The Computer Chip Inspired by a Brain
July 6, 2016
Artificial intelligence is humanity’s attempt to replicate the complicated thought processes in their own brains through technology. IBM is trying to duplicate the human brain and they have been successful in many ways with supercomputer Watson. The Tech Republic reports that IBM has another success under their belt, except to what end? Check out the article, “IBM’s Brain-Inspired Chip TrueNorth Changes How Computers ‘Think,’ But Experts Question Its Purpose.”
IBM’s TrueNorth is the first computer chip with an one million neuron architecture. The chip is a collaboration between Cornell University and IBM with the BARPA SyNAPSE Program, using $100 million in public funding. Most computer chips use the Von Neumann architecture, but the TrueNorth chip better replicates the human brain. TrueNorth is also more energy efficient.
What is the purpose of the TrueNorth chip, however? IBM created an elaborate ecosystem that uses many state of the art processes, but people are still wondering what the real world applications are:
“ ‘…it provides ‘energy-efficient, always-on content generation for wearables, IoT devices, smartphones.’ It can also give ‘real-time contextual understanding in automobiles, robotics, medical imagers, and cameras.’ And, most importantly, he said, it can ‘provide volume-efficient, unprecedented neural network acceleration capability per unit volume for cloud-based streaming processing and provide volume, energy, and speed efficient multi-modal sensor fusion at an unprecedented neural network scale.’”
Other applications include cyber security, other defense goals, and large scale computing and hardware running on the cloud. While there might be practical applications, people still want to know why IBM made the chip?
” ‘It would be as if Henry Ford decided in 1920 that since he had managed to efficiently build a car, we would try to design a car that would take us to the moon,’ [said Nir Shavit, a professor at MIT’s Computer Science and Artificial Intelligence Laboratory]. ‘We know how to fabricate really efficient computer chips. But is this going to move us towards Human quality neural computation?’ Shavit fears that its simply too early to try to build neuromorphic chips. We should instead try much harder to understand how real neural networks compute.’”
Why would a car need to go to the moon? It would be fun to go to the moon, but it doesn’t solve a practical purpose (unless we build a civilization on the moon, although we are a long way from that). It continues:
” ‘The problem is,’ Shavit said, ‘that we don’t even know what the problem is. We don’t know what has to happen to a car to make the car go to the moon. It’s perhaps different technology that you need. But this is where neuromorphic computing is.’”
In other words, it is the theoretical physics of computer science.