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Mind in the MACHINE. By: PIORE, ADAM, Discover, 02747529, Jun2013, Vol. 34, Issue 5

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Mind in the MACHINE

A visionary engineer aims to transform computing with technology modeled on the human brain

The day he got the news that would transform his life, Dharmendra Modha, 17, was supervising a team of laborers scraping paint

off iron chairs at a local Mumbai hospital. He felt happy to have the position, which promised steady pay and security -- the most a

poor teen from Mumbai could realistically aspire to in 1986.

Modha's mother sent word to the job site shortly after lunch: The results from the statewide university entrance exams had come

in. There appeared to be some sort of mistake, because a perplexing telegram had arrived at the house. Modha's scores hadn't

just placed him atop the city, the most densely inhabited in India -- he was No. 1 in math, physics and chemistry for the entire

province of Maharashtra, population 100 million. Could he please proceed to the school to sort it out?

Back then, Modha couldn't conceive what that telegram might mean for his future. Both his parents had ended their schooling

after the 11th grade. He could count on one hand the number of relatives who went to college. But Modha's ambitions have

expanded considerably in the years since those test scores paved his way to one of India's most prestigious technical

academies, and a successful career in computer science at IBM's Almaden Research Center in San Jose, Calif.

Listen American Accent

POST UNIVERSITY

LIBRARY

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Recently, the diminutive engineer with the bushy black eyebrows, closely cropped hair and glasses sat in his Silicon Valley office

and shared a vision to do nothing less than transform the future of computing. "Our mission is clear," said Modha, now 44, holding

up a rectangular circuit board featuring a golden square.

"We'd like these chips to be everywhere -- in every corner, in everything. We'd like them to become absolutely essential to the

world."

Traditional chips are sets of miniaturized electrical components on a small plate used by computers to perform operations. They

often consist of millions of tiny circuits capable of encoding and storing information while also executing programmed commands.

Modha's chips do the same thing, but at such enormous energy savings that the computers they comprise would handle far more

data, by design. With the new chips as linchpin, Modha has envisioned a novel computing paradigm, one far more powerful than

anything that exists today, modeled on the same magical entity that allowed an impoverished laborer from Mumbai to ascend to

one of the great citadels of technological innovation: the human brain.

TURNING TO NEUROSCIENCE

The human brain consumes about as much energy as a 20-watt bulb -- a billion times less energy than a computer that simulates

brainlike computations. It is so compact it can fit in a two-liter soda bottle. Yet this pulpy lump of organic material can do things no

modern computer can. Sure, computers are far superior at performing pre-programmed computations -- crunching payroll

numbers or calculating the route a lunar module needs to take to reach a specific spot on the moon. But even the most advanced

computers can't come close to matching the brain's ability to make sense out of unfamiliar sights, sounds, smells and events,

and quickly understand how they relate to one another. Nor can such machines equal the human brain's capacity to learn from

experience and make predictions based on memory.

Five years ago, Modha concluded that if the world's best engineers still hadn't figured out-how to match the brain's energy

efficiency and resourcefulness after decades of trying using the old methods, perhaps they never would. So he tossed aside

many of the tenets that have guided chip design and software development over the past 60 years and turned to the literature of

neuroscience. Perhaps understanding the brain's disparate components and the way they fit together would help him build a

smarter, more energy-efficient silicon machine.

These efforts are paying off. Modha's new chips contain silicon components that crudely mimic the physical layout of, and

connections between, microscopic carbon-based brain cells. (See "Inside Modha's Neural Chip," page 55.) Modha is confident

that his chips can be used to build a cognitive computing system on the scale of a human brain for only 100 times more power,

making it 10 million times more energy efficient than the computers of today.

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Already, Modha's team has demonstrated some basic capabilities. Without the help of a programmer explicitly telling them what to

do, the chips they've developed can learn to play the game Pong, moving a bar along the bottom of the screen and anticipating the

exact angle of a bouncing ball. They can also recognize the numbers zero through nine as a lab assistant scrawls them on a pad

with an electronic pen.

Of course, plenty of engineers have pulled off such feats -- and far more impressive ones. An entire subspecialty known as

machine learning is devoted to building algorithms that allow computers to develop new behaviors based on experience. Such

machines have beaten the world's best minds in chess and Jeopardy! But while machine learning theorists have made progress

in teaching computers to perform specific tasks within a strict set of parameters -- such as how to parallel park a car or plumb

encyclopedias for answers to trivia questions -- their programs don't enable computers to generalize in an open-ended way.

