Category: Big Ideas

https://thenewstack.io/ai-designers-try-reverse-engineering-rat-brain/

“Animal intelligence may be the next big thing in artificial intelligence,” proclaims a video on the IEEE Spectrum site. “But first, scientists must digitize a rat brain.”

The video accompanied a 3,000-word article that explained that, currently, “there are some situations when a three-year-old can easily defeat the fanciest AI in the world.” Specifically, children excel at “one-shot learning” — the ability to recognize something after only seeing it once.

“Humans have an amazing ability to make inferences and generalize,” said Harvard University neuroscientist David Cox, who is also an expert on artificial intelligence. Or, as IEEE Spectrum puts it, AI researchers are “deeply envious of toddlers’ facility with it.”

So there’s now an ambitious $100 million project funded by the U.S. the Intelligence Advanced Research Projects Agency (IARPA, an intelligence community cross-agency office) to reverse engineer this kind of smarts. Its five-year mission: to identify a brain’s “strategy” for simple feats of identification, so it can be recreated with algorithms.

The Machine Intelligence from Cortical Networks (MICRONS) program is focusing on that area where the brain processes what it sees — the visual cortex. But it’s actually attempting to map every neuron in one cubic millimeter of brain tissue. 50,000 neurons — interconnected through a half a billion synapses. The ultimate big data set.

No one has ever attempted this before.

The article points out today’s neural networks are “loosely inspired by the brain’s structure,” but that human brains ultimately have 86 billion neurons — or, 1.7 million times more than appear in that cubic millimeter. Trillions of connections are possible. That’s 1,000,000,000,000. But apparently, the important thing seems to be identifying the mechanism that underlies it all.

“The big gap is understanding operations on a circuit level,” the IEEE quoted (now former) MICRONS program manager R. Jacob Vogelstein, “how thousands of neurons work together to process information.”

If they succeed, the article suggests, the payoff could be enormous: “The government’s big bet is that brainlike AI systems will be more adept than their predecessors at solving real-world problems.”

One obvious application would be identifying faces from security-camera footage — but the hope is to apply this to more than computer vision. The entirety of the cerebral context has a “suspiciously similar” structure, according to Cox — which suggests there’s one fundamental circuit that the brain uses to process information. Identifying it would be a major step toward human-like general intelligence.

Their research focuses on embedding state-of-the-art technological equipment on the brains of rats and mice. “It all starts with a rat in a cage learning to play a video game,” quipped the video that accompanies the article. The rat gets rewarded with a drop of sweet juice if they correctly identify one of two images flashed on a small display.

Using a two-photon excitation microscope, the researchers first scan a live rat’s brain with an infrared laser to record flashes from a fluorescent tag that indicates when a neuron is active. “The microscope makes movies of neural activities,” explained the video, while the article adds poetically that “The 3D video shows patterns that resemble green fireflies winking on and off in a summer night.”

“You can watch a rat having a thought,” Cox explained.

“It’s very similar to how you’d try to reverse engineer an integrated circuit,” Vogelstein added. “You could stare at the chip in extreme detail, but you won’t really know what it’s meant to do unless you see the circuit in operation.”

“It may be much easier to engineer the brain than to understand it.” — George Church, Wyss Institute for Biologically Inspired Engineering

But that’s just the beginning. The rat’s tissue is then FedEx’d from Massachusetts to Illinois, where the U.S. Department of Energy’s Argonne National Laboratory performs a sophisticated imaging using a particle accelerator to produce “extremely bright” X-rays. After the tissue is captured from different angles, the X-ray images can be combined into a three-dimensional image.

Then the brain returns to Massachusetts, where it’s sliced into 33,000 strips, each one just 30 nanometers wide, captured with tape strips and delivered to silicon wafers. The 33,000 slides then meet “the world’s fastest scanning electron microscopes,” which runs continuously, and creates an image of each one at a 4-nanometer resolution.

Now it’s possible to see the axons that connect the neurons, and — since there’s millions of them — there’s another piece of automation, a piece of software that can automatically trace the axons from one tissue slice to the next, along with its thousands of connections to other neurons. Or, as IEEE Spectrum puts it, it “reconstructs all the neural wiring within the cube of brain tissue.”

