What makes software intelligent?

Everyone, from journalists to investors, sees the growing impact of AI in our lives. But few know what’s under the hood. How do machine learning algorithms work in practice? What’s next? A detailed yet approachable explanation for the intelligent (human) reader.

  • 240-page online book
  • Lifetime digital access
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Length: 240 pages
Edition: e1.0.2
Last Updated: 2022-11-02
Language: English
ISBN (Holloway.com):

Making Things Think

How AI and Deep Learning Power the Products We Use

by Giuliano Pezzolo Giacaglia

Machine learning affects almost every part of our digital lives. One credible prediction is that artificial intelligence could scale from about $2.5 trillion to $87 trillion in enterprise value by 2030; for comparison, the internet has generated around $12 trillion dollars of enterprise value from 1997 to 2021.*

Yet the algorithms are opaque to most people, even to tech journalists and many software engineers. We see both fanfare and fear in the news about the capabilities of artificial intelligence. Headlines often dramatize certain problems, focus on unrealistic scenarios, and neglect important facts about how AI and recent developments in machine learning work—and how they are likely to affect our lives.

Giuliano Giacaglia is an engineer who studied at MIT (with Marvin Minsky and Patrick Henry Winston, in the famed Artificial Intelligence Lab there) and built AI systems at Microsoft, Facebook, and Bluecore (a company that personalizes emails and ads for the largest e-commerce brands in the world). He realized how much these systems were affecting products people use every day, from self-driving car software to video recommendations.

AI systems do not need to be black boxes. This book aims to explain both how these systems work so you can have an informed opinion, and assess for yourself the wide-ranging risks and opportunities presented by intelligent software.

This means giving you context and technical detail. The past informs our view of the future, so the book includes a brief history of artificial intelligence (including accurate and inaccurate predictions from the past) as well as recent developments in deep learning. The result is an essential guide for the intelligent reader to gain a practical and realistic understanding of the past, present, and possible future of artificial intelligence.

The guide also includes a new survey of key companies and developments in AI for 2022. As with other Holloway titles, purchase includes future updates.

Wes CowleyEditor
Artificial Intelligence has become more important in society over time. Most of the software that people interact with has AI embedded in it. It is used broadly, from powering TikTok to helping predict cancer. These are still the early days of the development and impact of AI in different industries, and that will only increase. Making Things Think explains how these systems work in a simple way.Paul English(co-founder, Kayak)
About the Author
Giuliano Pezzolo Giacaglia
Giuliano has held several roles in technology and AI companies, from startups like Bluecore to Microsoft and Facebook. He has long been interested in the rise and applications of artificial intelligence. He holds two degrees from MIT, where he studied under Marvin Minsky, one of the founding fathers of artificial intelligence and founder of the MIT Media Lab. Giuliano also worked under Patrick Henry Winston, former director of the MIT Artificial Intelligence Laboratory, in the Genesis Group, helping to develop an account of human intelligence. This is his first book.
AI is going to be the engine of so much progress in the near-mid future across fields in tech, climate, medicine, retail and more. In this book, Giuliano goes over all the important developments in AI over the last decade, I'd recommend it to anyone who wants an overview of how these systems work.Ruchi Shangvi(former early engineer, Facebook and VP of Operations, Dropbox)

Table of Contents

Why Another Book about AI?
A Brief History of AI
The Birth of Artificial Intelligence (1952–1956)
The Golden Years of AI (1956–1974)
The First AI Winter (1974–1980)
The AI Boom (1980–1987)
The Second AI Winter (1987–1993)
Probabilistic Reasoning (1993–2011)
IBM Watson
A Brief Overview of Deep Learning
Deep Neural Networks
Convolutional Neural Networks
Google Brain: The First Large-Scale Neural Network
DeepMind: Learning from Experience
AlphaGo: Defeating the Best Go Players
Software 2.0
Dueling Neural Networks
Data Is the New Oil
Data Privacy
Deep Learning Processors
What Can AI Learn from Animal Brains?
Learning to Learn Theory
Sleeping and Learning
Predicting the Future
Robots in the Industry
Machine Learning and Robotics
Self-Driving Cars
The Future of Self-Driving Cars
Industry Applications
Voice Assistants (Siri)
AI in Medicine
AI and Space
AI in E-Commerce
AI and the Law
AI and Real Estate
Risks and Impact of AI
Recommendation Algorithms
Interpretability of Neural Networks
Economic Impact of AI
Artificial General Intelligence
Emerging Developments in Artificial Intelligence
A Landscape of Top Artificial Intelligence Teams
Notable Developments in AI in 2022
Further Readings in AI
Machine Learning Blogs
Courses and Programs
Year in Review Reports
Short Biographies of Key Figures in AI
Notable AI Tools and Efforts

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Does this sound like you?

I want to learn more about the algorithms used by systems like Siri or self-driving cars.
I would like to know more about the history of AI to extrapolate future trends.
I’m concerned about my data being harvested by tech companies and used by AI systems.
I don’t know whether media articles about artificial intelligence surpassing human intelligence are realistic.
What’s next with deepfakes and other AI-powered disinformation?

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