Jeremy Howard is the CEO of Enlitic, an advanced machine learning company in San Francisco.
Traditional programming of a computer means telling it in absolute detail how to achieve a task. This is difficult unless the programmer is an expert in the task he is teaching, and prevents the computer being better than the programmer. Machine learning allows a computer to learn on it’s own – as Arthur Samuel programmed a computer to beat himself at checkers. Nowadays machine learning has been successfully commercialised. Google is based on machine learning, LinkedIn and Facebook have learnt how to recommend friends, Amazon can recommend products using machine learning.
Deep learning is an algorithm inspired by how the human brain works, so assuming enough computation time and learning time, there are no limits. For example, deep learning can:
- Drive cars
- understand English, translate to Chinese, and read back in Chinese.
- Image recognition through Deep Learning has an error rate down to 6% – better than human levels.
- look at an image and identify similar images
- write a caption for an image
- understand sentence structure and language.
These are very human-centric that humans are now able to do.
Computers are also exceeding and enhancing human performance. In cancer diagnosis, a computer analysed tumours and discovered some features unknown to human doctors that can help predict survival rate and treatment. Computer predictions of survival were more accurate than humans and the discoveries improved the science of cancer treatment. This system can be developed with no background in medicine, and replaces the data analysis and diagnostics of the medical process. This leaves doctors more time to gather input data and apply treatments. The number of doctors in the developing world is 10 – 20 times less than what is needed, and will take many generations to train enough. If computers can learn to fill these roles, lives will be saved.
On the flipside, computers will wipe out a service industry whose role is to read documents, drive cars, talk. This is >80% of the jobs in the developed world. In the past (eg industrial revolution) a large number of jobs were obsolete at the same time as new jobs came into being, but computer learning is much more disruptive than this since it takes very few people to develop and roll out the algorithms. Once fully rolled out, computers will far surpass humans at an exponential rate – when computers can redesign themselves to be better and better.
To fix the high unemployment, better education and incentives to work will not help if there are no jobs to do. We need to look at this problem differently – by decoupling labour from earnings or moving to a craft based economy. Jeremy asks us all to think about how to adjust to this new reality.
Google emailed me a Youtube update suggesting I watch this video. As someone who watches a lot of TED talks and an interest in Artificial Intelligence, Google knew I would watch it.
Jeremy makes a lot of good points. Personally I think he used too many examples and it was a little disorienting to follow. Nonetheless, he pulls out the important points. To me his final point is the most important: this massive change in our economy is coming and very few people seem prepared for it. How will we deal with a world with 80% unemployment, where all those jobs are no longer necessary for us to maintain the same standard of living?