Novartis and Microsoft announced they are joining forces to apply artificial intelligence to some of the most intractable problems in healthcare, in one of the most expansive tie-ups so far between big pharma and big tech. Under one part of the five-year agreement, which will be reviewed annually, Microsoft will work on new tools intended to make it easier to apply AI to all areas of the Swiss pharmaceutical company's business, from finance to manufacturing. A second part of the work will focus on using deep learning -- the technique that has brought the biggest recent advances in AI -- to improve the speed and precision with which it develops new medicines. Vas Narasimhan, Novartis' chief executive, suggested Tuesday that AI could hold particular promise in the field of personalized medicine, helping to identify subgroups of patients most likely to benefit from new treatments. The pharmaceutical industry, initially slow to recognize the potential of digital technologies, has been picking up the pace in recent years, with companies including GlaxoSmithKline and Sanofi exploring how big data can accelerate research and development.

Robots aren't replacing everyone, but a quarter of U.S. jobs will be severely disrupted as artificial intelligence accelerates the automation of existing work, according to a new Brookings Institution report. The report, published Thursday, says roughly 36 million Americans hold jobs with "high exposure" to automation -- meaning at least 70 percent of their tasks could soon be performed by machines using current technology. Among those most likely to be affected are cooks, waiters and others in food services; short-haul truck drivers; and clerical office workers. "That population is going to need to upskill, reskill or change jobs fast," said Mark Muro, a senior fellow at Brookings and lead author of the report. Muro said the timeline for the changes could be "a few years or it could be two decades."

Cybersecurity has been continuously evolving, not just as a hot topic for discussion but as the mainstream challenge and priority for a large number of organizations. Recently, we have seen several cyberattack incidents turning into global epidemic events, such as WannaCry (May 2017; damaging 200,000 computers across 150 countries), Petya/NotPetya (June 2017; $10 billion damage estimated), Mirai (Oct 2016; initial level impact on 300,000 insecure IoT-devices worth $100 million, further variants and consequences still getting unveiled). And even on the corporate front, the world has witnessed several massive breach incidents, including Yahoo (2013-14; impacting 3 billion users), Equifax (July 2017; impacting 150 million U.S. citizens), and Aadhaar (Aug 2017 to Jan 2018, 1.1 billion Indian citizens impacted), just to name a few. With every passing day, cybercriminals are learning and adopting new and innovative methods of attack. To withstand such attacks, security agencies also need to ramp up their game. Besides the established players, there are a large number of startups using advanced techniques like machine learning and artificial intelligence to prevent such cyberattacks.

Innovation Endeavors, the fund backed by Google's Eric Schmidt, has for years now been taking a novel approach to working on difficult and still-evolving problems, like cybersecurity and food shortages: it sets up incubators that bring together different stakeholders to identify, develop and fund ways of tackling these issues. Today, Innovation unveiled the latest of these: a new project called Deep Life, which aims to identify tricky problems in the world of life sciences, and figure out how to use computer science -- specifically innovations in areas like machine learning -- to help fix them. Target areas will include therapeutics, diagnostics and industrial life sciences in biology, chemistry and other fields; and Deep Life will provide startups with "investment capital across all stages of growth; access to experts, including scientists and decision-makers; proprietary data sets; early feedback on product; identification of market needs; initial customers and potential partners. In exchange for their startup support, Deep Life member organizations gain access to emerging technologies and hard-to-find talent," according to a blog post introducing the new project penned by Innovation Endeavors' co-founder Dror Berman. Deep Life will unveil the first fruits of its efforts during a pitch day on May 30, and it's accepting applications for places as of right now.

Let's assume you are not an expert highly trained in medical imaging. And let's assume you were invited one day to try out a new technology for heart ultrasounds -- diagnostic tools that are notoriously difficult to use because of the chest wall and because some shots must be made while the heart is in motion. When I was given the shot on a recent day, I was able to take the ultrasound in a matter of minutes with the help of software, developed by a San Francisco-based startup called Caption Health. The software told me how to hold the ultrasound probe against the ribs of a model who had been hired for the purpose of my visit and knew on its own when to snap the image. It was a little like having Richard Avedon's knowledge of photography uploaded into the guts of my iPhone camera. You can see the image I took of the parasternal long axis view of the heart pumping at the top of this page.

