Average base pay for US jobs in artificial intelligence is more than twice that of the average full-time job.

Microsoft's artificial intelligence simulator has been expanded to include autonomous car research.

Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking. If this party identifies the story as misinformation, it is marked as disputed. However, given the uncertain number of exposures, the high cost of fact checking, and the trade-off between flags and exposures, the above mentioned procedure requires careful reasoning and smart algorithms which, to the best of our knowledge, do not exist to date. In this paper, we first introduce a flexible representation of the above procedure using the framework of marked temporal point processes. Then, we develop a scalable online algorithm, Curb, to select which stories to send for fact checking and when to do so to efficiently reduce the spread of misinformation with provable guarantees. In doing so, we need to solve a novel stochastic optimal control problem for stochastic differential equations with jumps, which is of independent interest. Experiments on two real-world datasets gathered from Twitter and Weibo show that our algorithm may be able to effectively reduce the spread of fake news and misinformation.

A new artificial intelligence platform has been launched, marketed as an'in-meeting' intelligence that can listen, make notes and identify key moments …

Machine learning and predictive analytics are pervasive in our lives today. AI impacts nearly everything we do and interact with including retail and wholesale pricing, consumer habits and behaviors, marketing and advertising, politics, entertainment, sports, medicine, business logistics and planning, fraud and risk detection, airline and truck route planning, pricing strategy, gaming, AI speech recognition, AI image recognition, self-driving cars, and robotics. Yet whether you are creating a self-driving car, predicting customer churn, or cresting a product recommendation system, all machine learning projects follow the same process and the same five basic phases. Data is the new oil. It is quickly becoming the most valuable commodity in the world. Data is like oil because it fuels machine learning projects. Without data, there is no machine learning and no predictive analytics. And just like grades of oil, there are grades of data. Supreme data is like rocket fuel for machine learning models, and buyers pay a premium for it.

Health care may be on the cusp of a major transformation as IT leaders prepare for the increased adoption of disruptive technology capabilities. The health care industry has hardly been an early adopter of technology-enabled transformation, having been slower to embrace the emerging systems and tools that have remade other industry sectors. However, that is beginning to change. Consider how the development and implementation of exponentially improving technologies have revolutionized genomics. In 1999, scientists spent five months and approximately $300 million generating the first-draft sequence of a human genome.

Silicon Valley investment giant Sequoia Capital has pumped $50m (£38m) into Bristol-headquartered artificial intelligence chip producer Graphcore – a major vote of confidence in the UK's booming startup scene. Sequoia is best known as an early investor in some of the technology sectors' most prominent names – including Apple, Oracle, Yahoo, Google, YouTube, PayPal, Instagram, WhatsApp and Airbnb. Nigel Toon, chief executive officer of Graphcore, said that Sequoia was a suitable fit for his company because of its understanding of the importance of machine learning and how this will "reshape computing". "We're not obsessed with raising money, we're obsessed with who we work with – our customers, our team, our partners and our investors," Mr Toon said. The cash injection will help Graphcore, which was founded last year, to accelerate growth to meet the expected global demand for its machine-intelligence processor, the company said.

This Is An extract in English of Pierre Pinna's Interview on CNN Turkey. IPFC Online Founder and CEO Pierre Pinna, one of the world-renowned names in artificial intelligence and digital transformation issues that brings together entrepreneurial companies in Turkey, national and international investors, participated at the T-Bootcamp event in Izmir, explaining the future. One of the most important meeting points of entrepreneurs and investors, the T-Bootcamp event, which emphasizes the innovation efforts in Turkey, was organized at the Selçuk Yaşar Campus in Izmir with the cooperation of Yaşar University Minerva Incubation Center and technology platform creation agency WBCONS-HD. Among the leading names in the world of artificial intelligence and digital convergence, IPFC Online Founder and CEO Pierre Pinna, Fintech and Blockchain applications that are rapidly expanding include Inclusivity Network Founder Antonio Javier Ruiz, Peak State Consulting Director Anuj Khanna, Technology and Human College Founder Zeynep Dereli and entrepreneurs from Izmir and Istanbul participated as speakers. Entrepreneurs brought in prominent projects.

If you asked people about Artificial Intelligence, most, if they had an idea of what it was, would likely respond with concerns about the rise of the robots, job losses, sentient machine armies, privacy and transparency. You only need to look at the comments about Boston Dynamics back-flipping robot this week to see how quickly we get dystopian about the prospect of advances in this area. Dave Heiner, Vice President and Deputy General Counsel of the Regulatory Affairs team at Microsoft, sees things a bit differently. His main concern is we're not deploying it fast enough. It's a surprising statement from a man whose job contains the words'regulatory affairs' but he's a self-proclaimed AI convert and has spent the last few years contemplating all manner of issues in relation to AI including data privacy, ethics, transparency and trust, and has been advising Microsoft, as well as governments and organisations on behalf of Microsoft, on AI policy frameworks and regulation.

This with an application as in DeepWalk, as you mention in your blogpost. Followup question: is there a way to train a GCN to take in a graph (let's say with a constant number of nodes) and classify each node of said graph? In other words, instead of selecting features such as betweenness, I want a network that learns relevant graph features for my task at hand.