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Geoffrey Hinton and Demis Hassabis: AGI is nowhere close to being a reality

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Re: Geoffrey Hinton and Demis Hassabis: AGI is nowhere close to being a reality

#111

Post by antidesitter »

odyssey7 wrote: post #20
karmasimida wrote: post #17

And no one should be surprised by this. The NN advancement of late doesn't help addressing human-style symbolic reasoning at all. All we have is a much more powerful function approximator with a drastic increased capacity (very deep networks with billions of parameters) and scalable training scheme (SGD and its variants). Such architecture works great for differentiable data, such's images/audios, but the improvement…

Are there ways that an AI practitioner would be able to tell whether a neural network is doing human-style symbolic reasoning?

IQ tests [1] and one-shot learning come to mind.

[1] https://arxiv.org/abs/1807.04225

Re: Geoffrey Hinton and Demis Hassabis: AGI is nowhere close to being a reality

#112

Post by alexnewman »

nuguy wrote: post #68

I take huge offense to this article. They claim that when it comes to AGI, Hinton and Hassabis “know what they are talking about.” Nothing could be further from the truth. These are people who have narrow expertise in one framework of AI. AGI does not yet exist so they are not experts in it, in how long it will be, or how it will work. A layman is just as qualified to speculate about AGI as these people so I find it…

"These are people who have narrow expertise in one framework of AI." Proof that you don't know who you are insulting

Re: Geoffrey Hinton and Demis Hassabis: AGI is nowhere close to being a reality

#113

Post by epicureanideal »

https://en.wikiquote.org/wiki/Incorrect_predictions

"Hence, if it requires, say, a thousand years to fit for easy flight a bird which started with rudimentary wings, or ten thousand for one which started with no wings at all and had to sprout them ab initio, it might be assumed that the flying machine which will really fly might be evolved by the combined and continuous efforts of mathematicians and mechanicians in from one million to ten million years--provided, of course, we can meanwhile eliminate such little drawbacks and embarrassments as the existing relation between weight and strength in inorganic materials. [Emphasis added.] The New York Times, Oct 9, 1903, p. 6."

-----

A couple of the leading minds in AGI say it's a long ways away... just because the universe likes to give us the finger, maybe AGI is on the horizon. Maybe we'll look back at this in 10 years and laugh (if we're here).

Re: Geoffrey Hinton and Demis Hassabis: AGI is nowhere close to being a reality

#115

Post by nuguy »

alexnewman wrote: post #112
nuguy wrote: post #68

I take huge offense to this article. They claim that when it comes to AGI, Hinton and Hassabis “know what they are talking about.” Nothing could be further from the truth. These are people who have narrow expertise in one framework of AI. AGI does not yet exist so they are not experts in it, in how long it will be, or how it will work. A layman is just as qualified to speculate about AGI as these people so I find it…

"These are people who have narrow expertise in one framework of AI." Proof that you don't know who you are insulting

I don’t remember insulting anyone. And how is that not true?

Re: Geoffrey Hinton and Demis Hassabis: AGI is nowhere close to being a reality

#116

Post by joe_the_user »

Retric wrote: post #110
joe_the_user wrote: post #106

Earlier quoted context omitted.

Well, neural nets and similar things laughably worse than AI systems when confronted with "real world" situation. I wouldn't argue with the point that humans use rigorous logic and overt rules-based behavior much less than they imagine (your summary is very much a summary of the other-NLP model of mind, which I know). I'd argue that while "refined" logic, systematic logic, might be rare, fairly crude logic, more or l…

Intelegence is not limited to what Humans are good at. People are really bad at several tasks where current AI tech excels, but those things tend to be excluded from the conversation. AGI that is as smart as say a rat would easily qualify as AGI even without language skills.

Intelligence is not limited to what Humans are good at.

Being able to implement all the things human are good at, however, should be able to get us everything that we could do, because anything we could create, it could create too.

AGI that is as smart as say a rat would easily qualify as AGI even without language skills.

Indeed, but while a full language-using AI is ways a way at least, using language is one thing that's at sort-of describable/comprehensible as a goal. A rat is a lot more robust than any human made robot but how? Overall, I keep hearing these "there's intelligence that's totally unlike what we conceive" argument but it seems like computer programs as they exist now either do what a human could do rationally and more quickly (a conventional program) or heuristic duplicate human surface behavior (neural nets). You could sort-of argue for more but it's a bit tenuous. Human behavior is very flexible already (that's the point, right). And assuming AI is hard to create, creating something who properties we to-some-extent understand is more like than creating the wild unknown AI.

