At the China Online Literature conference last week, Chinese search engine Sogou announced plans to create artificial intelligence-powered avatars that look and sound like two of the country's most famous authors--taking the experience of listening to an audiobook to an entirely new level. The first authors to get the A.I. avatar treatment will be Yue Guan and Bu Xin Tian Shang Diao Xian Bing. But if the project is successful, it could be a jumping off point for the industry to create avatars of even more authors. The audiobook industry is already big business in China and is expected to be worth more than $1 billion in the country by next year, according to iiMedia Research Group. A.I. avatars have the potential to give that an even greater boost.

NEW YORK; Aug. 21, 2019 – The semiconductor industry is the most bullish about adopting artificial intelligence (AI) and understanding the significant impact it will have on their industry, according to Accenture Semiconductor Technology Vision 2019, the annual report from Accenture (NYSE: ACN) that predicts key technology trends likely to redefine business over the next three years. Three-quarters of semiconductor executives surveyed for the report (77%) said they have adopted AI within their business or are piloting the technology. In addition, nearly two-thirds of semiconductor executives (63%) expect that AI will have the greatest impact on their business over the next three years, compared with just 41% of executives across 20 industries. This ranks AI higher for chipmakers than other new disruptive technologies surveyed, including d istributed ledgers, extended reality and q uantum computing. AI, comprising technologies that range from machine learning to natural language processing, enables machines to sense, comprehend, act and learn in order to extend human capabilities.

As demonstrated by many researchers [1, 2], the use of Machine Learning (ML) in Medicine is nowadays becoming more and more important. Researchers are now using ML in applications such as EEG analysis and Cancer Detection/Analysis. For example, by examining biological data such as DNA methylation and RNA sequencing can then be possible to infer which genes can cause cancer and which genes can instead be able to suppress its expression. In this post, I will walk you through how I examined 9 different datasets about TCGA Liver, Cervical and Colon Cancer. All the datasets have been provided by the UCSC Xena (University of California, Santa Cruz website).

If you're looking to get a taste of the future of transport, then you should head over to Berlin, where a driverless bus has just begun ferrying real-life passengers along a 1,2-kilometre circuit for the first time. The maiden voyage took place at a quarter past ten last Friday morning, as the first computer-controlled bus set off from U-Bahn Alt-Tegel. According to Berlin's transport authority, the BVG, it is the first vehicle of its kind to drive on public roads in Germany's capital city. When you picture an automated vehicle, you might think in sleek lines, futuristic chrome and glass, but the BVG's sunshine-yellow driverless bus is - there's no other word for it - undeniably cute. Rather oddly-proportioned, it is capable of speeds of up to 15 kilometres per hour and can carry a total of six passengers at a time, as well as an attendant - just in case.

Can facial recognition technology reduce the number of missing people in mature tech countries by 80% by 2023 as Gartner predicts? According to Gartner's predictions, by 2023 there will be an 80% reduction in missing people in mature tech countries compared to 2018, due to AI technology. Knowit recently won the SAS Nordic Hackathon competition with their case, "Where is my daughter?" Tune in to this episode of #Analytics4Society where we discuss use of AI for the greater good of society, the ethics and real-life examples of how such technology create value within a wide range of sectors.

SAN FRANCISCO – Applications of Artificial Intelligence, machine learning, and deep learning are relatively useless without a lucid understanding of how the outputs of their predictive models are derived. Explainable AI hinges on explainability--a clear verbalizing of how the various weights and measures of machine learning models generate their outputs. Those explanations, in turn, are determined by interpretability: the statistical or mathematical understanding of the numerical outputs of decisions made by predictive models. Interpretability is foundational to unraveling some of the more consistent issues plaguing AI today. Facilitating interpretability--and using it as the impetus for refining machine learning models and the data on which they're trained--is indispensable for overcoming the threat of biased models once and for all.

"There's no simple answer," said Stuart Russell, a computer scientist at UC Berkeley, an adjunct professor of neurological surgery at UC San Francisco and the author of a forthcoming book, "Human Compatible: Artificial Intelligence and the Problem of Control." "But in the long run nearly all current jobs will go away, so we need fairly radical policy changes to prepare for a very different future economy. In his book, Russell writes, "One rapidly emerging picture is that of an economy where far fewer people work because work is unnecessary." That's either a very frightening or a tantalizing prospect, depending very much on whether and how much you (and/or society) think people ought to have to work and how society is going to put a price on human labor. There will be less work in manufacturing, less work in call centers, less work driving trucks, and more work in health care and home care and construction. MIT Technology Review tried to track all the different reports on the effect ...

Nvidia CEO Jensen Huang said AI would drive long-term demand because it is the "single most powerful force of our time." Nvidia reported earnings and revenues that beat analysts' expectations as demand for graphics and artificial intelligence chips picked up in the second fiscal quarter. Huang also said his company's near-term growth will come from gaming and a couple of variants of the company's artificial intelligence chip business: inferencing and AI at the edge. During a conference call with analysts, Huang said artificial intelligence is the "single most powerful force of our time" and that there are more than 4,000 AI startups working with the company -- as compared to 2,000 AI startups in April 2017. In an interview with VentureBeat, Huang said the actual number of AI startups Nvidia is tracking is closer to 4,500.

Very first (and very draft) round of Machine Learning and Artificial Intelligence Frameworks and Applications Fingerprinting and Internet Census project. Based on the Grinder framework. Why? AI systems are complex and fragile. For example "The TensorFlow server is meant for internal communication only. It is not built for use in an untrusted network."

In a recent study, NIA-supported researchers at the University of California (UC) demonstrated how a computer system with the ability to learn and improve, known as machine learning, could help analyze amyloid plaques in the human brain. Amyloid plaques are abnormal clumps of protein that accumulate in the brains of people with Alzheimer's disease, but not all plaques look alike. As published in Nature Communications, the researchers from both UC San Francisco and UC Davis describe a technique that automates the process of measuring plaques and their different characteristics. This approach could enable larger-scale analysis of brain tissue to help accelerate research on the possible causes of Alzheimer's and how the disease progresses. Using 43 healthy and diseased human brain samples donated to the UC Davis Alzheimer's Disease Center Brain Bank, the researchers taught a computer to detect different types of amyloid plaques within each sample.