# Innovation Potentials and Pathways Merging AI, CPS, and IoT
## 2 State of the Art and Recent Advances
### Artificial Intelligence
\begin{table}
\begin{tabular}{c l} \hline \hline
**Game** & \multicolumn{1}{c}{**Chronological Development**} \\ \hline Backgammon & 1979: The backgammon program BKG by [PERSON] defeats the world champion—the first computer program to defeat (in an exhibition match) a world champion in any game—though [PERSON] attributes the win to luck with the dice rolls. \\ Backgammon & 1992: The backgammon program TD-Gammon by [PERSON] reaches championship level ability, using temporal differences learning (a form of reinforcement learning) and repeated plays against itself \\ Chess & 1997: [PERSON] beats the world chess champion, [PERSON]. [PERSON] claims to have seen \\  & glimpses of true intelligence and creativity in some of the computer’s moves. Since then, chess engines have continued to improve. \\ \hline Scrabble & As of 2002, Scrabble-playing software surpasses the best human players. \\ \hline Jeopardy! & 2010: IBM’s [PERSON] defeats the two all-time-greatest human jeopardy! Champions, [PERSON] and \\  & [PERSON]. Jeopardy! is a televised game show with trivia questions about history, literature, sports, geography, pop culture, science, and other topics. Questions are presented in the form of clues, \\  & and often involve wordplay. \\ \hline Go & As of 2012, the Zen series of go-playing programs has reached rank 6 dan in fast games (the level of a very strong amateur player), using Monte Carlo tree search and machine learning techniques. \\  & _Go-playing programs have been improving at a rate of about 1 dan/year in recent years. If this rate of improvement continues, they might beat the human world champion in about a decade._ * \\  & * Addendum: AI application AlphaGo beat human world champion [PERSON] in March 2016, [42; 43]. \\  & Addendum II: The further improved AI application AlphaGo Zero was trained during 2017 without \\  & prior human knowledge, only with the game rules and by playing against itself—it is now unbeatable \\  & by humans and the strongest AI application regarding the game of Go [13]. \\ \hline \multirow{3}{*}{Poker} & Addendum III: In 2017, also the first AI program was able to beat human players at head-up no-limit \\  & poker, in contrast to the above mentioned games of e.g., chess and go a non-perfect information setting [44; 45; 46]. Similarly to e.g., jeopardy! in this case it was expected to take much longer for AI instances to \\ \cline{1-1}  & master such ’human’ game settings including incomplete information and cheating. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advances in AI Game Applications (from [14], pp. 12–13 and author additions). For the last line in this table taken from [PERSON] ([14], pp. 12-13) we again recognize how hard future prognosis are in this field: [PERSON] posits in 2014 that 'Go' as a very strategic and intuitive game may be mastered by AI on superhuman levels (meaning leaving human players no real chance as you may try with any free chess app on your smartphone already today) \"in about a decade\"; this would indicate about 2024. But in reality, this level was already reached when the AI program _AlphaGo_ beat the human go world champion [PERSON] in March 2016, [42; 43]--eight years earlier than predicted just two years before. As can be seen from the text in Table 1, a typical human error led to this understated prognosis as AI development was perceived at a 'linear' rate (\"at a rate of about 1 \(\text{ dan}/\text{year}\) in recent years\"). In reality, the AI evolution takes on progressively--leading to a situation where already in 2017--one year later--the further improved AI instance _Alpha Go Zero_ was able to teach itself the ability to beat the old AlphaGo system without human examples or help [13]. The same happened with the first chess AI from 1997--up to a state where today easy-to-use and free smartphone applications for chess leave no chance to even the best human players.