“AlphaStar is the first agent to achieve Grandmaster level in StarCraft II, and the first to reach the highest league of human players in a widespread professional esport without simplification of the game. Like StarCraft, real-world domains such as personal assistants, self-driving cars, or robotics require real-time decisions, over combinatorial or structured action spaces, given imperfectly observed information. Furthermore, similar to StarCraft, many applications have complex strategy spaces that contain cycles or hard exploration landscapes, and agents may encounter unexpected strategies or complex edge cases when deployed in the real world. The success of AlphaStar in StarCraft II suggests that general-purpose machine learning algorithms may have a substantial effect on complex real-world problems.”
“Google has been accused of breaking promises to patients, after the company announced it would be moving a healthcare-focused subsidiary, DeepMind Health, into the main arm of the organisation.The restructure, critics argue, breaks a pledge DeepMind made when it started working with the NHS that “data will never be connected to Google accounts or services”. The change has also resulted in the dismantling of an independent review board, created to oversee the company’s work with the healthcare sector, with Google arguing that the board was too focused on Britain to provide effective oversight for a newly global body.”
“We don’t just want this to be an academically interesting result – we want it to be used in real treatment. So our paper also takes on one of the key barriers for AI in clinical practice: the “black box” problem. For most AI systems, it’s very hard to understand exactly why they make a recommendation. That’s a huge issue for clinicians and patients who need to understand the system’s reasoning, not just its output – the why as well as the what.
Our system takes a novel approach to this problem, combining two different neural networks with an easily interpretable representation between them. The first neural network, known as the segmentation network, analyses the OCT scan to provide a map of the different types of eye tissue and the features of disease it sees, such as haemorrhages, lesions, irregular fluid or other symptoms of eye disease. This map allows eyecare professionals to gain insight into the system’s “thinking.” The second network, known as the classification network, analyses this map to present clinicians with diagnoses and a referral recommendation. Crucially, the network expresses this recommendation as a percentage, allowing clinicians to assess the system’s confidence in its analysis”.