Hosted by Eleanor Drage and Kerry McInerney, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. In this episode, Eleanor talks to Alexander Thomas, a filmmaker and academic who leads the BA in Media Production at the University of East London. They discuss his new book about transhumanism, a philosophical movement that aims to improve human capabilities through technology and whose followers includes Jeff Bezos, Elon Musk, Larry Page, and also apparently the DJ Steve Aoki. Alex is himself one of the foremost commentators on transhumanism. He explores transhumanist fantasies about the future of the human, is obsessed with the extremes of possibility: they either think that AI will bring us radical abundance or total extinction.

Linsker has reported the development of centre---surround receptive fields and oriented receptive fields in simulations of a Hebb-type equation in a linear network. The dynamics of the learning rule are analysed in terms of the eigenvectors of the covariance matrix of cell activities. Analytic and computational results for Linsker's covariance matrices, and some general theorems, lead to an expla(cid:173) nation of the emergence of centre---surround and certain oriented structures. Linsker [Linsker, 1986, Linsker, 1988] has studied by simulation the evolution of weight vectors under a Hebb-type teacherless learning rule in a feed-forward linear network. The equation for the evolution of the weight vector w of a single neuron, derived by ensemble averaging the Hebbian rule over the statistics of the input patterns, is:!

Sarvapali (Gopal) Ramchurn is a Professor of Artificial Intelligence, Turing Fellow, and Fellow of the Institution of Engineering and Technology. He is the Director of the UKRI Trustworthy Autonomous Systems hub and Co-Director of the Shell-Southampton Centre for Maritime Futures. He is also a Co-CEO of Empati Ltd, an AI startup working on decentralised green hydrogen technologies. His research is about the design of Responsible Artificial Intelligence for socio-technical applications including energy systems and disaster management. Ferdinando Rodriguez y Baena is Professor of Medical Robotics in the Department of Mechanical Engineering at Imperial College, where he leads the Mechatronics in Medicine Laboratory and the Applied Mechanics Division. He has been the Engineering Co-Director of the Hamlyn Centre, which is part of the Institute of Global Health Innovation, since July 2020.

An Artificial Intelligence Incubation Hub (AIIH), set up jointly by the Army and Bharat Electronics Limited (BEL) as part of a pact inked earlier this year, will be identifying artificial intelligence projects that may be critical for the Army and coordinate the evaluation of trials in those cases, News18 has learnt. As per defence sources, the AIIH will also be tasked with identifying partners from the industry, academia or start-ups to check the feasibility of ideas related to AI projects for implementation and will be responsible for hiring of resources and necessary hardware for validation of suggested AI projects for the force. Based on BEL's research and development expertise and areas where the Army could use AI, the new hub will be working in major domain areas of machine learning, auto-platforms, AI-based swarm technology, robotics, big data analytics, image processing as well as cyber security and AI-based response mechanism. In March this year, the Army and BEL had inked a pact to collaborate in AI for defence applications. The step was taken after the defence ministry had pushed for fast-tracking incorporation of the new tech for defence applications, as part of which the defence services were asked to collaborate with defence PSUs.

Hosted by Eleanor Drage and Kerry Mackereth, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. In this episode, the historian of science Lorraine Daston explains why science has long been allergic to emotion, which is seen to be the enemy of truth. Instead, objective reason is science's virtue. She explores moments where it's very difficult for scientists not to get personally involved, like when you're working on your pet hypothesis or theory, which might lead you to select data that confirms your hypothesis, or when you're confronted with some anomalies in your dataset that threaten a beautiful and otherwise perfect theory. But Lorraine also reminds us that the desire for objectivity can itself be an emotion, as it was when Victorian scientists expressed their heroic masculine self-restraint.

Artificial intelligence (AI) and data science (DS) centers are becoming ubiquitous in academic institutions around the globe. These centers serve to focus research efforts and bring together large teams to address important problems. AI centers in more mature research ecosystems tend to be multi-institutional, such as the Alan Turing Institute in the U.K. with 13 academic partners12 and Mila in Montreal with four academic partners and numerous industry partners.8 Often such centers are also focused on a specific theme, such as the 18 AI institutes funded by NSF.10 In contrast, the centers in India tend to be contained in only one institute--this facilitates the institute to identify AI/DS as a growth area and an area of interest to the Institute.

This prestigious event took place in Lisbon and was hosted by the Champalimaud Foundation at its world class facilities. The Champalimaud Foundation has the world's first pancreatic cancer research and treatment centre. The Botton-Champalimaud Pancreatic Cancer Centre has the first unit in the world designed and built specifically with the aim of researching and treating pancreatic cancer. This Centre is the result of a partnership between the Champalimaud Foundation and Maurício and Carlotta Botton, who contributed 50 million euros to its construction. Over the past 20 years, the number of people with pancreatic cancer has increased exponentially throughout most of the world, especially in the most industrialised countries.

Bringing together public, private and third sector organisations together with computer scientists, health scientists, engineers and social scientists in a shared physical and virtual space, creating the conditions for innovation.

A common artificial intelligence model known as deep convolutional neural networks (DCNNs) does not see objects the way humans do – and that could be dangerous in real-world AI applications. That is the conclusion of Professor James Elder, co-author of a York University study published recently, which finds that AI cannot use something called "configural shape perception", which is standard in human perception for recognising shapes. Published in the Cell Press journal iScience, the paper Deep learning models fail to capture the configural nature of human shape perception is a collaborative study by Elder, who holds the York research chair in human and computer vision and is co-director of York's Centre for AI & Society, co-authored with assistant psychology professor Nicholas Baker at Loyola College in Chicago, a former postdoctoral fellow at York. The study employed novel visual stimuli called "Frankensteins" to explore how the human brain and DCNNs process holistic, configural object properties. "Frankensteins are simply objects that have been taken apart and put back together the wrong way around," says Elder. "As a result, they have all the right local features, but in the wrong places."

Every stage of life is impacted by mental health diseases, which range from dementia to schizophrenia. The World Health Organization estimates that one in eight people worldwide suffer from a mental condition and that poor mental health costs the world economy $1 trillion in lost productivity each year. Effective treatment for mental health illnesses depends on an early and precise diagnosis, just like it does for many illnesses. Nevertheless, unlike, for instance, a heart attack, which can be detected through tests that detect particular signs or "biomarkers" linked with the disorder, no clear-cut biomarkers for mental health issues have yet been identified. This is due to the intricate interplay of factors that causes mental diseases, such as heredity, biological predisposition, and unfavorable living circumstances.