Artificial Intelligence

Easily Explained for Beginners

De: Axel Mammitzsch

Narrado por: Axel Mammitzsch

Duración: 52 m

Resumen del Editor

This audio book on artificial intelligence is aimed at beginners and is designed to teach you the basics within the historical development of AI. For this reason, our journey begins 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

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.

• Proof Program - Logical Theorist,
• Example from "Human Problem Solving" (Simon),
• The structure of a problem.

III. Expert Systems

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.

• 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.

IV. Neuronal Networks

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.

• The human neuron,
• Signal processing of a neuron,
• The Perceptron.

V. Machine Learning, Deep Learning & Computer Vision

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.

• Example - potato harvest,
• The birth year of deep learning,
• Layers of deep learning networks,
• Machine Vision / Computer Vision,
• Convolutional Neural Network.

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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