Artificial Intelligence Programming Python

1
Artificial Intelligence Programming Python
2
Contents 1. What is Artificial
Intelligence?.....................................
..................................................
.............1 2. Problems in
AI................................................
..................................................
...........................1 a. Reasoning and
Problem Solving...................................
..................................................
............2 b. Knowledge Representation......
..................................................
.................................................2
c. Planning...................................
..................................................
..................................................
2 d. Learning..................................
..................................................
..................................................
3 e. Natural Language Processing...............
..................................................
.....................................3 f.
Perception........................................
..................................................
..........................................3 g.
Motion and Manipulation...........................
..................................................
...............................3 h. Social
Intelligence......................................
..................................................
...............................3 i. General
Intelligence......................................
..................................................
.............................3 3. Approaches
to Artificial Intelligence........................
..................................................
.................3 a. Cybernetics and Brain
Simulation........................................
..................................................
.....4 b. Symbolic.............................
..................................................
..................................................
....4 c. Sub-Symbolic .........................
..................................................
..................................................
. 4 d. Statistical Learning ...................
..................................................
................................................
4 4. Python AI Tutorial - Artificial
Intelligence Tools................................
.........................................4 a.
Search and Optimization...........................
..................................................
................................5 b.
Logic.............................................
..................................................
............................................5 c.
Probabilistic Methods for Uncertain
Reasoning.........................................
................................5 d.
Classifiers and Statistical Learning
Methods...........................................
...................................5 e.
Artificial Neural Networks........................
..................................................
................................6 f. Evaluating
Progress..........................................
..................................................
.........................6 5. Python AI
Tutorial - Applications of Artificial
Intelligence......................................
...................6
3
Python AI Tutorial
Artificial Intelligence With lt, Python
1. What is Artificial Intelligence? Artificial
Intelligence, often dubbed AI, is the
intelligence a machine demonstrates. With machine
intelligence, it is possible to give a device the
ability to discern its environment and act to
maximize its chances of success in achieving its
goals. In other words, AI is when a machine can
mimic cognitive functions like learning and
problem-solving. AI is whatever hasnt been done
yet. As we said, an AI takes in its environment
and acts to maximize its chances of success in
achieving its goals. A goal can be simple or
complex, explicit or induced. It is also true
that many algorithms in AI can learn from data,
learn new heuristics to improve and write other
algorithms. One difference to humans is that AI
does not possess the features of human
commonsense reasoning and folk psychology. This
makes it end up making different mistakes than a
human would.
2. Problems in AI When simulating or creating AI,
we may run into problems around the following
traits-
1
https//data-flair.training/big-data-hadoop/
4
Python AI Tutorial
Data EM FUir
General Intelligence
Problems in AI
Social Intelligence
Reasoning and Problem Solving
Learning
Perception
Natural Language . Processing Motion
and Manipulation
Knowledge Representation
a. Reasoning and Problem Solving Earlier,
algorithms mimicked step-by-step reasoning that
humans display. AI research later introduced
methods to work with incomplete and uncertain
information. However, as the problems grew
larger, these algorithms became exponentially
slower. Humans often use fast, intuitive
judgments instead of a step-by-step deduction.
b. Knowledge Representation Some expert systems
accumulate esoteric knowledge from experts. A
comprehensive commonsense knowledge base holds
many things including- objects, properties,
categories, relations between objects,
situations, events, states, time, causes,
effects, knowledge about knowledge, and other
domains. When we talk about ontology, we talk
about what exists. Under knowledge
representation, we observe the following
domains- Default reasoning Qualification
problem Breadth of commonsense knowledge
Subsymbolic form of some commonsense knowledge
c. Planning An intelligent agent should be
capable of setting goals, achieving them, and
visualizing the future. Assuming it is the only
system in the world, an agent can be certain of
their actions consequences. If there are more
actors, the agent should be able to reason under
uncertainty. For this, it should be able to
assess its environment, make predictions,
evaluate predictions, and adapt according to its
assessment. With multi-agent planning, we observe
multiple agents cooperate and compete to achieve
a goal.
2
https//data-flair.training/big-data-hadoop/
5
Python AI Tutorial
d. Learning AI is related to Machine Learning in
some way. We have often talked about unsupervised
learning- the ability to take a stream of input
and find patterns in it. This includes
classification and numerical regression. We
classify things into categories and produce a
function that describes how inputs and outputs
relate and change each other. These function
approximators.
e. Natural Language Processing NLP is an area of
Computer Science that gives machines the ability
to read the human language and understand it.
With it, we can retrieve information, mine text,
answer questions, and translating using machines.
We use strategies like keyword spotting and
lexical affinity.
f. Perception With machine perception, we can
take input from sensors like cameras,
microphones, and lidar to recognize objects. We
can use it for applications like speech
recognition, facial recognition, and object
recognition. We can also analyze visual input
with Computer Vision.
g. Motion and Manipulation With AI, we can
develop advanced robotic arms and more for modern
factories. These can use the experience to learn
to deal with friction and gear slippage. The term
Motion Planning means dividing a task into
primitives like individual joint movements.
h. Social Intelligence Should I go to bed,
Siri?, I ask as I reach home from a busy day. I
think you should sleep on it, Siri quips back.
Affective Computing, an umbrella term,
encompasses systems that can recognize,
interpret, process, or simulate human affects/
emotions. In this domain, we have observed
textual sentiment analysis and multimodal affect
analysis. The aim is to allow AI to understand
others motives and emotional states to predict
their actions. It can mimic human emotion and
expressions to appear sensitive and interact with
humans. A robot with rudimentary social skills is
Kismet, developed at MIT by Dr. Cynthia Breazeal.
i. General Intelligence Lately, many AI
researchers have begun working on tractable
narrow AI applications like a medical diagnosis.
The future could hold machines with Artificial
General Intelligence(AGI) that combines such
narrow skills. Googles DeepMind will be an
example of this. 3. Approaches to Artificial
Intelligence We observe four different approaches
to AI-
3
https//data-flair.training/big-data-hadoop/
6
Python AI Tutorial
Python AI Tutorial - AI Approaches
a. Cybernetics and Brain Simulation Some machines
exist that use electronic networks to display
rudimentary intelligence.
b. Symbolic This approach considers reducing
human intelligence to symbolic manipulation. This
includes cognitive simulation, logic-based,
anti-logic or scruffy, and knowledge-based
approaches.
c. Sub-Symbolic For processes of human cognition
like perception, robotics, learning, and pattern
recognition, sub-symbolic approaches came into
picture. This includes approaches like embodied
intelligence and computational intelligence and
soft computing.
d. Statistical Learning Statistical learning
techniques like HMM and neural networks deliver
better accuracy in practical domains like data
mining. Limitations of HMM include that it cannot
model the infinite possible combinations of
natural language.
4. Python AI Tutorial - Artificial Intelligence
Tools
For AI, we have the following tools-
4
https//data-flair.training/big-data-hadoop/
7
Python AI Tutorial
gtiData ( jf Flair
gt-1 Search and Optimization 02 r Logic

