Artificial Intelligence Programming with Python - Python AI Tutorial
1. Python AI Tutorial
Today, in this Python AI Tutorial, we will take on an introduction to Artificial Intelligence. Moreover, in this Artificial Intelligence Programming, we will see AI Problems, Tools in AI, and Artificial Intelligence approaches. If you read this article carefully you will definitely feel the knowledge about Artificial Intelligence with Python.
So, let’s start the Python AI Tutorial
2. 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 hasn’t 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.
Do you know about AI 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.
3. Python AI Tutorial – AI Problems
When simulating or creating AI, we may run into problems around the following traits-
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-
Have a look at Robotics and Artificial Intelligence
- Default reasoning; Qualification problem
- The breadth of commonsense knowledge
- The 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.
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.
Do you know expert systems in Artificial Intelligence to Solve Problems
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. Google’s DeepMind will be an example of this.
4. Python AI Tutorial – Approaches
We observe four different approaches to AI-
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.
Read More PYTHON AI TUTORIAL