Step by step, condition by condition, AI systems are slowly learning to diagnose disease as well as any human doctor, and they could soon be working in a hospital near you. The latest example is from London, where researchers from Google's DeepMind subsidiary, UCL, and Moorfields Eye Hospital have used deep learning to create software that identifies dozens of common eye diseases from 3D scans and then recommends the patient for treatment. The work is the result of a multiyear collaboration between the three institutions. And while the software is not ready for clinical use, it could be deployed in hospitals in a matter of years. Those involved in the research described is as "ground-breaking."

A new machine-learning system is as good as the best human experts at detecting eye problems and referring patients for treatment, say scientists. The groundbreaking artificial intelligence system, developed by the AI-outfit DeepMind with Moorfields eye hospital NHS foundation trust and University College London, was capable of correctly referring patients with more than 50 different eye diseases for further treatment with 94% accuracy, matching or beating world-leading eye specialists. "The results of this pioneering research with DeepMind are very exciting and demonstrate the potential sight-saving impact AI could have for patients," said Prof Sir Peng Tee Khaw, the director of the NIHR Biomedical Research Centre at Moorfields eye hospital and the UCL Institute of Ophthalmology. The two-stage AI system takes a more human-like and intelligible approach to analysing the highly complex optical coherence tomography (OCT) scans of patient retinas. These are commonly used to triage patients with sight problems into four clinical categories: urgent, semi-urgent, routine and observation only.

DeepMind has successfully developed a system that can analyze retinal scans and spot symptoms of sight-threatening eye diseases. Today, the AI division -- owned by Google's parent company Alphabet -- published "early results" of a research project with the UK's Moorfields Eye Hospital. They show that the company's algorithms can quickly examine optical coherence tomography (OCT) scans and make diagnoses with the same accuracy as human clinicians. In addition, the system can show its workings, allowing eye care professionals to scrutinize the final assessment. At the moment, hospitals and clinics use flesh-and-bone specialists to dissect OCT scans.

"OpenAI Five plays 180 years worth of games against itself every day." Artificial Intelligence vs Humans is a hotly debated topic these days and the gaming arena holds one of the prime witnesses of that. DOTA 2 players have taken on an AI algorithm before in a head-to-head match and have lost. The story seems to continue to date, a recent match between a team of 5 humans vs a mix of AI programs, being a proof of that. The AI programs, developed by OpenAI - an AI research lab founded by Elon Musk and Y Combinator president Sam Altman, won 2 out of 3 matches against the 5 semi-professional humans working together as a team.

U.K.-based Deepmind has created artificial intelligence software that can read lips. An artificial intelligence (AI) program from DeepMind can read lips better than professional lip readers after reviewing thousands of hours of YouTube videos along with transcripts via machine learning. The researchers tested the program on 37 minutes of video it had not previously viewed, and it misidentified only 41% of the words. In comparison, the best previous computer method, which focuses on individual letters instead of phonemes, had a 77% word error rate, while professional lip readers had a 93% error rate in the same test, which lacked context or body language. Columbia University's Hassan Akbari says the AI, if incorporated into a phone, would enable hearing-impaired users to have a "translator" with them wherever they go.

Zero-shot learning transfers knowledge from seen classes to novel unseen classes to reduce human labor of labelling data for building new classifiers. Much effort on zero-shot learning however has focused on the standard multi-class setting, the more challenging multi-label zero-shot problem has received limited attention. In this paper we propose a transfer-aware embedding projection approach to tackle multi-label zero-shot learning. The approach projects the label embedding vectors into a low-dimensional space to induce better inter-label relationships and explicitly facilitate information transfer from seen labels to unseen labels, while simultaneously learning a max-margin multi-label classifier with the projected label embeddings. Auxiliary information can be conveniently incorporated to guide the label embedding projection to further improve label relation structures for zero-shot knowledge transfer. We conduct experiments for zero-shot multi-label image classification. The results demonstrate the efficacy of the proposed approach.

The human team – made up of popular Twitch streamers and former professionals ranked in the 99.95th percentile – hunkered down to play against the bots known as OpenAI Five in San Francisco on Sunday. OpenAI Five smashed its opponents, winning comfortably in two out of three games. It did lose one game, however, after spectators watching the match live and on Twitch were allowed to pick the pool of heroes – the playable characters in the game. Each hero comes with its own strengths and weaknesses and picking a balanced combination is paramount to winning. If you have too many characters for the same role, the other team will steamroll you.

Google battled Facebook to acquire artificial intelligence lab DeepMind in 2014 for a reported $660 million - but not everyone was happy with the move. "How in the world can you build a truly neutral artificial general intelligence under that company?" questions Humayun Sheikh, an early investor who helped London-based DeepMind commercialise the AI technology that Google now owns. Sheikh, who personally donated to DeepMind founder Demis Hassabis during the technology's very early stages, cut ties with the firm when it was sold to Google four years ago. DeepMind software lead Toby Simpson, creator of the "Creatures" game series in the 1990s, left the company. He believed the Google acquisition...

Later this month, the best Dota 2 teams in the world will meet in Vancouver for the biggest tournament of the year, The International. The annual contest consistently boasts the highest prize pool in eSports (it's up to $23.5 million already this year), not to mention the glory that comes with winning the prestigious event. It may not be long, however, before a team of non-human players becomes worthy of such success. This weekend, the all-bot roster of OpenAI Five took on a team of Dota 2 casters and ex-pro players that individually rank amongst some of the best in the world. OpenAI Five won the best-of-three exhibition match convincingly, and the only reason the human team took a game was thanks to a little help from the audience.

OpenAI's machine learning bots have beaten another team of semi-professionals in Dota 2, in their second public match in the traditional five-versus-five settings. You can watch the action on Twitch – complete with commenters typing in SKYNET! The human team – made up of popular Twitch streamers and former professionals ranked in the 99.95th percentile – hunkered down to play against the bots known as OpenAI Five in San Francisco on Sunday. OpenAI Five smashed its opponents, winning comfortably in two out of three games. It did lose one game, however, after spectators watching the match live and on Twitch were allowed to pick the pool of heroes – the playable characters in the game.