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Palm Founders Form AI Company
Mentifex writes "As reported in the New York Times, Kansas City Star and other news media, Jeff Hawkins (co-author of On Intelligence) and Donna Dubinsky, co-founders of Palm Computing and Handspring, along with Dileep George as the principal engineer, are starting an AI company named Numenta as a follow-up to Hawkins' recent work on visual processing."
It appears the article summary might be misleading. From the first sentence of www.numenta.com:
Numenta is developing a new type of computer memory system modeled after the human neocortex. The applications of this technology are broad and can be applied to solve problems in computer vision, artificial intelligence, robotics and machine learning.
They further go on to say:
Numenta is a technology platform provider rather than an application developer. The Company is creating a scalable software toolkit that will allow developers and partners to configure and adapt HTM systems to particular problems.
My reading on this is that they aren't an AI company - they're just developing a technology that could be used for AI or many, many other uses.
I'm a big tall mofo.
I'm surprised that the short summary, from my brief perusal, does not include reference to work by Peter Foldiak (1991, 199?) and Wallis (1996). Both these authors published numerous papers on temporal and spatial coherence. My MSc in 1996 was also on the same topic followed by human research on the same problem. All of the computational work was with unsupervised learning algorithms varying whether the temporal processing was at the input our output stage.
I guess I'll have to read the original paper. However, the notion of temporal processing has been around for a long time.
Note: My own human research has yielded reliable data that addresses the acquisition of invariant object recognition.
I mean, heck, if it gets us even one step closer to having competent automated tech support, I'm all for it.
If a 'bot passes the Turing Test, it will be big news, trust me.
As the submission noted, this work will be building on what Hawkins wrote about in his recent book, On Intelligence. The companion web site for the book is here:
...
There are also a some reviews of the book:
http://blogger.iftf.org/Future/000605.html
http://www.computer.org/computer/homepage/0105/ran dom/index.htm
(By Bob Colwell, who was Intel's chief IA32 architect)
http://www.techcentralstation.com/112204B.html
http://www.corante.com/brainwaves/archives/026649. html
A quote from his book:
The agenda for this book is ambitious. It describes a comprehensive theory of how the brain works. It describes what intelligence is and how your brain creates it. The theory I present is not a completely new one. Many of the individual ideas you are about to read have existed in some form or another before, but not together in a coherent fashion. This should be expected. It is said that "new ideas" are often old ideas repackaged and reinterpreted. That certainly applies to the theory proposed here, but packaging and interpretation can make a world of difference, the difference between a mass of details and a satisfying theory. I hope it strikes you the way it does many people. A typical reaction I hear is, "It makes sense. I wouldn't have thought of intelligence this way, but now that you describe it to me I can see how it all fits together." With this knowledge most people start to see themselves a little differently. You start to observe your own behavior saying, "I understand what just happened in my head." Hopefully when you have finished this book, you will have new insight into why you think what you think and why you behave the way you behave. I also hope that some readers will be inspired to focus their careers on building intelligent machines based on the principles outlined in these pages.
Weren't neural networks supposed to lead to intelligent machines?
Of course the brain is made from a network of neurons, but without first understanding what the brain does, simple neural networks will be no more successful at creating intelligent machines than computer programs have been.
Why has it been so hard to figure out how the brain works?
Most scientists say that because the brain is so complicated, it will take a very long time for us to understand it. I disagree. Complexity is a symptom of confusion, not a cause. Instead, I argue we have a few intuitive but incorrect assumptions that mislead us. The biggest mistake is the belief that intelligence is defined by intelligent behavior.
What is intelligence if it isn't defined by behavior?
The brain uses vast amounts of memory to create a model of the world. Everything you know and have learned is stored in this model. The brain uses this memory-based model to make continuous predictions of future events. It is the ability to make predictions about the future that is the crux of intelligence. I will describe the brain's predictive ability in depth; it is the core idea in the book.
How does the brain work?
The seat of intelligence is the neocortex. Even though it has a great number of abilities and powerful flexibility, the neocortex is surprisingly regular in its structural details. The different parts of the neocortex, whether they are responsible for vision, hearing, touch, or language, all work on the same principles. The key to understanding the neocortex is understanding these common principles and, in particular, its hierarchical structure. We will examine the neocortex in sufficient detail to show how its structure captures the structure of the world. This will b
There are actually quite a few projects now taking similar, cortex-centric approaches to AI hard problems. Are we up to something here? The guys responsible of these projects are not wacko types at all, but established entrepreneurs and/or well-known researchers:
CCortex "A 20-billion neuron simulation of the Human Cortex and peripheral systems."
Cyc a knowledge base with vast collection of facts about the real world and logical reasoning ability. Financed by Paul Allen AI related investment company,Vulcan.
Numenta is developing a new type of computer memory system modeled after the human neocortex.
They seem to we well financed, and knowledgeable. Are we witnessing the start of something big here?
The work Hawkins describes has roots in research on perceptrons back in the 1950s.
Did you even READ the book?
Most of it speaks about how theories about how the brain classifies and processes information - and spends very little time on existing artificial intelligence constructs such as neural networks. Another good piece of the book details the author's troubles with trying to do academic research into AI, a viewpoint that I share.
..don't panic
I'm no AI expert, but it seems unlikely to me that one can make an AI that can "understand" the message without making a full-blown Touring-test-passing AI, and if you had such a thing there are certainly better things it could be applied to than filtering spam.
What I mean when I say it's like bayesian filtering is that you could add another meta level to the filter that compares strings of words, or something similar.
In a way, it seems to me that Bayesian filtering is a form of AI, simply because it "learns" and has emergent behaviors that can't entirely be predicted. Does it being a simple algorithm make it not "real AI"? Perhaps it's just not real "smart" AI... I dunno...