AI Chapter 6

6.3 Artificial intelligence
https://www.youtube.com/watch?v=rE00609d-f4

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6.3 Artificial intelligence
Understand what is meant by artificial intelligence (AI)

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Understand what is meant by artificial intelligence (AI)
Types of artificial intelligence
Weak AI or Narrow AI
Weak AI also called Narrow AI is AI trained and focused to perform specific tasks. Weak AI drives
most of the AI that surrounds us today. ‘Narrow’ might be a more accurate descriptor for this
type of AI as it is anything but weak; it enables some very robust applications, such as Apple's
Siri, Amazon's Alexa, IBM Watson, and autonomous vehicles.

General Intelligence (AGI)
Is a theoretical form of AI where a machine would have an intelligence equaled to humans; it
would have a self-aware consciousness that has the ability to solve problems, learn, and plan for
the future.

Strong AI
.Artificial Super Intelligence (ASI) also known as superintelligence would surpass the intelligence
and ability of the human brain.

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Examples of AI include:
» news generation based on live news feeds
» smart home devices (such as Amazon Alexa, Google Now, Apple Siri and Microsoft Cortana):
– the AI device interacts with a human by recognizing verbal commands
– it learns from its environment and the data it receives
– the device becomes increasingly sophisticated in its responses, thus showing the ability to use
automated repetitive learning

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6.3 Artificial intelligence
Examples of AI include:
Deep learning: is a machine learning technique that teaches computers to do what comes naturally to
humans, to learn Take an example of Self Driving feature in cars like Tesla(Autopilot)
Facial Recognition: Artificial Intelligence has made it possible to recognize individual faces using biometric
mapping. For example, Clearview AI, an American technology company, offers surveillance technology for
law agencies to monitor entire cities with a network of CCTV.
Automate Simple and Repetitive Tasks: AI has the ability to execute the same kind of work over and over
again without breaking a sweat.
Data Ingestion: With every passing day, the data that we are all producing is growing exponentially, which is
where AI steps in. Instead of manually feeding this data, AI-enabled not just gathers this data but also
analyzes it with the help of its previous experiences.
Chatbots: are software to provide a window for solving customer problems’ through either audio or textual
input. Earlier the bots used to respond only to specific commands. If you say the wrong thing, it didn’t know
what you meant.

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Examples of AI include:
Quantum Computing: AI is helping solve complex quantum physics problems with the accuracy
of supercomputers with the help of quantum neural networks. This can lead to path-breaking
developments in the near future for example, A pioneer in this field is Google AI Quantum

Cloud Computing: Next Artificial Intelligence characteristics is Cloud Computing. With such a
huge amount of data being churned out every day, data storage in a physical form would have
been a major problem for example Microsoft Azure is one of the prominent players in the cloud
computing industry. It offers to deploy your own machine learning models to your data stored in
cloud servers without any lock-in.

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https://www.youtube.com/watch?v=T-X2VhfIzCw

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6.3 Artificial intelligence
Basic operations and components of AI systems

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An expert system: is a computer program that uses artificial intelligence (AI) technologies to
simulate the judgment and behavior of a human or an organization that has expertise and
experience in a particular field.
Expert systems are usually intended to complement, not replace, human experts.

Note: It is important to remember that an expert system is not used to replace the human experts;
instead, it is used to assist the human in making a complex decision. These systems do not have human
capabilities of thinking and work on the basis of the knowledge base of the particular domain

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An expert system:
Below are some popular examples of the Expert System:
DENDRAL: It was an artificial intelligence project that was made as a chemical analysis expert system. It was
used in organic chemistry to detect unknown organic molecules with the help of their mass spectra and
knowledge base of chemistry.
MYCIN: It was one of the earliest backward chaining expert systems that was designed to find the bacteria
causing infections like bacteremia and meningitis. It was also used for the recommendation of antibiotics
and the diagnosis of blood clotting diseases.

There are many applications that use expert systems:

» oil and mineral prospecting
» diagnosis of a patient’s illness
» fault diagnostics in mechanical and electronic equipment
» tax and financial calculations
» strategy games, such as chess
» logistics (efficient routing of parcel deliveries)
» identification of plants, animals and chemical/biological compounds.

