New research from Tohoku University (1) demonstrating pattern finding using low energy solid state devices, representing synapses (spintronics), has potential to reduce the hype of contemporary artificial intelligence and move the field forward incrementally. Computer scientists have been wasting time with conventional computers and inefficient software solutions on what they hope to be a replication of intelligence. However, it has been clear from the inception of the field that engineering processes and know-how fall significantly short of its intended goals. The problem has always been hardware design and the fact that there are more software engineers in the world than those who focus on hardware, has acted as a brake on progress.
The brain has always been a bad model for artificial intelligence. A massive energy hog that has to prop itself up on a large and fat storing gut just to survive, has always been an inefficient design to create intelligence. Largely designed to keep track of routine systems, the brain accidently took on a foreign role that allowed abstract thinking. The over design of the system meant that it could do so with relatively small incremental cost. Computer scientists' attempts to replicate the energy inefficient organ, designed primarily for routine and repeating tasks, on the promise of intelligence have left many skeletons in the long and unsuccessful path to artificial intelligence. The fact that there is unabated noise in the universe of millennials about artificial intelligence is symptomatic of a lack of understanding of what could be possible.
Practical mathematicians and engineers are a bad combination for effecting ground breaking innovation. In the 60s, this potent combination of technologists designed the neural nets - to simulate what they felt was happening inside the funny looking organ. For decades, their attempts to "train," their nets met with failure with the artificial constructs taking too long to learn anything or spontaneously becoming unstable. They continued with the brute force method as the cost of computers and memory started to decline rapidly. Lately, they have found some short cuts that allows faster training. However, natural language processing, clever video games and autonomous cars are not examples of artificial intelligence by any stretch of the imagination.
To make artificial intelligence happen, technologists have to turn to fundamental innovation in hardware. And, they may be well advised to lose some ego and seek help from very different disciplines such as philosophy, economics and music. After all, the massive development of the human brain came when they started to think abstractly and not when they could create fire and stone tools at will.
- William A. Borders, Hisanao Akima, Shunsuke Fukami, Satoshi Moriya, Shouta Kurihara, Yoshihiko Horio, Shigeo Sato, Hideo Ohno. Analogue spin–orbit torque device for artificial-neural-network-based associative memory operation. Applied Physics Express, 2017; 10 (1): 013007 DOI: 10.7567/APEX.10.013007