Artificial Intelligence (AI) - Simply Explained for Beginners

For this reason, our journey bs with the section "Introduction and historical background of AI".

Topics and contents of the lessons:

I. Introduction and historical background

What is AI - a philosophical consideration

Strong and Weak AI

The Turing Test

The birth of the AI

The era of great expectations

Catching up with reality

How to teach a machine to learn

Distributed systems in the AI

Deep Learning, Machine Learning, Natural Language Processing

II. The general problem solver

Proof Program - Logical Theorist

Example from "Human Problem Solving" (Simon)

The structure of a problem

In this section, we first take up the initial techniques of AI. You will learn about the concepts and famous example systems that triggered this early phase of euphoria.

III. Expert Systems

Factual knowledge and heuristic knowledge

Frames, Slots and Filler

Forward and backward chaining

The MYCIN Programme

Probabilities in expert systems

Example - Probability of hairline cracks

In this section, we discuss expert systems that, similar to the general problem solvers, only deal with specific problems. But instead, they use excessive rules and facts in the form of a knowledge base.

IV. Neuronal Networks

The human neuron

Signal processing of a neuron

The Perceptron

This section heralds a return to the idea of being able to reproduce the human brain and thus make it accessible to digital information processing in the form of neural networks. We look at the early approaches and highlight the ideas that were still missing to help neural networks achieve a breakthrough.

V. Machine Learning (Deep Learning & Computer Vision)

Example - potato harvest

The birth year of Deep Learning

Layers of deep learning networks

Machine Vision / Computer Vision

Convolutional Neural Network.

The idea of an agent and its interaction in a multi-agent system is described in the fifth section. The main purpose of such a system is to distribute complexity over several instances.

The sixth section deals with the breakthrough of multi-layer neural networks, machine learning, machine vision, speech recognition and some other applications of today's AI.



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