President Donald Trump's new "American A.I. Initiative" is designed to place the United States at the forefront of artificial intelligence research. But the executive order itself is reportedly pretty broad about how the nation can actually achieve global A.I. superiority. According to Axios and other sources, there's no new federal funding allocated for artificial-intelligence and machine learning projects; instead, government agencies are asked to shift existing funding to A.I. research, as well as open up datasets, models, and other resources to researchers and other tech pros--potentially fueling new inventions. The National Institute of Standards and Technology (NIST) is also tasked with creating standards for safe and reliable A.I. systems. Government agencies will introduce fellowships and skills programs that will retrain workers to deal with an A.I.-centric future.
The Secretary of State for Health and Social Care commissioned The Topol Review: Preparing the healthcare workforce to deliver the digital future, as part of the draft health and care Workforce Strategy for England to 2027 – Facing the Facts, Shaping the Future. The Topol Review, led by cardiologist, geneticist, and digital medicine researcher Dr Eric Topol, explores how to prepare the healthcare workforce, through education and training, to deliver the digital future. Dr Topol appointed a Review Board and three Expert Advisory Panels. HEE provided the secretariat team to facilitate the Review. The Topol Review is now published and it makes recommendations that will enable NHS staff to make the most of innovative technologies such as genomics, digital medicine, artificial intelligence and robotics to improve services.
I have worked on the problem of open-sourcing Machine Learning versus sensitivity for a long time, especially in disaster response contexts: when is it right/wrong to release data or a model publicly? This article is a list of frequently asked questions, the answers that are best practice today, and some examples of where I have encountered them. The criticism of OpenAI's decision included how it limits the research community's ability to replicate the results, and how the action in itself contributes to media fear of AI that is hyperbolic right now. It was this tweet that first caught my eye. Anima Anankumar has a lot of experience bridging the gap between research and practical applications of Machine Learning.
I just got back from attending IBM Think in San Francisco. Though it was a quick trip across the country, I was inundated with IBM's vision, covering topics from A (i.e. Despite the wide-ranging discussion, IBM's main focus was on three areas: 1) hybrid cloud, 2) advanced analytics, and 3) security. For example, IBM's hybrid cloud discussion centered on digital transformation and leaned heavily on its Red Hat acquisition, while advanced analytics included artificial intelligence (AI), cognitive computing (Watson), neural networks, etc. To demonstrate its capabilities in these areas, IBM paraded out customers such as Geico, Hyundai Credit Corporation, and Santander Bank, who are betting on IBM for game-changing digital transformation projects.
AI startups experienced their best funding year ever, raising a record $9.33 billion, or nearly 10% of last year's total VC investments that reached $99.5 billion, an 18-year high since the dot-com era.Getty The Artificial Intelligence (AI) winter is definitely over. As venture capital (VC) funding nears record since the dot-com era, with U.S. companies raising $99.5 billion versus $119.6 billion in 2000 according to the latest PwC MoneyTree Report, AI startups also experienced their best year ever, raising a record $9.33 billion, or nearly 10% of last year's total VC investments. Since 2013, VC investments in AI startups had regularly increased over the following four years, with a compound annual growth rate (CAGR) of about 36%. However, AI-related funding significantly jumped last year, increasing 72% compared to 2017, despite a dip in deal activity, with 466 startups funded from 533 in 2017, and after increasing for four years. The report also reveals that seed-stage deal activity among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%.
The U.S. military wants to expand its use of artificial intelligence in warfare, but says it will take care to deploy the technology in accordance with the nation's values. The Pentagon outlined its first AI strategy in a report released Tuesday. The plan calls for accelerating the use of AI systems throughout the military, from intelligence-gathering operations to predicting maintenance problems in planes or ships. It urges the U.S. to advance such technology swiftly before other countries chip away at its technological advantage. "Other nations, particularly China and Russia, are making significant investments in AI for military purposes, including in applications that raise questions regarding international norms and human rights," the report says.
On Wednesday, the Alberta government announced a $100 million investment, over a period of five years, to attract more artificial intelligence-based high-tech companies to Alberta. The five-year plan will support both Alberta Innovates and the Alberta Machine Intelligence Institute (Amii) to leverage partnerships with Alberta's research universities, while also creating jobs. In addition, Alberta will undertake a significant campaign to market Alberta's tech talent to the world in order to attract new investment. Since 2002, Alberta has invested $42 million in AI research at the University of Alberta and Amii. "Businesses around the world are turning to machine learning and artificial intelligence as key drivers of innovation across every industry sector."
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This week, President Donald Trump signed a new executive order on artificial intelligence and the Pentagon declassified part of its AI strategy. Neither was a first attempt at a national AI strategy. In 2016, the Obama administration published a comprehensive plan on the future of AI, which never had time to gain the momentum it needed in government. The Pentagon has been researching intelligent machines for the better part of 60 years, and only recently did it come to a consensus: Our future wars will be fought in code, using data and algorithms as powerful weapons. Using AI techniques, a military can "win" by destabilizing an economy rather than demolishing countrysides and city centers.