The man-made brain

Image from: Rick Bolin

It seems to me that the most exciting thing is to know what is possible in the near future. I have been interested in artificial intelligence for years, but I was still surprised to get to know that we are now so near to an artificial brain.

IBM and HRL are making a neuromorphic microprocessor as capable as a cat brain by 2016, which will be the outcome of the SyNAPSE project started in 2011.  

This may seem trivial given that IBM Watson, a supercomputer equipped with intelligent software, has already beat human in chess and the quiz show Jeopardy. It is now being promoted to be applied in medical care as well. Nevertheless, the breakthrough of SyNAPSE is to have an intelligent chip instead of a supercomputer. The technology of artificial neural network has long been widely used, but at a huge cost for high performance. IBM Watson is as large as several rooms, and your three meals each day cannot keep it working for even one hour. Realizing neural network at the chip level does make a big different here.

The basic idea is to use a fundamentally different architecture when building the microprocessor. Traditionally all computers follows the Von Neumann structure, where the system has separate units for memory, processing, control and input-output units. Information is processed sequentially. In a neuromorphic chip, however, memory, processing and control units are mixed, and a high level of parallelism is realized. I have thought that this kind of circuit would inevitably require new materials and device characteristics, but I really have overlooked the potential of the current transistor based integrated circuit technology.

IBM is building such processors by implementing neuromorphic algorithms in the hardware design. Many of these algorithms have already been used in software. The “hardware coding” makes the system much more efficient than just running intelligent software in normal computers.

In an introduction video, Dr. John Arthur, one of the researcher from IBM, mentioned “current computers can do a fantastic job in adding numbers, but they do really poorly in recognizing faces where human brain is very good at.” The goal is to build systems that can do recognition tasks automatically.

In the same video, Dr. Horst Simon, the deputy director of Lawrence Berkeley National Lab made a very interesting analogy to the invention of plane to describe this objective. Unlike birds, the planes have rigid wings instead of flapping wings. Dr. Simon said:

“Reorganization computing is exactly at the stage where we are looking at “flapping wings” and “rigid wings”. We don’t want to build a bird; we want to build a device that allows humans to fly. So, we don’t want to build here a human brain; we want to build devices that can help solve the tasks that current computers cannot solve at ease.”

“We don’t want to build a bird; we want to build a device that allows humans to fly.” I found this comment quite inspiring: take whatever reference to help finish the task. I always wonder what Dr. Von Neumann’s reference was when he outlined the architecture that gave birth to our current computers. But now, our brain is the natural and powerful reference.