Physics of the Future: How Science Will Shape Human Destiny and Our Daily Lives by the Year 2100 (18 page)

What I was interested in was the fact that Blue Gene was simulating the thinking process of a mouse brain, which has about 2 million neurons (compared to the 100 billion neurons that we have). Simulating the thinking process of a mouse brain is harder than you think, because each neuron is connected to many other neurons, making a dense web of neurons. But while I was walking among rack after rack of consoles making up Blue Gene, I could not help but be amazed that this astounding computer power could simulate only the brain of a mouse, and then only for a few seconds. (This does not mean that Blue Gene can simulate the behavior of a mouse. At present, scientists can barely simulate the behavior of a cockroach. Rather, this means that Blue Gene can simulate the firing of neurons found in a mouse, not its behavior.)

In fact, several groups have focused on simulating the brain of a mouse. One ambitious attempt is the Blue Brain Project of Henry Markram of the École Polytechnique Fédérale de Lausanne, in Switzerland. He began in 2005, when he was able to obtain a small version of Blue Gene, with only 16,000 processors, but within a year he was successful in modeling the rat’s neocortical column, part of the neocortex, which contains 10,000 neurons and 100 million connections. That was a landmark study, because it meant that it was biologically possible to completely analyze the structure of an important component of the brain, neuron for neuron. (The mouse brain consists of millions of these columns, repeated over and over again. Thus, by modeling one of these columns, one can begin to understand how the mouse brain works.)

In 2009, Markram said optimistically, “It is not impossible to build a human brain and we can do it in ten years. If we build it correctly, it should speak and have an intelligence and behave very much as a human does.” He cautions, however, that it would take a supercomputer 20,000 times more powerful than present supercomputers, with a memory storage 500 times the entire size of the current Internet, to achieve this.

So what is the roadblock preventing this colossal goal? To him, it’s simple: money.

Since the basic science is known, he feels that he can succeed by simply throwing money at the problem. He says, “It’s not a question of years, it’s one of dollars …. It’s a matter of if society wants this. If they want it in ten years, they’ll have it in ten years. If they want it in a thousand years, we can wait.”

But a rival group is also tackling this problem, assembling the greatest computational firepower in history. This group is using the most advanced version of Blue Gene, called Dawn, also based in Livermore. Dawn is truly an awesome sight, with 147,456 processors with 150,000 gigabytes of memory. It is roughly 100,000 times more powerful than the computer sitting on your desk. The group, led by Dharmendra Modha, has scored a number of successes. In 2006, it was able to simulate 40 percent of a mouse’s brain. In 2007, it could simulate 100 percent of a rat’s brain (which contains 55 million neurons, much more than the mouse brain).

And in 2009, the group broke yet another world record. It succeeded in simulating 1 percent of the human cerebral cortex, or roughly the cerebral cortex of a cat, containing 1.6 billion neurons with 9 trillion connections. However, the simulation was slow, about 1/600th the speed of the human brain. (If it simulated only a billion neurons, it went much faster, about 1/83rd the speed of the human brain.)

“This is a Hubble Telescope of the mind, a linear accelerator of the brain,” says Modha proudly, remarking on the mammoth scale of this achievement. Since the brain has 100 billion neurons, these scientists can now see the light at the end of the tunnel. They feel that a full simulation of the human brain is within sight. “This is not just possible, it’s inevitable. This will happen,” says Modha.

There are serious problems, however, with modeling the entire human brain, especially power and heat. The Dawn computer devours 1 million watts of power and generates so much heat it needs 6,675 tons of air-conditioning equipment, which blows 2.7 million cubic feet of chilled air every minute. To model the human brain, you would have to scale this up by a factor of 1,000.

This is a truly monumental task. The power consumption of this hypothetical supercomputer would be a billion watts, or the output of an entire nuclear power plant. You could light up an entire city with the energy consumed by this supercomputer. To cool it, you would need to divert an entire river and channel the water through the computer. And the computer itself would occupy many city blocks.

Amazingly, the human brain, by contrast, uses just 20 watts. The heat generated by the human brain is hardly noticeable, yet it easily outperforms our greatest supercomputer. Furthermore, the human brain is the most complex object that Mother Nature has produced in this section of the galaxy. Since we see no evidence of other intelligent life-forms in our solar system, this means that you have to go out to at least 24 trillion miles, the distance to the nearest star, and even beyond to find an object as complex as the one sitting inside your skull.

We might be able to reverse engineer the brain within ten years, but only if we had a massive Manhattan Project–style crash program and dumped billions of dollars into it. However, this is not very likely to happen any time soon, given the current economic climate. Crash programs like the Human Genome Project, which cost nearly $3 billion, were supported by the U.S. government because of their obvious health and scientific benefits. However, the benefits of reverse engineering the brain are less urgent, and hence will take much longer. More realistically, we will approach this goal in smaller steps, and it may take decades to fully accomplish this historic feat.

So computer simulating the brain may take us to midcentury. And even then, it will take many decades to sort through the mountains of data pouring in from this massive project and match it to the human brain. We will be drowning in data without the means to meaningfully sort out the noise.

But what about the second approach, identifying the precise location of every neuron in the brain?

