The partnership between Sensyne Health and Microsoft will entail the aim of improving, augmenting, and reducing the cost of patient care. Expected to deliver latest generation healthcare'cloud-first' systems and predictive machine learning algorithms, while reducing demand on global health systems and increasing scalability of care, the agreement includes the following aspects: Sensyne's AI healthcare expertise will be combined with Micosoft's latest healthcare capabilities. These include clinical workflow and patient engagement tool Health Cloud, digital waiting room and remote consultation service Virtual Consult, and natural language interaction system Health Bot. Richard Farrell, chief innovation officer at Netcall, explores how healthcare can undergo effective digital transformation one step at a time. "This strategic partnership with Microsoft will further enhance Sensyne's ability to advance and scale the benefits that advanced clinical AI can bring to improve patient outcomes and accelerate the development of new medicines through its research partnerships with NHS Trusts," said Lord Paul Drayson, CEO of Sensyne Health.

The Hoover Institution's Condoleezza Rice and HAI Co-Director Fei-Fei Li discuss AI ethics, technology advances, and the...

With the advent of pharmacogenomics, machine learning research is well underway to predict patients' drug response that varies by individual from the algorithms derived from previously collected data on drug responses. Entering high-quality learning data that can reflect a person's drug response as much as possible is the starting point for improving the accuracy of the prediction. Previously, preclinical studies of animal models were used which were relatively easier to obtain compared to human clinical data. In light of this, a research team led by Professor Sanguk Kim in the Department of Life Sciences at POSTECH is drawing attention by successfully increasing the accuracy of anti-cancer drug response predictions by using data closest to a real person's response. The team developed this machine learning technique through algorithms that learn the transcriptome information from artificial organoids derived from actual patients instead of animal models.

Walmart Inc. has ended its effort to use roving robots in store aisles to keep track of its inventory, reversing a yearslong push to automate the task with the hulking machines after finding during the coronavirus pandemic that humans can help get similar results. The retail giant has ended its contract with robotics company Bossa Nova Robotics Inc., with which it joined over the past five years to gradually add six-foot-tall inventory-scanning machines to stores. Walmart had made the robots a frequent topic of conversation at media and investor events in recent years, hoping the technology could help reduce labor costs and increase sales by making sure products are kept in stock. Walmart ended the partnership because it found different, sometimes simpler solutions that proved just as useful, said people familiar with the situation. As more shoppers flock to online delivery and pickup because of Covid-19 concerns, Walmart has more workers walking the aisles frequently to collect online orders, gleaning new data on inventory problems, said some of these people.

Robots have been a mainstay in factories for decades, but their use has been expanding everywhere else, from warehouses and hospitals to retail. That trend continued last year, and the novel coronavirus pandemic has accelerated service robot demand for automated logistics, disinfection, and delivery, according to the International Federation of Robotics. The Frankfurt, Germany-based IFR said that the sales value of professional service robots increased by 32% to $11.2 billion (U.S.) worldwide between 2018 and 2019. The organization published its full research in the "World Robotics 2020 – Service Robots" report, which is available for download. Sales of medical robotics accounted for 47% of the total service robot value turnover in 2019, according to the IFR.

Researchers from Penn State University and Houston Methodist Hospital recently outlined their work on a machine learning tool that uses a smartphone camera to quickly gauge facial movements for sign of a stroke. The tool – which was presented as a virtual poster at this month's International Conference on Medical Image Computing and Computer Assisted Intervention – relies on computational facial motion analysis and natural language processing to spot sagging muscles, slurred speech or other stroke-like symptoms. To build and train it, the researchers used an iPhone to record 80 Houston Methodist patients who were experiencing stroke symptoms as they performed a speech test. According to a Penn State release, the machine learning model performed with 79% accuracy when tested again on that dataset, which the researchers said is roughly on par with emergency room diagnoses using CT scans. "Currently, physicians have to use their past training and experience to determine at what stage a patient should be sent for a CT scan," James Wang, professor of information sciences and technology at Penn State, said in a release from the university.

AI has long been enabling innovation, with both big and small impacts. From AI-generated music, to enhancing the remote fan experience at the U.S. Open, to managing coronavirus patients in hospitals, it seems like the future is limitless. But, in the last few months, organizations from all sectors have been met with the realities of both Covid-19 and increasing anxiety over social justice issues, which has led to a reckoning within companies about the areas where more innovation and better processes are required. In the AI industry, specifically, organizations need to embrace their role in ensuring a fairer and less-biased world. It's been well-established that machine learning models and AI systems can be inherently biased, some more than others -- a result most commonly attributed to the data being used to train and develop them.

Sometime next April, a 50-foot-long autonomous ship will shake loose the digital bonds of its human controllers, scan the horizon with radar, and set a course westward across the Atlantic. The Mayflower Autonomous Ship won't be taking commands from a human captain like the first Mayflower did during its crossing back in 1620. Instead it will get orders from an "AI captain" built by programmers at IBM. The Mayflower's computing system processes data from 30 onboard sensors and six cameras to help the ship sail across the ocean, obey shipping rules (like how to pass other ships at sea), and control electrical and mechanical systems like the engine and rudder. There won't be anyone on board if something goes wrong, although it does have to send a daily report to a human operator back in the UK.

Researchers are using machine learning algorithms trained on simulations of space debris as part of a key project. With more than 34,000 pieces of junk orbiting around the Earth, their removal is becoming a matter of safety. Earlier this month an old Soviet Parus navigation satellite and a Chinese ChangZheng-4c rocket were involved in a near miss and in September the International Space Station conducted a manoeuvre to avoid a possible collision with an unknown piece of space debris. A project led by ClearSpace-1, a spin off from research lab EPFL in Zurich, will recover the now obsolete Vespa Upper Part, a payload adapter orbiting 660km above the Earth that was once part of the European Space Agency's Vega rocket. The mission, set for 2025, aims to ensure that it re-enters the atmosphere and burns up in a controlled way.

Technology keeps creating challenges for intellectual property law. The infamous case of the "monkey selfie" challenged the notion of not just who owns a piece of intellectual property, but what constitutes a "who" in the first place. Last decade's semi-sentient monkey is giving way to a new "who": artificial intelligence. The rapid rise of AI has forced the legal field to ask difficult questions about whether an AI can hold a patent at all, how existing IP and patent laws can address the unique challenges that AI presents, and what challenges remain. The answers to these questions are not trivial; stakeholders have poured billions upon billions of dollars into researching and developing AI technologies and AI-powered products and services across academia, government, and industry.