by Ernst Dickmanns (whose team built the world's first reliable robot cars in the early 1980s with the help of Mercedes-Benz, e. g., [3]) autonomously drove 1000 miles from Munich to Denmark and back, in traffic at up to 120 mph, automatically passing other cars (a safety driver took over only rarely in critical situations). Japanese labs (Honda, Sony) and Pfeiffer's lab at TU Munich built famous humanoid walking robots.

Engineering problems often seemed more challenging than AI-related problems.

Another source of progress was the dramatically improved access to all kinds of data through the WWW, created by Tim Berners-Lee at the European particle collider CERN (Switzerland) in 1990. This greatly facilitated and encouraged all kinds of "intelligent" data mining applications. However, there were few if any obvious fundamental algorithmic breakthroughs; improvements / extensions of already existing algorithms seemed less impressive and less crucial than hardware advances. For example, chess world champion Kasparov was beaten by a fast IBM computer running a fairly standard algorithm. Rather simple but computationally expensive probabilistic methods for speech recognition, statistical machine translation, computer vision, optimization, virtual realities etc. started to become feasible on PCs, mainly because PCs had become 1000 times more powerful within a decade or so.

2006. As noted by Stefan Artmann (personal communication, 2006), today's AI
textbooks seem substantially more complex and less unified than those of several decades ago, e. g., [18], since they have to cover so many apparently quite different subjects. There seems to be a need for a new unifying view of intelligence. In the author's opinion this view already exists, as will be discussed below.

## 2 Subjective Selected Highlights Of Present Ai

The more recent some event, the harder it is to judge its long-term significance. But this biased author thinks that the most important thing that happened recently in AI
is the begin of a transition from a heuristics-dominated science (e.g., [24]) to a real formal science. Let us elaborate on this topic.

## 2.1 The Two Ways Of Making A Dent In Ai Research

There are at least two convincing ways of doing AI research: (1) construct a (possibly heuristic) machine or algorithm that somehow (it does not really matter how) solves a previously unsolved interesting problem, such as beating the best human player of Go (success will outshine any lack of theory). Or (2) prove that a particular novel algorithm is optimal for an important class of AI problems.

It is the nature of heuristics (case (1)) that they lack staying power, as they may soon get replaced by next year's even better heuristics. Theorems (case (2)), however, are for eternity. That's why formal sciences prefer theorems.

For example, probability theory became a formal science centuries ago, and totally formal in 1933 with Kolmogorov's axioms [13], shortly after G¨odel's paper [5]. Old but provably optimal techniques of probability theory are still in every day's use, and in fact highly significant for modern AI, while many initially successful heuristic approaches eventually became unfashionable, of interest mainly to the historians of the field.