Thursday, August 11, 2011

Rebel Speech Recognition

Rebel Speech

I started yet another artificial intelligence project, Rebel Speech. Actually, it is part of the Rebel Cortex project, which now consists of Rebel Vision and Rebel Speech. Both subprojects will use the same sensory cortex for learning and recognition. Programming wise, speech recognition is less complex than visual recognition because the sensory mechanism is easier to implement. It's mostly a matter of using a Fast Fourier Transform to convert a time domain audio signal from a microphone into a frequency domain signal. In addition, only a fraction of the detected frequency spectrum is required for good performance. I envision that someone will one day design a microphone that works like the human ear, i.e., it would use many microscopic hair-like sensors that respond directly to various frequencies. In the meantime, a regular microphone and an FFT will do.

Population Modulation

I've been writing some Windows C# code for Rebel Speech in my spare time in the last few days. I have already implemented the microphone capture and FFT code. Well, it's not all that hard considering that there is a lot of good and free FFT code on the net and Microsoft provides a handy Microphone class in its XNA framework. I am now working on designing the audio sensors and the sensory layer. It's a little complicated not just because I need to design both signal onset and offset sensors but also because dealing with stimulus amplitude is counterintuitive. In the brain, all signals are carried by pulses which have pretty much equal amplitude. One would think that changes in the intensity of a stimulus should be converted into frequency modulation but that is not the way it works either. The brain uses a technique that I call population modulation to encode amplitude. In other words, there are many sensors that handle a single phenomenon. The number of sensors that fire in response to a sensory stimulus is a function of the intensity of the stimulus.

In the brain, this sort of parse activation is accomplished with the use of inhibitory connections between the cells in a group. Luckily, in a computer brain simulation, all we need is a list of cells. Stay tuned.

See Also:

Rebel Cortex
Invariant Visual Recognition, Patterns and Sequences
Rebel Cortex: Temporal Learning in the Tree of Knowledge

Friday, August 5, 2011

How Jeff Hawkins Reneged on His Own Principles

"Vision Is More Like a Song than a Painting"

About two weeks ago, in Part II of my article A Fundamental Flaw in Numenta's HTM Design, I wrote that "visual recognition is not unlike speech or music recognition." Yesterday, I did a little search on Google and discovered that Jeff Hawkins and I are pretty much in agreement. On page 58 of his book, On Intelligence, Hawkins writes that "vision is more like a song than a painting." On page 31, he writes, "this is how people learn practically everything, as a sequence of patterns." Hawkins and I are on the same page, at least as far as this aspect of intelligence is concerned.


One would think that Hawkins would stick to his original principles about how the brain learns but he turns around and contradicts himself after he co-founded Numenta Inc. In Numenta's HTM model, Hawkins abandons his gut instincts and gives in to Dileep George's erroneous ideas on how visual learning should work. George believes that the visual cortex sees an image as a hierarchy of small patterns, as opposed to a hierarchy of sequences of patterns. He thinks that the brain sees an entire image all at once and that the visual receptive field increases as one goes up the hierarchy. As I have shown previously on this blog, George is wrong, period. Hawkins is correct that we learn and see everything as a sequence of patterns. We never see a whole picture all at once, in terms of small concurrent patches. There is no pattern learning except at the bottom level of the memory hierarchy and it does not work the way George believes it does either. Why did Hawkins change his mind?

Blinded by that Old Math Magic of Academia

I think Hawkins allowed himself to be bamboozled by George's mathematical sleights of hand. It's the old "when all you've got is a hammer, everything looks like a nail" sort of thing, all over again. George is still playing the math hand at his new AI venture, Vicarious Systems, Inc. I wish him well but he will fail, in my opinion. AI and the brain are not about math. As Hawkins claims in his book, On Intelligence, intelligence is almost entirely about prediction and sequences of patterns. When we finally unravel the workings of the brain, we will be amazed at the simplicity of its principles.

Bayesian Crap

I am going to say something that will come as a surprise to many. Some may even take offense. But, you know me, I always tell it like I see it. That whole Bayesian learning crap that Dileep George introduced to Numenta's HTM is just that, crap. The brain does not use Bayesian statistics to learn which pattern may succeed another. The brain learns as many patterns and sequences as it can, regardless of their probability of occurrence. What matters is that they occur frequently enough to be more than just random noise. Probability only comes into play long after the learning phase, during decision making, e.g., when it comes time to determine whether an object is an apple or an orange. Recognition happens correctly because many branches of the tree of knowledge compete for attention and the strongest (i.e., the most appropriate) branch wins. It's that simple. No Bayesian crap is required. I'll have more to say about temporal learning in an upcoming article.

Shooting AI in the Foot and Bragging About it

My advice to Hawkins is to be true to his original vision and stop listening to academia. Academia have been shooting AI in the foot for the last sixty years or so. That whole symbolic AI nonsense would be laughable if it weren't so pathetic. What a waste of time and brains. But guess what, they are not about to change anytime soon. Worse, they have no shame about their failure. They brag about their "accomplishments".

You've heard it here first. :-)

See Also:

Jeff Hawkins Is Close to Something Big
A Fundamental Flaw in Numenta's HTM Design