[m0zg]

The problem is that not only is this "not full blown AGI". The problem is that, if you understand how this works, it's not "intelligence" at all (using the layperson meaning of the word, not the marketing term), and it's not even on the way to get us there.

[phist_mcgee]

It reminds me of that pithy remark by someone I read a while ago which was (paraphrased): "Any time someone pushes forward AI as a field, people will almost alway remark: 'but that's not real AI.'"

It's true, the mundanity quickly settles in, and we look to the next 'impossible hurdle' and disregard the fact that only a few years ago, natural language generation like this was impossible.

[abernard1]

> "Any time someone pushes forward AI as a field, people will almost alway remark: 'but that's not real AI.'"

This statement reveals a widespread, and in my opinion, a not-entirely-correct, assumption that increases in the ML field means we're actually pushing forward on AI. It also implies a belief that the pre-1970s people were somehow less right than the 2000s+ ML crowd, when a lot of ML's success is related to compute power that simply did not exist in the 1970s.

ML computational machines to transform inputs->outputs are great, but there's no compelling reason to believe they're intrinsic to intelligence, as opposed to functioning more like an organ.

We might be making great image classifier "eyes", or spam-filtering "noses", or music-generating "ears". But it's not clear to me that will incrementally get us closer to an intelligent "brain", even if all those tools are necessary to feed into one.

[liuliu]

I disagree. Yes, it is just a decoder of transformer. But it looks like we are really close, with some tweaks on the network structure, reward function design and inputs / outputs. On the same time, GPT-3 also points how far away we are at hardware level.

Let me put it this way: I don't know how challenging the rest is going to be, but it surely looks like we are on the right path finally.

[m0zg]

It fundamentally has no _reasoning_. There is no AGI without reasoning.

[hackinthebochs]

What makes you think this? The fact that it can produce working code from a prompt in some cases shows rudimentary non-trivial reasoning. Hell, GPT-2 demonstrated rudimentary reasoning of the trivial sort.

[abernard1]

> The fact that it can produce working code from a prompt in some cases shows rudimentary non-trivial reasoning.

It doesn't at all. It indicates that it read stackoverflow at some point, and that on a particular user run, it replayed that encoded knowledge. (I'd also argue it shows the banality of most React tutorials, but that's perhaps a separate issue.)

Quite a lot of these impressive achievements boil down to: "Isn't it super cool that people are smart and put things on the internet that can be found later?!"

I don't want to trivialize this stuff because the people who made it are smarter than I will ever be and worked very hard. That said, I think it's valid for mere mortals like myself to question whether or not this OpenAI search engine is really an advancement. It also grates on me a bit when everybody who has a criticism of the field is treated like a know-nothing Luddite. The first AI winter was caused by disillusionment with industry claims vs reality of what could be accomplished. 2020 is looking very similar to me personally. We've thrown oodles of cash and billions of times more hardware at this than we did the first time around, and the most use we've gotten out of "AI" is really ML: classifiers. They're super useful little machines, but they're sensors when you get right down to it. AI reality should match its hype, or it should have less hype (e.g. not implying GPT-3 understands how to write general software).

[hackinthebochs]

>It doesn't at all.

Assertions aren't particularly useful in this discussion. Nothing you said supports your claim that GPT-3 doesn't show any capacity for reasoning. The fact that GPT-3 can create working strings of source code from prompts it (presumably) hasn't seen before means it can compose individual programming elements into a coherent whole. If it looks like a duck and quacks like a duck, then it just might be a duck.

Here's an example of rudimentary reasoning I saw from GPT-2 in the context of some company that fine-tuned GPT-2 for code completion (made up example but captures the gist of the response):

[if (variable == true) { print("this sentence is true") } else] { print("this sentence is false") }

Here's an example I tested using talktotransformer.com: [If cars go "vroom" and my Ford is a car then my Ford] will also go "vroom"...

The bracketed parts where the prompt. If this isn't an example of rudimentary reasoning then I don't know what is. If your response is that this is just statistics then you'll have to explain how the workings of human brains aren't ultimately "just statistics" at some level of description.

[abernard1]

> working strings of source code from prompts it (presumably) hasn't seen before

I'm saying that "presumably" is wrong, especially on what it was: a simple React program. It would not surprise me if the amount of shared structure and text in the corpus is all over the place.

This can be tested by making more and more sophisticated programs in different languages, and seeing how often it returns the correct result. I don't really care, because it can't reliably do basic arithmetic if the numbers are in different ranges. This is dead giveaway it hasn't learned a fundamental structure. If it hasn't learned that, it hasn't learned programming.

The examples are not really that impressive either. They are boolean logic. That a model like this can do copy-pasta + encode simple boolean logic and if-else is... well.. underwhelming. Stuff like that has been happening for a long time with these models, and no one has made claims that the models were "reasoning".

[jpttsn]

Couldn’t you yourself learn how to do that, in a foreign language, without knowing what the words mean?

[dragonwriter]

> The fact that it can produce working code from a prompt in some cases shows rudimentary non-trivial reasoning.

No, “in some cases” doesn't show reasoning. It is, arguably, weak evidence for reasoning that supports other explanations. With the right input corpus, a Markov chain generator will produce working code from a prompt “in some cases”, and I don't think any one has a weak enough definition of reasoning to admit Markov chains.

[hackinthebochs]

Of course we need to quantify "in some cases" for your argument to hold. Humans aren't perfect reasoners, for example. The examples I saw were impressive and were mostly correct, apart from some minor syntax errors or edge cases. This wasn't a Markov chain generator where the "interesting" responses where cherry picked from a pile of nonsense.

[Bx6667]

So under your logic we won’t have any idea that we are close to having agi until we have a machine that can reason... which is agi. You are missing the big picture

[opless]

There's clearly no planning for a solution, I think that's what GP is getting at.

[Bx6667]

You don’t understand how it works. You can’t explain how the model works. Go ahead and correct me if I’m wrong.