It seems to me it is just multi-task learning with online training, and with examples shuffled at every iteration. BTW, they mentioned that labels are shuffled from dataset-to-dataset. Is this the same as shuffling examples?

You've probably heard about OpenAI -- a new nonprofit to focus on artificial intelligence research that is good for humanity. So here we have a case of knowledgeable people recognizing a threat and deciding that the way forward is to create a nonprofit organization. This moment has some historical precedent. In 1955, Albert Einstein, Bertrand Russell and several other scientists got together and issued a manifesto about the dangers of nuclear technologies. It would launch decades of Pugwash conferences and the anti-nuclear movement. The founders of OpenAI also issued a manifesto.

DeepMind, an artificial intelligence company owned by Google, has attempted to justify why it needs access to millions of NHS patient records for a kidney monitoring app, after a new investigation from New Scientist questioned whether an ethical approval process should have been obtained first. The AI research lab, acquired by Google in 2014 for around 400 million, signed a data-sharing agreement with the Royal Free London NHS Foundation Trust on 29 September 2015. The agreement gives Google DeepMind access to the names, addresses, and medical conditions of the 1.6 million patients that are treated at Barnet, Chase Farm, and the Royal Free hospitals each year, as well as data on all patients treated by the Trust in the past five years. This week, New Scientist questioned why Google DeepMind needs access to so much data on so many people, including those who have never experienced kidney problems, for the app, which is called Streams. Streams -- used by Royal Free clinicians in three separate trials since December 2015 -- is designed to detect acute kidney injury (AKI), a condition that kills more than 1,000 people a month.

Gym-TORCS is the reinforcement learning (RL) environment in TORCS domain with OpenAI-gym-like interface. TORCS is the open-rource realistic car racing simulator recently used as RL benchmark task in several AI studies. Gym-TORCS is the python wrapper of TORCS for RL experiment with the simple interface (similar, but not fully) compatible with OpenAI-gym environments. The current implementaion is for only the single-track race in practie mode. If you want to use multiple tracks or other racing mode (quick race etc.), you may need to modify the environment, "autostart.sh" or the race configuration file using GUI of TORCS.

Three weeks ago, New Scientist revealed that Google's artificial intelligence company DeepMind has access to the identifiable personal medical information of millions of UK patients through a data-sharing agreement with the Royal Free London NHS Foundation Trust. Now, a New Scientist investigation has found that Google DeepMind deployed a medical app called Streams for monitoring kidney conditions without first contacting the relevant regulatory authority. Our investigation also asks whether an ethical approval process that covers this kind of data transfer should have been obtained, and raises questions about the basis under which Royal Free is sharing data with Google DeepMind. DeepMind's partnership with the Royal Free provides it with fully identifiable information – including names, addresses and details of medical conditions – for the 1.6 million patients treated at Barnet, Chase Farm and the Royal Free each year. It also includes complete data on all patients treated by the trust in the past five years.

"Because of AI's surprising history, it's hard to predict when human-level AI might come within reach," the group, OpenAI, said in a statement on their website. "When it does, it'll be important to have a leading research institution which can prioritise a good outcome for all over its own self-interest." The project is a non-profit effort and their main objective is to save humanity, not generate income. "OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return," the OpenAI website explains.

Congratulations are in order for the folks at Google Deepmind (https://deepmind.com) who have mastered Go (https://deepmind.com/alpha-go.html). However, some of the discussion around this seems like giddy overstatement. Wired says, "machines have conquered the last games" (http://bit.ly/200O5zG) The truth is nowhere close. For Go itself, it has been well known for a decade that Monte Carlo tree search (MCTS, http://bit.ly/1YbLm4M; that is, valuation by assuming randomized playout) is unusually effective in Go.

OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. You can use it from Python code, and soon from other languages. To get started, you'll need to have Python 2.7 or Python 3.5. You can later run pip install -e .[all] to do a full install (this requires cmake and a recent pip version).

EMR and other software systems are a partial solution to the underlying data problem, they help structure data in a way that is machine readable and more reproducible. It still doesn't fix it, because most medical data is interpreted rather than explicit. Two doctors will diagnose different conditions, try different treatments, report different findings. Two examples of the same disease behave stochastically but with tens or hundreds of hidden confounders. There isn't a 1-0 training signal, it is a smeared probability distribution that overlaps with other distributions.

The machine learning community has coalesced around Google's TensorFlow library. Interestingly, one major holdout was DeepMind, which did most of its research on the Torch7 library. Then, late last month, DeepMind announced it was moving to TensorFlow as well -- it was already using it for portions of AlphaGo. Why does any of this matter? Well, with standardization comes the opportunity for optimization, and Google has gone wild with optimization in this case.