On July 20, I was contacted by MIT Technology Review to comment about OpenAI’s efforts to train robots to do tasks in the home using reinforcement learning. I am posting the questions and answers below, the corresponding article is here.

On June 23, the UK will decide whether to remain in the EU or not (“Brexit”). There are arguments going back and forth whether being part of the EU is good thing or not. Many of these arguments are based on predictions and more-or-less justified assumptions what could happen (in a positive or negative way) if Brexit happens. Many numbers are mentioned in the campaigns (e.g., how much the economy would shrink, the pound would fall, the house prices go down), but these numbers are usually (educated) guesses.

However, a non-speculative but re-appearing number is **350 Million**. This is the number the Leave Campaign propagate as the amount of money the UK will save by getting out of the EU by simply not paying the contribution to the EU budget (“membership fee”). Per week, by the way.

350 Million or 350,000,000 is such an unimaginable and unreal number that I want to look at it a bit more closely to understand how they get to this number, what it means for the UK taxpayer and how it fits in the general context of other taxes we pay. I will try to relate this number to units that make more sense to most people than the number of zeros attached to 35.

On the way back from NIPS 2015, we got the idea of organizing an ICML workshop on data-efficient machine learning. Data-efficient machine learning is something that is currently somewhat out of the focus of the deeply hyped machine learning community, but there are so many applications where you can simply not collect enough data, e.g., personalized healthcare.

The workshop will be happening at ICML this year, and we are quite excited about the quality of the papers submitted, the invited speakers, and the breadth of topics that fall into the category of data-efficient machine learning.

## AlphaGo vs Lee Sedol – The new AI Challenge

Posted: 2016-03-12 in researchTags: Deep Learning, Reinforcement Learning

On March 4, I was contacted by the Xinhua News Agency to comment on the upcoming Go match between Google DeepMind’s AlphaGo algorithm and the top-Go player Lee Sedol. I am posting the questions and answers below:

Yoshua Bengio and Yann LeCun were giving this tutorial as a tandem talk.

The tutorial started off by looking at what we need in Machine Learning and AI in general. Two key points were identified:

- Distributed representation
- Compositional models

## NIPS 2015 – Deep RL Workshop

Posted: 2015-12-13 in conferences, researchTags: Deep Learning, Reinforcement Learning

This is a brief summary of the first part of the Deep RL workshop at NIPS 2015. I couldn’t get a seat for the second half…

## NIPS 2015 – Bayesian Optimization Workshop

Posted: 2015-12-13 in conferences, researchTags: Bayesian Optimization, Gaussian processes

I attended the Bayesian Optimization workshop at NIPS 2015, and the following summarizes what was going on in the workshop from my perspective. This post primarily serves my self-interest in not losing these notes. But it may be useful for others as well.

**Organizers:**

- Nando de Freitas (Oxford)
- Ryan P. Adams (Harvard University)
- Bobak Shahriari (University of British Columbia)
- Roberto Calandra (TU Darmstadt)
- Amar Shah (University of Cambridge)

The workshop was effectively run by Bobak Shahriari and Roberto Calandra. In the beginning of the workshop, **Bobak Shahriari** was giving a brief introduction to Bayesian Optimization (BO), motivating the entire setting of data-efficient global black-box optimization and the gap that this workshop will address. Read the rest of this entry »