Book Image

Cognitive Computing with IBM Watson

By : Rob High, Tanmay Bakshi
Book Image

Cognitive Computing with IBM Watson

By: Rob High, Tanmay Bakshi

Overview of this book

Cognitive computing is rapidly becoming a part of every aspect of our lives through data science, machine learning (ML), and artificial intelligence (AI). It allows computing systems to learn and keep on improving as the amount of data in the system increases. This book introduces you to a whole new paradigm of computing – a paradigm that is totally different from the conventional computing of the Information Age. You will learn the concepts of ML, deep learning (DL), neural networks, and AI with the help of IBM Watson APIs. This book will help you build your own applications to understand, and solve problems, and analyze them as per your needs. You will explore various domains of cognitive computing, such as NLP, voice processing, computer vision, emotion analytics, and conversational systems. Equipped with the knowledge of machine learning concepts, how computers do their magic, and the applications of these concepts, you’ll be able to research and apply cognitive computing in your projects.
Table of Contents (16 chapters)
Title Page
Copyright and Credits
About Packt

Advances in AI

Much of the larger and more profound impact that AI will have on our world is in the distant future. But we are seeing an incredible array of advances in technology occurring now—some of which will prove useful, and others, perhaps, not so much. Let's touch on a few of the more promising ones here.

Generative adversarial networks

One of the more interesting advances in recent years has been the introduction of generative adversarial networks. The principle behind this technology is fairly straightforward: two algorithms are developed.

The first algorithm is designed to generate a thing—this can be anything, such as a picture of a cat. Obviously, without having first been taught how to generate a picture of a cat the algorithm just generates some random image that it presents as a cat.

The second algorithm has been trained to recognize that thing—such as a picture of a cat in this example.

The two algorithms are then hooked together, with the first algorithm generating pictures...