Currently, Furber and his team are testing a version that includes a mere 50 “neurons” that can navigate a simple virtual environment described as “Pac-Man-like.”

Furber has big plans for the computer once it is completed:

That’s good enough for Furber, who wants to start teaching his brain-like computer about the world as soon as possible. His first goal is to teach it how to control a robotic arm, before working towards a design to control a humanoid. A robot controller with even a dash of brain-like properties should be much better at tasks like image recognition, navigation and decision-making, says Furber.

“Robots offer a natural, sensory environment for testing brain-like computers,” says Furber. “You can instantly tell if it is being useful.”

Processors for the project are currently being manufactured in Taiwan, and Furber intends to have a 10,000 chip version of the machine operational before the end of the year.

(Via PopSci)

If you’re interested in learning more about what goes on behind-the-scenes at the Blue Brain Project, an effort to reverse-engineer the mammalian brain, this short documentary is a good start. It features interviews with project leader Henry Markram explaining more about the project and shows how his team is going about its important work.

This video is only a taste of director Noah Hutton’s “10-year-in-the-making” film, although I’m not sure if that means the finished documentary will be released a decade from now, a time Markram has predicted that he and his team will ultimately be successful in their mission.

(Via BoingBoing)

While that computer was built for a particular function and with the best intentions in mind, so are other computers built with the best intentions. We currently have computers that are armed with missiles flying over foreign countries. While these are of course controlled by a human, and rely on a system of at least four control planes to provide radar support, it’s only a matter of time before they are autonomous.

Advancements in robotics are taking major leaps every day, soon we will see completely independent robots and androids. Similar to the science fiction we read, they will start out as our slaves. History has taught us the slavery lesson pretty well. I know it’s hard to fathom, robots rising up against their human masters and demanding equal rights and freedom, but with how inundated we are with science fiction is it really that hard to imagine?

The Daily Mail features a very in-depth feature story about the work of Henry Markram and the Blue Brain Project, an effort to “reverse-engineer the mammalian brain, in order to understand brain function and dysfunction through detailed simulations.” Markram and team, who have already been able to simulate the workings of “about 10,000 neurons,” firmly believe that they will be able to develop a copy of a rat’s brain within the decade:

‘We will do it by 2018,’ says the professor confidently. ‘We need a lot of money, but I am getting it. There are few scientists in the world with the resources I have at my disposal.’

There is, inevitably, scepticism. But even Markram’s critics mostly accept that he is on to something and, most importantly, that he has the money.

Tens of millions of euros are flooding into his laboratory at the Brain Mind Institute at the Ecole Polytechnique in Lausanne – paymasters include the Swiss government, the EU and private backers, including the computer giant IBM. Artificial minds are, it seems, big business.

The human brain is the most complex object in the universe. But Markram insists that the latest supercomputers will soon have its measure.

Although Markram has plenty of resources, he estimates he’ll need a custom-built “billion dollar machine” to simulate a human brain.

Today researchers at IBM’s Almaden research center unveiled  the “biggest artificial brain ever,” which is described as being as large as a cat’s brain. The hardware is impressive, consisting of 147,456 CPUs, but what is even more compelling is how the team plans to “teach” the computer – by creating a virtual world in which it can explore and learn:

The simulation that Modha will unveil today is just a starting point. It lacks the neural patterning that develops as real brains mature. Neuroscientists believe that this complexity can only evolve through “embodied learning”—stumbling around in a physical body, in which every action has instant consequences that are experienced through senses such as touch and sight. As Anil Seth, a neuroscientist at the University of Sussex in Britain, puts it, “The brain wires itself.” 

Seth demonstrated this principle while at the Neurosciences Institute in San Diego using a brain simulation called Darwin. He embodied Darwin’s 50,000 virtual neurons (about equal to the brain of a pond snail, or one-quarter of a fruit fly) in a wheeled robot. As Darwin wandered around, its virtual neurons rewired their connections to produce so-called hippocampal “place cells”—similar to neurons found in mammals—which helped it navigate. Scientists don’t know how to program these place cells, but with embodied learning the cells emerge on their own. 

Paul Maglio, a cognitive scientist at Almaden, has similar plans for Modha’s cortical simulation. He’s building a virtual world for it to inhabit using software from the video shootout game “Unreal Tournament” and data from Mars. Besides topographic maps and aerial photos, Maglio plans to use rover-level imagery to create terrain with lifelike boulders and craters. 

The video-game software provides a pallet of several dozen robotic bodies for Modha’s virtual cortex. Initially, it will use a simple wheeled robot to explore its world, driven by fundamental desires such as sustenance and survival. “It’s got to like some things and not like other things,” Maglio says. “Ultimately, it’s going to want not to roll off the edges of cliffs.”

Creating a virtual world for AIs has been suggested as a way to enable them to learn and interact with human avatars or other AIs without the danger of them making decisions that could affect the “real world.”

Of course there is no danger of this machine, even as powerful as it is, somehow gaining sentience and becoming a danger to others. In this case a virtual world is the only environment in which a machine of this size (which takes up a room the size of an acre) can learn from its actions in a way that roughly approximates the physical world. I expect this to be the norm until we’re able to come up with a way to shrink a machine with a comparable level of processing power down to a size that can practically exist as a robot – an event that is still decades away.

“We think exascale is a 100 million-core kind of enterprise, and there doesn’t seem any real pathway around it, said Turek. “Where the players in pursuit of exascale are today is [at] a state of investigation to see what the right model is. So if hybridization is the key, then what is the ratio of special-purpose cores to conventional cores?” he said.

