A compressed Overview over Artificial Intelligence (AI) Table of Contents: Definition of AI A short history of AI Roughly structuring AI Examples of AI Discussion

There is no universally accepted definition of AI! Source: Artificial Intelligence and life in 2030, Report of the 2015 Study Panel, Stanford University, September 2016 There is no universally accepted definition of AI! “Curiously, the lack of a precise, universally accepted definition of AI probably has helped the field to grow, blossom, and advance at an ever-accelerating pace. Practitioners, researchers, and developers of AI are instead guided by a rough sense of direction and an imperative to “get on with it.””

1. Definition of AI Source: Artificial Intelligence and life in 2030, Report of the 2015 Study Panel, Stanford University, September 2016 Perhaps a useful definition is that by Prof. Nils J. Nilsson, Stanford University: (*1933) “AI is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment.”

1980s: First success of “Expert Systems” 2. A short history of AI The field of AI research was born at a workshop at Dartmouth college in 1956. 1956-1970: Optimism : Machines will be capable, within twenty years, of doing any work a man can do. AI winter 1980s: First success of “Expert Systems” collapse AI winter

2. A short history of AI Late 1990s and early 21st century: Successes in data mining, medical diagnosis. 10/Feb/1996 Deep Blue won against Kasparov; (perhaps IBM cheated in order to increase stock value). 1. (* 12/6/1947) 2012: A breakthrough in “Deep Learning” by Prof. Hinton et al. (image recognition) 2. Rapid increase of AI applications in industry e. g. Google, Amazon

3. Roughly structuring AI Knowledge based systems Neural networks Others

Automatic generation of an indexed database from the web 4. Examples of AI Knowledge based systems Classical Expert Systems Google Search Indexed database Automatic generation of an indexed database from the web Search algorithm Classical chess programs

Prof. Schmidhuber * 01/17/1963 LSTM NN 4. Examples of AI Neural networks overview Prof. Schmidhuber * 01/17/1963 LSTM NN Based on the human brain one neuron y W1 W2 Wn W… Xy Connections from other neurons Connections to other neurons (Prof. Hinton* 12/6/1947) = Adjusted by a Deep Learning Algorithm which was developed from Prof. Hinton et al. in 1986! Deep Learning = Adjustment by numerous examples with known solution

Neural networks Example Car/Person detection 4. Examples of AI Neural networks Example Car/Person detection

Computers: 1202 CPUs and 178 CPUs 4. Examples of AI Neural networks Watson is Champion of Jeopardy! 2011: Watson from IBM Alphago beats Go world champion Lee Sedol! Computers: 1202 CPUs and 178 CPUs 2015: Alphago from DeepMind

Alpha Zero Shows Machines Can Become Superhuman Without Any Help 4. Examples of AI Neural networks 2017: Alphazero from DeepMind Googles AI-daughter DeepMind developed a self learning algorithm which learned Go, Chess and Shogi by itself. Initially only the game rules were known to Alphazero. The performance was achieved by playing against itself or other programs by self learning. After 3 days Alphazero beat the up to then leading programs Stockfish (Chess), Elmo (Shogi), AlphaGo (Go). Alpha Zero Shows Machines Can Become Superhuman Without Any Help An upgraded version of the game-playing AI teaches itself every trick in the Go book, using a new form of machine learning. by Will Knight October 18, 2017, MIT Technology Review

4. Examples of AI Neural networks 2016: Translate German Einen Offline-Modus haben die Entwickler in Google Übersetzer auch untergebracht, der funktioniert aber „nur“ mit 52 Sprachen. Besonders komfortabel ist die Live-Übersetzung: Damit können Sie Konversationen in ungleichen Sprachen für beide Gesprächsteilnehmer quasi in Echtzeit übersetzen lassen - Star Trek lässt grüßen. PC-Welt, 07.06.2017 2016: Translate From now on Google Translate translates with a single neural net from each of the 103 supported languages in each other even if Google Translate did not learn example sentences for the translation of a pair of languages . Translations into rare languages from this. Heise online, 2016 English Google translation An offline mode, the developers have housed in Google translators also, but it works "only" with 52 languages. The live translation is especially comfortable: It allows you to translate conversations in unequal languages for both participants in real time - Star Trek sends its regards. The developers have also integrated in Google translator an offline mode but this works "only" with 52 languages. Perhaps improved

5. Discussion “It’s sort of incredible to me that people are scared of computers taking jobs,” Dr. Eyal (MIT) says. “It’s not that computers can’t replace lawyers because lawyers do really complicated things. It’s because lawyers read and talk to people. It’s not like we’re close. We’re so far.” MIT Technology Review, September 2017 There is now hope that the same (deep learning) techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless other things to transform whole industries. But this won’t happen—or shouldn’t happen—unless we find ways of making techniques like deep learning more understandable to their creators and accountable to their users. Otherwise it will be hard to predict when failures might occur—and it’s inevitable they will. MIT Technology Review, June 2017

5. Discussion No one really knows how the most advanced algorithms do what they do. That could be a problem, e. g. for real autonomous cars (see above). The technical effort for e. g. alphazero is enormous. 5000 Tensor Processing Units (TPU) of the first generation and 64 TPUs of the second generation are needed for training. Only 4 TPUs are necessary after the training is accomplished. TPUs are special chips for processing neural networks (tensor processing units).

AI protagonists see things e. g. in the following way: 5. Discussion AI protagonists see things e. g. in the following way: Age Hunt and Gather - 2.000.000 years Agricultural Age - 10.000 Industrial Age 1760 Information Age 1969 Augmented Age 2020 ? years

5. Discussion Or: “A new type of life is going to emerge which will spread all over the galaxy “(Prof Schmidhuber, 2017) Self reproducing AI. May be a thread to mankind. Or: “AI will be either the best, or the worst thing, ever to happen to humanity” (Prof Steven Hawking, 2016)

5. Discussion Or: “This is the end of the beginning. The euphoria that the most problems are solved is not justified. We celebrate successes but we hardly do not talk about faults.” (Fei-Fei Li Ph.D. | Associate Professor CS, Stanford University, June 2017)