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4.0 out of 5 stars The Cortex Spins its Tales with Hidden Markov Models, November 14, 2012
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This review is from: How to Create a Mind: The Secret of Human Thought Revealed (Hardcover)
Like a news commentator explaining a bad day on Wall Street,
the cortex has an explanation for everything -
it generates our subjective universe. To paraphrase George Box,
all our brain's models of the world are wrong,
but some are useful, generative, and simple (but not too simple).

In How to Create a Mind acclaimed inventor Ray Kurzweil
puts forth a model of how the brain works:
the pattern recognition theory of mind (PRTM).
The brain successively interiorizes the world as a set of patterns.

Kurzweil's framework uses hierarchical hidden Markov models (HHMMs)
as its main stock in trade. HHMMs add to the PRTM model the notion
that those patterns are arranged into a hierarchy of nodes,
where each node is an ordered sequence of probabilistically matched lower nodes.

So, the key question for me is this: are HHMMs
really the key to understanding and building a mind?

Ray has been on this track since the sixties,
when he and I were classmates at MIT. In a spectacular
career spanning decades, Ray invented systems for OmniPage OCR,
text to speech (famously for Stevie Wonder), and
automated speech recognition as in Dragon Naturally Speaking.
Nuance bought Ray's precursor company.

All automatic speech recognition nowadays is done using HHMMs,
and the results are astounding. For example, see Microsoft Research
Chief Rick Rashid's YouTube "Speech Recognition Breakthrough."
A computer transcription of Rick's talk appears in
real time and is quite accurate.

The amazing success of HHMMs in handling speech and language is
a story that needs to be understood by AI aficionados, and
Kurzweil presents this topic in a beautifully comprehensible exposition.

Kurzweil elaborates a story here that 1) the cortex is
the key to thought; 2) it is hierarchically organized into
300 million pattern recognizers; 3) each pattern recognizer
consists of a 100 neurons in a vertical minicolumn, and
4) those pattern recognizers communicate with one another
via a Manhattan-like grid (similar to an FPGA) -
end of story for the neocortex.

This is a story similar to the one told by entrepreneur Jeff Hawkins in
On Intelligence, and one that Hawkins, his former associate Dileep George
(now at Vicarious), and Kurzweil himself are trying to capitalize on
in cortex-engineering startups. I eagerly follow their results.

So, HHMMs work well and are a required part of a computational
neuroscience curriculum, but ARE THEY THE MASTER KEY that will unlock
the doors not only to a full understanding of the mind
but also to a future of superintelligent AIs? How to Create a Mind
is a good story but IS IT FICTION or nonfiction?

While HHMMs are required reading for automatic speech recognition,
they DO NOT DO all the brain's heavy-lifting. Rather, the brain employs
MANY mechanisms (which robots that aspire to humanity
may need to incorporate or emulate.)

Five stars for HHMM exposition. Subtract one star for giving short shrift
to the following pivotal neuroscience principles: 1) attentional mechanisms,
2) brain-wide dynamical networks, 3) gamma oscillations and inhibitory networks
and also 5) the role of insula and brain stem in emotion, 6) reward based learning
including the essential role of basal ganglia and midbrain,
and 7) hippocampus and memory.

Despite its corticocentric focus, Kurzweil's impressive engineering
successes make this an important story; furthermore, it is engagingly told.
I cover neuroscience and AI at . Below are two recent 'DO NOT MISS'
FIVE STAR stories.)

Addendum: 30 Nov 2012 - Today's issue of SCIENCE (and Ray K's newsletter)
features a story about a new 2.5M spiking neuron model (SPAUN) that
performs 8 tasks and outputs to a physically modeled arm.
See the videos at NENGO > Videos > Collection of Spaun.
That is the state of the art!

Addendum: Jan 2013: Want to know where the brain stores meaning? (YOU DO!)
See Alex Huth's 5 min YouTube from Jack Gallant's lab. Search:
Alex Huth, gallantlabucb "Perceptual Object and Action Maps in the Human Brain."
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Showing 1-3 of 3 posts in this discussion
Initial post: Nov 14, 2012 4:18:24 PM PST
Ron L. Burk says:
Watched Rashid's speech. Seemed like a pretty bad error rate to me.
Rife with things like:
It really happens in two steps.
translated to:
It really happen to stop.
Many others. If that's a breakthrough, I guess the previous state of the
art was worse than I thought.

In reply to an earlier post on Nov 14, 2012 6:12:56 PM PST
Last edited by the author on Nov 14, 2012 6:22:18 PM PST
Bob Blum says:
Hi Ron, You're right. There are errors.
What's amazing to me is that it's as good as it is.
(To paraphrase Ray K's joke: I'm amazed that the dog plays chess,
but not bummed out that it hasn't perfected its endgame.)

Consider that this single pass, real-time algorithm uses no semantic information,
no anaphora, and no conjugations. Amazing that it can perform at this level - but not perfect.

Thanks, Bob

In reply to an earlier post on Dec 21, 2012 3:41:03 AM PST
ratty says:
pretty sure that joke was one of calvin trillin's and about chickens playing tic-tac-toe.
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