ILLMO: A new platform for interactive statistics

Jean-Bernard Martens, a Full Professor on Visual Interaction with the Department of Industrial Design of the Eindhoven University of Technology, Netherlands, has developed ILLMO (Interactive Log Likelihood MOdeling), a statistical modelling tool that provides an interactive environment offering an intuitive and interactive approach to statistics. Scientists and researchers, including those who are not specialists in statistics, can access modern statistical […]

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xAI-EWS — an explainable AI model predicting acute critical illness

Simon Meyer Lauritsen developed an explainable AI early warning score system based on electronic health records.

Early clinical predictions of acute critical illness have a vital influence on patient outcomes. For clinical medicine to benefit from the higher predictive power of Artificial Intelligence, however, explainable and transparent systems are essential. Simon Meyer Lauritsen and his collaborators at Enversion, Aarhus University, and Regional Hospital Horsens in Denmark have developed xAI-EWS – an explainable AI early warning score […]

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Choice‑making and choose‑ables: Making decision agents more choosy

choice-making for AI

Dr Lorraine Dodd, from Cranfield University, examines the Artificial Intelligence approach to modelling a decision agent’s choice-making. She explores the factors affecting an adaptive agent’s freedom of decision-making and investigates factors that can shape, extend, limit or re-focus an agent’s potential in terms of their ways forward. Dr Dodd also discusses the higher-order concept of choice-making that she refers to […]

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Optimising Particle Accelerators with Adaptive Machine Learning

Alexander Scheinker introduces new techniques based on machine learning.

Machine learning has become a staple of research into many of today’s most cutting-edge technologies. Until now, however, it has not been widely considered as a useful tool for online optimisation of the performance of particle accelerators. Through his research, Dr Alexander Scheinker at the Los Alamos National Laboratory in New Mexico, USA, introduces new techniques based on machine learning, […]

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Unsupervised feature extraction applied to bioinformatics

Unsupervised feature extraction applied to bioinformatics

In his new book, Professor Y-h Taguchi, from Chuo University, Tokyo, Japan, takes two classical mathematical techniques, principal component analysis and tensor decomposition, and demonstrates how they can be used to perform feature selection in his cutting-edge research. Both unsupervised learning methods are applied to carry out feature extraction in a wide range of ‘large p small n’ problems. This […]

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Nesta: Collective intelligence, people-powered results and experimentation

People are participating in the 100 Day Challenge.

The world is changing faster than ever, and solutions to the challenges faced by governments, the health and public sectors and educational institutions need to be innovative and inventive to match. At the Nesta foundation, experts are using research-based evidence to come up with frameworks and programmes to support those people trying to solve some of Europe’s trickiest public conundrums. […]

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Artificial intelligence allows quick and easy diagnosis of pancreatic cancer

Artificial intelligence allows quick and easy diagnosis of pancreatic cancer

The pancreas is an important organ that regulates food digestion and blood sugar levels. Cancers commonly arise in the pancreas, leading to death for over 50,000 patients per year in the U.S. alone. This is due to a lack of effective therapies and a low survival rate for this disease since symptoms arise only in the late stages of disease. […]

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Crisis management: Using Artificial Intelligence to help save lives

Climate change, and humanitarian issues such as the movement of refugees can create challenges for crisis management and emergency response, particularly in developing countries. Dr Mayank Kejriwal, based at the University of Southern California is passionate about applying Artificial Intelligence (AI) to social problems. He is working with colleagues to develop tools that extract critical data sent via social media […]

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Building artificial intelligence for social good

A science research article about Professor Tran-Thanh work developing AI algorithms aimed at tackling societal problems and his work focuses on increasing security for AI.

Artificial intelligence is one of the most disruptive technologies nowadays and as such is considered both an opportunity and a threat to society. On the one hand, it could make us vulnerable as we trust machines to make more and more decisions that so far have been reserved for humans. On the other hand, it has the potential to revolutionise […]

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Computational methods of researching cancer treatments

In this research article: Dr Haibe-Kains’ research focuses on the computational integration of high dimensional molecular data to analyse multiple facets of carcinogenesis.

Artificial Intelligence (AI) and machine learning algorithms have the potential to bring substantial advances in the fields of research exploring complex diseases and trying to identify effective treatments. Dr Benjamin Haibe-Kains, working at The Princess Margaret Cancer Centre in Toronto, has spent over a decade developing machine learning tools and databases that could help scientists gain a better understanding of […]

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