This approach is also a herculean task, and may also take many decades of painful research. Instead of using supercomputers like Blue Gene, these scientists take the slice-and-dice approach, starting by dissecting the brain of a fruit fly into incredibly thin slices no more than 50 nm wide (about 150 atoms across). This produces millions of slices. Then a scanning electron microscope takes a photograph of each, with a speed and resolution approaching a billion pixels per second. The amount of data spewing from the electron microscope is staggering, about 1,000 trillion bytes of data, enough to fill a storage room just for a single fruit fly brain. Processing this data, by tediously reconstructing the 3-D wiring of every single neuron of the fly brain, would take about five years. To get a more accurate picture of the fly brain, you then have to slice many more fly brains.

Gerry Rubin of the Howard Hughes Medical Institute, one of the leaders in this field, thinks that altogether, a detailed map of the entire fruit fly brain will take twenty years. “After we solve this, I’d say we’re one-fifth of the way to understanding the human mind,” he concludes. Rubin realizes the enormity of the task he faces. The human brain has 1 million times more neurons than the brain of a fruit fly. If it takes twenty years to identify every single neuron of the fly brain, then it will certainly take many decades beyond that to fully identify the neural architecture of the human brain. The cost of this project will also be enormous.

So workers in the field of reverse engineering the brain are frustrated. They see that their goal is tantalizingly close, but the lack of funding hinders their work. However, it seems reasonable to assume that sometime by midcentury, we will have both the computer power to simulate the human brain and also crude maps of the brain’s neural architecture. But it may well take until late in this century before we fully understand human thought or can create a machine that can duplicate the functions of the human brain.

For example, even if you are given the exact location of every gene inside an ant, it does not mean you know how an anthill is created. Similarly, just because scientists now know the roughly 25,000 genes that make up the human genome, it does not mean they know how the human body works. The Human Genome Project is like a dictionary with no definitions. Each of the genes of the human body is spelled out explicitly in this dictionary, but what each does is still largely a mystery. Each gene codes for a certain protein, but it is not known how most of these proteins function in the body.

Back in 1986, scientists were able to map completely the location of all the neurons in the nervous system of the tiny worm
C. elegans.
This was initially heralded as a breakthrough that would allow us to decode the mystery of the brain. But knowing the precise location of its 302 nerve cells and 6,000 chemical synapses did not produce any new understanding of how this worm functions, even decades later.

In the same way, it will take many decades, even after the human brain is finally reverse engineered, to understand how all the parts work and fit together. If the human brain is finally reverse engineered and completely decoded by the end of the century, then we will have taken a giant step in creating humanlike robots. Then what is to prevent them from taking over?

In
The Terminator
movie series, the Pentagon proudly unveils Skynet, a sprawling, foolproof computer network designed to faithfully control the U.S. nuclear arsenal. It flawlessly carries out its tasks until one day in 1995, when something unexpected happens. Skynet becomes conscious. Skynet’s human handlers, shocked to realize that their creation has suddenly become sentient, try to shut it down. But they are too late. In self-defense, Skynet decides that the only way to protect itself is to destroy humanity by launching a devastating nuclear war. Three billion people are soon incinerated in countless nuclear infernos. In the aftermath, Skynet unleashes legion after legion of robotic killing machines to slaughter the remaining stragglers. Modern civilization crumbles, reduced to tiny, pathetic bands of misfits and rebels.

Worse, in the
Matrix Trilogy,
humans are so primitive that they don’t even realize that the machines have already taken over. Humans carry out their daily affairs, thinking everything is normal, oblivious to the fact that they are actually living in pods. Their world is a virtual reality simulation run by the robot masters. Human “existence” is only a software program, running inside a large computer, that is being fed into the brains of humans living in these pods. The only reason the machines even bother to have humans around is to use them as batteries.

Hollywood, of course, makes its living by scaring the pants off its audience. But it does raise a legitimate scientific question: What happens when robots finally become as smart as us? What happens when robots wake up and become conscious? Scientists vigorously debate the question: not if, but when this momentous event will happen.

According to some experts, our robot creations will gradually rise up the evolutionary tree. Today, they are as smart as cockroaches. In the future, they will be as smart as mice, rabbits, dogs and cats, monkeys, and then they will rival humans. It may take decades to slowly climb this path, but they believe that it is only a matter of time before the machines exceed us in intelligence.

AI researchers are split on the question of when this might happen. Some say that within twenty years robots will approach the intelligence of the human brain and then leave us in the dust. In 1993, Vernor Vinge said, “Within thirty years, we will have the technological means to create superhuman intelligence. Shortly after, the human era will be ended …. I’ll be surprised if this event occurs before 2005 or after 2030.”

On the other hand, Douglas Hofstadter, author of
Gödel, Escher, Bach,
says, “I’d be very surprised if anything remotely like this happened in the next 100 years to 200 years.”

When I talked to Marvin Minsky of MIT, one of the founding figures in the history of AI, he was careful to tell me that he places no timetable on when this event will happen. He believes the day will come but shies away from being the oracle and predicting the precise date. (Being the grand old man of AI, a field he helped to create almost from scratch, perhaps he has seen too many predictions fail and create a backlash.)

A large part of the problem with these scenarios is that there is no universal consensus as to the meaning of the word
consciousness.
Philosophers and mathematicians have grappled with the word for centuries, and have nothing to show for it. Seventeenth-century thinker Gottfried Leibniz, inventor of calculus, once wrote, “If you could blow the brain up to the size of a mill and walk about inside, you would not find consciousness.” Philosopher David Chalmers has even catalogued almost 20,000 papers written on the subject, with no consensus whatsoever.

Nowhere in science have so many devoted so much to create so little.