"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.

Pure and simple, this is hubris. And, I am sorry to say, it is reflected in how DeepMind has acted in acquiring the NHS medical data: not bothering to ask for people's consent and not following ethics rules and regulations. What these actions communicate is that DeepMind views people's medical histories merely as a bunch of data it wants to feed into a learning algorithm, in the same way as it used the old Go games for training the AlphaGo algorithm. And if a company treats people as pieces in a board game, why would it care about privacy and ethics? Well, that is precisely why we shouldn't give DeepMind and its parent company a free hand in using our private data without proper supervision.

Neuroscientists at DeepMind, a Google-owned AI lab in London, have teamed up with academics at Oxford University and UCL to try and determine how the human brain navigates an underground train network. The group -- whose work was published in the journal Neuron this week -- asked humans to plan a journey in a virtual subway network. Participants were tasked with getting from A to B while MRI scans of their brain were taken. These scans showed which parts of the brain are involved in planning and making decisions. The group, which included Google DeepMind CEO Demis Hassabis, concluded that the brain splits the task of completing a journey into different jobs, with different parts of the brain handling different elements of the task.

The predictive powers of computers will work nicely in cases where reality does not change dramatically. However, it will fail in any case where there are dramatic, unpredictable, changes in the future. The authoritative science journal Nature announced recently that a computer designed by Google's DeepMind defeated a human master in the ancient Chinese board game, "Go." This impressive achievement once again raised the expectations for a predicted future in which computers will have artificial intelligence, with major media outlets worldwide touting this anticipated future. One of the major questions raised in response to DeepMind's achievement is what are the outer limits, if any, of intelligent machines?

An app being made by DeepMind, the Google-owned AI company, working in collaboration with the NHS Royal Free Trust in London and being used to help identify hospital patients who might be at risk of acute kidney disease (AKI) is not currently in use, TechCrunch has learned. The collaboration between the tech giant and a portion of the UK's publicly funded health service has drawn criticism for the breadth of patient data being used to power an app which targets a single medical condition. DeepMind and the Royal Free have also been criticized for not approaching the UK's medicines and healthcare devices regulator, the MHRA, prior to using the Streams app in hospitals. The MHRA is responsible for standards of safety, quality and efficacy for healthcare products, which can include software apps. It has emerged that DeepMind and the Royal Free Trust are now in discussions with the MHRA over whether the Streams app needs to be registered as a medical device.

As a taxi driver, you need to pick up and drop off passengers as fast as possible.