Modha hopes his energy-efficient chips will usher in change. "Modern computers were originally designed for three fundamental

problems: business applications, such as billing; science, such as nuclear physics simulation; and government programs, such

as Social Security," Modha states. The brain, on the other hand, was forged on the crucible of evolution to quickly make sense of

the world around it and act upon its conclusions. "It has the ability to pick out a prowling predator in huge grasses, amid a huge

amount of noise, without being told what it is looking for. It isn't programmed. It learns to escape and avoid the lion."

Machines with similar capabilities could help solve one of mankind's most pressing problems: the overload of information.

Between 2005 and 2012, the amount of digital information created, replicated and consumed worldwide increased over 2,000

percent -- exceeding 2.8 trillion gigabytes in 2012. By some estimates, that's almost as many bits of information as there are stars

in the observable universe. The arduous task of writing the code that instructs today's computers to make sense of this flood of

information -- how to order it, analyze it, connect it, what to do with it -- is already far outstripping the abilities of human

programmers.

Cognitive computers, Modha believes, could plug the gap. Like the brain, they will weave together inputs from multiple sensory

streams, form associations, encode memories, recognize patterns, make predictions and then interpret, perhaps even act -- all

using far less power than today's machines.

Drawing on data streaming in from a multitude of sensors monitoring the world's water supply, for instance, the computer might

learn to recognize changes in pressure, temperature, wave size and tides, then issue tsunami warnings, even though current

science has yet to identify the constellation of variables associated with the monster waves. Brain-based computers could help

emergency department doctors render elusive diagnoses even when science has yet to recognize the collection of changes in

body temperature, blood composition or other variables associated with an underlying disease.

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"You will still want to store your salary, your gender, your Social Security number in today's computers," Modha says. "But

cognitive computing gives us a complementary paradigm for a radically different kind of machine."

LIGHTING THE NETWORK

Modha is hardly the first engineer to draw inspiration from the brain. An entire field of computer science has grown out of insights

derived from the way the smallest units of the brain -- cells called neurons -- perform computations. It is the firing of neurons that

allows us to think, feel and move. Yet these abilities stem not from the activity of any one neuron, but from networks of

interconnected neurons sending and receiving simple signals and working in concert with each other.

The potential for brainlike machines emerged as early as 1943, when neurophysiologist Warren McCulloch and mathematician

Walter Pitts proposed an idealized mathematical formulation for the way networks of neurons interact to cause one another to fire,

sending messages throughout the brain.

In a biological brain, neurons communicate by passing electrochemical signals across junctions known as synapses. Often the

process starts with external stimuli, like light or sound. If the stimulus is intense enough, voltage across the membrane of

receiving neurons exceeds a given threshold, signaling neurochemicals to fly across the synapses, causing more neurons to fire

and so on and so forth. When a critical mass of neurons fire in concert, the input is perceived by the cognitive regions of the brain.

With enough neurons firing together, a child can learn to ride a bike and a mouse can master a maze.

McCulloch and Pitts pointed out that no matter how many inputs their idealized neuron might receive, it would always be in one of

only two possible states -- activated or at rest, depending upon whether the threshold for excitement had been passed. Because

neurons follow this "all-or-none law," every computation the brain performs can be reduced to series of true or false expressions,

where true and false can be represented by 1 and 0, respectively. Modern computers are also based on logic systems using Is

and 0s, with information coming from electric switches instead of the outside environment.

McCulloch and Pitts had captured a fundamental similarity between brains and computers. If endowed with the capacity to ask

enough yes-or-no questions, either one should presumably eventually arrive at the solution to even the most complicated of

questions. As an example, to draw a boundary between a group of red dots and blue dots, one might ask of each dot if it is red

(yes/no) or blue (yes/no). Then one might ask if two neighboring pairs of dots are of differing colors (yes/no). With enough layers

of questions and answers, one might answer almost any complex question at all.

Yet this kind of logical ability seemed far removed from the capacity of brains, made of networks of neurons, to encode memories

or learn. That capacity was explained in 1949 by Canadian psychologist Donald Hebb, who hypothesized that when two neurons

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fire in close succession, connections between them strengthen. "Neurons that fire together wire together" is the catchy phrase

that emerged from his pivotal work.

