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AI Business: Framework and Maturity Model

 Methods and Technologies

In the following, the various methods and technologies are briefly outlined

and explained.

Symbolic AI

Since the conference at Dartmouth College in 1956, a variety of different

methods and technologies have been developed for the construction of intel-

ligent systems.

Even if neuronal networks and thus the approach of sub-symbolic AI

dominates today, the field of research was dominated by the symbolic

approach for a long time. This “classical” approach by John Haugeland

called “Good Old-Fashioned Artificial Intelligence” (GOFAI) used defined

rules to come to intelligent conclusions depending on the input. Up to the

AI winter of the 1990s, “artificial intelligences” were developed by program-

ming and filling control equipment and standards and databases to then be

able to access them in practice. To this day, a large number of search, plan-

ning or optimisation algorithms and methods from the times of symbolic

artificial intelligence are applied in modern systems, which today are simply

regarded as excellent algorithms of informatics.

Natural Language Processing (NLP)

Computer linguistics covers the understanding, processing and generating

of languages. “Natural language processing” describes the ability comput-

ers have to work with spoken or written text by extracting the meaning

from the text or even generating text that is readable, stylistically natu-

ral and grammatically correct. With the help of NLP systems, computers

are put in a position of not only reacting to formalised computer lan-

guages such as Java or C, but also to natural languages such as German or

English.

Rule-Based Expert Systems

ule-based expert systems belong to one of the first profitable implementa-

tions of AI that are applied to this day. The fields of use are multifaceted and range from planning in logistics and air traffic over the production of con-

sumer and capital goods down to medical diagnostics systems.

They are distinguished by the fact that the knowledge represented inside

of them originates from experts (individual fields of expertise) in its nature

and origin. Depending on the input variables, automatic conclusions are

then derived from this knowledge. To this end, the knowledge (in the spirit

of symbolic AI) must be codified, i.e. furnished with rules, and be linked to

a derivation system to solve the challenges.

Sub-symbolic AI

The approach of symbolic AI to systematically capture and codify knowl-

edge was considered very promising for a long time. In a world that is being

digitalised further and further, in which knowledge implicitly lies in the

amounts of data, AI should be able to do something that knowledge-based

expert systems inherently find difficult: Self-learning. Deep Blue, for exam-

ple, was in fact able to beat Garry Kasparow in 1996 without the use of

artificial neuronal networks, but only because the chess game had been for-

malised by humans and because the computer was able to compute up to

200 million moves per second from which the most promising one was then

chosen.



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