What to Expect in This Book

Youโ€™re reading an excerpt of Making Things Think: How AI and Deep Learning Power the Products We Use, by Giuliano Giacaglia. Purchase the book to support the author and the ad-free Holloway reading experience. You get instant digital access, plus future updates.

In the first section of Making Things Think, I talk about the history and evolution of AI, explaining in laypersonโ€™s terms how these systems work. I cover the critical technical developments that caused big jumps in performance but not some of the topics that are either too technical or not as relevant, such as k-NN regression, identification trees, and boosting. Following that, I talk about deep learning, the most active area of AI research today, and cover the development trends of those methods and the players involved. But more importantly, I explain why Big Data is vital to the field of AI. Without this data, artificial intelligence cannot succeed.

The next section describes how the human brain works and compares it to the latest AI systems. Understanding how biological brains work and how animals and humans learn with them can shed light on possible paths for AI research.

We then turn to robotics, with examples of how industry is using AI to push automation further into supply chains, households, and self-driving cars.

The next section contains examples of artificial intelligence systems in industries such as space, real estate, and even our judicial system. I describe the use of AI in specific real-world situations, linking it back to the information presented earlier in the book. The final section contains risks and impacts of AI systems. This section starts with how these systems can be used for surveillance, and it includes the economic impact of AI and ends with a discussion of the possibility of AGI.

Why Another Book about AI?

I was born and raised in Sรฃo Paulo, Brazil. I was lucky enough to be one of the two Brazilians selected to the undergraduate program at MIT. Coming to the US to study was a dream come true. I was really into mathematics and ended up publishing some articles in the field,* but I ended up loving computer science, specifically focusing on artificial intelligence.

Iโ€™ve since spent almost a decade in the field of artificial intelligence, from my Masters in machine learning to my time working on a company that personalizes emails and ads for the largest e-commerce brands in the world. Over these years, Iโ€™ve realized how much these systems were affecting peopleโ€™s everyday lives, from self-driving car software to recommending videos. 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.*

Even though these systems are everywhere and will become more important over time as they become more capable, few people have a concrete idea of how they work. On top of that, we see both fanfare and fear in the news about the capability of these systems. But the headlines often dramatize certain problems, focus on unrealistic scenarios, and neglect important facts about how AI and recent developments in machine learning workโ€”and are likely to affect our lives.

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