AI has become central to ideas about the future. But what exactly it is, and what it's going to do for us, are still very much questions to be answered. At an event in which she was described as "California's coolest data scientist", Chowdury explained that she works for the consultants Accenture and has a clutch of high-powered degrees. Chowdury wants to challenge the emerging conventional wisdom about AI. For a start, let's not forget that we're talking about computer code, she says, not some kind of techy Frankenstein.

Artificial intelligence systems could outperform humans in all tasks within the next 45 years, according to a new study which also suggests that all human jobs will be automated in the next 120 years. According to a survey of over 350 artificial intelligence (AI) researchers, machines are predicted to be better than us at translating languages by 2024, writing high-school essays by 2026, driving a truck by 2027, working in retail by 2031, writing a bestselling book by 2049 and surgery by 2053. However, there is only a five per cent chance that computers will bring about outcomes that may lead to human extinction, researchers said. The survey, by the University of Oxford in the UK and Yale University in the US, was conducted among 352 researchers who had presented their research at the Conference on Neural Information Processing Systems or the International Conference on Machine Learning – the two major conferences in the field of AI. "There is accumulating evidence that machines can overpower human intelligence in complex, though specific tasks," Eleni Vasilaki at the University of Sheffield in the UK, told the'New Scientist'. Also Read: Even'dumb AI' can boost human performance, says study However, there is little evidence that AI with human-like versatility will appear any time soon, Vasilaki said.The survey results showed that researchers in Asia typically gave shorter time frames than those in North America – predicting that AI would outperform humans on all tasks within 30 years, compared with 74 years.

London: Artificial intelligence systems could outperform humans in all tasks within the next 45 years, according to a new study which also suggests that all human jobs will be automated in the next 120 years. According to a survey of over 350 artificial intelligence (AI) researchers, machines are predicted to be better than us at translating languages by 2024, writing high-school essays by 2026, driving a truck by 2027, working in retail by 2031, writing a bestselling book by 2049 and surgery by 2053. However, there is only a five per cent chance that computers will bring about outcomes that may lead to human extinction, researchers said. The survey, by the University of Oxford in the UK and Yale University in the US, was conducted among 352 researchers who had presented their research at the Conference on Neural Information Processing Systems or the International Conference on Machine Learning--the two major conferences in the field of AI. "There is accumulating evidence that machines can overpower human intelligence in complex, though specific tasks," Eleni Vasilaki at the University of Sheffield in the UK, told the New Scientist. However, there is little evidence that AI with human-like versatility will appear any time soon, Vasilaki said. The survey results showed that researchers in Asia typically gave shorter time frames than those in North America--predicting that AI would outperform humans on all tasks within 30 years, compared with 74 years.

Advancements in Artificial Intelligence -- the capability of machines to make informed decisions and perform tasks usually reserved for humans -- are moving at a rapid rate, and it's threatening workers from truck drivers to surgeons, according to a new study from the University of Oxford. Spearheaded by Katja Grace of the Future of Humanity Institute at Oxford, the report surveyed more than 350 AI experts on how long it'll take machines to master certain jobs, from remedial to advanced. Within the next decade, experts predict machines will outperform humans when it comes to translating languages, writing a quality high school essay, and driving trucks. AI proficiency in sales and retail is expected by the early 2030s. The essay data is especially intriguing.

I would like to introduce you all to my friend Bruce Oberhardt. Bruce is a brilliant and creative scientist. Please check out his bio below. It is fascinating to me what he does and how he speaks, teaches and explains his ideas. He brought the following article to my attention and I thought it was intriguing so here goes, time for sharing with all of you.

Scientists in the US have accurately reconstructed images of human faces by monitoring the responses of monkey brain cells. The brains of primates can resolve different faces with remarkable speed and reliability, but the underlying mechanisms are not fully understood. The researchers showed pictures of human faces to macaques and then recorded patterns of brain activity. The work could inspire new facial recognition algorithms, they report. In earlier investigations, Professor Doris Tsao from the California Institute of Technology (Caltech) and colleagues had used functional magnetic resonance imaging (fMRI) in humans and other primates to work out which areas of the brain were responsible for identifying faces.

SYS-CON Events announced today that EARP Integration will exhibit at SYS-CON's 20th International Cloud Expo, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. EARP Integration is a passionate software house. Since its inception in 2009 the company successfully delivers smart solutions for cities and factories that start their digital transformation. EARP provides bespoke solutions like, for example, advanced enterprise portals, business intelligence systems and mobile applications for international enterprises across different sectors such as Energy and Utilities, GreenTech, MedTech, FinTech, Facility Management and Housing, Automotive Manufacturing, and Sport. EARP also cooperates with international software houses by providing them with highly qualified and well-selected, multilingual teams for bigger projects.

SYS-CON Events announced today that Tappest will exhibit MooseFS at SYS-CON's 20th International Cloud Expo, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. MooseFS is a breakthrough concept in the storage industry. It allows you to secure stored data with either duplication or erasure coding using any server. The newest - 4.0 version of the software enables users to maintain the redundancy level with even 50% less hard drive space required. The software functions on all major operating systems and is used around the world by businesses, universities and NGOs.

In this paper, we propose a StochAstic Recursive grAdient algoritHm (SARAH), as well as its practical variant SARAH+, as a novel approach to the finite-sum minimization problems. Different from the vanilla SGD and other modern stochastic methods such as SVRG, S2GD, SAG and SAGA, SARAH admits a simple recursive framework for updating stochastic gradient estimates; when comparing to SAG/SAGA, SARAH does not require a storage of past gradients. The linear convergence rate of SARAH is proven under strong convexity assumption. We also prove a linear convergence rate (in the strongly convex case) for an inner loop of SARAH, the property that SVRG does not possess. Numerical experiments demonstrate the efficiency of our algorithm.

Many machine learning algorithms are based on the assumption that training examples are drawn independently. However, this assumption does not hold anymore when learning from a networked sample because two or more training examples may share some common objects, and hence share the features of these shared objects. We show that the classic approach of ignoring this problem potentially can have a harmful effect on the accuracy of statistics, and then consider alternatives. One of these is to only use independent examples, discarding other information. However, this is clearly suboptimal. We analyze sample error bounds in this networked setting, providing significantly improved results. An important component of our approach is formed by efficient sample weighting schemes, which leads to novel concentration inequalities.