Information on Aitificial Intelligence

1. ARTIFICIAL INTELLIGENCE
Name: Himani Patpatia
PRN: 20220901104
B.Tech Bioenginering, Sem I
Prsentation for Technical Communication

2. Intelligence is...
• Capacity for learning, reasoning, understanding and similar forms
of mental activity: aptitude in grasping truths , relationships,
facts,meanings,etc.
We are able to...
• Interact with the real world.
• Searching the best solution.
• Reasoning and planning.
• Learning and adaptation.

3. What is Artificial Intelligence?
• AI is the science and engineering of making intelligence
machines, especially intelligent computer programs.
• Complicated activities involve thousands of data sets and non-
linear relationships between variables.
• Machine learning: offloading optimization.
• Field of study that gives computers the ability to learn without
being explicitly programmed.
• Machine learning algorithms learn through training.
• 15 approaches to machine learning. One approach– ‘Deep
Learning’.

4. AI- The science of Intelligent programs
• Deep Learning: offloading feature specification.
• Deep Learning is achieved not by processing exhaustive rules but
through practice and feedback.
• Deep learning work using artificial ‘neural network’- a collection
of ‘neurons’ (software-based calculators) connected together.
• An artificial neuron has one or more inputs. It performs a
mathematical calculation based on these to deliver an output.
• A neural network is created when neurons are connected to one
another; the output one neuron becomes an input for another.

5. The Beginning of AI
• We believe AI is an evolution in computing as, or more,
important than the shifts to mobile or cloud computing.
• The team AI was first coined by John McCarthy in 1956.
• 5 years later Alan Turing wrote a paper on the notion of
machines being able to stimulate human beings and the ability to
do intelligent thing such as play Chess.
• There is an argument that since computers would always be
applying rote fact lookup they could never ‘understand’ a
subject.

6. Themes of AI
• The main advances over past 60 years have been advances in
search algorithms, machine learning algorithms, and integrating
statistical analysis.
• ‘AI Effect’ contributed to the downfall of US- beased AI
research in the 80s.
• After decades of research, no computer has come close to
passing Turing test (a model for measuring ‘intelligence’)
• While they’ve built software that can beat humans at some
games.

7. The Turing Test
• “Computing Machinery and Intelligence” – by Alan Turing.
• It put forward a Question ‘Can machines think?’
• Imitation Game - also called The Turing Test.
• The Turing Test takes a simple pragmatic approach, assuming
that a computer that is indistinguishable from an intelligent
human actually has shown that machines can think.
• The fact that that Turing teat is still discussed and researchers
attempt to produce software capable of passing it are
indications that Alan Turing and the propsed test provided a
strong and useful visual to field of AI.

8. Expert Systems: As subset of AI
• First emerged in the early 1950s.
• According to K.S.Metaxiotis et al, expert systems can be
characterized by:
1. Using symbolic logic rather than only numerical calculations;
2. The processing is data- driven;
3. A knowledge database containing explicit contents of a certain
area knowledge; and
4. The ability to interpret its conclusions in the way that is
understandable to the user.
• LISP: Programming language in AI and expert systems by
McCarthy.

9. • Expert systems were increasingly used in industrial applications
- DENDRAL (a chemical structure analyser), XCON(a computer
hardware configuration system), MYCIN(a medical diagnosis
system) and ACE (AT&T’s cable maintenance system).
• PROLOG: An alternative to LISP in logic programming – 1972.
• The success of these systems stimulated a near-magical fascination
with smart applications.
• More companies taken part in using expert system technology and
developing practical applications.
• Nowadays, expert systems has expanded into many sectors of our
society.

10. Technology Issues and Performance
Limitations
• Software Standards and Interoperability
• American Association of Artificial Intelligence (AAAI), the IEEE
Computer Society, DARPA, and the US government.
• Knowledge Acquisition and Analysis
• The problem- solving skills in humans oftentimes are more
complicated and complex than what knowledge collecetion can
achieve.
• Eg. Humans learn to walk at early age through practice and painful
experience.
• Handling Uncertain Situation
• The ability of expert system to derive correct output is often
compromised by lack of precision in rules and inputs.

11. THANK YOU!