The Department of Veterans Affairs announced on Thursday that it has officially launched a National Artificial Intelligence Institute in an attempt to be a "leader" in the development of the technology. The institute will work to "prioritize and realize" AI research and development programs. These include existing projects, like an effort to use AI to reduce veterans' wait times for health appointments, and another to scan their medical records to evaluate their suicide risk as part of the REACH VET program. "VA's artificial intelligence institute will usher in new capabilities and opportunities that will improve health outcomes for our nation's heroes," Secretary Robert Wilkie said in a statement. The institute is a partnership between the VA's Office of Research and Development and Wilkie's Center for Strategic Partnerships.

With late 2019 marking the 20th anniversary of the landmark report on medical errors "To Err is Human," now is time for a renewed focus on novel ways to improve patient safety. The report launched the modern patient safety movement by shedding some much-needed light on the prevalence of medical errors and preventable deaths in the U.S., spawning many improvements to patient safety over the subsequent two decades. But before the healthcare industry gets too self-congratulatory, we could use a quick reality check. Patient safety remains a persistent global issue that exacts a huge human cost, as well as a financial one, as a recent report from the World Health Organization (WHO) starkly illustrates. While it is estimated that there is a one in 3 million risk of dying while travelling by airplane, the risk of patient death while receiving healthcare due to a preventable medical accident is estimated to be one in 300, according to the WHO.

"If you want to command a multiyear, seven-figure salary, you used to have only four career options: chief executive officer, banker, celebrity entertainer, or pro athlete. Imagine a glass with balls. This glass is a field of Computer science knowledge, and balls are various fields: back end, front end development, embedded. One of these balls is artificial intelligence, and it is special because there are other balls inside: machine learning, natural language processing, and a whole slew of other things. Each of these units individually -- a powerful force and new opportunities to change any sphere.

Jen-Hsun Huang, president and chief executive officer of Nvidia Corp., gestures as he speaks during ... [ ] the company's event at the 2019 Consumer Electronics Show (CES) in Las Vegas, Nevada, U.S., on Sunday, Jan. 6, 2019. We found that if Nvidia Stock drops 10% or more in a week (5 trading days), there is a solid 36% chance it'll recover 10% or more, over the next month (about 20 trading days) Nvidia stock has seen significant volatility this year. While the company has been impacted by the broader correction in the semiconductor space and the trade war between the U.S. and China, the stock is being supported by a strong long-term outlook for GPU demand amid growing applications in Deep Learning and Artificial Intelligence. Considering the recent price swings, we started with a simple question that investors could be asking about Nvidia stock: given a certain drop or rise, say a 10% drop in a week, what should we expect for the next week? Is it very likely that the stock will recover the next week?

Tisch Multiple Sclerosis Research Center of New York is using artificial intelligence, machine learning and data science to find patterns that may relate to the cause of the disease. The disease affects more than 2.3 million individuals, and while researchers make progress in understanding MS, the cause remains unknown. However, research techniques are improving thanks to data science and technology, says Saud Sadiq, MD, director and chief research scientist at Tisch. The research center is getting help from Deloitte, Sadiq adds. "Given the complexity of MS and the urgent need to help patients living with this diagnosis, we wanted to explore new ways to infuse technology into our research. We met with Deloitte and discussed the possibility of applying tools like AI and machine learning to narrow down molecules that may be correlated to MS, as well as accelerate the discovery process."

NNAISENSE has successfully concluded its Series B financing round, with a number of high-profile industrial partners having invested in its vision to integrate True AI into intelligent automation. The company, which draws on more than 25 years of expertise in AI, will apply its state-of-the-art machine learning capabilities to deliver bottom-line improvement to the inspection, modelling, and control of complex industrial production processes. The lead investor in the round is Samsung Ventures Investment Corporation, whose focus is on future-oriented businesses based on new and innovative technologies, while other significant investors include Repsol Energy Ventures SA – the venture capital arm of integrated global energy company Repsol – and Schott AG, who are keen to explore the possibilities AI can deliver as part of its digitalisation program. B2B tech venture fund Alma Mundi Ventures, which was the lead investor in the Series A financing round, increased its position, while Jaan Tallinn's Metaplanet Holdings OÜ also invested further. Tallinn was a founding engineer at Skype and Kazaa and is keen to see AI put to uses that are beneficial and which align with human values.

At the Tackling Climate Change workshop at this year's NeurIPS conference, some of the top minds in machine learning came together to discuss the effects of climate change on life on Earth, how AI can tackle the urgent problem, and why and how the machine learning community should join the fight. The panel included Yoshua Bengio, MILA director and University of Montreal professor; Jeff Dean, Google's AI chief; Andrew Ng, cofounder of Google Brain and founder of Landing.ai; and Cornell University professor and Institute for Computational Sustainability director Carla Gomes. The Tackling Climate Change workshop explored a wide range of topics, from the use of deep reinforcement learning to improve performance for ride-hailing services like Uber and Lyft to the application of deep learning to predict wildfire risk, detect avalanche deposits, improve plane efficiency with better wind forecasts, and conduct a global census of solar farms. The workshop is put together by Climate Change AI, a group that hosts workshops at AI research conferences and a forum for collaboration between machine learning practitioners and people from other fields. One essential step in better addressing the world's pressing challenges, says Bengio, is changing the way AI research is valued.

If you ever get a chance, I HIGHLY recommend talking to people. Not just people around you *most* of the time, like your immediate or extended family, or the nosy neighbor, or the mail professional who happily delivers package upon package of online orders to your porch on what seems to be an hourly basis. No, I mean different people, ones you've never met before, especially around the world. I have had the unique opportunity to visit many countries in my still short life, and experienced several cultures, several languages, several routines and rites. I've broken bread around the world, and visited hundreds of heritage sites learning about the past and present of the indigenous.

On one hand, we know AI is the future of business. After all, manpower simply isn't fast enough to keep up with the pace of consumer demand. That said, there's a big difference between knowing AI is the future and actually implementing AI within your business successfully. That latter part--AI adoption--is where many companies are finding themselves stuck. No one said digital transformation would be easy--but you're not alone if you assumed AI adoption would be a cakewalk.

Algorithmic or automated decision systems use data and statistical analyses to classify people for the purpose of assessing their eligibility for a benefit or penalty. Such systems have been traditionally used for credit decisions, and currently are widely used for employment screening, insurance eligibility, and marketing. They are also used in the public sector, including for the delivery of government services, and in criminal justice sentencing and probation decisions. Most of these automated decision systems rely on traditional statistical techniques like regression analysis. Recently, though, these systems have incorporated machine learning to improve their accuracy and fairness. These advanced statistical techniques seek to find patterns in data without requiring the analyst to specify in advance which factors to use. They will often find new, unexpected connections that might not be obvious to the analyst or follow from a common sense or theoretic understanding of the subject matter. As a result, they can help to discover new factors that improve the accuracy of eligibility predictions and the decisions based on them. In many cases, they can also improve the fairness of these decisions, for instance, by expanding the pool of qualified job applicants to improve the diversity of a company's workforce.