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

Summary

In this chapter, we saw examples of prejudice in AI systems through several case studies. We saw how Cambridge Analytica developed an AI based on stolen personal data, how Amazon developed an AI that displayed sexist traits, and how the US justice system, to some extent, relies on AI that displays racist traits.

We built our own AI system that displayed some elements of prejudice and discussed how important it is to be aware of in-built biases, especially when using pre-trained models. We gained experience with the Python library spaCy and saw how word embeddings work. We verified that our sentiment analyzer worked on movie reviews, and then tested it further with some more words associated with prejudices.

In the next chapter, we will be studying the fundamentals of SQL and NoSQL databases by taking a practical approach. We will be learning and performing queries in MySQL, MongoDB, and Cassandra. Don't forget to consider the ethical considerations of any data that...