “statistical modelling” is way more descriptive of what we are doing the “intelligence”. That doesn’t mean it isn’t impressive. These non-classical statistical inference are going beyond what we have been able to do with classic statistics, it is indeed impressive. Personally I only grow skeptical when claims are made about some vague Artificial General In…

Given that NN components are demonstrated to have universal computation properties, it is more like deriving a program from training data. Technically a program here is a 'curve' but it's insanely complex one.

Do you consider programming math? Since technically you write a function which deals with 0s and 1s.

Re: AlphaCode as a dog speaking mediocre English

#260

Post by ahgamut »

moyix wrote: post #258
ahgamut wrote: post #242

Earlier quoted context omitted.

> It does not seem to be copying from the training data in any meaningful way. My point is, I would like to verify this claim with different metrics, because we probably have different interpretations of the word "meaningful". AlphaCode measures similarity between programs via longest common substrings . That's better than nothing, but that would mean two programs that differ only in variable naming would not be cons…

Sure, I think this would be a really interesting study to do! I have been meaning to scrape a big chunk of GitHub in order to do this kind of analysis. I think I would be surprised (just based on my own use of Codex and Copilot) if they were copying at the level of "same code but renamed variables" either. Past that I think comparisons get pretty difficult, and to some degree I would actually be more impressed if it…

> I would actually be more impressed if it understood enough about programs to be able to mimic higher-level structure without just memorizing text.

Agreed.

> Why do you think Python isn't good for comparing program structure?

From recent experience I prefer control flow analysis, or something that results in a graph structure. As you said, that's harder with dynamic languages. I also think some Python features (english-like syntax, f-strings, division converts int to float, whitespace indentation, loops vs generator expressions) make structural comparisons messy, but that may just be bias.

The ideal language would be one with minimal syntax, where we can target a decent range of programs, and obtain as much info as possible about program structure without actually running the program. I've come across LISP-without-macros in Dreamcoder (https://arxiv.org/abs/2006.08381), BF++ (https://arxiv.org/abs/2101.09571), and a couple of others which I can't remember right now. I think the APL family (APL/J/K) would interesting because fewer characters to generate, but each character has a lot of meaning.

Right now I'm looking at flow-based programming (FBP) for this: In FBP the control flow is explicit - the program code describes a directed acyclic graph (DAG), so comparing program structure becomes straightforward (subgraph isomorphism with some heuristics). I'm writing a toy FBP language that draws images (https://github.com/mayahq/flatland), with which I aim to test what these models understand.

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