Biological learning systems run the gamut from the lowly roundworm (Caenorhabditis elegans) with its 300 or so neurons, all the way up to the adult elephant brain, with its 200 billion neurons. Whether they’re located in fruit flies or cockroaches, chimpanzees or dolphins, all neurons do the same thing: they process and transmit information. And the reason for this is the same across the biological board: To avoid danger and maximize success in sustaining and propagating themselves, all organisms must be able to sense the environment, respond to it accordingly, and remember those stimuli that indicate risks and rewards. Learning, in short, is a prerequisite for the survival of individuals and species in the natural world. The same iron law, however, is becoming increasingly applicable to the world of man-made systems.
According to Dr. Volker Tresp, one of Siemens’ top machine learning authorities and a computer science professor at Ludwig Maximillian University in Munich, there are three kinds of learning: memorization (such as the ability to remember facts); skills (such as the ability to learn to throw a ball); and abstraction (such as the ability to form rules based on observations). Computers, which are born whizzes in the first area, are rapidly catching on to the other two. Take, for instance, the skill needed to produce a flawlessly even sheet of steel in a given thickness — an area in which Siemens has been a leader for over 20 years. “Here,” says Tresp, “the simplest learning schema is to make a prediction, and then check to see if the output product meets the desired specification.” Confronted with an output requirement for, say, a particularly high grade of steel, an automated rolling mill would take sensor data (composition, strip temperature, etc.) into account, estimate the required pressure based on previously learned information, and then adjust itself accordingly in real time in response to its measuring data until it achieved exactly the right pressure to get the desired thickness. “In a neural network-based learning system,” explains Tresp, “this would be achieved by adjusting the relative weight matrix (see diagram) of all the factors that influence a given parameter, such as thickness.”