Nanostructured layers boast countless potential properties--but how can the most suitable one be identified without any long-term experiments? A team from the Materials Discovery Department at Ruhr-Universität Bochum (RUB) has ventured a shortcut: using a machine learning algorithm, the researchers were able to reliably predict the properties of such a layer. Their report was published in the new journal Communications Materials from 26 March 2020. During the manufacture of thin films, numerous control variables determine the condition of the surface and, consequently, its properties. Relevant factors include the composition of the layer as well as process conditions during its formation, such as temperature.

Roundup Let's get cracking with some machine-learning news. Starksy Robotics is no more: Self-driving truck startup Starsky Robotics has shut down after running out of money and failing to raise more funds. CEO Stefan Seltz-Axmacher bid a touching farewell to his upstart, founded in 2016, in a Medium post this month. He was upfront and honest about why Starsky failed: "Supervised machine learning doesn't live up to the hype," he declared. Neural networks only learn to pick up on certain patterns after they are faced with millions of training examples.

Miso Robotics, a startup developing robots that can perform basic cooking tasks in commercial kitchens, today announced that it has deployed new tools to its platform in CaliBurger restaurants as part of an advanced approach with CaliGroup intended to improve safety and health standards. The hope is to minimize the threat of infection for patrons and delivery workers during the COVID-19 pandemic, which has sickened hundreds of thousands of people worldwide. In the coming weeks, in partnership with payment provider PopID, Miso will install a thermal-based screening device in a CaliBurger location in Pasadena, California, that attaches to doors to measure the body temperatures of people attempting to enter the restaurant, along with Miso's Flippy robot in the kitchen, to address health concerns. Before entering, the staff, delivery drivers, and guests will have to scan their faces, and if the device sensor detects the person has a fever, they won't be allowed to enter the building. Miso says that store owners will be able to opt into text messages alerting them that someone whose temperature reading is in line with health and safety standards is at the door, at which point employees will be able to open the door manually.

Google researchers recently published a paper describing a framework -- SEED RL -- that scales AI model training to thousands of machines. They say that it could facilitate training at millions of frames per second on a machine while reducing costs by up to 80%, potentially leveling the playing field for startups that couldn't previously compete with large AI labs. Training sophisticated machine learning models in the cloud remains prohibitively expensive. According to a recent Synced report, the University of Washington's Grover, which is tailored for both the generation and detection of fake news, cost $25,000 to train over the course of two weeks. OpenAI racked up $256 per hour to train its GPT-2 language model, and Google spent an estimated $6,912 training BERT, a bidirectional transformer model that redefined the state of the art for 11 natural language processing tasks.

The study tested five publicly available tools from Apple, Amazon, Google, IBM and Microsoft that anyone can use to build speech recognition services. These tools are not necessarily what Apple uses to build Siri or Amazon uses to build Alexa. But they may share underlying technology and practices with services like Siri and Alexa. Each tool was tested last year, in late May and early June, and they may operate differently now. The study also points out that when the tools were tested, Apple's tool was set up differently from the others and required some additional engineering before it could be tested.

"If you look at the key names in the global debate on AI ethics, it is in fact dominated by women who have many different types of backgrounds, not only tech backgrounds." Artificial Intelligence (AI) is the game-changer in the industry, turbocharging new use cases in transportation, law enforcement, e-commerce, retail, healthcare, and entertainment. However, the quick pace of transformation and adoption is not accompanied by concrete industry standards on AI ethics and fairness in Machine Learning algorithms. While ethics in AI have been a dominant narrative for sometime, Big Tech is still seeking ways to design a code of conduct when building ML algorithms. Some tech giants like Microsoft have laid down guidelines to responsible AI and has operationalized responsible AI at scale, others are yet to follow suit.

Waymo, a former Google project, is testing out self-driving trucks and building up its package delivery program.

University of Massachusetts Amherst researchers have invented a portable surveillance device powered by machine learning - called FluSense - which can detect coughing and crowd size in real time, then analyze the data to directly monitor flu-like illnesses and influenza trends. The FluSense creators say the new edge-computing platform, envisioned for use in hospitals, healthcare waiting rooms and larger public spaces, may expand the arsenal of health surveillance tools used to forecast seasonal flu and other viral respiratory outbreaks, such as the COVID-19 pandemic or SARS. Models like these can be lifesavers by directly informing the public health response during a flu epidemic. These data sources can help determine the timing for flu vaccine campaigns, potential travel restrictions, the allocation of medical supplies and more. "This may allow us to predict flu trends in a much more accurate manner," says co-author Tauhidur Rahman, assistant professor of computer and information sciences, who advises Ph.D. student and lead author Forsad Al Hossain.

Last week, Microsoft gathered experts from academia, civil society, policy making and more to discuss one of the most important topics in tech at the moment: responsible AI (RAI). Microsoft's Data Science and Law Forum in Brussels was the setting for the discussion, which focused on rules for effective governance of AI. Whilst AI governance and regulation may not be everyone's cup of tea, the event covered an array of subjects where this has become a red hot issue, such as the militarization of AI, liability rules in AI systems, facial recognition technology and the future of quantum computing and more. The event also gave Microsoft an opportunity to showcase its strategy around this important area. A few highlights are worth sharing, so let's dig a bit deeper into what Microsoft is doing in RAI, why it's important and what it means for the market moving forward.

Satz and Averso, who met while students at DSI, are deeply committed to using "data for good." The pair has worked together for several years at the intersection of data science and health care and formed EVQLV in December 2019 to use AI to accelerate the speed at which healing is discovered, developed, and delivered. The company has already grown to 12 team members with skills ranging from machine learning and molecular biology to software engineering and antibody design, cloud computing, and clinical development.