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Name: Architects of Intelligence
Rating: 4.3 (443 reviews)
Author: Martin Ford

Martin Ford

The truth about AI from the people building it

4.3 (443 ratings)

25 mins

Brief summary

'Architects of Intelligence' by Martin Ford is a collection of interviews with leading experts and thinkers in the field of artificial intelligence. It offers insights into the current state and future of AI, as well as its societal and ethical implications.

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Artificial Intelligence Human-Machine Interaction

Table of Contents

Architects of Intelligence
Summary of 8 key ideas

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Key idea 1 of 8

What’s in it for me? A big-picture look at the inner workings and implications of artificial intelligence.

Whether it’s the latest sci-fi films or just everyday news, it seems that artificial intelligence can be found everywhere. But perhaps unsurprisingly, there’s little agreement about the details surrounding AI: will automation lead to mass unemployment? Will traditional cars soon be replaced by self-driving vehicles? Are we destined to be subjugated by robotic overlords?

Despite the wide variance in opinions surrounding these issues, it’s often the most pessimistic and melodramatic voices that take center stage when it comes to discussions about AI and its potential effects.

Of course, AI does pose some real threats to our economy. But by focusing only on the downsides, we ignore AI’s potential to reshape the world, redefine work, and provide major boons to healthcare, among many other benefits. These blinks provide a holistic view of AI, from the way it works to the ways it could help or harm us in the future. Along the way, you’ll get a general sense of the opinions of the 23 AI experts the author interviewed in making his book.

In these blinks, you’ll learn

how AI learns what a cat is;
why we should be afraid of killer robots; and
why live concert tickets might become even more expensive.

Different deep learning methods can train AI to complete tasks.

Think back to when you were a child. Do you remember the first time you saw a cat, or a picture of a cat? How many cats did you need to see before you fully understood exactly what a cat was?

You most likely only needed to see one or two before you could easily differentiate a cat from another animal. This sort of learning, which involves viewing a very small number of examples, comes easily and naturally to humans – but for an AI, it’s very difficult. In order for an AI to understand what makes a cat a cat, it must be trained. Nowadays, that often happens through deep learning, a form of machine learning that has driven most of the major strides in AI over the last decade.

The key message here is: Different deep learning methods can train AI to complete tasks.

Whether an AI is being trained to recognize cats, dogs, or coffee cups, it all starts with a neural network. This is software with multiple layers of “neurons” that mimic the ones found in the human brain.

There are a few different methods scientists commonly use to train neural networks. One is supervised learning, a type of deep learning in which an AI is fed a set of training examples, each labeled with a description. After the AI has been trained, we could then show it a picture of a cat. Next, the collection of pixels in the picture flows through the neural network, after which the machine will confirm – we hope – that what it “sees” is, indeed, a cat.

Even if it guesses correctly, this AI still has absolutely no idea of what the word “cat” signifies – it doesn’t know what a cat does or whether it’s alive. For an AI to develop this understanding, it needs to be taught via grounded language learning. This is a deep learning approach where sentences or words are associated with images, videos, or objects in the real world.

All of these techniques enable deep learning to have all sorts of potential applications. For one, grounded language learning could help develop AI’s language skills, making it useful in personal assistants like Siri. And deep learning has already been used to train AI to play games. In one of the most famous instances, the AI AlphaGo was trained by observing many games of Go – and ultimately was able to beat the best human champion at his own game.

Deep learning is limited.

It’s hard not to be impressed when we see an AI beating the best human players at chess, Go, or shogi. But as monumental as that is, it doesn’t mean we’re any closer to achieving any kind of general intelligence. AI is still only good at completing specific, narrow tasks.

Take AlphaZero. It was trained through deep learning to play deterministic, two-player, and fully observable board games, like chess and Go. Despite AlphaZero’s skill in these games, it would be utterly useless if asked to learn to play any other type of game – like, say, poker.

Hybrid systems could be the key to further enhancements in AI.

Over the years, different machine learning techniques have fallen in and out of favor. This phenomenon is exactly what happened with the concept of deep learning. The technique emerged as early as the 1950s but was dismissed as useless by the 1960s – yet today it’s once again the dominant machine learning method.

