Human-Level AI Is Harder Than It Seemed in 1955 – John McCarthy (2006)

http://www-formal.stanford.edu/jmc/slides/wrong/wrong-sli/wrong-sli.html

by shawndumas • 11 years ago

40 41 11 years ago

www-formal.stanford.edu

It shouldn't actually be hard, especially when you look at how our brains lead to a recognizably human intelligence. Problem is, as has been pointed out by Jeff Hawkins, no one in AI research care(d/s) to look at biology to see what might be learned.... So much heat and funding without light.

Edit: this was meant somewhat ironically to get the point across that we can't be expected to succeed with AI unless we know how HI actually works.

simonh• 11 years ago

The problem with that is, we still dont really know how the brain works either. Yes we know roughly how neurons work, but how does that lead to the making of a decision? No clue. The best we can do is look at various activation patterns in the anterior cingulate cortex and wonder. It's like trying to figure out how a CPU works by measuring fluctuations in its temperature and power draw as it performs calculations.

atrilla• 11 years ago

And the closest to solving this we seem to be at present is fuelled by deep learning, which is basically a big neural network with an absurdly vast amount of neurons (i.e., parameters for learning, like the brain). We can observe how this brute-force technique works, but unfortunately no-one can explain why (it's a black box model). The same story has been on for decades.

I would relate it to being a discriminative model, which is tailored to solving a specific task, in contract to generative models, which try to model and explain the world. Perhaps the brain is not meant to understand how the world works but how to do take advantage of it.

superobserver10• 11 years ago

Well, yes, which is the point. If we don't know how our brains do this intelligencing, then how can we expect any program or machine to do the same? This is a part of the failure of AI research. Can't put the cart before the horse. Well, you can, but it probably won't work.

knodi1232• 11 years ago

seriously? no one in AI is looking at the human brain to try and understand intelligence? this is the most inaccurate comment I've seen in 2015, although it's a little too early in the year to call it a winner.

superobserver10• 11 years ago

I meant that as a part of the general argument put forward by Jeff Hawkins (cf. "On Intelligence"). Not my claim, to be precise. And it isn't exactly as ridiculous as it sounds either.

p1esk8• 11 years ago

Actually, the comment is fairly accurate. Very few people in AI are reading neuroscience papers, and even fewer are trying to implement the ideas from those papers in software. Those who do, are not really doing AI, but rather writing brain simulators, and not trying to perform any intelligent tasks. The closest examples that come to mind (aside from Jeff Hawkins) are Chris Eliasmith and guys behind Leabra.

If you know any others, please mention them!

alexhawdon• 11 years ago

There's a whole branch of CompSci/AI exactly to do with this, known (sometimes) as "Bio-Inspired Systems". It is as false to say that nobody is looking to nature for inspiration as it is to say that human-level intelligence is a trivial problem.

If you can substantiate your assertion then there are people waiting to give you enormous piles of money!

superobserver10• 11 years ago

No need to substantiate. Compare brain research funding over the same period of time versus AI. Who comes out ahead (pun not intended)?

Teodolfo3• 11 years ago

I completely agree that there is very little chance AI research will succeed any time soon, but not because AI researchers haven't listened enough to Jeff Hawkins. The way to understand human intelligence is to build computational models that reproduce some of its properties.

superobserver10• 11 years ago

My statement isn't so much that we listen to Hawkins but rather that the point put forward by Hawkins (among others) in recent years is the point around which we should focus our efforts. You can't build a computational model if you have nothing that realistically reflects the requirements of the thing that you wish to model as instantiated in reality as we know it.

tzs• 11 years ago

Edit: this was meant somewhat ironically to get the point across that we can't be expected to succeed with AI unless we know how HI actually works

That's only true if the way HI works is the only reasonable way at achieve AI.

Biological solutions can be a good inspiration for some problems, but not always. See the film Gizmo, which is the subject of another story currently on the first couple of pages of HN, for some footage of what happened when people tried to base aviation too closely on what birds do.

I can't see any reason that it is not plausible that someday, after we do have AI, the sentence "we can't be expected to succeed with AI unless we know how HI actually works" will be regarded similarly to the way the sentence "we can't be expected to build vehicles that travel 60 mph [1] unless we know how cheetahs actually work" would be regarded now.

[1] 97 km/hr

tr352• 11 years ago

What I miss in these notes, and what I think is the main question we should be asking ourselves, is the question of the black-box approach. That is, do we understand what intelligence amounts to? On the one hand, we want to be able to say yes but on the other hand, we haven't been able after all these years to formalize the concept of intelligence in a satisfactory or even meaningful way. So the answer seems to be no. Moreover, do we even know what intelligence is?

Thus, should we instead be building black-box systems that behave seemingly intelligently without understanding why they behave intelligently? I.e. neural networks, deep learning? Is this a good investment? It seems so, at least in the short term. But what about the long term? I don't know the answer.

Having said that, I've been working for some time on non-monotonic reasoning and related subjects, which are typical symbolic logic approaches to AI, and I believe that what I am doing, while intellectually very worthwhile, is a dead end in the context of AI. I'm sometimes surprised at how easily we get money to get to work on this stuff. So I agree with the point that lack of funding is not the problem.

harperlee• 11 years ago

Could you please elaborate on why do you believe it is a dead end? I'm learning about symbolic logic and I would love to have some pointers on the limitation of the approach... From what I've read to date, it's just too expensive to have a proper rule set, but I haven't found a good discussion on the limitations of the approach!

serf• 11 years ago

John von Neumann was busy dying. Anyway he disapproved of the Newell-Simon work on chess, probably of AI in general.

made me laugh.

When McCarthy had just died I remember a video from a fellow that worked with him somewhere talking about how until the day of his death he had massive amounts of bandwidth piped into his house, and that the fellow wondered what he did with it all, sort of like a mad scientist.

I still wonder sometimes when I think about him. I sort of like to think that he was busting out work towards the 'obstacle' points in this paper 'til the very end.

dicroce• 11 years ago

The human brain has about 100 billion neurons. The human genome is 770MB (in .2bit format)... So, we know the genome doesn't have even close to enough information to describe all of the connections within the brain... This means that the brain must be an emergent phenomenon.. There must be some gross structure, or general mechanisms described in the genome and the rest is left to learning...

Andrew Ng at Stanford built the largest neural network to date, with 11.2 billion parameters (which I'm going to take to mean 11.2 billion neurons, as I'm assuming the parameters are the neuron weights)... So we're still pretty far off of human numbers of neurons... In addition, humans have the be bathed in sensory input for years before they begin to show intelligence..

sk5t• 11 years ago

Did anyone ever suppose the genome could possibly describe the functioning of the brain? After all, learning to play an instrument doesn't change your genome, and who could even take a stab at estimating how many bits of storage it takes to "store" mastery?

tmzt• 11 years ago

I took it to mean that there isn't enough storage in the genome to encode the initial full linkage of neurons, not that learning would have to be written back to the genome in order to change the ongoing network.

Iv• 11 years ago

I'd wager it is 11.2 billion synapses. And probably at most half that as you need 2 parameters for a synapse: its destination and its weight.

Teodolfo3• 11 years ago

11 billion parameters is a number of connections, not neurons. Connection weights, not neuron weights.

cLeEOGPw• 11 years ago

We will have article with same title, just years changed to 2045 and 2015 respectively. And probably one or two 30 year periods after that too. People underestimate the complexity of intelligence because of how common sense it seems to us.

Human-Level AI Is Harder Than It Seemed in 1955 – John McCarthy (2006)

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