Climate change activist Greta Thunberg has been named one of the ten most influential people in science in 2019 by the journal Nature. The 16 year old has been named alongside a neurologist who brought pig brains back to life and a palaeontologist who shook up humanity's family tree. The prestigious British science journal, which celebrated its 150th anniversary this year, says the Swedish campaigner'channelled the rage of a generation'. She had outshone scientists who couldn't'galvanise global attention' the way she did and many are cheering her along, according to Nature. The ten most influential list also includes a physicist building quantum computers, a biologist editing genes in adult humans and a microbiologist fighting Ebola.

Using machines to augment human activity is nothing new. Egyptian hieroglyphs show the use of horse-drawn carriages even before 300 B.C. Ancient Indian literature such as "Silapadikaram" has described animals being used for farming. And one glance outside shows that today people use motorized vehicles to get around. Where in the past human beings have augmented ourselves in physical ways, now the nature of augmentation also is more intelligent. Again, all one needs to do is look to cars – engineers are seemingly on the cusp of self-driving cars guided by artificial intelligence.

This article was originally published May 11, 2018 on JAXenter. At Kublr we've been talking with customers and the community about the workloads they plan to run using containers and Kubernetes. Frameworks from MapReduce to Hadoop to Spark have created parallel processing capabilities that leverage clusters to speed processing tasks. These clusters have been frequently managed with their own cluster management solution (eg. Recent developments in Kubernetes for data science and machine learning include the 2.3 release of Apache Spark with "native" Kubernetes support.

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.