2. Randomness and creativity. After 60 years, results

2. Problem 
definition:
How can artificial intelligence be employed to increase the efficiency and
effectiveness in purchasing and supply chain management?
3. Current research on the topic
The official idea and definition of Artificial Intelligence was first coined by
John McCarthy in 1955 at the Dartmouth Conference. According to Mr. John
McCarthy “Every aspect of learning or any other feature of intelligence can in
principle be so precisely described that a machine can be made to simulate it.
An attempt will be made to find how to make machines use language, form
abstractions and concepts, solve kinds of problems now reserved for humans, and
improve themselves”.
In essence, artificial intelligence is a machine with the ability to solve
problems that are currently done by humans with their natural intelligence. A
computer will demonstrate a form of intelligence when it learns how to improve
itself as solving these problems.
The 1955 proposal defines 7 areas of A.I.:
1. simulating higher functions of the human brain.
2. Programming a computer to use general language.
3. Arranging hypothetical neurons in a manner so that they can form concepts.
4. A way to determine and measure problem complexity.
5. Self-improvement
6. Abstraction: Defined as the quality of dealing with ideas rather than
events.
7. Randomness and creativity.
After 60 years, results have been achieved in language, measuring problem
complexity and self-improvement. Randomness and creativity are just starting to
be explored in detail.
In the definition of the idea the word intelligence is mentioned. According to
Mr. Jack Copeland, who has written several books on A.I. some of the most
important factors of intelligence are: generalization learning – learning that
enables the learner to be able to perform better in situations not previously
encountered, reasoning- the ability to draw conclusions appropriate to the
situation at hand, problem solving – given such and such data find x,
perception – analyzing a scanned environment and analyzing features and
relationships between objects as example self-driving cars, language
understanding, understanding language by following sintax and other rules
similar to a human
Examples of A.I. are machine learning, computer vision, natural language
processing, robotics, pattern recognition and knowledge management
There are also different types of artificial intelligence in terms of approach:
Strong A.I. and weak A.I
Strong A.I. is simulating the human brain by building systems that think and in
the process give us an insight into how the brain works. In theory, It can do
anything as well/better than a human brain, but the current technological stage
is not near in achieving this
Weak A.I. is a system that behaves like a human but doesn’t give an insight
into how the human brain works, IBM’s Deep Blue, a chess playing A.I. is an
example: it processed millions of moves before it made any actual moves on the
chessboard. Also there is a middle ground between strong and weak A.I. This is
where a system is inspired by human reasoning but doesn’t have to stick to it.
IBM’s Watson is an example, like humans it reads a lot of information, recognizes
patterns and builds up evidence to say ” I am x percent confident that this is
the right solution to the question that you’ve asked me from the information
that I have read.
Google’s deep learning is similar as it mimics the structure of the human
brain, by using neural networks: ths system uses nodes that act as artificial
neurons connecting information, going deeper neural networks are a subset of
machine learning. Machine learning referes to algorithms that enable software
to improve its performance over time as it obtaines more data. This is
programing by input output examples rather than coding. As example: a
programmer would have no idea how to program a computer to recognize a dog but
he can create a program with a form of intelligence that can learn to do so, if
he gives the program enough image data in the form of dogs and let it process
and learn, when you give the program an image of a new dog that is never seen
before it would be able to tell that is a dog with relative ease.
The last concept on artificial intelligence: most artificial intelligence
algorithms are expert systems. An expert system is a system that employs human
knowledge in a computer to solve problems that ordinarily require human
expertise. Basically represents the practical application of a knowledge
database.
In summary, artificial intelligence can help to address some of the society’s
toughest and most pressing problems, from climate modeling to complex disease
analysis. We’re excited to see what we can use this technology to tackle first,
as mentioned y Demius hasibus, the co-creator of Deepmind.

AI can be used for the products that are classified as “tail-end spend”. AI can
receive information on past quotes what each individual seller can offer as discount
or price reduction, what are the minimum characteristics, factors and
requirements of each product and.

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4. Central question of the thesis
How can artificial intelligence help reduce costs and increase efficiency and
effectiveness of the corporate purchasing and supply chain management?

5. Knowledge interest of the author

6. The research objective and/or the underlying hypothesis
What actions do procurement and supply chain leaders to implement artificial
intelligence in their organizations?

7. Which methods lend themselves to work on the central question/hypothesis (theoretical
vs empirical, primary vs secondary analysis, qualitative vs quantitative, or a
combination of methods etc.)

8. Sources