Is Reinforcement Learning a Slow Learner?

A prominent AI researcher recently gave a webinar with the ACM (Association of Computing Machinery) expressing dismay at the current performance of AI systems and giving his thoughts on the directions research should take. Yann LeCun, Chief AI Scientist at Facebook and Professor at NYU, gave a talk titled “The Power and Limits of Deep Learning”. Current AI systems have no ability to model the real-world. For example, babies learn quite early that a truck that drives off a platform and hovers in the air is unexpected. Current AI systems do not have this ability – they might, after many, many training examples, might be able predict this type of behavior for very specific vehicles. “Sure, I know a red fire truck will fall down, but I have no idea what this Prius is going to do. Let’s watch…” This same type of thing happens in the simpler task of image recognition. A human can get the idea of an elephant from a few images, but our most sophisticated image recognition systems need many thousands of training examples to recognize a new object. And even then, it will have difficulty in recognizing a different view (Elephant rear-end?, Elephant with trunk hidden behind a wall?) if it has not specifically been trained with those types of views.

Similarly, Reinforcement Learning, a technique used to train AI systems to do things like play video games at (or above) human levels, is a slow learner. It takes 83 hours of real-time play for the RL systems to achieve a level a human player can achieve in 15 minutes.

Two basic algorithms used in AI are (1) supervised learning and (2) unsupervised learning. Supervised learning is an algorithm trained by showing it an image (or training example) along with the desired response. “Hello computer. This image is a car. This next image is a bird.” This goes on for millions of images (The ImageNet dataset, used in a lot of benchmark tests, has over 15 million images, and often a subset of over 1 million images is used for training). The training is also repeated over that same set many times (many “epochs”). On the other hand, unsupervised learning tries to make sense of the data without any human “supervised” advice. An example is a clustering algorithm that tries to group items into clusters, or groups, so that items within each group are similar to each other in some way.

Prof. LeCun’s suggestion is that unsupervised learning, or what he calls self-supervised learning, might provide a better approach. He said “Prediction is the essence of intelligence.” We will see whether computers will be able to generate predictions from a just a few examples.


  1. Karen Hao, Technology Review, The AI technique that could imbue machines with the ability to reason
  2. Yann LeCun, The Power and Limits of Deep Learning

Launching The MIT Institute for Data, Systems, and Society


The MIT Institute for Data, Systems, and Society held it’s Launch event Sept 22-23 to showcase it’s first year in operation.

IDDS works at addressing societal problems by focusing on the intersection of statistics, data science, information systems, and social sciences.

After introductions by MIT President Rafael Reif and IDDS Director Prof. Munther Dahleh, the seminar started with some great talks on the Future of Voting, including remarks by Nate Silver of, who gave some behind the scenes glimpses into the polling numbers and methodology for the upcoming Presidential election.

Exploring Data Sculptures at the MIT Museum


This past weekend, budding data scientists got to try their hand at communicating data through story telling.  Less bar charts, more paper and glue.  It’s easy to use Excel to create a graph, but is there a better way to grab reader’s attention?

Rahul Bhargava, a Research Scientist at the MIT Center for Civic Media, tried to get people thinking about using stories to convey numerical data. Visitors were encouraged to do quick mock-ups of one of three topics.  For example, a one-pager gave some statistics showing the rapid rise in the cost of higher education.  The images above were some of the results.

The workshop took place at the Idea Hub at The MIT Museum in Cambridge, Massachusetts.  The Idea Hub hosts a different topic each weekend day.