Via Forbes
by Roger Kay
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IBM researchers reverse engineered a macaque brain as a start to engineering one of their own
The gnomes at IBM’s research labs were not content to make merely a genius computer
that could beat any human at the game of jeopardy. They had to go and
create a new kind of machine intelligence that mimics the actual human
brain.
Watson, the reigning jeopardy champ, is smart, but it’s still
recognizably a computer. This new stuff is something completely
different. IBM is setting out to build an electronic brain from the
ground up.
Cognitive computing, as the new field is called, takes computing
concepts to a whole new level. Earlier this week, Dharmendra Modha, who
works at IBM’s Almaden Research Center, regaled a roomful of analysts
with what cognitive computing can do and how IBM is going about making a
machine that thinks the way we do. His own blog on the subject is here.
First Modha described the challenges, which involve aspects of neuroscience, supercomputing, and nanotechnology.
The human brain integrates memory and processing together, weighs
less than 3 lbs, occupies about a two-liter volume, and uses less power
than a light bulb. It operates as a massively parallel distributed
processor. It is event driven, that is, it reacts to things in its
environment, uses little power when active and even less while resting.
It is a reconfigurable, fault-tolerant learning system. It is
excellent at pattern recognition and teasing out relationships.
A computer, on the other hand, has separate memory and processing.
It does its work sequentially for the most part and is run by a clock.
The clock, like a drum majorette in a military band, drives every
instruction and piece of data to its next location — musical chairs with
enough chairs. As clock rates increase to drive data faster, power
consumption goes up dramatically, and even at rest these machines need a
lot of electricity. More importantly, computers have to be
programmed. They are hard wired and fault prone. They are good at
executing defined algorithms and performing analytics.
With $41 million in funding from the Defense Advanced Research
Projects Agency (DARPA), the scientists at the Almaden lab set out to
make a brain in a project called Systems of Neuromorphic Adaptive
Plastic Scalable Electronics (SyNAPSE).
The rough analogy between a brain and a computer posits roles for
cell types — neurons, axons, and synapses — that correspond to machine
elements — processors, communications links, and memory. The matches
are not exact, as brain cells’ functions are less distinct from each
other than the computer elements. But the key is that the brain
elements all reside near each other, and activity in any given complex
is stimulated by activity from adjacent complexes. That is, thoughts
stimulate other thoughts.
Modha and his team set out to map and synthesize a wiring diagram for
the brain, no trivial task, as the brain has 22 billion neurons and 220
trillion synapses. In May 2009, the team managed to simulate a system
with 1 billion neurons, roughly the brain of a lower mammal. Except
that it operates at one-thousandth of real time, not enough to perform
what Modha called “the four Fs”: food, fight, flight, and mating.
But the structure of this machine is entirely different from today’s
commercial computers. The memory and processing elements are built
close together. It has no clock. Operations are asynchronous and event
driven; that is, they have no predetermined order or schedule. And
instead of being programmed, they learn. Just like us.
Part of getting the power down to brain-like levels is not storing
temporary results (caching, in industry jargon). Sensing stimulates
action, which is sensed and acted upon further. And so on.
The team recently built a smaller hardware version of the brain
simulation, one with just 256 neurons, 262,000 programmable synapses,
and 65,000 learning synapses. The good news is that this machine runs
at within an order of magnitude of the power that a real brain
consumes. With its primitive capabilities, this brainlette is capable
of spatial navigation, machine vision, pattern recognition, and
associative memory and can do evidence-based hypothesis generation. It
has a “mind’s eye” that can see a pattern, for example, a badly written
number, and generate a good guess as to what the actual number is.
Already better than our Precambrian ancestors.
Modha pointed out that this type of reasoning is a lot like that of a
typical right hemisphere in the brain: intuitive, parallel, synthetic.
Not content with half a brain, Modha envisions adding a typical von
Neumann-type computer, which acts more like a reasoning left hemisphere,
to the mix, and having the two share information, just like a real
brain.
When this brain is ready to go to market, I’m going to send my own on holiday and let Modha’s do my thinking for me.
Oh, and, by the way, in case you were wondering whether the SyNAPSE
project has caused Watson to be put out to pasture, nothing could be
further from the truth. Watson is alive and well and moving on to new,
more practical applications.
For example, since jeopardy contestants can’t “call a friend,” Watson
was constrained to the data that could be loaded directly into the
machine (no Internet searches), but in the latest application of Watson
technology — medical diagnoses — the Internet is easily added to the
corpus within the machine, allowing Watson to search a much wider range
of unstructured data before rendering an answer.
Watson had to hit the bell faster than the human contestants, but the
doctors seeking advice on a strange set of symptoms can easily wait a
half hour or longer. So, Watson can make more considered choices.
Watson at work is a serious tool.
All this genius is causing my brain to explode.
Disclosure: Endpoint has a consulting relationship with IBM.