IEEE Spectrum June, 2008 - 39

Janelia's circuit diagrams but also other
work in neuroscience. Adler cites the work
of Stanford neuroscientist Stephen Smith
as "the first steps to finding the soul." At
Harvard's Center for Brain Science, neuroscientist Jeff Lichtman mapped mouse
neurons by "painting " them w ith
f luorescent proteins. Rubin believes
he'll live long enough to see an MRI-like
device that measures function with such
high-resolution output that neurons in
fruit flies, mice, or even humans can be
observed taking in and processing information in real time.
How would all these different systems
work together to show us how the brain
does what it does? With his 10-billionpixel-per-second microscope, Adler is
confident he'll be able to produce braintopography images like Google's satellite views, resolving fine details in sharp
focus. Smith's cartography, on the other
hand, he compares with Google's map
views, including street names. Rubin's
fMRI data would be like real-time traffic data. Layering these different maps
atop each other, says Adler, could lead to
a hybrid comparable to a Google map.
Such a Google-mapped brain, Adler
says, could do more than let us understand and cure disease: it could lead to
a map of human consciousness. And he
believes that understanding the wiring
of the brain could lead to transformative
technologies. What are memories, he
asks, but rewired patterns in our brains?
"If you can understand how memories are
formed," he says, "you can create memories." Just as today's sophisticated circuitediting tools can modify microchips after
they've been manufactured and packaged, a brain-editing tool could perhaps
one day modify the brain. Adler jokes
about an application straight out of Total
Recall: buying fond memories of a vacation instead of taking the actual trip.

I
TimoThy ArchibAld

n this heady context, the leap from
reverse engineering the human brain
to building a thinking machine doesn't
seem ridiculous. To Adler, the existence
of human beings is proof enough that
humans can be engineered. "When we
study biology, we're just studying a different version of nanotechnology-only it's a
more advanced nanotechnology." But he
quickly qualifies that statement: silicon
is the wrong material, he adds. The nanotechnology we use today is static; we can
move electrons around but not atoms,
which means the chip doesn't change
when you use it. "We may not ever be able

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to get there using the silicon technology of
moving electrons," he says. "But someone
could come along tomorrow and invent
a different way of making a circuit that's
closer to what the brain does. Then, within
50 to 100 years, we'll have something that
can do what the brain does."
But there's nothing like a little healthy
competition to speed up this timetable.
Janelia isn't the only player in the highspeed brain-imaging arena: both Harvard
and the Max Planck Institute for Medical
Research, in Heidelberg, Germany (where
the 3-D SEM method of brain reconstruction was actually invented), are
also working on the brain problem, and
they compete heavily for milestones. The
Harvard team may have solved the imagesettling problem: they plan to adapt a
conveyor-belt device used in the semiconductor industry as a continuously
moving stage that allows an uninterrupted panoramic image, eliminating the
need for time-wasting, steadying pauses.
Adler also consults for Harvard, helping its team push the limits of its existing
SEMs by "supercharging them" to hit their
full potential. Before a 10-billion-pixel-persecond microscope can be useful, he says,
many other roadblocks have to be negotiated. So in the meantime, he takes these
souped-up SEMs to the limits imposed on
them by physics, not factory settings. That
means, for instance, that a lab microscope
with a default rate of 10 million pixels per
second can jump to 100 million pixels per
second after Adler is finished tweaking it.
Despite all the obstacles, the good
news, Adler says, is that the fundamental physics of the superhigh-throughput
electron microscope has been resolved.
It's no longer a science problem, he says:
now it's an engineering problem. Hess
agrees. "Finding that one 65-nm shortedwire defect in a Pentium chip and that one
miswired neuron in a fruit-fly brain," he
says, are fundamentally similar problems.
They're counting on the inexorable climb
of Moore's curve to aid them in their process. Rubin describes the phenomenon
in terms of his Ph.D. work sequencing a
single yeast gene. "Thirtyish years later,
DNA-sequencing machines are at the point
where students are doing 100 of my Ph.D.s
per second," he says, laughing. "We're at
millisecond data acquisition. These are
the kinds of advances we'll need to make
a map of the human mind."
In Rubin's mind, solving the fruit-fly
brain is a 20-year problem. "After we solve
this, I'd say we're one-fifth of the way to
understanding the human mind." o

ExpERT ViEw:

eric Hahn

WHO HE IS
Serial entrepreneur and
early-stage investor who
founded Collabra Software
(sold to Netscape) and
Lookout Software (sold
to Microsoft) and backed
Red Hat, Loudcloud, and
Zimbra. CTO of Netscape
during the browser wars.
SINGULARITY
WILL OCCUR
Within 70 years
MACHINE
CONSCIOUSNESS
WILL OCCUR
"Yes, in that they eventually
pass the Turing Test for 'Is it
thinking?' "
MOORE'S LAW
WILL CONTINUE FOR
30 more years
THOUGHTS
"I think that machine
intelligence is one of the
most exciting remaining
'great problems' left in
computer science. For
all its promise, however,
it pales compared with
the advances we could
make in the next few
decades in improving
the health and education
of the existing human
intelligences already on
the planet. I believe the
first thing a tabula rasa
intelligence (machine or
otherwise) would conclude
is that humans are very
poor stewards of their
own condition."

june 2008 * ieee spectrum * na

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Table of Contents for the Digital Edition of IEEE Spectrum June, 2008