Wednesday, November 30, 2016

True Artificial Intelligence Will Arrive Suddenly and Will Stun the World

Abstract

In this article, I argue that true artificial intelligence, aka artificial general intelligence or AGI, may arrive on the world scene within the next ten/fifteen years or even sooner. It will not be a gradual process. It will arrive suddenly and take the world completely by surprise.

True AI Will Not Come from the Mainstream AI Community

When I say that the arrival of true AI will take the world by surprise, what I mean is that it will come from an unexpected place. Don't wait for the mainstream AI community to figure out intelligence. That will not happen. Knowing what I know about the brain and intelligence, there is no doubt in my mind that mainstream AI scientists are completely clueless as to how to even approach the problem. They are clueless because over 99 % of AI research money currently goes into funding deep learning, which is, as I have explained elsewhere, a hindrance to progress toward true AI. The most important ingredient in intelligence is time. And yet, amazingly, time is a mere afterthought in AI research, especially deep learning.

There are a handful of AI researchers who do understand the crucial importance of time to intelligence but, as I explained in my previous article, they are handicapped by their continued adherence to a representational approach to intelligence. In other words, in spite of all the hype, they are still doing symbolic AI or GOFAI. Please read, The World Is its Own Model or Why Hubert Dreyfus Is Still Right About AI for more on this topic.

Another obvious reason that the mainstream AI community is clueless is that they believe that the brain is performing some kind of massive parallel computation on sensory inputs. They assume that the brain continually generates an internal model of the world using statistical calculations on its input signals. The problem with this view is that neurons are way too slow for this kind of signal processing. The surprising truth is that the brain does not compute anything when it perceives the world. The brain assumes that the world is deterministic and does its own computations. It learns how the world behaves and expects that this behavior is perfect and will not deviate. The mechanism is akin to an automatic coin sorting machine whereby the machine assumes that the different sizes of the coins automatically determine which slots they belong to.

True AI Will Arrive Suddenly

A truly intelligent system, such as the human brain, consists of multiple, highly integrated modules. What I mean is that every module that comprises an intelligent system has a specific function, organization and operation that complement the other modules. No single module can function in isolation. It is not possible to solve one aspect of intelligence without also solving all the other aspects. In other words, one cannot understand sensory perception without also understanding motor behavior, and vice versa. There will be no evolution during which advances are made a little at a time while machines become gradually more intelligent over the years until a time is reached when they achieve human-like intelligence. True AI will appear suddenly.

The Secret of True AI Will Come from a Completely Unexpected Source

The most surprising thing about the arrival of true AI on the world scene will not be that it is finally here (although that will certainly make the front pages) but where it came from. I am not going to say too much about this other than the following. True AI is so counterintuitive that it would take us (humanity) hundreds, if not thousands of years to figure it out on our own. Fortunately for us, there is an ancient source of scientific knowledge about the brain and intelligence that the world has chosen to ignore. I have worked for more than a decade to decipher and understand this knowledge and I have made great progress. But whether or not I publish my work is not up to me. The only caveat here is that I am a known internet nut. Stay tuned.

The World Is its Own Model or Why Hubert Dreyfus Is Still Right About AI
Why Deep Learning Is a Hindrance to Progress Toward True AI
In Spite of the Successes, Mainstream AI is Still Stuck in a Rut

12 comments:

Rick Deckard said...

I am already here )

Louis Savain said...

Hi Rick,

You and I are just two internet AI crackpots. LOL.
May the best crackpot win.

Rick Deckard said...

What do pyramids, star of David & the cross have in common? The AI algorithm of course! )

Louis Savain said...

What do pyramids, star of David & the cross have in common? The AI algorithm of course! )

Of course!

Alex said...

Hi Louis,

would love to hear more about time based- and non-representational AI algorithms. But I am not holding my breath since we all know how grave the consequences would be. (Total surveillance and a society not worth living.) I went to one of NVIDIA's conferences recently and, if you want to truly frighten yourself, look at what the deep learning- and GPU folks are working on. They are attempting tracking behind every surveillance camera in our cities. Fortunately, as you say, they haven't made that much progress since the 1980ies neural nets. Except that they have much more powerful chips.

Given all these implications I am more interested in parallel computing than in self learning algorithms. Any plans you might take up your work and publish in these areas again? I think that the recent success of GPUs have made this field much more relevant.

