Neural Information Processing Systems http://nips.cc/

Dayan's SR [3] is well-suited for transfer learning in settings with fixed dynamics, as the decomposition ofthevaluefunction intorepresentations ofexpected outcomes (future stateoccupancies) andcorresponding rewards allowsustoquickly recompute values under newrewardsettings.

However, it is not clear how such representations might be learned and computed in partially observed, noisy environments. Here, we introduce a neurally plausible model using distributional successor features, which builds on the distributed distributional code for the representation and computation of uncertainty, and which allows for efficient value function computation in partially observed environments via the successor representation.

Reinforcement learning is a paradigm through which an agent interacts with its environment by trying out different actions at different states and observing the outcome. Each of these interactions can change the state of the environment, and can also provide rewards to the agent. The goal of the agent is to learn the value of performing each action on each state. By value, we mean the biggest amount of rewards that is possible for the agent to obtain after performing that action in that state. If the agent achieves this goal, it can then act optimally on its environment by choosing, at every state, the action that has the biggest value.

The wake-sleep algorithm (Hinton, Dayan, Frey and Neal 1995) is a rel(cid:173) atively efficient method of fitting a multilayer stochastic generative model to high-dimensional data. In addition to the top-down connec(cid:173) tions in the generative model, it makes use of bottom-up connections for approximating the probability distribution over the hidden units given the data, and it trains these bottom-up connections using a simple delta rule. We use a variety of synthetic and real data sets to compare the per(cid:173) formance of the wake-sleep algorithm with Monte Carlo and mean field methods for fitting the same generative model and also compare it with other models that are less powerful but easier to fit.

Decision making lies at the very heart of many psychiatric diseases. It is also a central theoretical concern in a wide variety of fields and has undergone detailed, in-depth, analyses. We take as an example Major Depressive Disorder (MDD), applying insights from a Bayesian reinforcement learning framework. We focus on anhedonia and helplessness. Helplessness--a core element in the conceptual- izations of MDD that has lead to major advances in its treatment, pharmacolog- ical and neurobiological understanding--is formalized as a simple prior over the outcome entropy of actions in uncertain environments.

The research was sparked by the pandemic and set out to build an AI tool to predict how much extra oxygen a Covid-19 patient may need in the first days of hospital care, using data from across four continents. The technique, known as federated learning, used an algorithm to analyse chest x-rays and electronic health data from hospital patients with Covid symptoms. To maintain strict patient confidentiality, the patient data was fully anonymised and an algorithm was sent to each hospital so no data was shared or left its location. Once the algorithm had'learned' from the data, the analysis was brought together to build an AI tool which could predict the oxygen needs of hospital Covid patients anywhere in the world. Published today in Nature Medicine, the study dubbed EXAM (for EMR CXR AI Model), is one of the largest, most diverse clinical federated learning studies to date.

Addenbrooke's Hospital in Cambridge along with 20 other hospitals from across the world and healthcare technology leader, NVIDIA, have used artificial intelligence (AI) to predict Covid patients' oxygen needs on a global scale. The research was sparked by the pandemic and set out to build an AI tool to predict how much extra oxygen a Covid-19 patient may need in the first days of hospital care, using data from across four continents. The technique, known as federated learning, used an algorithm to analyse chest x-rays and electronic health data from hospital patients with Covid symptoms. To maintain strict patient confidentiality, the patient data was fully anonymised and an algorithm was sent to each hospital so no data was shared or left its location. Once the algorithm had'learned' from the data, the analysis was brought together to build an AI tool which could predict the oxygen needs of hospital Covid patients anywhere in the world.

Addenbrooke's Hospital in Cambridge and 20 other hospitals from across the world and healthcare technology leader NVIDIA have used artificial intelligence (AI) to predict COVID patients' oxygen needs on a global scale. The research was sparked by the pandemic and set out to build an AI tool to predict how much extra oxygen a COVID-19 patient might need in the first days of hospital care, using data from across four continents. The technique, known as federated learning, used an algorithm to analyze chest X-rays and electronic health data from hospital patients with COVID symptoms. To maintain strict patient confidentiality, the patient data was fully anonymized and an algorithm was sent to each hospital so no data was shared or left its location. Once the algorithm had "learned" from the data, the analysis was brought together to build an AI tool which could predict the oxygen needs of hospital COVID patients anywhere in the world.

Unpicking the secrets of the brain's reward system has earned three neuroscientists a reward of their own. Wolfram Schultz, Peter Dayan, and Ray Dolan have today been awarded the €1 million Brain Prize by Denmark's Lundbeck Foundation. The prize recognises researchers who have made vital contributions to understanding how our brains work. Together, their research has revealed how reward systems in the brain that involve the signalling chemical dopamine influence our behaviour and survival, playing important roles in decision-making, gambling, drug addiction, psychopathic tendencies, and schizophrenia. "This is the biological process that makes us want to buy a bigger car or house, or be promoted at work," says Wolfram Schultz, at the University of Cambridge.