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It takes just 3.7 seconds of audio to clone a voice. This impressive--and a bit alarming--feat was announced by Chinese tech giant Baidu. A year ago, the company's voice cloning tool called Deep Voice required 30 minutes of audio to do the same. This illustrates just how fast the technology to create artificial voices is accelerating. In just a short time, the capabilities of AI voice generation have expanded and become more realistic which makes it easier for the technology to be misused.

No matter the domain, data-intensive apps share one requirement in common: a reliable file system that ensures data is available to them on demand. Pure Storage, NetApp, VAST Data, IBM Spectrum Scale, and Dell EMC provide this, as does San Jose, California-based company WekaIO. WekaIO's high-velocity Matrix platform takes advantage of flash storage, off-the-shelf components, and sophisticated software techniques to deliver enormous speedups at exabyte scale. In fact, the company claims Matrix is the fastest parallel file system on the market for AI and technical compute workloads, as measured by independent SPEC SFS 2014 benchmark tests. To lay the groundwork for future growth in AI and analytics, life sciences, manufacturing, media and entertainment, and financial services, WekaIO has closed a $31.7 million series C financing round led by Hewlett Packard Enterprise (HPE), with participation from a host of storage and computing industry giants including Mellanox, Nvidia, Seagate, Western Digital Capital, and Qualcomm.

Artificial intelligence can help to bring together the twin megatrends of digitalization and decarbonisation. There has been a lot of talk about how artificial intelligence (AI) will affect various aspects of our lives, but little has been said to date about how the technology can help to make the world more sustainable. A new report from the consultancy PwC, commissioned by software giant Microsoft, looks at how the twin, powerfully disruptive megatrends of digitization and decarbonisation could come together in future and it concludes that AI could make a significant dent in global greenhouse gas (GHG) emissions. PwC defines AI as "a collective term for technologies that can sense their environment, think, learn, and take action in response to what they're sensing and their objectives". Applications can range from automation of routine tasks to augmenting human decision-making and beyond to automation and discovery – huge amounts of data to spot, and act on patterns, which are beyond our current capabilities.

Artificial Intelligence is on the cusp of becoming the biggest technology of the information age, says Horacio Rozanski, president and CEO of Booz Allen Hamilton. However, we need to bake human judgement into it before it is too late, he writes in this opinion piece. The exponential pace of technological advancement has made it more challenging than ever before to address its unintended consequences. We have seen it with digital innovation, especially social media, and we are only just beginning to contemplate it with artificial intelligence, which holds the promise of being the most transformational technology of the information era. The unanticipated consequences of digital innovation have been well documented.

Institutes gear up India for age of artificial intelligence Shuriah Niazi 11 May 2019 The Indian workforce, academics and students are gearing up for the advent of the age of artificial intelligence (AI). According to the National Association of Software and Services Companies (NASSCOM), the country will require nearly 238,000 AI professionals in the next three years and the AI industry will be worth US$16 billion by 2025. AI is a field that has a comparatively long history but continues to actively grow and evolve. NASSCOM last month launched the programme titled'AI Foundation for Faculty Development' to hone faculty's skills in AI at Bengaluru (formerly Bangalore) in the southern Indian state of Karnataka. In addition, some top institutions like the Indian Institute of Technology (IIT) Madras, IIT Hyderabad and Indian Institute of Science in Bengaluru are training students and faculty in AI.

Industry has mobilized to shape the science, morality and laws of artificial intelligence. On 10 May, letters of intent are due to the US National Science Foundation (NSF) for a new funding programme for projects on Fairness in Artificial Intelligence, in collaboration with Amazon. In April, after the European Commission released the Ethics Guidelines for Trustworthy AI, an academic member of the expert group that produced them described their creation as industry-dominated "ethics washing". In March, Google formed an AI ethics board, which was dissolved a week later amid controversy. In January, Facebook invested US$7.5 million in a centre on ethics and AI at the Technical University of Munich, Germany.

The global artificial-intelligence (AI) market could grow at a compound annual growth rate of 55.6% between 2018 and 2025, according to Allied Market Research, as a wide range of industries use AI applications to streamline and improve their businesses. However, AI is such a hot buzzword in tech circles that it can be tough to identify the industry's worthy long-term investments. Today, a trio of our Motley Fool investors will highlight three promising AI plays -- Tencent (NASDAQOTH: TCEHY), Microsoft (NASDAQ: MSFT), and NVIDIA (NASDAQ: NVDA). Leo Sun (Tencent): Tencent, one of China's top tech companies, owns the country's most popular mobile messaging platform in WeChat, as well as one of its most popular payment systems in WeChat Pay. Tencent Video is one of China's leading streaming video platforms, and Tencent Music (NYSE: TME), which was spun off in an IPO last year, is the country's leading music streaming platform.

Why did the machine think the turtle was a rifle? Artificial intelligence is easily confused by so-called adversarial examples and like many others, Aleksander Madry at the Massachusetts Institute of Technology had thought they were bugs that would disappear with better algorithms or ways to train them.

We present a framework that enables estimation of low-dimensional sub-resolution reservoir properties directly from seismic data, without requiring the solution of a high dimensional seismic inverse problem. Our workflow is based on the Bayesian evidential learning approach and exploits learning the direct relation between seismic data and reservoir properties to efficiently estimate reservoir properties. The theoretical framework we develop allows incorporation of non-linear statistical models for seismic estimation problems. Uncertainty quantification is performed with Approximate Bayesian Computation. With the help of a synthetic example of estimation of reservoir net-to-gross and average fluid saturations in sub-resolution thin-sand reservoir, several nuances are foregrounded regarding the applicability of unsupervised and supervised learning methods for seismic estimation problems. Finally, we demonstrate the efficacy of our approach by estimating posterior uncertainty of reservoir net-to-gross in sub-resolution thin-sand reservoir from an offshore delta dataset using 3D pre-stack seismic data.