Collaborating Authors


The Fight Over Which Uses of AI Europe Should Outlaw


The system, called iBorderCtrl, analyzed facial movements to attempt to spot signs a person was lying to a border agent. The trial was propelled by nearly $5 million in European Union research funding, and almost 20 years of at Manchester Metropolitan University, in the UK. Polygraphs and other technologies built to detect lies from physical attributes have been widely declared unreliable by psychologists. Soon, errors were reported from iBorderCtrl, too. Media reports indicated that its [lie-prediction algorithm didn't and the project's own website that the technology "may imply risks for fundamental human rights."

Tiny nanoturbine is an autonomous machine smaller than most bacteria

New Scientist

A tiny turbine made from DNA looks like a windmill and is hundreds of times smaller than most bacteria. It rotates when immersed in salty water and could be used as a molecular machine for speeding up chemical reactions or transporting particles inside cells. Cees Dekker at Delft University of Technology in the Netherlands and his colleagues created the turbine after being inspired by a rotating enzyme that helps catalyse energy-storing molecules in our cells.

Are babies the key to the next generation of artificial intelligence?


Babies can help unlock the next generation of artificial intelligence (AI), according to Trinity neuroscientists and colleagues who have just published new guiding principles for improving AI. The research, published today in the journal Nature Machine Intelligence, examines the neuroscience and psychology of infant learning and distills three principles to guide the next generation of AI, which will help overcome the most pressing limitations of machine learning. Dr. Lorijn Zaadnoordijk, Marie Sklodowska-Curie Research Fellow at Trinity College explained: "Artificial intelligence (AI) has made tremendous progress in the last decade, giving us smart speakers, autopilots in cars, ever-smarter apps, and enhanced medical diagnosis. These exciting developments in AI have been achieved thanks to machine learning which uses enormous datasets to train artificial neural network models. "However, progress is stalling in many areas because the datasets that machines learn from must be painstakingly curated by humans.

Focus on machine learning models in medical imaging – Physics World


Join the audience for an AI in Medical Physics Week live webinar at 3 p.m. BST on 23 June 2022 based on IOP Publishing's special issue, Focus on Machine Learning Models in Medical Imaging Want to take part in this webinar? An overview will be given of the role of artificial intelligence (AI) in automatic delineation (contouring) of organs in preclinical cancer research models. It will be shown how AI can increase efficiency in preclinical research. Speaker: Frank Verhaegen is head of radiotherapy physics research at Maastro Clinic, and also professor at the University of Maastricht, both located in the Netherlands. He is also a co-founder of the company SmART Scientific Solutions BV, which develops research software for preclinical cancer research.

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The SIOP Foundation has awarded the 2022 Visionary Grant to an international and interdisciplinary team that plans to advance the current understanding of human–artificial intelligence (AI) teamwork and the role of trust in that collaboration. The winning proposal, "We Are In This Together: When an AI Agent Becomes Your Teammate," was submitted by the team of Eleni Georganta, Technical University of Munich; Anna-Sophie Ulfert, Eindhoven University of Technology; Myrthe Tielman, Delft University of Technology; and Tal Oron-Gilad and Shanee Honig, both of Ben-Gurion University. The team will use the $100,000 prize to explore humans and AI working as teammates. "To unlock the potential of these new teams, trust will be the key," Ulfert said. "But what is the meaning of trust in teams consisting of human and AI teammates?"

Forthcoming machine learning and AI seminars: June 2022 edition


This post contains a list of the AI-related seminars that are scheduled to take place between 13 June 2022 and 31 July 2022. All events detailed here are free and open for anyone to attend virtually. Learning to predict complex outputs: a kernel view Speaker: Florence d'Alché-Buc Organised by: London School of Economics and Political Science Register here. Title to be confirmed Speaker: Miguel Rodrigues (University College London) Organised by: One World Signal Processing To sign up, subscribe to the mailing list here. Title to be confirmed Speaker: Maurice Weiler (University of Amsterdam) Organised by: UCL ELLIS Zoom link is here.

Apple tweaks third-party dating app payment rules to comply with Dutch regulator's order


Apple has announced a handful of changes to its rules related to dating app payments in order to comply with orders from the Netherlands Authority for Consumers and Markets (ACM). If you'll recall, the regulator had ordered the tech giant to allow third-party payments in locally available dating apps by January this year. A Reuters report from March said the company had yet to adhere to the orders in a way that truly complies with what the regulator wanted, though -- until now, that is. In its announcement, Apple said it has made adjustments to the user interface for third-party payments. As part of its efforts to comply with the ACM, it started showing a warning whenever someone tries to pay with a third-party payment option, warning them that they'll have to contact the developer for a refund.

Theory suggests quantum computers should be exponentially faster on some learning tasks than classical machines


A team of researchers affiliated with multiple institutions in the U.S., including Google Quantum AI, and a colleague in Australia, has developed a theory suggesting that quantum computers should be exponentially faster on some learning tasks than classical machines. In their paper published in the journal Science, the group describes their theory and results when tested on Google's Sycamore quantum computer. Vedran Dunjko with Leiden University City has published a Perspective piece in the same journal issue outlining the idea behind combining quantum computing with machine learning to provide a new level of computer-based learning systems. Machine learning is a system by which computers trained with datasets make informed guesses about new data. And quantum computing involves using sub-atomic particles to represent qubits as a means for conducting applications many times faster than is possible with classical computers.

Machine Learning Using TensorFlow Cookbook: Create powerful machine learning algorithms with TensorFlow: Audevart, Alexia, Banachewicz, Konrad, Massaron, Luca: 9781800208865: Books:


Konrad is a data science manager with experience stretching longer than he likes to ponder on. He holds a PhD in statistics from Vrije Universiteit Amsterdam, where he focused on problems of extreme dependency modeling in credit risk. He slowly moved from classic statistics towards machine learning and into the business applications world. Konrad worked in a variety of financial institutions on an array of data problems and visited all the stages of a data product cycle: from translating: business requirements ("what do they really need"), through data acquisition ("spreadsheets and flat files? He currently leads a central data science team at Adevinta.

GitHub - jboynyc/textnets: Text analysis with networks.


This provides novel possibilities for the visualization and analysis of texts. Network of U.S. Senators and words used in their official statements following the acquittal vote in the 2020 Senate impeachment trial (source). Initially begun as a Python implementation of Chris Bail's textnets package for R, textnets now comprises several unique features for term extraction and weighing, visualization, and analysis. It uses the Leiden algorithm for community detection, which is able to perform community detection on the bipartite (word–group) network. That means that you can use textnets to analyze and visualize your data in Jupyter notebooks!