The headquarters of Boundless Mind looks as if it were created by a set designer to satisfy a cultural cliché. The tech startup is run out of a one-car garage a few blocks from California's Venice Beach. On the morning I visited, in March, it was populated by a dozen screens–phones, tablets, monitors–and half as many 20-something engineers, all of whom were male and bearded, and one of whom wore a cowboy hat. Someone had written in blue marker across the top of a whiteboard in all caps: You're building amazing sh-t. But that, more or less, is where the Silicon Valley stereotypes end. Ramsay Brown, 29, and T. Dalton Combs, 32, the co-founders of Boundless Mind, are hardly the college dropouts of tech lore; they're trained neuroscientists. And unlike most tech entrepreneurs, they are not trying to build the next big thing that will go viral.

The University of Southampton is set to join The Alan Turing Institute (ATI), the UK's prestigious national institute for data science. Subject to signing a partnership agreement, it is anticipated that Southampton and the University of Bristol will be working with the Institute and its university partners to develop collaborative programmes of research from summer 2018. The Institute is named in honour of Alan Turing, whose pioneering work in theoretical and applied mathematics, engineering and computing are considered to be the key disciplines comprising the emerging field of data science. All new universities joining the Turing network have been admitted based on the excellence of their research and its alignment with the Institute's research interests, their ability to bring new expertise and opportunities which add to the core strengths of the Institute and its existing partners, and their willingness to contribute financially to the Institute. Professor Mark Spearing, Vice-President (Research and Enterprise), University of Southampton, commented: "We are a pioneering world-leader in computer, data and web science and becoming a university partner of The Alan Turing Institute is an important step as we seek to further expand our capabilities in harmony with other world-leaders in these fields. This partnership comes at a time when the digital world faces many challenges and it is only through collaborative efforts like this that we'll succeed in addressing the world's societal and scientific needs."

Fintech startups around the world are changing the way people manage their money. Apps are reshaping entire payment systems, people are learning to live with chatbots, and innovative interfaces are challenging the way banking gets done. Much is happening abroad, but the impact of these trends on the U.S. market is very hard to predict. Our financial system is far more complex and well entrenched than systems in other countries, with thousands of institutions and a complex regulatory environment featuring countless state-level authorities and multiple federal agencies. U.S. banks tend to be slower to adopt technology, in part because of the gargantuan task of updating old, siloed systems.

April 12, 2018 --Visit this online directory of the nearly 200 faculty members at Hampshire College and you'll find that, listed between a professor of communications and a visiting professor of video and film, is a petri dish of yellow schmutz. The schmutz is a plasmodial slime mold, Physarum polycephalum, a glob of living cells that exhibits decidedly non-schmutzlike behavior, such as solving mazes and anticipating periodic events – so much so that in 2017 Hampshire, a private liberal arts school in Amherst, Mass., awarded it a position of "visiting non-human scholar." The abilities of non-animals to remember events, recognize patterns, and solve problems are prompting scientists and philosophers to rethink what thinking is. In the 20th century, science demolished the notion that humans are the only animals to exhibit complex thinking; in the 21st, biologists are beginning to see cognition in other biological kingdoms – not just slime molds, but also plants. This shift in thought could not only help scientists better understand cognition's workings and its origins, but it could also help in the search for intelligence beyond Earth.

Getting depressed might seem a strange affliction for a robot, but artificially intelligent brains may also suffer from similar mental health problems to people. That's the claim put forward by an American neuroscientist, who says that machines could even hallucinate, if not monitored correctly. Developers will need to create software equivalents to anti-depressants, which help to control levels of the neurotransmitter serotonin in the brain, he recommends. Artificial intelligence and robotic brains may suffer from some of the mental health issues traditionally associated with humans. Zachary Mainen works at the Champalimaud Centre for the Unknown in Lisbon and studies how the brain makes decisions.

Hino Motors, Toyota Motor Corp.'s group truck manufacturer, and Volkswagen Truck & Bus of Germany agreed Thursday to form a partnership on the technologies behind hybrids, electric cars, autonomous driving and connectivity. Each company will maintain independence under the deal, but executive teams will work together to map out cooperation on innovative technology and leveraging their market positions. The arrival of the digital age and robotics in the auto industry is pressuring automakers around the world to deliver vehicles that connect to the net or avoid crashes automatically. Partnerships can save on costs and boost competitiveness. Automakers are also trying to respond to a global push to reduce global warming and pollution by developing cleaner powertrains.

The Food and Drug Administration has approved the first artificial intelligence software that can decide, without a clinician's involvement, whether a patient might have a certain disease, the agency announced Wednesday. The software, called IDx-DR, looks for diabetic retinopathy, an eye disease that afflicts individuals with diabetes. With minimal training, health care providers can use a special camera to take a picture of the back of the patient's retina, which an algorithm then analyzes to look for the disease. If the software finds evidence of the disease, it recommends that a patient see an eye specialist. A computer program that can analyze medical images could save time and money, cutting down on unnecessary, expensive trips to specialists.

Heather Roff is a fellow at the University of Cambridge. She was formerly a Future of War fellow with New America in Washington D.C. Roff is scheduled to speak this week at the Conference on World Affairs at the University of Colorado Boulder. Colorado Public Radio reserves the right to use the comments we receive, in whole or in part, and to use the commenter's name and location, in any medium. By commenting below, you agree to these terms. For additional information, please consult our Privacy Policy & Terms of Use as well as our Community Standards.

In 2017, Slice, a New York tech company that builds software solutions for independent pizzerias, had a management problem. The company's tech staff is based in Macedonia, where high unemployment rates mean most of their new hires have never held a formal job prior to Slice. "We have a lot of first-time managers who need coaching," said Rick Pereira, chief people officer. Instead of moving to Macedonia himself, Pereira implemented Butterfly.ai, The tools uses anonymous employee survey results and past performance data to rate managers' performance, then offers tips and training content to help them improve.

In this video from 2018 Swiss HPC Conference, Torsten Hoefler from (ETH) Zürich presents: Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis. "Deep Neural Networks (DNNs) are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this talk, we describe the problem from a theoretical perspective, followed by approaches for its parallelization. We discuss the different types of concurrency in DNNs; synchronous and asynchronous stochastic gradient descent; distributed system architectures; communication schemes; and performance modeling. Based on these approaches, we extrapolate potential directions for parallelism in deep learning."