Cheers,
Alex

Louis Savain said...

Alex,

Thanks for the comment. I agree that the introduction of true AI into the world would spell doom for human civilization.

You wrote:

Given all these implications I am more interested in parallel computing than in self learning algorithms. Any plans you might take up your work and publish in these areas again? I think that the recent success of GPUs have made this field much more relevant.

Yes. I have not abandoned my interest in the foundations of computer hardware and software. As you know, I am of the opinion that we have been doing it wrong from the beginning, since the days of Babbage and Lovelace.

If you look at a cross section of the brain, you'll notice that there is more white matter than grey matter. The white matter consists of the axonic fibers that provide the communication pathways between the neurons in the grey matter. The lesson here is that communication is just as or even more important than computation.

With this in mind I have a design for a new sort of computer with billions of computational micro-units (both data and operators) connected by a 3D lattice consisting of hundreds of billions of nano-switches. There would be no central processor. I envision a future computer about the size of the human brain with the same computational capacity and energy efficiency as the brain. Programming this computer would consist of connecting computational units to other units by turning various switches on or off.

This would be a complete reinvention of computing as we know it. I hope to one day raise enough money to see this project to fruition. This is important in more ways than one. True human-level AI cannot become widespread unless it is housed in small, low power, inexpensive and powerful computing hardware. Our current Von Neumann computer model is not up to the task. Not even GPUs can come close to what will be required.

Hang in there.

William Spearshake said...

Mapou: "You and I are just two internet AI crackpots"

Well, you definitely have the "crackpot" part correct. At least for you.

norman_h said...

The machines are already learning to learn.
The next problem we face with our offspring will be abstracting the machine design patterns into reusable blocks, rather like object-orientation within the computer programming world.

https://papers.nips.cc/paper/6461-learning-to-learn-by-gradient-descent-by-gradient-descent.pdf

Best of luck to you!

Spent Death said...

@norman_h you have no idea what you are talking about. The fundemental problem in AI is they don't know what learning is, and why are humans so efficient at it. The brain consumes about 20 watts of power, and neurons are unbelievably slower than the modern cpu. Yet these so called AI algorithms are no where near as efficient. I can guarantee you that no one in the field of AI is remotely close to understanding what intelligence is, and they are heading in the wrong direction. Nothing from the theories with statistical inference/learning matches with theories done in psychology, neuroscience and biology. The techniques like machine learning, deep learning, clustering and such aren't new and go back to the 60s. What is fundamentally missing is a theory of intelligence that needs to explain and link concepts in psychology, neuroscience and theory of computing.

To give you an analogy, before Newton formalized the theory of gravity, there were many mathematical equations that were derived from the Kepler and Copernicus era to model planetary motion. At each century, they realized they were slightly off, and invented more complicated math to fix their models. The entire field of hyperbolic geometry was invented to "hack" mathematics to describe the motion of planets.

It wasn't until Newton came and shifted the paradigm and theorized that the mass of an object affects its trajectories. Newton gave us an explanation of why we are seeing what we are seeing, then created models to describe nature and predict it with a level of accuracy that was unprecedented, it just blew everything that came before out of the water. Centuries worth of math made obsolete in an instant.

With respect to AI, we are in the same boat. Every single AI algorithm out there can be reduced to a single philosophy: There is an uncertainty in the world, and the best tool to learn from the world is by statistical inference. We have decades worth of research derived from this single philosophy, many complicated mathematical models, with very little to show or explain. This is a clear case of history repeating itself.

The current paradigm inspired from the 30s era break throughs in statistical inference will be destroyed in an instant once the real thing comes out. The real theory will have this, and it will explain what intelligence is, what learning is, and create great things and a much better world.

Alexander Buianov said...

Spent Deatch... I agreee.
Spent Death, Lous Savain
Do you know any chat, forum, reddit or facebook group for these 1% to communicate?
Skype?

Louis Savain said...

Alexander,

Thanks for the comment. The biggest scientific breakthroughs in history are done by lone wolves and rebels. They don't communicate much with others until they have something that will completely blow everything else out of the water.

As Spent Death mentioned, Isaac Newton was such a person. Newton had the answer for universal gravitation and sat on it for 10 years. Why? Because he wanted to prove that the gravitational attraction of a planet or star acted as if its entire mass was concentrated at the center. He had to invent calculus before he could do it.

Take care.

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