Book Image

The Artificial Intelligence Infrastructure Workshop

By : Chinmay Arankalle, Gareth Dwyer, Bas Geerdink, Kunal Gera, Kevin Liao, Anand N.S.
Book Image

The Artificial Intelligence Infrastructure Workshop

By: Chinmay Arankalle, Gareth Dwyer, Bas Geerdink, Kunal Gera, Kevin Liao, Anand N.S.

Overview of this book

Social networking sites see an average of 350 million uploads daily - a quantity impossible for humans to scan and analyze. Only AI can do this job at the required speed, and to leverage an AI application at its full potential, you need an efficient and scalable data storage pipeline. The Artificial Intelligence Infrastructure Workshop will teach you how to build and manage one. The Artificial Intelligence Infrastructure Workshop begins taking you through some real-world applications of AI. You’ll explore the layers of a data lake and get to grips with security, scalability, and maintainability. With the help of hands-on exercises, you’ll learn how to define the requirements for AI applications in your organization. This AI book will show you how to select a database for your system and run common queries on databases such as MySQL, MongoDB, and Cassandra. You’ll also design your own AI trading system to get a feel of the pipeline-based architecture. As you learn to implement a deep Q-learning algorithm to play the CartPole game, you’ll gain hands-on experience with PyTorch. Finally, you’ll explore ways to run machine learning models in production as part of an AI application. By the end of the book, you’ll have learned how to build and deploy your own AI software at scale, using various tools, API frameworks, and serialization methods.
Table of Contents (14 chapters)
Preface
4
4. The Ethics of AI Data Storage

Introduction

In the previous chapter, you learned how to prepare data using extract, transform, and load (ETL) pipelines to feed it efficiently into an AI system. In contrast, in this chapter, we'll take a break from looking at how things could be done, and we'll start asking whether they should be done. As with many new fields, AI has run up against ethical considerations. Ethics itself is always a topic that sparks controversy but combine that with a field that people often still associate with killer robots, and you're bound to find some very difficult and hotly debated topics.

Even outside of AI, robots can get into ethical trouble. For example, in 2014, the artistic group "!Mediengruppe Bitnik" created an automated trading bot that could buy random items from the so-called "dark web." The dark web is like the world wide web that most of us use every day, but you will not find its pages indexed on search engines such as Google. Instead, pages...