Ironically, the computer isn’t as good as a human scientist, but “there aren’t enough humans on earth to trace this much data,” said Cox, who the article describes as “obsessively focused on automating every step of the process.” It’s a problem that software engineers at Harvard and the Massachusetts Institute of Technology are already working on. But however the diagram gets made, researchers will then combine it with the fluorescent images of the rat brain’s activity, which according to the article should reveal the brain’s computational structures.

“It should show which neurons form a circuit that lights up when a rat sees an odd lumpy object, mentally flips it upside down, and decides that it’s a match for object A,” the article’s writer, Eliza Strickland, noted.

In an interesting twist, they also plan to train a neural network on the same image-recognition task, and compare the results.

Meanwhile, there’s also another MICRONS project taking an entirely different approach. Researchers at Harvard and Carnegie Mellon University have genetically engineered mice so there’s a unique sequence of molecules on both ends of each axon (a technique called “DNA barcoding.”) Then their software can generate a map by connecting each pair sharing the same axon-identifying code. One of the team’s leaders — George Church, a professor at Harvard’s Wyss Institute for Biologically Inspired Engineering — thinks the technique could ultimately map the whole brain of a mouse. That’s all 70 million neurons and 70 billion connections.

Church ultimately even suggests that one day it may be possible to ditch the silicon altogether, and engineer brains with special circuits that speed up their cognition — in effect, to build better biological brains: “I think we’ll soon have the ability to do synthetic neurobiology, to actually build brains that are variations on natural brains.”

In the article, he shares a near-heretical thought — that successfully recreating a brain with circuits and algorithms may not ultimately provide an answer. “I think understanding is a bit of a fetish among scientists,” he tells IEEE Spectrum, in a refreshing counterpoint to the prevailing belief that everything we humans experience can be reduced to patterns of electric pulses.

“It may be much easier to engineer the brain than to understand it.”

Feature image: IEEE video.

http://www.economist.com/blogs/buttonwood/2017/06/1914-effect

Industrialisation meant that new sources of power emerged to challenge the old aristocratic elites—industrialists and factory workers. Workers were able to use their muscle to demand more rights and, increasingly, the vote. Elites turned to nationalism as a way of distracting voters from economic issues and shoring up their support. This nationalism led to clashes with the other great powers where their interests diverged; between Britain and Russia in Asia; Russia and Austria in the Balkans; Germany and France in north Africa.

So let us move to the current era of globalisation, during which the export share of global GDP has more than doubled since the 1960s. New economic powers have emerged to challenge American dominance; first, Japan, and now China and potentially India. Imperial overstretch threatens America as it did Edwardian Britain. The ability and, more recently, willingness of America to act as global policeman has been eroded. Indeed, unlike Britain in 1914, America is a net debtor not a creditor. Can it hold China’s ambitions in check in the Pacific, counterbalance Iran and the terrorists of Islamic State in the middle east and deal with a nationalist Russian regime? It seems clear that other powers think it can’t. They are pushing to see whether America will react.

Within the economy two big changes have occurred. Manufacturing capacity has moved from the developed world to Asia. Technology has rewarded skilled workers and widened pay gaps. Voters have rebelled by turning to parties that reject globalisation. This didn’t happen in France but generally it has made life more difficult for centre-left parties and turned centre-right parties more nativist. America’s Republicans used to be enthusiasts for free trade. Now they have elected Donald Trump.

Just as in the first era, globalisation has disrupted international and domestic power structures. Thankfully this does not mean that another world war is inevitable. But it is easy to imagine regional conflicts: Iran against Saudi Arabia, or an American attack on North Korea that provokes a Chinese reaction.

The real danger is that this a zero-sum game. Governments will appear to grab a larger share of global trade for their own countries. In doing so, they will cause trade to shrink. That might make voters even angrier. From the early 1980s to 2008, most companies could count on a business-friendly political environment in the developed world. But it looks as if that era has ended with the financial crisis. Globalisation has caused another counter-reaction.

The best hope is that technology can deliver the economic growth and rising prosperity voters want. If that happens, these threats will not disappear but they will be much reduced. But for all the hype about new technology, productivity has been sluggish. The omens are not great.