[s1dechnl]

> I'm not sure what you're arguing but it seems like my key point wasn't communicated well.

It was and quite well. Were speaking the same language. We just have different conclusions.

> Yes, every narrow AI is a system of systems to an extent. So expand on that concept but outside of a single firm/system. Such that the self driving car system is one single solution path solving "transportation" which would comprise automated flight/rail etc... and is a node in a larger general system - like hub and spoke.

And you still have nothing more than a hub and spoke system of systems authored for specific problems spaces and you're spokes will increase with every new problem space until you overwhelm your hub. A horrible architectural approach that if not caught in the initial stages will result in catastrophe down the road... Weak AI is weak AI no matter how you scale it.

> You seem to be arguing that a single entity will fail if it attempts to take a narrow AI system and make it generalizable. Of which I am in agreement with.

This is a start in the right direction...

> If however there were 10,000 or 100,000 or 1,000,000 narrow AI companies/systems (like a self driving car system or alphago etc...) those could fill the corpus of solutions which an executive function system could utilize depending on the application and together they would be what we call AGI.

No, its strung together weak AI. It will require significantly and unreasonable amounts of resources. Its capability will increasingly reach diminishing returns and you'll end up with a frankenstein monster code base that no one can manage or understand.. Sounds a lot like the path Weak AI is already heading down.. At such a point, it's best to just scrap it and start all over. Something that Hinton and other prominent figures are finally admitting. Something I concluded year ago which lead me down a different path. Now, you're more than welcome to state : Well hey man that's your opinion and you're wrong and I'll wish the 10s,100s, million of narrow AI companies the best just as was conveyed to me a umber of years ago. Weak AI is Weak AI. It is a class of optimization algorithms. You can jerry rig this all you want.. You still have nothing more than a system of systems of optimization algos. If you think this is what intelligence is, I'm not sure what to say.

[AndrewKemendo]

You still have nothing more than a system of systems of optimization algos. If you think this is what intelligence is, I'm not sure what to say.

Until someone comes up with a better definition of intelligence that's what I'm sticking with. I think you're looking for an elegant solution right out of the box - the "one algorithm to rule them all" and I don't think that is feasible from an engineering perspective if for no other reason than no singular system has anything near the data collection nodes needed for specificity on the range of tasks that would suffice any definition of "General."

Having raised three other humans and observing them while building DL systems myself for a living, I feel more strongly everyday that human intelligence is a hodgepodge of "weak AI" systems glued together with an exceptionally efficient executive function. AGI is as much a community building and humanity wide input collection challenge as it is a math problem. We need to think about it that way.

[nl]

I feel more strongly everyday that human intelligence is a hodgepodge of "weak AI" systems glued together with an exceptionally efficient executive function.

I’m in complete agreement with this. I try to avoid AGI discussions because people get upset when I argue that the vast majority of human “intelligence” seems to be strong pattern matching, and we can’t really define the parts that aren’t in any useful way.

Take the person you are discussing this with. The majority of their point seems to be a hang-up on the word “intelligence”.

I find that a pointless thing to argue over. Just agree and say it is an intelligence simulator which is indistinguishable from a real intelligence.

[s1dechnl]

> Until someone comes up with a better definition of intelligence that's what I'm sticking with.

You'll get a capability demo instead. It wont fail to impress. Definitions and designs are for another day.

> I think you're looking for an elegant solution right out of the box - the "one algorithm to rule them all" and I don't think that is feasible from an engineering perspective if for no other reason than no singular system has anything near the data collection nodes needed for specificity on the range of tasks that would suffice any definition of "General."

What else is one looking for who claims they're trying to solve the Intelligence problem? Marketing an optimization algorithm as the next coming might make you rich in the short term but it doesn't bring you closer to the truth. It does in fact take your further away. So, 'the elegant solution'/'the hard problem' was the only thing I set out to tackle some years ago. Otherwise, i'd have been wasting my time/not being truthful with myself. It's feasible from a research and engineering perspective. Few commit themselves to the TRUE task and the likelihood of failure. I was ok with that it and stuck with it. I self-funded my work. It mainly centered on research. Thus, there were no exits. I either saw it through and achieve it or I didn't.

As far as :

> no singular system has anything near the data collection nodes needed for specificity on the range of tasks that would suffice any definition of "General."

Sure it does. Look in the mirror and log onto the web. I've let the misses play online for a bit now ;).

> Having raised three other humans and observing them while building DL systems myself for a living, I feel more strongly everyday that human intelligence is a hodgepodge of "weak AI" systems glued together with an exceptionally efficient executive function. AGI is as much a community building and humanity wide input collection challenge as it is a math problem. We need to think about it that way.

My graduate work centered on the underpinnings of DL (Distributed Optimization). After years of industry experience, I searched for a new challenge. After some open ended research in physics/photonics, I came to Artificial Intelligence. I scratched my head for 3-4 months as to why (Distributed Optimization) was being called Artificial Intelligence. I took the broad lot of it and threw it in the trash as prominent figures are only now stating : https://www.axios.com/artificial-intelligence-pioneer-says-w...

You're thinking about AGI as if its a chain of DL systems because that's what's made you money and where your work has centered on over the years. I took the broad majority and trashed it as Hinton now indicated others should do and started from scratch. I have no such bias. However, as my graduate work centered on the fundamental underpinnings of statistical optimization / distributed optimization, I know exactly what its limits are.

The human race is far more than a hodgepodge of optimization algos w/ an executive function (whatever that might be given the clearly varied forms of it).

[pdimitar]

Pardon my question: what kind of education did you have?

I deeply regret not studying Computer Science but yours seems to be deeper than that. I'd be very interested in the courses you took.