SAN FRANCISCO – Tech titans Mark Zuckerberg and Elon Musk recently slugged it out online over the threat that artificial intelligence might one day pose to the human race, although you could be forgiven if you don't see why this seems like a pressing question. Thanks to AI, computers are learning to do a variety of tasks that have long eluded them -- everything from driving cars to detecting cancerous skin lesions to writing news stories. But Musk, the founder of Tesla Motors and SpaceX, worries that AI systems could soon surpass humans, potentially leading to our deliberate (or inadvertent) extinction. Two weeks ago, Musk warned U.S. governors to get educated and start considering ways to regulate AI in order to ward off the threat. "Once there is awareness, people will be extremely afraid," he said.

What did we learn Ability to predict BTC ZAR price based on USD seems possible..? Ability to quickly dump and store data for later analysis hugely valuable Spark not so great for time-series data (?) Future plans incl.

Tech titans Mark Zuckerberg and Elon Musk recently slugged it out online over the possible threat artificial intelligence might one day pose to the human race, although you could be forgiven if you don't see why this seems like a pressing question. Thanks to AI, computers are learning to do a variety of tasks that have long eluded them -- everything from driving cars to detecting cancerous skin lesions to writing news stories. But Musk, the founder of Tesla Motors and SpaceX, worries that AI systems could soon surpass humans, potentially leading to our deliberate (or inadvertent) extinction. Two weeks ago, Musk warned U.S. governors to get educated and start considering ways to regulate AI in order to ward off the threat. "Once there is awareness, people will be extremely afraid," he said at the time.

Some of the world's top researchers in AI have proved their mettle by taking top honors in three challenges posed by the Seattle-based Allen Institute for Artificial Intelligence. The institute, also known as AI2, was created by Microsoft co-founder Paul Allen in 2014 to blaze new trails in the field of artificial intelligence. One of AI2's previous challenges tested the ability of AI platforms to answer eighth-grade-level science questions. The three latest challenges focused on visual understanding – that is, the ability of a computer program to navigate real-world environments and situations using synthetic vision and machine learning. These aren't merely academic exercises: Visual understanding is a must-have for AI applications ranging from self-driving cars to automated security monitoring to sociable robots.

And how can businesses, as well as members of the public, best keep themselves informed about the extent to which advances in AI may impact on the economy, as well as our society? A recent consultation by the UK House of Lords Select Committee on Artificial Intelligence has called for evidence on the economic, ethical and social implications of advances in artificial intelligence [call for evidence PDF]. The consultation poses a range of questions in particular topic areas, such as the impact of AI on society and the public perception of it, as well as ethical considerations and the role of the governemnt in responding to AI's development and use. For example, one question, targeted at experts in the field, asks "What is the current state of artificial intelligence and what factors have contributed to this?". Another, that could be answered by a much wider audience, seeks to explore the extent to which "efforts [should be] be made to improve the public's understanding of, and engagement with, artificial intelligence" and how they should be pursued.

A deep neural network based architecture was constructed to predict amino acid side chain conformation with unprecedented accuracy. Amino acid side chain conformation prediction is essential for protein homology modeling and protein design. Current widely-adopted methods use physics-based energy functions to evaluate side chain conformation. Here, using a deep neural network architecture without physics-based assumptions, we have demonstrated that side chain conformation prediction accuracy can be improved by more than 25%, especially for aromatic residues compared with current standard methods. More strikingly, the prediction method presented here is robust enough to identify individual conformational outliers from high resolution structures in a protein data bank without providing its structural factors. We envisage that our amino acid side chain predictor could be used as a quality check step for future protein structure model validation and many other potential applications such as side chain assignment in Cryo-electron microscopy, crystallography model auto-building, protein folding and small molecule ligand docking.

This paper describes an implementation of the L-BFGS method designed to deal with two adversarial situations. The first occurs in distributed computing environments where some of the computational nodes devoted to the evaluation of the function and gradient are unable to return results on time. A similar challenge occurs in a multi-batch approach in which the data points used to compute function and gradients are purposely changed at each iteration to accelerate the learning process. Difficulties arise because L-BFGS employs gradient differences to update the Hessian approximations, and when these gradients are computed using different data points the updating process can be unstable. This paper shows how to perform stable quasi-Newton updating in the multi-batch setting, studies the convergence properties for both convex and nonconvex functions, and illustrates the behavior of the algorithm in a distributed computing platform on binary classification logistic regression and neural network training problems that arise in machine learning.

A variety of representation learning approaches have been investigated for reinforcement learning; much less attention, however, has been given to investigating the utility of sparse coding. Outside of reinforcement learning, sparse coding representations have been widely used, with non-convex objectives that result in discriminative representations. In this work, we develop a supervised sparse coding objective for policy evaluation. Despite the non-convexity of this objective, we prove that all local minima are global minima, making the approach amenable to simple optimization strategies. We empirically show that it is key to use a supervised objective, rather than the more straightforward unsupervised sparse coding approach. We compare the learned representations to a canonical fixed sparse representation, called tile-coding, demonstrating that the sparse coding representation outperforms a wide variety of tilecoding representations.

Artificial Intelligence is reshaping your relationship with the world and it's just getting started. Tesla's autopilot, job automation, the products you'stumble upon' online - it's entering our daily lives, careers, businesses, even our homes with such blistering pace you probably haven't even realized it. There's a reason Andrew Ng, the founder of $100m company Coursera said "Artificial Intelligence is the new electricity" - soon it'll be as much a part of your daily life as your smartphone, except without the off button. But here's where things get really crazy. This time round, the revolution will see machines taking on tasks no human intellect could ever perform.

Much has been made of the "food boom" in Pittsburgh, and the city has long had a thriving arts scene. But perhaps the secret, underlying driver for both the economy and the cool factor -- the reason Pittsburgh now gets mentioned alongside Brooklyn and Portland, Ore., as an urban hot spot for millennials -- isn't chefs or artists but geeks. In a 2014 article in The Pittsburgh Post-Gazette, Mayor Bill Peduto compared Carnegie Mellon, along with the University of Pittsburgh, to the iron ore factories that made this city an industrial power in the 19th century. The schools are the local resource "churning out that talent" from which the city is fueled. Because of the top students and research professors at Carnegie Mellon, tech companies like Apple, Facebook, Google and Uber have opened offices here.