What appealed to me the most is that OpenAI is a new, historically unprecedented model for innovation. The existing models include academia, an industry research lab, a startup, and we also have some other singular but still interesting experiments such as Bell Labs, a massive research lab enabled by a government-regulated monopoly. These all have their interesting pros and cons. I felt that OpenAI presented a very interesting experiment in innovation in this sense, it combines elements of things I love (it's an academia/startup hybrid), I felt that there was a lot of potential value that a...

Open AI, a non-profit artificial intelligence research company backed by Elon Musk, launched a toolkit for developing and comparing reinforcement learning algorithms. OpenAI Gym is a suite of environments that include simulated robotic tasks and Atari games as well as a website for people to post their results and share code. OpenAI researcher John Schulman shared some details about his organization, why reinforcement learning is important and how the OpenAI Gym will make it easier for AI researchers to design, iterate and improve their next generation applications.

Google's DeepMind already proved it could beat a Go world champion, and it's AI has even been used to cut the company's energy costs. How about improving the way we treat cancer? DeepMind recently announced a partnership with the Radiotherapy Department at University College London Hospitals NHS Foundation Trust. The department provides world-leading cancer treatment, but there's one area in particular where Google's AI could help speed up the process. When it comes to certain types of cancer in areas like the head and neck doctors need to plan carefully to avoid damaging important organs and body parts.

Working out how to zap a tumor with radiation is a laborious process for physicians. Google's machine-learning division, DeepMind, thinks AI can help ease the burden. When medics apply radiotherapy to a cancer patient, they have to carefully determine which parts of the body should be exposed to radiation in order to kill the tumor while ensuring that as much healthy surrounding tissue as possible is preserved. The process, known as segmentation, requires the doctor to manually draw areas that can and can't be treated onto a 3-D scan of the patient's tumor site. The process is particularly complex for head and neck cancers, in which the tumor often sits immediately next to many important anatomical features.

The release continues, "The purpose of the research collaboration between UCLH and DeepMind is to develop artificial intelligence technology to assist clinicians in the segmentation process so that it can be done more rapidly but just as accurately. Clinicians will remain responsible for deciding radiotherapy treatment plans but it is hoped that the segmentation process could be reduced from up to four hours to around an hour. The research involves anonymised radiotherapy images of up to 700 former head and neck cancer patients who have consented to their data being used for research purposes. Dr Yen-Ching Chang, clinical lead for radiotherapy at UCLH, said: 'This is very exciting research which could revolutionise the way in which we plan radiotherapy treatment. Developing machine learning which can automatically differentiate between cancerous and healthy tissue on radiotherapy scans will assist clinicians in planning radiotherapy treatment. This has the potential to free up clinicians to spend even more time on patient care, education and research, all of which would be to the benefit of our patients and the populations we serve'."

Google's DeepMind is aiming to help cut the time spent identifying key areas to treat, and avoid, in radiotherapy. Google's DeepMind is partnering with the UK's NHS to explore how machine learning could help doctors treat head and neck cancers. DeepMind, the Google subsidiary that beat a human contestant in the notoriously complex game of Go and is helping cut Google's datacenter costs, will be conducting cancer treatment research at the Radiotherapy Department at University College London Hospitals (UCLH) NHS Foundation Trust. While privacy and regulation will slow the pace of adoption, AI will bring some profound changes to healthcare. As the AI-research unit notes in a blog post, radiotherapy involving sensitive parts of the body, such as the mouth and sinuses, requires careful planning prior to treatment to avoid damaging key nerves and organs.

Google DeepMind is launching a project to reduce the time it takes doctors to prepare treatment for head and neck cancers. Alphabet's London-based artificial intelligence division has partnered with the UK's National Health Service and will be conducting the research in coordination with the University College London Hospital. Head and neck cancers are hard to plan treatment for because of their close proximity to important parts of the body. Before any kind of radiation treatment, clinicians will prepare a detailed map of where radiation will be administered on a patient in order to avoid damaging surrounding tissue. DeepMind says planning can take doctors up to four hours for head and neck cancers, and it hopes that by applying machine learning it will be able to automate parts of the process and reduce that planning time down to an hour.

The OpenAI site is centered around an Open Source project and community involving artificial intelligence. The term "Open Source" means that the source code for the project is available for free and can be used by others free of charge. Artificial Intelligence refers to the general aim of intelligent computing, making machines think and learn. The project itself is the creation of a set of tools that are considered to be models of human intelligence. These tools are intended to be integrated into programs or used stand alone for research.

In a recent media release, Sir Peng Tee Kaw, head of Moorfields' ophthalmology research centre, stated: "Our research with DeepMind has the potential to revolutionize the way professionals carry out eye tests and could lead to earlier detection and treatment of common eye diseases such as age-related macular degeneration."

Zero-Shot learning has been shown to be an efficient strategy for domain adaptation. In this context, this paper builds on the recent work of Bucher et al. [1], which proposed an approach to solve Zero-Shot classification problems (ZSC) by introducing a novel metric learning based objective function. This objective function allows to learn an optimal embedding of the attributes jointly with a measure of similarity between images and attributes. This paper extends their approach by proposing several schemes to control the generation of the negative pairs, resulting in a significant improvement of the performance and giving above state-of-the-art results on three challenging ZSC datasets.