Probabilistic Methods for Uncertain .Reasoning 04 Classifiers and Statistical Learning Methods

Artificial Neural Networks _i 06 Evaluating Progress
Artificial Intelligence Tools
Python AI Tutorial - Artificial Intelligence Tools
a. Search and Optimization To intelligently
search through possible solutions and use
reasoning to do so is a tool for AI. For
real-world problems, simple exhaustive searches
rarely suffice. This is because these have really
large search spaces. This leads to a slow search
or one that never ends. To get around this, we
can use heuristics.
b. Logic AI research uses different forms of
logic. Propositional logics use truth functions
like or and not. The fuzzy set theory holds a
degree of truth (values between 0 and 1) to vague
statements. First-order logic adds quantifiers
and predicates. Fuzzy logic helps with control
systems to contribute vague rules.
c. Probabilistic Methods for Uncertain
Reasoning We often use tools like Bayesian
networks for reasoning, learning, planning, and
perception. We can also use probabilistic
algorithms to filter, predict, smoothen, and
explain streams of data.
d. Classifiers and Statistical Learning
Methods Classifiers and controllers work
together. Consider an object. If it is shiny, the
classifier knows it is a diamond. If it is shiny,
the controller picks it up. But before inferring
an action, a controller classifies conditions. As
a function, a classifier matches patterns to find
the closest match. Supervised learning puts each
pattern into a predefined class.
5
https//data-flair.training/big-data-hadoop/
8
Python AI Tutorial
e. Artificial Neural Networks ANNs are
collections of nodes that are interconnected-
inspired by the huge network of neurons in the
human brain.
Python AI Tutorial - Artificial Neural
Networks Under these, we have categories like
feedforward neural networks and recurrent neural
networks. We will take up ANNs as a separate
topic in another tutorial.
f. Evaluating Progress Since AI is general
purpose, there is no way to find out which
domains it excels in. Games are a good benchmark
to assess progress. Some of these include AlphaGo
and StarCraft.
5. Python AI Tutorial - Applications of
Artificial Intelligence Like we said, AI is
pretty general-purpose. Here are a few domains it
finds use in- Automotive Healthcare
Video games
6
https//data-flair.training/big-data-hadoop/
9
Python AI Tutorial
Military
Finance and Economics
Art
Auditing
Advertising
So, this was all in Python AI Tutorial. Hope you
like our explanation.
Next step to follow
Copy in Python - Shallow Copy and Deep Copy
NLTK Python Tutorial (Natural Language
Toolkit)
Python Assert Statements Assertion Error
in Python
Python Speech Recognition - Artificial
Intelligence
7
https//data-flair.training/big-data-hadoop/