These future systems will have to use less memory per core and will need more memory bandwidth. Systems running 100 million cores will continually see core failures and the tools for dealing with them will have to be rethought “in a dramatic kind of way,” said Turek.

IBM’s design goal for an exascale system is to limit it to 20 megawatts of power and keep it at a size of between 70 and 80 racks. Jaguar is entirely built of CPUs, but Bland also sees future systems as hybrids, and points to chip development by both Intel and AMD that combine CPUs and co-processors.

“We believe that using accelerators is going to be absolutely critical to any strategy to getting to exaflop computers,” he said.

Addison Snell, CEO of InterSect Research, an HPC research firm in Sunnyvale, Calif., said accelerators are capable of providing vast computational capability for specific applications, and the applications that can take advantage of them can move toward exascale first.” Eventually, a general-purpose exascale system will arrive, “but special-purpose will probably come first.”

This estimate comes hot on the heels of news that Jaguar, the supercomputer at Oak Ridge National Laboratory, has claimed the title of “world’s most powerful supercomputer” with a theoretical top speed of 2.3 petaflops and consisting of 250,000 cores.

Creating more energy efficient computers is, therefore, a huge step in the quest for AI. Kwabena Boahen, a researcher at Stanford University, has a solution for this problem by creating a new chip as the basis of a computer called “Neurogrid” that incorporates “noise” into computing. This noise simulates the neural noise, or misfiring synapses, that occurs in organic brain:

Radically improving that efficiency, Boahen says, will involve trade-offs that would horrify a chip designer. Forget about infinitesimal error rates like one in a trillion; the transistors in Neurogrid will crackle with noise, misfiring at rates as high as 1 in 10. “Nobody knows how we’re going to compute with that,” Boahen admits. “The only thing that computes with this kind of crap is the brain.”

It sounds cockamamy, but it is true. Scientists have found that the brain’s 100 billion neurons are surprisingly unreliable. Their synapses fail to fire 30 percent to 90 percent of the time. Yet somehow the brain works. Some scientists even see neural noise as the key to human creativity. Boahen and a small group of scientists around the world hope to copy the brain’s noisy calculations and spawn a new era of energy-efficient, intelligent computing. Neurogrid is the test to see if this approach can succeed.

Most modern supercomputers are the size of a refrigerator and devour $100,000 to $1 million of electricity per year. Boahen’s Neurogrid will fit in a briefcase, run on the equivalent of a few D batteries, and yet, if all goes well, come close to keeping up with these Goliaths.

In addition to reducing power consumption, engineers theorize that the introduction of noise into computing may also enable machines to exhibit true creative thinking, another benchmark for true human-like AI.

In fact, the problem with the AI effort in general is that we have little progress to show after decades of attempts, likely for the very good reason that human intelligence is not algorithmic, at least not in the same sense in which computer programs are. I am most certainly not invoking mysticism or dualism here, I think that intelligence (and consciousness) are the result of the activity of a physical brain substrate, but the very fact that we can build machines with a degree of computing power and speed that greatly exceeds those of the human mind, and yet are nowhere near being “intelligent,” should make it pretty clear that the problem is not computing power or speed.

I’m not familiar with David Chalmers’ presentation, so I can’t comment on the information it contained or how it was presented. I can, however, take issue with Pigliucci’s argument that the idea of artificial intelligence is best left to science fiction.

The problem is, computers do not have the processing power of the human mind – aren’t even close, in fact. Ray Kurzweil estimates it will be a good 20 years, at least, before we create machines that approach this benchmark. From there, he estimates it will take at least 25 more years before machines possess the required level of intelligence to create machines smarter than themselves, which in theory would spark the singularity.

While humans have created very fast and powerful computers, we still haven’t created anything approaching the complexity of a mammalian brain. Researchers are, however, taking steps in that direction with endeavors such as the Blue Brain Project, which is an attempt to reverse-engineer the mammalian brain, but this research is still in its infancy.

Furthermore, I would dispute Pigliucci’s claim that we have “very little progress to show after decades of attempts” to create artificial intelligence. There have been great strides in using artificial intelligence to provide medical diagnoses, play chess, choose stocks to purchase, and so on.

Given the advances in AI and computing power we’ve seen over the past 40 years, is it so hard to believe that we’ll be successful in creating a human-level intelligence in the next two decades?

Video is now available of Henry Markram’s presentation at TED, in which he talks about The Blue Brain Project’s efforts to build a supercomputer that will attempt to “reverse engineer the mammalian brain.” The project has made impressive progress to date, showing a proof of concept by modeling half a rodent brain, and the team expects to have a model of the human brain completed in the next 10 years.

Despite the immense computing power demonstrated by Blue Brain, the team makes it clear that they are not attempting to create an artificial intelligence, instead working to “understand brain function and dysfunction through detailed simulations.”

The project has focused, however, not only on building a model of the neocortical column, but on developing a generic facility that could allow rapid modeling, simulation and experimentation of any brain region, if the data can be measured and provided according to specifications. The facility has been used to build the first model of the neocortical column, which consists of 10,000 3D digitizations of real neurons that are populated with model ion channels constrained by the genetic makeup of over 200 different types of neurons. A parallel supercomputer  is used to build the model and perform the experiments so that the behavior of the tissue can be predicted through simulations.

More information on the Blue Brain Project at their Web site here.