- Kurzweil, Ray.
How to Create a Mind.
New York: Penguin Books, 2012.
ISBN 978-0-14-312404-7.
-
We have heard so much about the exponential growth of computing power
available at constant cost that we sometimes overlook the fact that
this is just one of a number of exponentially compounding technologies
which are changing our world at an ever-accelerating pace. Many of
these technologies are interrelated: for example, the availability of
very fast computers and large storage has contributed to increasingly
making biology and medicine information sciences in the era of
genomics and
proteomics—the
cost of sequencing a human genome,
since the completion of the Human Genome Project, has fallen faster
than the increase of computer power.
Among these seemingly inexorably rising curves have been
the spatial and temporal resolution of the tools we use
to image and understand the structure of the brain.
So rapid has been the progress that most of the detailed
understanding of the brain dates from the last decade, and
new discoveries are arriving at such a rate that the author
had to make substantial revisions to the manuscript of this
book upon several occasions after it was already submitted
for publication.
The focus here is primarily upon the
neocortex,
a part of the brain which exists only in mammals and
is identified with “higher level thinking”:
learning from experience, logic, planning, and, in humans,
language and abstract reasoning. The older brain, which
mammals share with other species, is discussed in chapter 5,
but in mammals it is difficult to separate entirely from the
neocortex, because the latter has “infiltrated”
the old brain, wiring itself into its sensory and action
components, allowing the neocortex to process information
and override responses which are automatic in creatures such
as reptiles.
Not long ago, it was thought that the brain was a soup of
neurons connected in an intricately tangled manner, whose
function could not be understood without comprehending the
quadrillion connections in the neocortex alone, each with
its own weight to promote or inhibit the firing of a neuron.
Now, however, it appears, based upon
improved technology for observing the structure and operation
of the brain, that the fundamental unit in the brain is
not the neuron, but a module of around 100 neurons which
acts as a pattern recogniser. The internal structure of
these modules seems to be wired up from directions from
the genome, but the weights of the interconnections within
the module are adjusted as the module is trained based upon
the inputs presented to it. The individual pattern recognition
modules are wired both to pass information on matches to
higher level modules, and predictions back down to lower level
recognisers. For example, if you've seen the letters
“appl” and the next and final letter of the word
is a smudge, you'll have no trouble figuring out what the word
is. (I'm not suggesting the brain works literally like this,
just using this as an example to illustrate hierarchical
pattern recognition.)
Another important discovery is that the architecture of these
pattern recogniser modules is pretty much the same regardless
of where they appear in the neocortex, or what function
they perform. In a normal brain, there are distinct portions of
the neocortex associated with functions such as speech, vision,
complex motion sequencing, etc., and yet the physical structure
of these regions is nearly identical: only the weights
of the connections within the modules and the dyamically-adapted
wiring among them differs. This explains how patients recovering
from brain damage can re-purpose one part of the neocortex
to take over (within limits) for the portion lost.
Further, the neocortex is not the rat's nest of random connections
we recently thought it to be, but is instead hierarchically
structured with a topologically three dimensional “bus”
of pre-wired interconnections which can be used to make long-distance
links between regions.
Now, where this begins to get very interesting is when we
contemplate building machines with the capabilities of the
human brain. While emulating something at the level of neurons
might seem impossibly daunting, if you instead assume the building
block of the neocortex is on the order of 300 million more or less
identical pattern recognisers wired together at a high level in
a regular hierarchical manner, this is something we might be able
to think about doing, especially since the brain works almost
entirely in parallel, and one thing we've gotten
really good at in the last half century is making
lots and lots of tiny identical things. The implication
of this is that as we continue to delve deeper into the structure
of the brain and computing power continues to grow exponentially,
there will come a point in the foreseeable future where emulating
an entire human neocortex becomes feasible. This will permit
building a machine with human-level intelligence without translating
the mechanisms of the brain into those comparable to conventional
computer programming. The author predicts “this will first
take place in 2029 and become routine in the 2030s.”
Assuming the present exponential growth curves continue (and I
see no technological reason to believe they will not), the
2020s are going to be a very interesting decade. Just as
few people imagined five years ago that self-driving cars
were possible, while today most major auto manufacturers
have projects underway to bring them to market in the near future,
in the 2020s we will see the emergence of computational power
which is sufficient to “brute force” many problems
which were previously considered intractable. Just as search
engines and free encyclopedias have augmented our biological
minds, allowing us to answer questions which, a decade ago, would have
taken days in the library if we even bothered at all, the 300
million pattern recognisers in our
biological brains are on the threshold of having access to
billions more in the cloud, trained by interactions with
billions of humans and, perhaps eventually, many more artificial
intelligences. I am not talking here about implanting direct
data links into the brain or uploading human brains to
other computational substrates although both of these may
happen in time. Instead, imagine just being able to ask a
question in natural language and get an answer to it based upon
a deep understanding of all of human knowledge. If you think this
is crazy, reflect upon how exponential growth works or imagine
travelling back in time and giving a demo of Google or Wolfram
Alpha to yourself in 1990.
Ray Kurzweil,
after pioneering inventions in music synthesis, optical
character recognition, text to speech conversion, and
speech recognition, is now a director of engineering at Google.
In the Kindle edition, the index cites
page numbers in the print edition to which the reader can
turn since the electronic edition includes real page numbers.
Index items are not, however, directly linked to the text
cited.
February 2014 