We have actual people being affected by these algorithms. We see things in the news such as algorithms that predict recidivism -- whether someone will re-commit a particular crime -- and set a bail amount or pass that information on to a judge who decides whether or not to set bail. The algorithms used to make these predictions end up relying on correlations with socioeconomic status, or race, or gender. So someone who might have a very similar background to you but differs across race or gender might have a very different outcome because of what the algorithm predicts. Do you think people are generally aware of the degree to which these algorithms are already part of everyday life?

Wageningen University and Research in the Netherlands is digging even deeper into how artificial intelligence can be integrated into greenhouse production. A new research project focuses on whether it might be possible to predict cucumber harvest, and what information artificial intelligence needs to make correct predictions. To answer this, the Business Unit Greenhouse Horticulture at Wageningen University & Research is working on the development of an AI yield prediction model and associated database. A greenhouse is a complex system with several components such as crop, climate, and irrigation set-up. As a result, the plant itself acts as a sensor of its own biological status and its environment.

Washington, DC (November 7, 2019) -- Machine learning, a form of artificial intelligence, may help improve care for patients with kidney failure. The findings come from a study that will be presented at ASN Kidney Week 2019 November 5-November 10 at the Walter E. Washington Convention Center in Washington, DC. For the study, Ollie Fielding (pulseData, in New York) and his colleagues deployed a machine learning model to identify patients at risk of requiring kidney replacement therapy, such as dialysis or kidney transplantation. An electronic health record database of 110,998 patients was used to create a machine learning model to predict progression to kidney failure. The system calculates weekly risk scores for patients, and for those with high risk scores, an alert is sent so that treatment discussions can be made by a multidisciplinary team of clinicians.

With improvements in artificial intelligence technology, a proliferation of robots are now among us, so it's important to remember that humans and machines inevitably have a lot in common. After all, we designed them to do the same things we do and to be capable of learning, just like us. The visceral drive for humans to automate processes using machines is not new. Humans and machines share a long history of working together to continually make improvements in almost every aspect of our lives. AI is another disruptive technology of our time, and just like its predecessors, it will have a profound impact on our existence.

President Xi Jinping wants China to dominate artificial intelligence by 2030. But all it seems the Middle Kingdom's new AI entrepreneurs want to talk about ancient history. Take Kai-Fu Lee, for example. The founder of Face was born in Taiwan and emigrated to America when he was eleven. After earning a Ph.D. from Carnegie Mellon in the 1980s, he worked his way up at Apple, Microsoft, and eventually, Google. In his book AI Superpowers, he likens Chinese entrepreneurs to "gladiators" fighting in a new techwar arena, where it's "kill or be killed."

Robotic arms can move fast enough to snatch thrown objects right out of the air… but should they? Not unless you want them to unnerve the humans they're interacting with, according to work out of Disney Research. Roboticists there found that slowing a robot's reaction time made it feel more normal to people. Disney has of course been interested in robotics for decades, and the automatons in its theme parks are among the most famous robots in the world. But there are few opportunities for those robots to interact directly with people. Hence a series of research projects at its research division aimed at safe and non-weird robot-human coexistence.

Tel Aviv, New Delhi - November 7 th, 2019: Tech Mahindra Ltd., a leading provider of digital transformation, consulting and business re-engineering services and solutions, announced today its collaboration with Atidot, an Israel based InsurTech that offers predictive, analytics, Artificial Intelligence (AI) and Machine Learning (ML) tools for the life insurance and annuity companies. Through this partnership, Tech Mahindra and Atidot will provide a one-of-a-kind platform to increase profitability for insurance companies by predicting customer behavior, lapse patterns and improve their cross-sell as well as up-sell capabilities. The solution brings new benefits to life insurance policyholders by offering them policies that are more appropriate, have better coverage as well as protect retirement savings. The solution will also empower insurance providers to assess coverage needs and remedy under or over-insured policies in a timely and responsible manner. For life insurance providers, the solution will help them effectively capitalize on revenue potential - be it addressing and correcting underinsured policies to increase premium collection, leading better customer engagement, and reducing lapse rates thereby maximizing the number of profitable policies for the provider.

Physicists have designed artificial intelligence that thinks like the astronomer Nicolaus Copernicus by realizing the Sun must be at the centre of the Solar System.Credit: NASA/JPL/SPL Astronomers took centuries to figure it out. But now, a machine-learning algorithm inspired by the brain has worked out that it should place the Sun at the centre of the Solar System, based on how movements of the Sun and Mars appear from Earth. The feat is one the first tests of a technique that researchers hope they can use to discover new laws of physics, and perhaps to reformulate quantum mechanics, by finding patterns in large data sets. The results are due to appear in Physical Review Letters1. Physicist Renato Renner at the Swiss Federal Institute of Technology (ETH) in Zurich and his collaborators wanted to design an algorithm that could distill large data sets down into a few basic formulae, mimicking the way that physicists come up with concise equations like E mc2.

Astonishingly, this expectation was set out after several fatal crashes in Arizona. The communications infrastructure used in cars today (known as a Controller Area Network) was designed back in the 80s. It was developed for exchanging information between different micro controllers. Essentially, what we have is a peer-to-peer network – and an old one at that. The main issue here, is that these networks weren't built with security in mind, as it was not a key concern back then.

PITTSBURGH, PA (November 7, 2019) Mayor William Peduto, the City of Pittsburgh Department of Innovation & Performance, the Urban Redevelopment Authority of Pittsburgh, the Housing Authority of the City of Pittsburgh, and Allegheny County Airport Authority today announced the fifth cohort of the PGH Lab program. PGH Lab connects local startup companies with the City of Pittsburgh and local authorities, and independent institutions to explore new ways to use technology and innovative solutions to help improve city operations. The program provides an opportunity for local startups to test their beta-stage products and services in a real-world environment for three-four months. The City of Pittsburgh and the participating authorities have successfully completed four cycles and engaged 21 local startups, putting forth a variety of technological and innovative solutions ranging from waste management and composting to business processes and automation to immigrant inclusion initiatives. For the fifth cycle, three different startups will be joining PGH Lab.