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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

#21

Post by ulber »

yters wrote: post #12
siekmanj wrote: post #7

Earlier quoted context omitted.

We know the brain is doing something - if you don't want to call it computation, then you might as well call it magic.

There are other possibilities. For example, there can be an immaterial mind that operates as a halting oracle and interfaces with the world through the brain. Halting oracles are well defined, and we can empirically test for their existence. So, no reason why we have to assume everything humans do is reducible to some sort of automata. The only reason we make the assumption is because of prior materialistic commitmen…

There doesn't seem to be much reason to challenge the assumption: humans aren't good at solving any problems that we know to be uncomputable (e.g. the halting problem). Sure, it's a thing you could investigate, but the explanation for why it's not a popular topic is that it doesn't seem like a fruitful area of research.

Also, personally what my mind is doing doesn't feel like it's invoking an oracle for my problem solving. Generally when the search space for a problem that I'm solving increases I experience the kinds of blowups in the difficulty that would arise from me following an algorithm. Now, not everybody is the same. Do you feel like your problem solving calls an oracle?

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

#22

Post by karmasimida »

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?

Good question. I don't think we do actually.

The only reason I am convinced it is NOT doing a good job, is how utterly difficult to apply NN to dialog generation/management domain of business, often time it behaves much worse than rule-based systems.

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

#25

Post by mortivore »

yters wrote: post #6

As I always ask regarding this sort of story, why do we believe human intelligence is computable? The only answer I've heard is the materialist presupposition and sneers at any other metaphysic as "magic," which is not exactly a valid form of argument. As an alternative, the human mind could be some sort of halting oracle. That's a well defined entity in computer science which cannot be reduced to Turing computation,…

Part of me almost hopes it is a halting oracle of some sort because then we could start looking into either hooking up multiple brains to a single oracle or a single brain to many oracles.

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

#26

Post by ionforce »

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.

The problem is when non-technical people write articles or respond to posts about Deepmind. They think all AIs are the same and that one specific AI achievement means the Matrix is coming.

People lack nuance and critical thinking.

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

#27

Post by codekilla »

hacker_9 wrote: post #8

Behind every successful neural network is a human brain. Neural networks are a tool, an advanced tool for sure, but still just a tool. If we are looking for AGI, and assuming the brain is an AGI, then there are still many differences to resolve. For example, back propagation has not been observed in nature. Nor has gradient descent. So the core mechanisms for learning in nature have still to reveal their secrets.

> Behind every successful neural network is a human brain.

I've spent a lot of time trying to explain this to people, that there is a confluence between the human brain and the machine, people tend to look at the machine separately, which is a mistake. When I say unequivocally, 'there is no such thing as machine intelligence', I just get blank stares.

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

#28

Post by mortivore »

izzydata wrote: post #24

I'm not even convinced that a real AI is possible with conventional computer hardware or anything remotely similar to it. Not even considering software I get the impression there is a fundamental limitation of hardware.

I'm not convinced we've even defined the problem space well enough to solve it. Like what is the concrete measure(something to target) for intelligence? If we develop general intelligence is it going to be human, dog, or fish?

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

#29

Post by skybrian »

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…

Hmm, it seems like natural language translation has been getting quite a bit better with statistical techniques, though? I guess it depends what you mean by "only incremental".

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

#30

Post by roenxi »

The computational power of the hardware is getting really close to what a human brain is capable of (on an exponential scale, anyway). If "nowhere close" means not in the next 5 years then sure.

Over the medium term I'm not sure AI researchers are the best people to ask. They are completely dependent on how much power the electrical engineers give them - I doubt there is a deeper understanding what a doubling or quadrupling of computer power will do than any programmer learning about neural networks.

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