Connections between neurons explain how narrative memory is formed. In a famous literary example, Marcel Proust s childhood

flooded back when he dipped a madeleine in his cup of tea and took a bite. The ritual was one he had performed often during

childhood. When he repeated it years later, neurons fired in the areas of the brain storing these taste and motor memories. As

Hebb had suggested, those neurons had strong physical connections to other neurons associated with other childhood

memories. Thus when Proust tasted the madeleine, the neurons encoding those memories also fired -- and Proust was flooded

with so many associative memories he filled volumes of his masterwork, In Search of Lost Time.

By 1960, computer researchers were trying to model Hebb's ideas about learning and memory. One effort was a crude brain

mock-up called the perceptron. The perceptron contained a network of artificial neurons, which could be simulated on a computer

or physically built with two layers of electrical circuits. The space between the layers was said to represent the synapse. When

the layers communicated with each other by passing signals over the synapse, that was said to model (roughly) a living neural

net. One could adjust the strength of signals passed between the two layers -- and thus the likelihood that the first layer would

activate the second (much like one firing neuron activates another to pass a signal along). Perceptron learning occurred when the

second layer was instructed to respond more powerfully to some inputs than others. Programmers trained an artificial neural

network to "read," activating more strongly when shown patterns of light depicting certain letters of the alphabet and less strongly

when shown others.

The idea that one could train a computer to categorize data based on experience was revolutionary. But the perceptron was

limited: Consisting of a mere two layers, it could only recognize a "linearly separable" pattern, such as a plot of black dots and

white dots that can be separated by a single straight line (or, in more graphic terms, a cat sitting next to a chair). But show it a plot

of black and white dots depicting something more complex, like a cat sitting on a chair, and it was utterly confused.

It wasn't until the 1980s that engineers developed an algorithm capable of taking neural networks to the next level. Now

programmers could adjust the weights not just between two layers of artificial neurons, but also a third, a fourth -- even a ninth

layer -- in between, representing a universe where many more details could live. This expanded the complexity of questions such

networks could answer. Suddenly neural networks could render squiggly lines between black and white dots, recognizing both the

cat and the chair it was sitting in at the same time.

OUT OF BOMBAY

Just as the neural net revival was picking up steam, Modha entered India's premier engineering school, the Indian Institute of

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Technology in Bombay. He graduated with a degree in computer science and engineering in 1990.

As Modha looked to continue his education, few areas seemed as hot as the reinvigorated field of neural networks. In theory, the

size of neural networks was limited only by the size of computers and the ingenuity of programmers.

In one powerful example of the new capabilities around that time, Carnegie Mellon graduate student Dean Pomerleau used

simulated images of road conditions to teach a neural network to interpret live road images picked up by cameras attached to a

car's onboard computer. Traditional programmers had been stumped because even subtle changes in angle, lighting or other

variables threw off preprogrammed software coded to recognize exact visual parameters.

Instead of trying to precisely code every possible image or road condition, Pomerleau simply showed a neural network different

kinds of road conditions. Once it was trained to drive under specific conditions, it was able to generalize to drive under similar but

not identical conditions. Using this method, a computer could recognize a road with metal dividers based on its similarities to a

road without dividers, or a rainy road based on its similarity to a sunny road -- an impossibility using traditional coding techniques.

After being shown images of various left-curving and right-curving roads, it could recognize roads curving at any angle.

Other programmers designed a neural network to detect credit card fraud by exposing it to purchase histories of good versus

fraudulent card accounts. Based on the general spending patterns found in known fraudulent accounts, the neural network was

able to recognize the behavior and flag new fraud cases.

The neural networking mecca was San Diego -- in 1987, about 1,500 people met there for the first significant conference on

neural networking in two decades. And in 1991, Modha arrived at the University of California, San Diego to pursue his Ph.D. He

focused on applied math, constructing equations to examine how many dimensions of variables certain systems could handle,

and designing configurations to handle more.

By the time Modha was hired by IBM in 1997 in San Jose, another computing trend was taking center stage: the explosion of the

World Wide Web. Even back then, it was apparent that the flood of new data was overwhelming programmers. The Internet

offered a vast trove of information about human behavior, consumer preferences and social trends. But there was so much of it:

How did one organize it? How could you begin to pick patterns out of files that could be classified based on tens of thousands of

characteristics? Current computers consumed way too much energy to ever handle the data or the massive programs required to

take every contingency into account. And with a growing array of sensors gathering visual, auditory and other information in

homes, bridges, hospital emergency departments and everywhere else, the information deluge would only grow.