Deep learning will undoubtedly play a role in the AI of the future, but its limitations mean that it can’t be the only technique we use to create AGI. Rather, the development of AGI may require researchers to combine several different machine learning methods to create a hybrid system.

Artificial intelligence has the potential to make life easier and better for everyone.

In the recent past, there’s been a lot of talk about AI’s potential to reinforce the biases and stereotypes humans might unintentionally program into them. Lots of academic researchers understand the problem of bias when it comes to AI and are modifying algorithms to respond to it. But, this could be flipped and used to our advantage: we could use AI to eliminate biases.

It’s much more difficult to zero in on and correct biases in ourselves. But, as computer scientist Fei-Fei Li points out, when we see our own biases reflected back at us through technology, we can find ways to correct them. And so, eliminating bias is just one example of AI’s ability to improve people’s lives.

Artificial intelligence aids scientific advances, particularly in healthcare.

Walk into any hospital or care facility in America today and you’re likely to find a lot of stressed-out doctors and nurses. These days, healthcare professionals work long shifts, have tight schedules, and are highly susceptible to burnout and stress.

Not only that, rushed doctors and nurses mean patients are receiving lower-quality care than they otherwise would. In fact, the current third-leading cause of death in American hospitals is physician error.

Artificial intelligence could be weaponized.

It’s no secret that humanity’s destructive capabilities have increased greatly over time, from the bows and arrows of the medieval age to the specialized bombs and drones most militaries use today.

These days, even average citizens have access to drones they could potentially weaponize with small bombs. Fortunately, though, this sort of weapon isn’t scalable. Each drone can only hold one bomb, and be piloted by a single person, meaning its destructive capabilities are limited.

Universal basic income or stipends for education could solve the problem of job automation.

Imagine that, in ten years, you never have to work another day in your life. Does that sound like a utopia or a nightmare?

Whatever your answer, the growing presence and skill of AI – especially when it comes to efficiently completing repetitive tasks – may mean that many jobs will soon be automated. A few commonly-cited examples of the people who could soon be out of a job are cashiers, truck drivers, accountants, and factory workers.

The potential downsides of Artificial General Intelligence are hotly contested.

Without a doubt, the rise of AI has brought with it a whole host of concerns. But perhaps none has been as well-publicized as the threat posed by humanlike AGI. What happens if robots eclipse humanity, becoming faster, smarter, and stronger than all of us?

A world dominated by robot overlords is still probably a strictly science-fiction scenario. But according to philosopher Nick Bostrom and a few other researchers, it’s worthwhile to consider how the mission to create AGI could end up going very wrong.

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What is Architects of Intelligence about?

Architects of Intelligence (2018) is a collection of interviews with researchers, scientists, businessmen, and thinkers at the forefront of digital technology and artificial intelligence. There isn’t much agreement to be found among them about how fast the technology is developing, how soon we’ll all be driving autonomous cars, or the possibility of a breakthrough in general intelligence. But we can rest assured that AI technology is destined to shake the core of society, the economy, and life itself in unimaginable and unprecedented ways.

Architects of Intelligence Review

Architects of Intelligence (2018) provides a glimpse into the minds of the world's leading AI experts. Here's why this book is worth reading:

Featuring in-depth interviews with industry pioneers, it offers valuable insights into the future of AI and its impact on society.
This book goes beyond the technical aspects of AI, delving into the ethical and philosophical implications of this groundbreaking technology.
With its diverse range of perspectives from researchers, entrepreneurs, and theorists, it offers a comprehensive understanding of AI's potential and challenges.

Who should read Architects of Intelligence?

Futurists who love to consider how our world may soon change
AI-fearers who worry that technology could be destructive to humanity
Anyone curious about developments in AI

About the Author

Martin Ford is a futurist, public speaker, and author who has written on AI and digital technology for many publications, including the New York Times and the Washington Post. His other books include The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future and the bestselling Rise of the Robots: Technology and Threat of a Jobless Future, which won the Financial Times and McKinsey Business Book of the Year.

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