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Likelihood of discontinuous progress around the development of AGI

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Re: Likelihood of discontinuous progress around the development of AGI

#101

Post by yonkshi »

s1dechnl wrote: post #98
yonkshi wrote: post #97

Earlier quoted context omitted.

I don't agree with this sentiment, although I agree that AI is not nearly at the level of the hype that pop culture makes it out to be. AlphaZero is still a significant contribution to 'AGI' that shouldn't be buried. It's true that AlphaZero's knowledge is unable to be generalized for other systems, but its biggest contribution is a _stable_ RL system that can solve problems that no other systems can. This is the fir…

Having not defined what intelligence is and having not declared the nature of General Intelligence, you're sure an aspect of clearly defined weak AI is a significant contribution to 'AGI'... Interesting. > It's true that AlphaZero's knowledge is unable to be generalized for other systems Interesting admission. > This is the first piece of the puzzle of more general AI. The first piece is generalized intelligence. Arc…

Hinton's criticism is very valid, but it's not quite about AlphaGo and its branch of ML. his criticism revolves around supervised learning and back prop that cannot be used to achieve the so called AGI. Because ANN is nothing like our brain's real NN. When Hinton gave the speech back in 2014, NN had a huge explosion of hype, and NN was mostly for supervised learning, which is really only good at classification and regression problems, it cannot make decisions outside of its training.

The famous DeepMind DQN paper (the core of AlphaGo) were published after Hinton's talk. the DQN paper practically opened a new chapter in reinforcement learning field. I am not sure if you are familiar reinforcement learning. RL is learning by trial and error, model-less and largely non-bayesian, similar to how humans learn. Up until AlphaGo, RL field was stuck in a limbo because it was having a very hard time learning non-linear problems (which is the majority of problems in nature)

When I say generalization, I meant generalization of knowledge. Generalization is the second piece of the puzzle because, even as humans, we learn from experienc. After enough examples we began to generalize. Up until DQN came out, we couldn't even effectively learn. It's the equivalent of a human baby with severe memory problem. With deepmind's DQN, we can achieve much more stable learning on non-linear systems, and we can begin to add components such as generalization (such as transfer learning), intuitions (such as intuitive physics), symbolic network.

I am not too sure what you meant by weak AI and general AI. For me, an AI which can learn similar to how humans learn, able to use apply generalized knowledge when facing a brand new problem, independently think and make decisions without human assistance, that's general enough.

Yes, much work needs to be done, but I don't believe this is the wrong direction we are going. Though I'd be glad to be proven wrong and I am very fascinated by this debate, if you would like, we could continue discussing it over email/chat?

Re: Likelihood of discontinuous progress around the development of AGI

#102

Post by yonkshi »

YeGoblynQueenne wrote: post #48
petermcneeley wrote: post #42

Earlier quoted context omitted.

Computational power and memory estimates could be made based on existing knowledge of the human brain. The one big assumption being that the neuron is the source for human intelligence.

An even bigger assumption is that intelligence in computers will require the same amount of computational power that it requires in humans. The AI we have so far is completely different than human intelligence (e.g. machine learning requires vast amounts of data; human learning can learn from single examples, etc). Computers themselves have completely different abilities than humans. Intelligence on the human brain i…

I agree a true general AI will probably be fairly different on computer than human. Although I want to mention that humans can learn from one example is mostly because we already have a large a priori from our life experience. We spend years learning how to talk, communicate, write and read. Through which we have built very structured symbolic logic system, which is _learnt_.

A example of this would be mathematics. If a person is never taught mathematics, she/he are only limited to basic math operations. It would take the person years of learning and practice in order to comprehend a mathematical literature. Once we have a symbolic logic network built for a certain aspect of our life, we can rapidly retrieve information based on previous logical patterns, thus allowing us to learn from one example.

Re: Likelihood of discontinuous progress around the development of AGI

#103

Post by icebraining »

s1dechnl wrote: post #81
laughingman2 wrote: post #68

Earlier quoted context omitted.

It's highly disingenuous to portray the researchers in machine learning as some sort of sell outs . Hinton was working in neural nets even in the time when they were uncool and fringe area of research. They didn't sell out AGI for some profit motive . The thing is we don't have any clear path laid to follow to achieve AGI . Various paths proposed earlier turned out over optimistic and dead end. The current "weak AI"…

I don't recall portraying anyone in that light. I highlighted the nature of an industry. Who aligns themselves to this nature was left unstated. If this clear and present truth offends, its likely because it has merit and is validated by the level of offense one encounters. It quite clearly cannot be defended. If you believe what I have stated can be, you're more than welcome to produce sound arguments that try. We c…

I want to preface that I know no more about AI than the average technologist, so I'm not making any claims.

>> Various paths proposed earlier turned out over optimistic and dead end.

> False. Various paths proposed earlier are the same ones being turned into profitable solutions today. They are the same ones underlying the bulk of white papers today. There weren't over optimistic. They weren't dead ends which is why various groups are using them to suck down billions of dollars today. What happened that caused the previous AI winter is that people rushed fundamental research into applied engineering.

How do you fit full brain emulation in that description? And in particular Henry Markram's work with Blue Brain and Human Brain? As far as I know it doesn't have any profitable outcomes, and people have been working at it for decades - and now even with pledge funding by the EU and others to the tune of a billion euros - yet nine years after Markram said we could have a functional human brain in ten years, nothing much seems to have emerged.