Novartis and Microsoft have inked a five-year deal to use artificial intelligence to design molecules, personalize medication dosing, and optimize the manufacturing of CAR-T cancer therapy, the two companies announced Tuesday. The partnership, whose financial terms were not disclosed, marks the latest and one of the more high-profile signs of the inroads that tech is making into drug development. It promises benefits for both sides: Novartis (NVS) will get Microsoft's help in mining data in search of insights that could help it save and make money, while Microsoft will get a chance to refine its own software for use in the life sciences. Unlock this article by subscribing to STAT Plus and enjoy your first 30 days free! STAT Plus is STAT's premium subscription service for in-depth biotech, pharma, policy, and life science coverage and analysis.

If there is one thing the general public is familiar with when the use of artificial intelligence than it is facial recognition. Whether it is opening their mobile phone or the algorithms Facebook uses to find eyes or other parts of a face in images, facial recognition has become a standard. But now scientists dealing with complex questions like the composition of the universe are starting to use a modified version of the'standard' facial recognition in an attempt to discover how much of the dark matter there is in the universe and where it is possibly located. As Digital Trends and Futurity note in their reports on the subject, "physicists believe that understanding this mysterious substance is necessary to explain fundamental questions about the underlying structure of the universe." It is the researchers gathered in Alexandre Refregier's group at the Institute of Particle Physics and Astrophysics at ETH Zurich, Switzerland that has started to use deep neural network methods that lie behind facial recognition to develop new, special tools to attempt to discover what is still a secret of the universe for us. As Janis Fluri, one of the researchers working on the project told Digital Trends, "The algorithm we [use] is very close to what is commonly used in facial recognition," adding that"the beauty of A.I. is that it can learn from basically any data.

Artificial Intelligence (alternate unit) was written and developed by Beverly Clarke. She is author of the book "Computer Science Teacher – insight into the computing classroom." Additionally, she is an Education consultant and former teacher. In writing this unit the following are acknowledged for their contributions in proof reading, checking for technical accuracy, testing activities in the classroom, filming, being sound boards and committed to seeing an AI curriculum available for high school students – Mike Mendelson (NVIDIA), James McClung (formerly of NVIDIA), Joanna Goode (University of Oregon), Alison Lowndes (NVIDIA), Rosie Lane (South Wilts Grammar School for Girls), Peter McOwan (Queen Mary University of London), Paul Curzon (Queen Mary University of London), Liz Austin (NVIDIA), Gemma Bond (Screen Boo Productions) and Neil Rickus (University of Hertfordshire). Morals and Ethics supporting cards were sampled from material by Andrew Csizmadia (Newman University).

Many fear the potential for Artificial Intelligence (AI) to be weaponised against humanity, but Australian lifesavers are showcasing the positive power of the technology with a drone that can prevent crocodile attacks. On Thursday, lifesavers in Queensland demonstrated how a drone backed by a'CrocSpotter' AI-algorithm can reveal the presence of crocs before they become a danger to humans. Developed by researchers from the University of Technology Sydney (UTS) in collaboration with Westpac Little Ripper and Amazon Web Services, the technology was initially designed to protect beachgoers from sharks. CrocSpotter and SharkSpotter are two of five'spotter' AI algorithms developed by The Ripper Group and UTS. In 2018, a Westpac Little Ripper drone performed the world's first rescue by a drone at Lennox Head in NSW, dropping an inflatable pod to save two teenagers in massive surf.

You are, and welcome to the present and the future of automated machine learning programs that have the ability to significantly increase the speed of analysis of specialized MRI scans. Don't worry though--it's not ready for prime time just yet! Now, new research sheds light on just far we have come in terms of development of such machine learning programs. According to a new study published in the journal, Circulation: Cardiovascular Imaging, analysis of cardiac MRI scans using automated machine learning can be performed significantly faster and with comparable accuracy to human interpretation by trained cardiologists. It generally takes about 13 minutes for a trained physician (cardiologist) to interpret a cardiac MRI.