Formetrics, weuse Mean Integrated Square Error (MISE), Dosage Policy Error (DPE) and Policy Error (PE) [19, 25].

Machine learning is now being used to make crucial decisions about people's lives.

We provide theoretical results which prove that average pooling corresponds to projection within the space of square-integrable functions and show that U-Nets with average pooling implicitly learn a Haar wavelet basis representation of the data.

Exploring the socio - technical challenges confronting the adoption of AI in organisational settings is something that has so far been largely absent from the related literature . In particular, r esearch into requirements for trustworthy AI typically overlooks how people deal with the problems of trust in the tools that they use as part of their everyday work practices . This article presents some findings from an ongoing ethnographic study of how current tools are used in air traffic control work and what it r eveals about requirements for trustworthy AI in air traffic control and other safety - critical application domains.

A new AI weather prediction system, developed by a team of researchers from the University of Cambridge, can deliver accurate forecasts which use less computing power than current AI and physics-based forecasting systems. The system, Aardvark Weather, has been supported by the Alan Turing Institute, Microsoft Research and the European Centre for Medium Range Weather Forecasts. It provides a blueprint for a new approach to weather forecasting with the potential to improve current practices. The results are reported in the journal Nature. "Aardvark reimagines current weather prediction methods offering the potential to make weather forecasts faster, cheaper, more flexible and more accurate than ever before, helping to transform weather prediction in both developed and developing countries," said Professor Richard Turner from Cambridge's Department of Engineering, who led the research.

A single researcher with a desktop computer will be able to deliver accurate weather forecasts using a new AI weather prediction approach that is tens of times faster and uses thousands of times less computing power than conventional systems. Weather forecasts are currently generated through a complex set of stages, each taking several hours to run on bespoke supercomputers, requiring large teams of experts to develop, maintain and deploy them. Aardvark Weather provides a blueprint to replace the entire process by training an AI on raw data from weather stations, satellites, weather balloons, ships and planes from around the world to enable it to make predictions. This offers the potential for vast improvements in forecast speed, accuracy and cost, according to research published on Thursday in Nature from the University of Cambridge, the Alan Turing Institute, Microsoft Research and the European Centre for Medium-Range Weather Forecasts (ECMWF). Richard Turner, a professor of machine learning at the University of Cambridge, said the approach could be used to quickly provide bespoke forecasts for specific industries or locations, for example predicting temperatures for African agriculture or wind speeds for a renewable energy company in Europe.

Machine learning is now being used to make crucial decisions about people's lives. For nearly all of these decisions there is a risk that individuals of a certain race, gender, sexual orientation, or any other subpopulation are unfairly discriminated against. Our recent method has demonstrated how to use techniques from counterfactual inference to make predictions fair across different subpopulations. This method requires that one provides the causal model that generated the data at hand. In general, validating all causal implications of the model is not possible without further assumptions.