A CANONICAL PATH

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The more Modha thought about it, the more he became convinced that the solution might be found by turning back to the brain, the

most effective and energy-efficient pattern recognition machine in existence. Looking to the neuro-scientific literature for

inspiration, he found the writings of MIT neuroscientist Mriganka Sur. Sur had severed the neurons connecting the eyes of

newborn ferrets to the brain's visual cortex; then he reconnected those same neurons to the auditory cortex. Even with eyes

connected to the sound-processing areas of the brain, the rewired animals could still see as adults.

To Modha, this revealed a fascinating insight: The neural circuits in Sur's ferrets were flexible -- as interchangeable, it seemed, as

the back and front tires of some cars. Sur's work implied that to build an artificial cortex on a computer, you only needed one

design to create the "circuit" of neurons that formed all its building blocks. If you could crack the code of that circuit -- and embody

it in computation -- all you had to do was repeat it. Programmers wouldn't have to start over every time they wanted to add a new

function to a computer, using pattern recognition algorithms to make sense of new streams of data. They could just add more

circuits.

"The beauty of this whole approach," Modha enthusiastically explains, "is that if you look at the mammalian cerebral cortex as a

road map, you find that by adding more and more of these circuits, you get more and more functionality."

In search of a master neural pattern, Modha discovered that European researchers had come up with a mathematical description

of what appeared to be the same as the circuit Sur investigated in ferrets, but this time in cats. If you unfolded the cat cortex and

unwrinkled it, you would find the same six layers repeated again and again. When connections were drawn between different

groups of neurons in the different layers, the resulting diagrams looked an awful lot like electrical circuit diagrams.

Modha and his team began programming an artificial neural network that drew inspiration from these canonical circuits and could

be replicated multiple times. The first step was determining how many of these virtual circuits they could they link together and run

on IBM's traditional supercomputers at once. Would it be possible to reach the scale of a human cortex?

At first Modha and his team hit a wall before they reached 40 percent of the number of neurons present in the mouse cerebral

cortex: roughly 8 million neurons, with 6,300 synaptic connections apiece. The truncated circuitry limited the learning, memory

and creative intelligence their simulation could achieve.

So they turned back to neuroscience for solutions. The actual neurons in the brain, they realized, only become a factor in the

organs overall computational process when they are activated. When inactive, neurons simply sit on the sidelines, expending little

energy and doing nothing. So there was no need to update the relationship between 8 million neurons 1,000 times a second.

Doing so only slowed the system down. Instead, they could emulate the brain by instructing the computer to focus attention only

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on neurons that had recently fired and were thus most likely to fire again. With this adjustment, the speed at which the

supercomputer could simulate a brain-based system increased a thousandfold. By November 2007, Modha had simulated a

neural network on the scale of a rat cortex, with 55 million neurons and 442 billion synapses.

Two years later his team scaled it up to the size of a cat brain, simulating 1.6 billion neurons and almost 9 trillion synapses.

Eventually they scaled the model up to simulate a system of 530 billion neurons and 100 trillion synapses, a crude approximation

of the human brain.

BUILDING A SILICON BRAIN

The researchers had simulated hundreds of millions of repetitions of the kind of canonical circuit that might one day enable a new

breed of cognitive computer. But it was just a model, running at a maddeningly slow speed on legacy machines that could never

be brainlike, never step up to the cognitive plate. In 2008, the federal Defense Advanced Research Projects Agency (DARPA)

announced a program aimed at building the hardware for an actual cognitive computer. The first grant was the creation of an

energy-efficient chip that would serve as the heart and soul of the new machine -- a dream come true for Modha.

With DARPA's funding, Modha unveiled his new, energy-efficient neural chips in summer 2011. Key to the chips' success was

their processors, chip components that receive and execute instructions for the machine. Traditional computers contain a small

number of very fast processors (modern laptops usually have two to four processors on a single chip) that are almost always

working. Every millisecond, these processors scan millions of electrical switches, monitoring and flipping thousands of circuits

between two possible states, 1 and 0 -- activated or not.

To store the patterns of ones and zeros, today's computers use a separate memory unit. Electrical signals are conveyed between

the processor and memory over a pathway known as a memory bus. Engineers have increased the speed of computing by

shortening the length of the bus. Some servers can now loop from memory to processor and back around a few hundred-million

times per second. But even the shortest buses consume energy and create heat, requiring lots of power to cool.

