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."

Comments

Re:neocortex?

[AKAImBatman]

From what I remember from my neural networks days the human brain/neocortex works so well because of its massively parallel nature (not because of the processing power of any one neuron), and that computers simply aren't able to exploit this as they aren't designed to work like this

Computers aren't *normally* designed like this. They can be however, and in recent years have been moving in that direction. When neural networks were first being researched, a Cray supercomputer was about the closest you could get to that sort of parallelism. Fast forward to today and we find that Intel (Pentium), AMD (AMD64), Sun (Sparc), and Sony (Emotion Chip) are all building machines that are highly parallel in nature.

Even more interesting is that today you can build yourself a custom, massively parallel computer on a shoestring budget. All you need is a handful of FPGAs, a PCB layout service like Pad2Pad, a few other parts, and reasonable VHDL or Verilog skills. That's more or less what OpenRT did to build their SaarCORE architecture. :-)

Belief Propagation

[songbo]

The idea seems simple enough. Create a hierarchical inference structure. Train it on some data. Let the nodes learn what are the most frequent data. This forms the basic alphabet set. Propagate this up the hierachy. Learn the conditional probability distribution. Voila, you have a working visual recognition system. Problem is, the system will be slow, unless you have a processor capable of parallel or vector processing. Try implementing the system on Matlab with a 320x200 image, and see your processor crawl to a halt. Now, imagine doing this on a 320x200 video, and pray! Well, that's why we need a different processor architecture to make this work. But the theory is simple.

I'm skeptical that this is ready for prime-time

[DoctoRoR]

The article gives little detail of the technology, and it's not like the general ideas Hawkins describes haven't been explored by people during the many decades of AI/neural networks research. The Numenta website gives the following:

HTM is "hierarchical" because it consists of memory modules connected in a hierarchical fashion. The hierarchy resembles an inverted tree with many memory modules at the bottom of the hierarchy and fewer at the top. HTM is "temporal" because each memory module stores and recalls sequences of patterns. HTM is hierarchical both temporally and spatially. An HTM system is not programmed in a traditional sense; instead it is trained. Sensory data is applied to the bottom of the hierarchy and the HTM system automatically discovers the underlying patterns in the sensory input. You might say it "learns" what objects are in the world and how to recognize them. Time is an essential element of how HTM systems work. First, to learn the patterns in the world, the sensory data must flow over time just as we move our eyes to see and move our hands to feel. Second, because every memory module stores sequences of patterns, HTM systems can be used to make predictions of the future. They not only discover and recognize objects but they can make predictions about how objects will behave going forward in time.

That sounds like a number of neural network approaches, including Stephen Grossberg's work at BU. Although Hawkins seems to be a very bright guy, this field is littered with very bright researchers who made bold claims, and none of those efforts have yielded revolutionary businesses. Anyone remember (Stanford AI researcher) Edward Feigenbaum's Fifth Generation book in the 1980s? Doug Lenat's Cyc project?

Remember the huge difference between one neuron's firing rate and the clock speed for processors. The brain operates in a way that's fundamentally different from how we program and how computers operate: massive parallelism with slow components versus (mostly) serial computation. So when a company says they'll market a software solution to something which scientists haven't figured out yet, I am indeed skeptical. This is really more research effort than commercial venture, and Numenta admits this: "It may well take several years before products based on HTM systems are commercially available."

I hope there's something here. I'd love to see an outsider come in with fresh ideas and create a software platform to explore neuro-inspired programs. But let's be realistic and remember the history of AI. A red flag is the lack of any scientific papers available from the Numenta web site. If they are far enough along to make a software development kit, they should have been publishing results in peer-reviewed journals (with appropriate patent filings if necessary). So far, the only literature published is a trade book: On Intelligence.