IBM has unveiled new experimental brain-inspired chips that are able to learn based on experience.
Back in February, an artificial intelligence (AI) computer called Watson appeared on Jeopardy! to demonstrate its capabilities. Watson was developed by IBM who describes him/it as follows:
Watson is a workload optimized system designed for complex analytics, made possible by integrating massively parallel POWER7 processors and the IBM DeepQA software to answer Jeopardy! questions in under three seconds. Watson is made up of a cluster of ninety IBM Power 750 servers (plus additional I/O, network and cluster controller nodes in 10 racks) with a total of 2880 POWER7 processor cores and 16 Terabytes of RAM. Each Power 750 server uses a 3.5 GHz POWER7 eight core processor, with four threads per core. The POWER7 processor’s massively parallel processing capability is an ideal match for Watson’s IBM DeepQA software which is embarrassingly parallel (that is a workload that is easily split up into multiple parallel tasks).
Watson competed against 74-time champion Ken Jennings and $3.3 million-winning Brad Rutter. Watson kicked their butts scoring $77,147 compared to $24,000 for Jennings and $21,600 for Rutter.
Now, IBM has unveiled new experimental brain-inspired chips that are able to learn based on experience. From the press release:
Today, IBM researchers unveiled a new generation of experimental computer chips designed to emulate the brain’s abilities for perception, action and cognition. The technology could yield many orders of magnitude less power consumption and space than used in today’s computers. In a sharp departure from traditional concepts in designing and building computers, IBM’s first neurosynaptic computing chips recreate the phenomena between spiking neurons and synapses in biological systems, such as the brain, through advanced algorithms and silicon circuitry. Its first two prototype chips have already been fabricated and are currently undergoing testing. Called cognitive computers, systems built with these chips won’t be programmed the same way traditional computers are today. Rather, cognitive computers are expected to learn through experiences, find correlations, create hypotheses, and remember – and learn from – the outcomes, mimicking the brains structural and synaptic plasticity.
The press release also outlines the possible uses of such chips:
Future chips will be able to ingest information from complex, real-world environments through multiple sensory modes and act through multiple motor modes in a coordinated, context-dependent manner. For example, a cognitive computing system monitoring the world’s water supply could contain a network of sensors and actuators that constantly record and report metrics such as temperature, pressure, wave height, acoustics and ocean tide, and issue tsunami warnings based on its decision making. Similarly, a grocer stocking shelves could use an instrumented glove that monitors sights, smells, texture and temperature to flag bad or contaminated produce. Making sense of real-time input flowing at an ever-dizzying rate would be a Herculean task for today’s computers, but would be natural for a brain-inspired system.
The question many people will ask is whether advances such as this mean we’re at the point where computers will soon be smarter than humans – whether AI will be able to outperform real, biological intelligence. I don’t think so. The fact is that we don’t understand intelligence at all. As discussed in a Big Think article by Max Miller, we don’t understand the biological process behind it and cannot even satisfactorily define it. What sets an Einstein apart from the rest of us? We really have no clue. And if we don’t understand how intelligence works, then how the heck can we possibly create artificial intelligence?
Inevitably, computers will become ever better at processing information but, no matter how you define it, that’s a very long way from real intelligence.
What do you think? Will man soon play second fiddle to machine? Leave a comment and share your thoughts.