Rebooting AI

Building Artificial Intelligence We Can Trust

De: Gary Marcus, Ernest Davis

Narrado por: Kaleo Griffith

Two leaders in the field offer a compelling analysis of the current state of the art and reveal the steps we must take to achieve a truly robust artificial intelligence.

Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we have been led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer beating a human in Jeopardy! does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements in the field thus far have occurred in closed systems with fixed sets of rules, and these approaches are too narrow to achieve genuine intelligence.

The real world, in contrast, is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Taking inspiration from the human mind, Marcus and Davis explain what we need to advance AI to the next level, and suggest that if we are wise along the way, we won't need to worry about a future of machine overlords. If we focus on endowing machines with common sense and deep understanding, rather than simply focusing on statistical analysis and gatherine ever larger collections of data, we will be able to create an AI we can trust—in our homes, our cars, and our doctors' offices. Rebooting AI provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of how a new generation of AI can make our lives better.

The need for Deep understanding.

The need for AI Deep understanding, is critical ti any future AI system with real world applications.

entertaining

if you follow/enjoy Gary's online jousting vs the AI "greats" on Twitter, you will enjoy this book.

it's required reading for AI researchers and enthusiasts.

Insightful & nuanced take on AI, cuts through hype

This is a very enjoyable book to read and offers a very nuanced view of the state of AI. Its great to read a book like this after the successful of "Superintelligence" earlier. This book present a real contrast to that work and to other works which really hype the promise and potential of AI.

Excellent

I have read / listened too many books on AI. This is the first that provided such a clear understanding of the difference between narrow and general AI and why moving from narrow to general AI is far from a slam dunk The book provides many useful examples. It also provides a possible route map to general AI. I thoroughly enjoyed listening, great.

Great Epilogue

Moving towards AI with values & common sense… maybe a few more sprinkles. Great piece of research.

Cheers

Insightful

There are many things I still don’t understand about AI. This book helps answer a lot of questions that we may have regarding the topic.
The story is very well written, and couldn’t have come at a better time!

I’ve wondered about the lack of cognitive models in deep learning systems, this book does as well

I learned that Ai needs common sense and deep understanding, I’ve learned AI needs cognitive models. I didn’t learn how to accomplish any of those essentials. That’s my next step.

A must read for anyone in NLP

After the last five years of deep learning hype in NLP, Marcus and Davis offer a refreshing and uplifting account of the real state of machine understanding and reasoning.

Full of pragmatic examples of how and why deep learning fails in NLP, it also offers different frameworks for how and where to start building actual systems that can understand.

Serious and thoughtful redirect for AI

I have been waiting for the industry to finally get critical with itself. Thanks for this thoughtful treatment of a subject that has had misleading and frothy treatment from the media, unrealistic and misguided expectation-setting from software vendors, and too little serious self examination from practitioners. The authors have set out a pretty good set of “functional requirements” for a generalized AI here. They’ve done a good job of articulating 1. Why the current data-driven deep learning cannot progress beyond simple and very narrow tasks, 2. A common sense understanding of the world is missing from these approaches 3. a conceptual faculty able to learn without 10,000 high quality labeled examples beforehand is needed; and 4. What potential corrective actions might be taken.

Importantly, they have suggested a reunification of the two divergent AI traditions is ultimately what is needed to progress and fix the current course. Additionally they implied (or I read into it) that a more trans-disciplinary approach is needed, given the stated need for understanding the functionality of the brain, not just at a biological/ chemical level, but at a fundamental metaphysical / philosophical level. They paint a but challenging road forward, but “tough love” for AI is the only hope for the field, and so I say, thank you.

Cuts Through the Massive Hype Behind ML

I have gone through dozens of books on technology and AI and this book was a refreshing breath of air. It cuts through much of the hype behind machine learning and other AI techniques and helps to frame a more realistic perspective of what has been achieved and what can be achieved with existing techniques. There is a lot of healthy criticism of the current state of AI research and many practical suggestions to address existing problems.

This book is a must "read" for anyone who follows AI.