A Pessimistic View Of The State Of Deep Learning

William Vorhies provides us a negative (and necessary) look at the current state of Deep Learning solutions:

Reinforcement Learning (RL) is arguably the hottest research area in AI today because it appears RL can be adapted to any problem that has a well-defined reward function.  That encompasses game play, robotics, self-driving cars, and frankly pretty much else in machine learning.

Within RL, the hottest research area is Deep RL which means using a deep neural net as the ‘agent’ in the training.  Deep RL is seen as the form of RL with the most potential to generalize over the largest number of cases and perhaps the closest we’ve yet come to AGI (artificial general intelligence).

Importantly, Deep RL is also the technique used to win at Alpha Go which brought it huge attention.

The problem is, according to Alex Irpan, a researcher on the Google Brain Robotics team that about 70% of the time they just don’t work.

Alex has written a very comprehensive article critiquing the current state of Deep RL, the field with which he engages on a day-to-day basis.  He lays out a whole series of problems and we’ve elected to focus on the three that most clearly illustrate the current state of the problem with notes from his work.

Vorhies is not unduly negative and is optimistic in the medium to long term, but he is right in noting that there is a lot of work yet to do in this field.

Related Posts

Analyzing Customer Churn With Keras And H2O

Shirin Glander has released code pertaining to a forthcoming book chapter: This is code that accompanies a book chapter on customer churn that I have written for the German dpunkt Verlag. The book is in German and will probably appear in February: https://www.dpunkt.de/buecher/13208/9783864906107-data-science.html.The code you find below can be used to recreate all figures and analyses from this […]

Read More

Working With Images In Spark 2.4

Tomas Nykodym and Weichen Xu give us an update on working with images in the most recent version of Apache Spark: An image data source addresses many of these problems by providing the standard representation you can code against and abstracts from the details of a particular image representation.Apache Spark 2.3 provided the ImageSchema.readImages API (see Microsoft’s post […]

Read More


November 2018
« Oct Dec »