The processes underlying artificial intelligence today are in fact quite dumb. Researchers from Bochum are attempting to make them smarter.
Radical change, revolution, megatrend, maybe even a risk: artificial intelligence has penetrated all industrial segments and keeps the media busy. Researchers at the RUB Institute for Neural Computation have been studying it for 25 years. Their guiding principle is: in order for machines to be truly intelligent, new approaches must first render machine learning more efficient and flexible.
"There are two types of machine learning that are successful today: deep neural networks, also known as Deep Learning, as well as Reinforcement learning," explains Professor Laurenz Wiskott, Chair for Theory of Neuronal Systems.
Neural networks are capable of making complex decisions. They are frequently utilized in image recognition applications. "They can, for example, tell from photos if the subject is a man or a woman," says Wiskott.
The architecture of such networks is inspired by networks of nerve cells, or neurons, in our brain. Neurons receive signals via several input channels and then decide whether they pass the signal in the form of an electrical pulse to the next neurons or not.
Neural networks likewise receive several input signals, for example pixels. In a first step, many artificial neurons calculate an output signal from several input signals by simply multiplying the inputs by different but constant weights and then adding them up. Each of these arithmetic operations results in a value that – to stick with the example of man/woman – contributes a little to the decision for female or male. "The outcome is slightly altered, however, by setting negative results to zero. This, too, is copied from nerve cells and is essential for the performance of neural networks," explains Laurenz Wiskott.
The same thing happens again in the next layer, until the network comes to a decision in the final stage. The more stages there are in the process, the more powerful it is – neural networks with more than 100 stages are not uncommon. Neural networks often solve discrimination tasks better than humans.
The learning effect of such networks is based on the choice of the right weighting factors, which are initially chosen at random. "In order to train such a network, the input signals as well as what the final decision should be are specified from the outset," elaborates Laurenz Wiskott. Thus, the network is able to gradually adjust the weighting factors in order to finally make the correct decision with the greatest probability.
Reinforcement learning, on the other hand, is inspired by psychology. Here, every decision made by the algorithm – experts refer to it as the agent – is either rewarded or punished. "Imagine a grid with the agent in the middle," illustrates Laurenz Wiskott. "Its goal is to reach the top left box by the shortest possible route – but it doesn't know that." The only thing the agent wants is to get as many rewards as possible, otherwise it's clueless. At first, it will move across the board at random, and every step that does not reach the goal will be punished. Only the step towards the goal results in a reward.