The brain's architecture is fundamentally different, and a computer based on the brain would reflect that. Instead of a small

number of large, powerful processors working continuously, the brain contains billions of relatively slow, small processors -- its

neurons -- which consume power only when activated. And since the brain stores memories in the strength of connections

between neurons, inside the neural net itself, it requires no energy-draining bus.

The processors in Modha's new chip are the smallest units of a computer that works like the brain: Every chip contains 256 very

slow processors, each one representing an artificial neuron (By comparison, a roundworm brain consists of about 300 neurons.)

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Only activated processors consume significant power at any one time, making energy consumption low. But even when activated,

the processors need far less power than their counterparts in traditional computers because the tasks they are designed to

execute are far simpler: Whereas a traditional computer processor is responsible for carrying out all the calculations and

operations that allow a computer to run, Modha's tiny units only need to sum up the number of signals received from other virtual

neurons, evaluate their relative weights and determine whether there are enough of them to prompt the processor to emit a signal

of its own.

Modha has yet to link his new chips and their processors in a large-scale network that mimics the physical layout of a brain. But

when he does, he is convinced that the benefits will be vast. Evolution has invested the brain's anatomy with remarkable energy

efficiencies by positioning those areas most likely to communicate closer together; the closer neurons are to one another, the less

energy they need to push a signal through. By replicating the big-picture layout of the brain, Modha hopes to capture these and

other unanticipated energy savings in his brain-inspired machines. He has spent years poring over studies of long-distance

connections in the rhesus macaque monkey brain, ultimately creating a map of 383 different brain areas, connected by 6,602

individual links. (See "Mapping the Monkey Brain," page 58.) The map suggests how many cognitive computing chips should be

allocated to the different regions of any artificial brain, and which other chips they should be wired to. For instance, 336 links begin

at the main vision center of the brain. An impressive 1,648 links emerge from the frontal lobe, which contains the prefrontal cortex,

a centrally located brain structure that is the seat of decisionmaking and cognitive thought. As with a living brain, the neural

computer would have most connections converging on a central point.

Of course, even if Modha can build this brai-niac, some question whether it will have any utility at all. Geoff Hinton, a leading neural

networking theorist, argues the hardware is useless without the proper "learning algorithm" spelling out which factors change the

strength of the synaptic connections and by how much. Building a new kind of chip without one, he argues, is "a bit like building a

car engine without first figuring out how to make an explosion and harness the energy to make the wheels go round."

But Modha and his team are undeterred. They argue that they are complementing traditional computers with cognitive-computing-

like abilities that offer vast savings in energy, enabling capacity to grow by leaps and bounds. The need grows more urgent by the

day. By 2020, the world will generate 14 times the amount of digital information it did in 2012. Only when computers can spot

patterns and make connections on their own, says Modha, will the problem be solved.

Creating the computer of the future is a daunting challenge. But Modha learned long ago, halfway across the world as a teen

scraping the paint off of chairs, that if you tap the power of the human brain, there is no telling what you might do.

In a biological brain, neurons communicate by passing electrochemical signals across junctions known as synapses. Often the

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process starts with external stimuli, like light or sound.

"We'd like these chips to be everywhere -- in every corner, in everything. We'd like them to become absolutely essential to the

world."

They simulated a system of 530 billion neurons and 100 trillion synapses, a crude rendition of the human brain.

Gathering in front of the brain wall this February are the Cognitive Computing Lab team members (from left) John Arthur, Paul

Merolla, Bill Risk, Dharmendra Modha, Bryan Jackson, Myron Flickner and Steve Esser.

Dharmendra Modha and team member Bill Risk stand by a supercomputer at the IBM Almaden facility. Using the supercomputers

at Almaden and Lawrence Livermore National Laboratory, the group simulated networks that crudely approximated the brains of

mice, rats, cats and humans.

Dharmendra Modha stands alongside the brain wall, used by his cognitive computing team to simulate brain activity and model

neural chips at IBM. In his hand is a neurosynaptic chip, the core component of a new generation of computers based on the

architecture of the brain. The neon swirl on the opposite page was inspired by the neural architecture of a rhesus macaque brain,

used by Modha to help him design the chip. (Initials around the swirl's rim indicate discrete regions in the macaque brain.)

~~~~~~~~

By ADAM PIORE

PHOTOGRAPH BY MAJED ABOLFAZLI

Adam Piore is a contributing editor at DISCOVER.

© 2013 Discover Magazine