Also, "Getting to rat level" might not be the useful path to AGI. If we simply created a rat like thing, we might win the prize of "real AGI" but it would be far less useful than something we could tell what to do the way we tell humans what to do.

Re: Geoffrey Hinton and Demis Hassabis: AGI is nowhere close to being a reality

#117

Post by balfirevic »

mindgam3 wrote: post #90
rozim wrote: post #67

Earlier quoted context omitted.

I suspect "general purpose artificial intelligence system" means the same architecture applied to 3 games (western Chess, Shogi, Go).

Wouldn't that be a "general purpose game-playing intelligence system" at best? (without mentioning that it only applies to certain types of perfect-information games) Maybe it's just me, but "general purpose artificial intelligence system" sounds like, well, General Artificial Intelligence. Which sounds like Artificial General Intelligence, which is the holy grail.

> Maybe it's just me, but "general purpose artificial intelligence system" sounds like, well, General Artificial Intelligence.

Well, it doesn't sound like that at all to me and I think the phrasing is fair. Also, it folds proteins.

Re: Geoffrey Hinton and Demis Hassabis: AGI is nowhere close to being a reality

#118

Post by joe_the_user »

anonytrary wrote: post #23

Tangential: This title is weird. As if no one but the top minds in AI didn't know this? This isn't big news to anyone who has done even just a modicum of AI research.

Research can tell you current effort fall far short. Research can tell you current efforts are moving incrementally towards the goal, even. But research won't you when something we don't understand will happen. "A long time" but overall, it seems like the kind of situation where probably as such isn't particularly applicable.

Re: Geoffrey Hinton and Demis Hassabis: AGI is nowhere close to being a reality

#119

Post by MAXPOOL »

mrdoops wrote: post #9

I wonder to what extent the data being fed to these models are the issue. Or rather the problem is the systems that generate these data-sets and how representative of reality they are. If we make an app that involves humans and that data is used in a model - to what extent does user experience and other factors warp reality? Maybe our existing methods are good enough given enough compute to reach AGI but our datasets…

The problem is not the data. The problem is the need for high quality data. Current ML is data driven statistical learning. ML tries to learn a model that describes the distribution. It's impossible to get similar performance as the best reference implementation (human brain) using this approach. https://i.redd.it/kvvgv6zzhtp11.png

Think of 16 year old human:

* it has received less than 400 million wakeful seconds of data + 100 millions seconds of sleep,

* it has made only few million high level cognitive decisions where feedback is important and delay is tens of second or several minutes (say few thousand per day). From just few million samples it has learned to behave in the society like a human and do human things.

* Assuming 50 ms learning rate at average, at the lowest level there is at most 10 billion iterations per neuron (Short-term synaptic plasticity acts on a timescale of tens of milliseconds to a few minutes.)

Humans generate very detailed model of their environment with very little data and even less feedback. They can learn complex concept from one example. For example you need only one example of pickpocket to understand the whole concept.

Re: Geoffrey Hinton and Demis Hassabis: AGI is nowhere close to being a reality

#120

Post by visarga »

joe_the_user wrote: post #46
sdenton4 wrote: post #36

Earlier quoted context omitted.

Maaaaybe. I tend to think that symbolic reasoning is a learning tool, rather than a goalpost for general intelligence. For example, we use symbolic reasoning quite extensively when learning to read a new language, but once fluent can rely on something closer to raw processing - no more reading and sounding out character sequences. Similarly with chess - eventually we have good mnemonics for what make good plays, and…

Once someone is fluent in a language, the logical operations and judgements involved stop being overt and highly visible to the conscious mind. But that doesn't mean that one stops getting the benefits and results of logical operations. What you might see as logical operations "not mattering", I would see as logical operations integrated so deeply into reflexive operations that it's hard to see where one ends and the…

I think "a better set of inputs" is the real world or much better simulators to train our RL agents. François Chollet (author of Keras) was saying a similar thing - focusing too much on architectures and algorithms we forget the importance of the environment, an agent can only become as smart as the hardest problem it has to solve in its environment, and depends on the richness of said environment for learning. Humans are not general intelligences either, we're just good at being human (surviving in our environment). We'd be much smarter in a richer environment, too.

https://intelligence.org/2017/12/06/chollet/

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