AlphaCode as a dog speaking mediocre English • Hacker News Board

Re: AlphaCode as a dog speaking mediocre English

#261

Post by runarberg » Tue, Feb 08, 2022, 3:57 PM UTC

killerstorm wrote: post #259

runarberg wrote: post #231

Earlier quoted context omitted.

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.

There is no need to reduce programing to math (even though you can technically do so) just as there is no need to reduce ‘talking’ to math just because speech can be represented with numbers. In fact you can represent most things (probably all things) with numbers.

I do think it is interesting philosophically how much powerful machine learning algorithms are over the traditional linear models. And perhaps it is a good description to say that “best fit program” is inferred. However these algorithms are still limited to inference. And—while useful—calling it intelligent is not very accurate—at least in the way most people use the word—no matter how complected of a construct they are inferring.

Re: AlphaCode as a dog speaking mediocre English

#262

Post by robertlagrant » Wed, Feb 09, 2022, 1:08 AM UTC

ArnoVW wrote: post #66

gambiting wrote: post #48

Earlier quoted context omitted.

Definitely not with AI? I mean Photoshop could do that 10+ years ago without "machine learning" even being a thing people talked about.

This is sort of the interesting thing with AI. It's a moving target. Every time when an AI problem gets cracked, it's "yea but that's not really AI, just a stupid hack". Take autonomous cars. Sure, Musk is over-hyping, but we are making progress. I imagine it will go something like: Step 1) support for drivers (anti sleep or colision).. done? Step 2) autonomous driving in one area, perfect conditions, using expensive…

Arguably most autonomous driving solutions can already perfectly emulate typical driving in Paris in the rain.

Re: AlphaCode as a dog speaking mediocre English

#263

Post by posterboy » Fri, Feb 18, 2022, 2:25 PM UTC

GreenWatermelon wrote: post #156

belter wrote: post #97

Earlier quoted context omitted.

I am willing take on your bet, as long you agree with my condition...you can't feed it with any of the millions of lines of code previously created by humans you aim to beat...;-)

How would the AI learn then? Humans learn by looking at existing code and gleaning new ideas, and does the AI although it requires much more data. Give the AI as much data as it wants, if it manages to solve something, it means it's a problem worth automating (is this a cat?)

> Humans learn by looking at existing code and gleaning new ideas, and does the AI although it requires much more data.

Actually I doubt it. There are different ways to learn, the cheapest of which is observation indeed. There's also trial and error, how most enthusiast start with little more than a description of the grammar if it's primitive enough, famously so for the BASIC varieties. Of course this leads to local minima and frustration quickly, Turing Tarpits e.g., but it also leads to independent discovery and rediscovery. At the next step of learning, I believe, it's the differentiation that makes ... all the difference – ie. learning about the development of languages and eventually of computing machines per se. At a higher order of learning it cannot be limited to calculation without a semantic interface (and vice versa).

I mean, etymologies like ominous calx (Latin "pebble", cp. chalk, calcium) for calculation, and symbolical notation show the kind of "code" that we copy from. That said, I'd think at the current state of the art, the ai would have to look at the machine code as the representation of its output eventually (perhaps because I found myself wanting to do that), even if encoded through macros and programming language devices, which it could do entirely unsupervised.

However, understanding the task is a more general problem, isn't it?