Book SummaryRebooting AI, by Gary Marcus and Ernest Davis

1-Page Summary1-Page Book Summary of Rebooting AI

Artificial Intelligence (AI) has the potential to revolutionize every aspect of the way we live. From diagnostic tools for medicine to electronic personal assistants, self-driving cars, and household robotics, AI’s future applications could be enormous—but only if we can develop machine intelligence that can accurately and reliably carry out its tasks. While some see AI as a cornucopia that could free the human race from drudgery, others see potential dangers in ceding so much power over society’s systems to digital, non-human minds.

In Rebooting AI, published in 2019, Gary Marcus and Ernest Davis argue that AI proponents oversell what modern AI can accomplish, while AI in its current form underdelivers on its creators’ promises. Marcus and Davis also suggest that those who fear an AI takeover are worried about the wrong thing. The danger isn’t that an evil AI will conquer the world, but that we’ll cede power to unreliable systems which, through their lack of real-world comprehension, are likely to endanger people’s lives by making idiotic mistakes no human could conceive of.

Marcus and Davis are AI advocates,...

Rebooting AI Summary Don’t Believe the Hype About AI

Before discussing whether AI works or doesn’t, Marcus and Davis address how public perception of artificial intelligence is skewed. The computer industry, science fiction, and the media have primed the public to imagine “strong AI”—computer systems that actually think and can do so much faster and more powerfully than humans. Instead, what AI developers have delivered is “narrow AI”—systems trained to do one specific task while having no more awareness of the larger world than a doorknob can understand what a door is for. The authors explain how AI’s capabilities are currently being oversold and why software engineers and the public at large are susceptible to overestimating narrow AI’s capabilities.

(Shortform note: In this guide, we’ll discuss two levels of AI, though some software engineers now divide them into three: narrow, general, and strong. Narrow, or “weak,” AI is trained to perform specific tasks, such as chatbots that mimic human conversation or...

Rebooting AI Summary How Narrow AI Works

To understand Davis and Marcus’s concerns about narrow AI, it’s important to grasp how narrow AI operates. The current paradigm of AI development combines artificial “neural networks” that can be trained to produce desired outputs when they’re fed vast amounts of data. The authors describe how this process developed, the way that it permeates—and benefits—modern technology, and what it lacks in terms of reaching true intelligence.

(Shortform note: A neural network is a computer model composed of computational “nodes” that simulate the behavior of neurons in the brain. In contrast to a computer’s traditional use of binary signals, each node in a neural network fires according to a combination of inputs whose relative proportion and influence are heightened or lessened as the system “learns” to produce desired outputs. Despite the terminology used by software engineers, neural networks don’t truly embody the full range of behaviors of neurons in the brain, which leads Davis and Marcus to suggest that the term “neural network” itself is misleading.)

In the 20th century,...

Rebooting AI Summary How Narrow AI Fails

So far, AIs have been successful when they’ve been designed to do a single task. Their problem is reliability—even if they work most of the time, we never know when they’ll make nonsensical mistakes that no human ever would. Marcus and Davis blame AI’s problems on fundamental design deficiencies, including issues with how machine learning works, the nature of the data it’s trained on, problems with how computers process language, and the way that machines perceive the physical world.

Problems With Machine Learning and Data

Davis and Marcus’s main objection to training neural networks using large amounts of data is that when this strategy is employed to the exclusion of every other programming tool, it’s hard to correct for a system’s dependence on statistical correlation instead of logic and reason. Because of this, neural networks can’t be debugged in the way that human-written software can, and they’re easily fooled when presented with data that don’t match what they’re trained on.

AI Hallucinations

When neural networks are solely trained on input data rather than programmed by hand, it’s impossible to say exactly why the system produces a particular result...

Rebooting AI Summary The Road to Strong AI

To be clear, Marcus and Davis aren’t against AI—they simply believe that what the world needs is more research on strong AI development. The path to achieving strong AI systems that can genuinely understand and synthesize information requires drawing on more than big data and current machine learning techniques. The authors advocate for AI developers to make use of current research in neuroscience and psychology to build systems capable of human-level cognition, ones that learn like the human brain does instead of merely correlating data. The authors add that these systems should be developed with more rigorous engineering standards than have been employed in the industry so far.

Davis and Marcus don’t deny that modern AI development has produced amazing advances in computing, but they state that we’re still falling short of AI’s true potential. An AI with the ability to understand data would be able to read all the research in a field—a task no human expert can do—while synthesizing that information to solve problems in medicine, economics, and the environment that stump even the brightest human minds. The advent of strong AI will be transformative for the whole human race, but...

Shortform Exercise: Reflect on the Impact of AI in Your Life

AI is becoming omnipresent in our lives, including smartphone tools, search engines, automated product recommendations, and the chatbots that now write much of the news. However, Marcus and Davis argue that AI could be doing much more and that we overestimate what AI does because we don’t really understand its inner workings. Think about how you use AI at present and how that might shift in the future.

What AI application do you use the most often, such as a search engine or the map app on your phone? Do you feel that it gives the best results that it can? In what way have you ever found its results frustrating?