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An expert system:
Expert systems have many advantages:
» they offer a high level of expertise
» they offer high accuracy
» the results are consistent
» they have the ability to store vast amounts of ideas and facts
» they can make traceable logical solutions and diagnostics
» it is possible for an expert system to have multiple expertise
» they have very fast response times (much quicker than a human expert)
» they provide unbiased reporting and analysis of the facts
» they indicate the probability of any suggested solution being correct.
Expert systems also have disadvantages:
» users of the expert system need considerable training in its use to ensure the
system is being used correctly
» the set up and maintenance costs are very high
» they tend to give very ‘cold’ responses that may not be appropriate in certain
medical situations
» they are only as good as the information/facts entered into the system
» users sometimes make the very dangerous assumption that they are infallible.

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Basic operations and components of AI systems
An expert system:
Components of Expert System
An expert system mainly consists of three components:
1- User Interface
2- Inference Engine (Rules of Engine)
3- Knowledge Base
4- Rules base

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Basic operations and components of AI systems
An expert system:
Structure of an Expert System:

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Basic operations and components of AI systems
An expert system:
1- User Interface
With the help of a user interface, the expert system interacts with the user, takes queries as an
input in a readable format, and passes it to the inference engine. After getting the response
from the inference engine, it displays the output to the user.

» interaction can be through dialogue boxes, command prompts or other input
methods.

» the questions being asked usually only have Yes/No answers and are based on
the responses to previous questions.

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Basic operations and components of AI systems
An expert system:
2. Inference Engine(Rules of Engine)
- The inference engine is known as the brain of the expert system as it is the main processing
unit of the system.
» this is the main processing element of the expert system
» the inference engine acts like a search engine examining the knowledge base for
information/data that matches the queries
» it is responsible for gathering information from the user by asking a series of questions and
applying responses where necessary; each question being asked is based on the previous
responses.
» the inference engine is the problem-solving part of the expert system that makes use of
inference rules in the rules base.
» since the knowledge base is a collection of objects and attributes, the inference engine
attempts to use information gathered from the user to find an object that matches (making use
of the rules base to find a match).

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An expert system:
3. Knowledge base
» the knowledge base is a repository of facts
» it stores all the knowledge about an area of expertise obtained from a number of expert resources.
» it is basically a collection of objects and their attributes; for example:

» so if we had a series of questions:
– is it a mammal? YES
– can it be a pet? NO
– does it live in water? YES
– does it make sonic sounds? YES
– is its body covered in skin? YES
– does it have any legs? NO

conclusion: it is a WHALE.
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An expert system:
4. Rules base

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» the rules base is a set of inference rules
» inference rules are used by the inference engine to draw conclusions (the
methods used closely follow human reasoning)
» they follow logical thinking like the example above; usually involving a series of ‘IF’
statements, for example:

6.3 Artificial intelligence
Basic operations and components of AI systems
An expert system:
Setting up an expert system
» information needs to be gathered from human experts or from written sources such as
textbooks, research papers or the internet

» information gathered is used to populate the knowledge base that needs to be first created

» a rules base needs to be created; this is made up of a series of inference rules so that the
inference engine can draw conclusions

» the inference engine itself needs to be set up; it is a complex system since it is the main
processing element making reasoned conclusions from data in the knowledge base

» the user interface needs to be developed to allow the user and the expert system to
communicate

» once the system is set up, it needs to be fully tested; this is done by running the system with
known outcomes so that results can be compared and any changes to the expert system made.

6.3 Artificial intelligence
Basic operations and components of AI systems
An expert system:
Setting up an expert system

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Machine learning

Mimics = to imitate or copy in action, speech, etc., often playfully

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Basic operations and components of AI systems
Machine learning
Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability
of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to
perform complex tasks in a way that is similar to how humans solve problems.
Example that uses machine learning are the most sophisticated search engines or Image
recognition (x-ray as cancerous or not)

The search engine will learn from its past performance, meaning its ability to carry out searches
becomes more and more sophisticated and accurate.

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Machine learning
Difference between AI and machine learning

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Machine learning
Examples of machine learning

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Machine learning

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Click the link below to practice Machine learning using python programming

https://www.w3schools.com/python/python_ml_getting_started.asp

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