Ideas worth spreading

The jobs we'll lose to machines -- and the ones we won't

3,033,347 views |

Anthony Goldbloom |

TED2016

• February 2016

Machine learning isn't just for simple tasks like assessing credit risk and sorting mail anymore -- today, it's capable of far more complex applications, like grading essays and diagnosing diseases. With these advances comes an uneasy question: Will a robot do your job in the future?

Machine learning isn't just for simple tasks like assessing credit risk and sorting mail anymore -- today, it's capable of far more complex applications, like grading essays and diagnosing diseases. With these advances comes an uneasy question: Will a robot do your job in the future?

This talk was presented at an official TED conference. TED's editors chose to feature it for you.

If you're interested in better understanding machine learning, Kaggle has easy data sets to get you started. For example, you can predict who survived the Titanic based on gender, class, price paid for ticket. You can start with Microsoft Excel and step up to Python when you're ready for a bigger challenge.

About the speaker

Anthony Goldbloom

Anthony Goldbloom

Machine learning expert

See speaker profile

Anthony Goldbloom crowdsources solutions to difficult problems using machine learning.

The Economist | 2016 | Article

The return of the machinery question

This is a clear and accurate description of what machine learning can and can't do. It also offer a well-reasoned perspective on the potential impact on society.

Pedro Domingos | Basic Books, 2015 | Book

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

This book is a nice middle ground between a popular science book and a text book. It goes into enough depth that readers get a sense for some of the concepts behind the major machine learning algorithms. It covers the algorithms that are currently trendy (neural networks and ensembles of decisions trees) and approaches that are out of favor. The idea of a 'Master Algorithm' is a nice narrative device to tie the book together, but a little contrived as a standalone concept.

Erik Brynjolfsson and Andrew McAfee | W. W. Norton & Company, 2014 | Book

The Second Machine Age

This book is a clear and thoughtful account of how AI and robotics are going to impact the future. It also touches on policy remedies that might help us as we confront that future.

This talk was presented at an official TED conference. TED's editors chose to feature it for you.

If you're interested in better understanding machine learning, Kaggle has easy data sets to get you started. For example, you can predict who survived the Titanic based on gender, class, price paid for ticket. You can start with Microsoft Excel and step up to Python when you're ready for a bigger challenge.