through a focus on reducing search spaces by co-evolving the comparatively small weight vectors of individual recurrent neurons [7]. Such RNNs can learn to create memories of important events, solving numerous RL / optimization tasks unsolvable by traditional RL methods [6, 7]. They are among the most promising methods for practical program learning, and currently being applied to the control of sophisticated robots such as the walking biped of TU Munich [16].

## 3 The Next 25 Years

Where will AI research stand in 2031, 25 years from now, 100 years after G¨odel's ground-breaking paper [5], some 200 years after Babbage's first designs, some 400 years after the first automatic calculator by Schickard (and some 2000 years after the crucifixion of the man whose birth year anchors the Western calendar)?

Trivial predictions are those that just naively extrapolate the current trends, such as: computers will continue to get faster by a factor of roughly 1000 per decade; hence they will be at least a million times faster by 2031. According to frequent estimates, current supercomputers achieve roughly 1 percent of the raw computational power of a human brain, hence those of 2031 will have 10,000 "brain powers"; and even cheap devices will achieve many brain powers. Many tasks that are hard for today's software on present machines will become easy without even fundamentally changing the algorithms. This includes numerous pattern recognition and control tasks arising in factories of many industries, currently still employing humans instead of robots.

Will theoretical advances and practical software keep up with the hardware development? We are convinced they will. As discussed above, the new millennium has already brought fundamental new insights into the problem of constructing theoretically optimal rational agents or universal AIs, even if those do not yet immediately translate into practically feasible methods. On the other hand, on a more practical level, there has been rapid progress in learning algorithms for agents interacting with a dynamic environment, autonomously discovering true sequence-processing, problemsolving programs, as opposed to the reactive mappings from stationary inputs to outputs studied in most of traditional machine learning research. In the author's opinion the above-mentioned theoretical and practical strands are going to converge. In conjunction with the ongoing hardware advances this will yield non-universal but nevertheless rather general artificial problem-solvers whose capabilities will exceed those of most if not all humans in many domains of commercial interest. This may seem like a bold prediction to some, but it is actually a trivial one as there are so many experts who would agree with it.

Nontrivial predictions are those that anticipate truly unexpected, revolutionary breakthroughs. By definition, these are hard to predict. For example, in 1985 only very few scientists and science fiction authors predicted the WWW revolution of the 1990s.

The few who did were not influential enough to make a significant part of humanity believe in their predictions and prepare for their coming true. Similarly, after the latest stock market crash one can always find with high probability some "prophet in the desert" who predicted it in advance, but had few if any followers until the crash really occurred.