Re: Likelihood of discontinuous progress around the development of AGI

#104

Post by AndrewKemendo »

sgt101 wrote: post #100
AndrewKemendo wrote: post #96

Earlier quoted context omitted.

What does this comment mean?

The point of soar was/is to provide a framework for a collection of problem solvers with diverse methods to work together in a single agent. This was developed because of the insight that humans use diverse strategies to negotiate the challenges of their environment. Or... Lots of weak Ai adding up fyi general Ai.

Oh yes, of course.

Re: Likelihood of discontinuous progress around the development of AGI

#105

Post by icebraining »

tristanm wrote: post #93
AndrewKemendo wrote: post #77

Earlier quoted context omitted.

I don't think your interpretation of that prediction is accurate. Rather it would be that there are "bursts" of technological progress followed by slower or no gains while the world "catches up." That seems to more accurately follow the history of technological progress. I think a better interpretation would be that those "bursts" happen at tighter intervals, and if you look at the course of history that seems to be…

I think that bursts of technological progress follows more from the model of individualized intelligence, whereas continuous progress follows from the distributed, networked model of intelligence. A promoter of the distributed model of intelligence might argue that Einstein was only able to produce the general theory of relativity because of the knowledge already contained within society, such as the mathematics and…

I think you're making too many assumptions about the inner workings of "the brain".

If we look at actual brains - including Einstein's - are they not bursty? Don't people have periods of greater intellectual output with lulls in between? Seems to match pretty well.

Re: Likelihood of discontinuous progress around the development of AGI

#106

Post by whereismypwd »

mannykannot wrote: post #99
whereismypwd wrote: post #70

Earlier quoted context omitted.

I think the better argument is that most "breakthroughs" are exponentially hard, or we wouldn't consider them breakthroughs!

'Exponential' means something more specific than 'big' or 'largest in class'.

Exponential is probably the easiest way to say "a search without usable gradients in a high-dimensional problem space."

Re: Likelihood of discontinuous progress around the development of AGI

#107

Post by mannykannot »

whereismypwd wrote: post #106
mannykannot wrote: post #99

Earlier quoted context omitted.

'Exponential' means something more specific than 'big' or 'largest in class'.

Exponential is probably the easiest way to say "a search without usable gradients in a high-dimensional problem space."

'Breakthrough' means something less specific than 'a solution to a problem having a search without usable gradients in a high-dimensional problem space.' This is rather beside the point, however, as the the claim that kicked off this thread (most problems are exponentially hard) is about the difficulty of problem-solving in general, not the subset of problems that are pretty much hard by definition, in that their solution was/would be a breakthrough.

Re: Likelihood of discontinuous progress around the development of AGI

#108

Post by pdimitar »

AndrewKemendo wrote: post #45
YeGoblynQueenne wrote: post #29

About AlphaZero particularly, a few things must be kept in mind. First, AlphaZero still makes use of a Monte Carlo Tree Search algorithm to search for good moves. MCTS is a powerful algorithm with a very limited scope: zero-sum, perfect information games. So for instance, it would be very difficult to see how to use MCTS-based AlphaZero in, e.g., training self-driving cars. Second, the AlphaZero architecture is preci…

So, AlphaZero doesn't mean we're closer to _general_ AI. Quite the contrary: it's a very specialised form of AI that will be very difficult to use in any different task than chess, shoggi or go. This is a very true statement and one that I think a lot of people who aren't in ML/DL, but are "worried" about AGI, miss. There is however a common thread with everyone in AI, that they tend to think of AGI as "One algorithm…

Apologies for the off-topic question but I am simply too tempted not to ask them, I admit:

How does one make a living out of being an AGI researcher? How does one pay mortgage and has money and freedom to go on impulsive vacations with their wife?

And what does an AGI practitioner even mean?

Re: Likelihood of discontinuous progress around the development of AGI

#109

Post by pdimitar »

s1dechnl wrote: post #78
AndrewKemendo wrote: post #67

Earlier quoted context omitted.

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…

> 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…

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.

Re: Likelihood of discontinuous progress around the development of AGI

#110

Post by AndrewKemendo »

pdimitar wrote: post #108
AndrewKemendo wrote: post #45

Earlier quoted context omitted.

So, AlphaZero doesn't mean we're closer to _general_ AI. Quite the contrary: it's a very specialised form of AI that will be very difficult to use in any different task than chess, shoggi or go. This is a very true statement and one that I think a lot of people who aren't in ML/DL, but are "worried" about AGI, miss. There is however a common thread with everyone in AI, that they tend to think of AGI as "One algorithm…

Apologies for the off-topic question but I am simply too tempted not to ask them, I admit: How does one make a living out of being an AGI researcher? How does one pay mortgage and has money and freedom to go on impulsive vacations with their wife? And what does an AGI practitioner even mean?

How does one make a living out of being an AGI researcher? How does one pay mortgage and has money and freedom to go on impulsive vacations with their wife?

Well you don't, unless you work for OpenAI (which I don't). Not sure how OpenAI does it or how well they pay but I'm sure it's good. They have good donors.

And what does an AGI practitioner even mean?

To clarify, I'm a ML practitioner. AGI practitioner means nothing

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