# Innovation Potentials and Pathways Merging AI, CPS, and IoT
## 2 State of the Art and Recent Advances
### Artificial Intelligence  
Artificial Intelligence (AI) can be captured in short as'solving tasks like humans'. The concept is more than sixty years old as the term was coined at Dartmouth in 1956 by [PERSON] and colleagues ([PERSON] and colleagues, 1956). At that time, a new science field was envisioned for AI as \"science and engineering of making intelligent machines.\" (see ([PERSON] and colleagues, 1956; [PERSON] and colleagues, 1956), p. S37). But the core missions of making machines 'understand, think, and learn in a similar way to human beings\" was in the first four decades blocked by many technical hardware and software hindrances--the time was just not yet ripe and therefore many expectations and projects were huge disappointments (cp. ([PERSON] and colleagues, 1956), p. 410). But since about two decades, slow and increasing success is imminent in different fields, making it one of the most promising research fields today. For further analysis and connections towards logistics and supply chain management, it is informative to reconsider the literature distinction between'strong' and 'weak' AI: Whereas strong AI should be able to solve a large number or even more tasks like human beings, weak AI is supposed to be able to solve one specific task similarly or better than humans, such as playing chess for example. As can be recognized from many applications already today, weak AI is a reality in our everyday lives when machines play chess, navigate cars, compose music or suggest shopping items as well as social contacts and places to go. Strong AI on the other hand is currently yet a vision for the future, recognizable roughly in major movies such as 'Chappie', 'Automata' or 'Ex Machina'--where AI robots are capable of complex bundles of activities such as understanding language, speaking, recognizing other persons and devising plans of their own actions. This point of artificial intelligence reaching human intelligence levels is labelled 'Artificial General Intelligence (AGI)' or strong AI. The point in time where such AGI entities are reached is called \"singularity\" (see [38]). After this point, we might expect the entity featuring AGI to self-improve further (also called a 'take-off'), reaching a state of 'Artificial Super Intelligence' (ASI). Such an ASI entity would be unfathomable and unpredictable to us humans--such we humans are utmost incomprehensible for an ant or a mouse. Furthermore, it has to be recognized that 'tasks' in the nutshell definition usually involve a complex bundle of information, analysis, decision and activity processes. This is surely true for logistics processes such as for example order picking, route scheduling or truck driving. Therefore, usually AI innovations are happening in \"jumps\" and breakthroughs rather than as a continuous development--which makes predictions in this field very hard and sketchy (cp. [39; 40; 41]). In order to outline the technological and innovation advance of AI, the following Table 1 is providing details regarding the capability development in human games. As indicated in Figure 1, weak AI applications have been able to increasingly master single tasks or clearly restricted bundles of tasks such as playing chess or other accomplishments. This is indicated chronologically in Table 1 for some gaming examples-showing that beside the point that this list will ever grow longer and longer, the process can also be recognized to accelerate.