Robot developers say they are close to a breakthrough--getting a machine to pick up a toy and put it in a box. It is a simple task for a child, but for retailers it has been a big hurdle to automating one of the most labor-intensive aspects of e-commerce: grabbing items off shelves and packing them for shipping. HBC -1.08% and Chinese online-retail giant JD.com Inc., JD 0.37% have recently begun testing robotic "pickers" in their distribution centers. Some robotics companies say their machines can move gadgets, toys and consumer products 50% faster than human workers. Retailers and logistics companies are counting on the new advances to help them keep pace with explosive growth in online sales and pressure to ship faster.

In the first of the three leading-edge sessions on artificial intelligence at this year's ILTACon, folks will be hearing from Martin Tully, Co-Chair, of Akerman LLP's Data Law Practice, and Samuel Whitman, Mayer Brown's Knowledge Management Leader (see below for speaker bios). I will be moderating this panel along with the other two in the AI series at ILTACon 2017. At the end of the panel discussion those in attendance should be well on their way towards determining a communication strategy to educate their teams about AI technologies, have an understanding about real use-cases of AI in legal today, and lastly, have the basics down when it comes to understanding terms like natural language processing and machine learning. Have suggestions for questions I should ask Martin and Samuel? Send them my way via Twitter!

China's government has announced a goal of becoming a global leader in artificial intelligence in just over a decade, putting political muscle behind growing investment by Chinese companies in developing self-driving cars and other advances. Communist leaders see AI as key to making China an "economic power," said a Cabinet statement on Thursday. It calls for developing skills and research and educational resources to achieve "major breakthroughs" by 2025 and make China a world leader by 2030.

Streetlib is an online self-publishing solution that is geared towards independent authors. When you register for an account you can upload your e-book use a free ISBN that they give you. There are a ton of distribution options, but Amazon, Apple, Kobo, Google Play and Tolino generate the most sales. There are over 180,000 titles from 75,000 authors and the average indie is earning $35,000 per year. The markets that Streetlib finds that are most successful are mainly outside the United States, such as Italy, France, Germany, Spain, UK, Mexico, Canada and India.

Billionaire Mark Cuban made an appearance today in New York City's Central Park at the second annual "OZY Fest", and he didn't disappoint. Naturally, the conversation first gravitated towards President Trump, with moderator Carlos Watson leading a panel that also included Republican presidential candidate Jeb Bush and comedian Samantha Bee. Watson first asked if any of the panelists would join President Trump's cabinet. Cuban proclaimed that he wouldn't join Trump's cabinet, but he would meet with the President to converse about the state of our nation. When it was Jeb Bush's turn, the former governor simply replied: "Let's move onto something [more] fun." Watson then shifted gears to the hot-button topic of police brutality. "I think every city is different," Cuban responded when asked if our police system nationwide is broken.

Artificial Intelligence is making headlines everywhere. If you haven't been living under a rock, you've definitely come across the terms AI, Machine Learning, Natural Language Processing and so on. Anyone who knows a thing or two about the tech industry, knows that it is no stranger to catchphrases, fads, buzzwords and hype. AI, ML etc have become buzzwords. The hype around AI has reduced in other parts of the world but India is still very much part of the hype cycle. Students want to learn AI even before they have a base in computer science.

Is it possible to generally construct a dynamical system to simulate a black system without recovering the equations of motion of the latter? Here we show that this goal can be approached by a learning machine. Trained by a set of input-output responses or a segment of time series of a black system, a learning machine can be served as a copy system to mimic the dynamics of various black systems. It can not only behave as the black system at the parameter set that the training data are made, but also recur the evolution history of the black system. As a result, the learning machine provides an effective way for prediction, and enables one to probe the global dynamics of a black system. These findings have significance for practical systems whose equations of motion cannot be approached accurately. Examples of copying the dynamics of an artificial neural network, the Lorenz system, and a variable star are given. Our idea paves a possible way towards copy a living brain.

We consider the problem of minimizing the sum of an average function of a large number of smooth convex components and a general, possibly non-differentiable, convex function. Although many methods have been proposed to solve this problem with the assumption that the sum is strongly convex, few methods support the non-strongly convex cases. Adding a small quadratic regularization is a common trick used to tackle non-strongly convex problems; however, it may worsen the quality of solutions or weaken the performance of the algorithms. Avoiding this trick, we propose a new accelerated stochastic mirror descent method for solving the problem without the strongly convex assumption. Our method extends the deterministic accelerated proximal gradient methods of Paul Tseng and can be applied even when proximal points are computed inexactly. Our direct algorithms can be proven to achieve the optimal convergence rate $O(\frac{1}{k^2})$ under a suitable choice of the errors in calculating the proximal points. We also propose a scheme for solving the problem when the component functions are non-smooth and finally apply the new algorithms to a class of composite convex concave optimization problems.

Evaluating expression of the Human epidermal growth factor receptor 2 (Her2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognised importance as a predictive and prognostic marker in clinical practice. However, visual scoring of Her2 is subjective and consequently prone to inter-observer variability. Given the prognostic and therapeutic implications of Her2 scoring, a more objective method is required. In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring. The contest dataset comprised of digitised whole slide images (WSI) of sections from 86 cases of invasive breast carcinoma stained with both Haematoxylin & Eosin (H&E) and IHC for Her2. The contesting algorithms automatically predicted scores of the IHC slides for an unseen subset of the dataset and the predicted scores were compared with the 'ground truth' (a consensus score from at least two experts). We also report on a simple Man vs Machine contest for the scoring of Her2 and show that the automated methods could beat the pathology experts on this contest dataset. This paper presents a benchmark for comparing the performance of automated algorithms for scoring of Her2. It also demonstrates the enormous potential of automated algorithms in assisting the pathologist with objective IHC scoring.

Robot developers say they are close to a breakthrough--getting a machine to pick up a toy and put it in a box. It is a simple task for a child, but for retailers it has been a big hurdle to automating one of the most labor-intensive aspects of e-commerce: grabbing items off shelves and packing them for shipping. HBC -1.08% and Chinese online-retail giant JD.com Inc., JD 0.37% have recently begun testing robotic "pickers" in their distribution centers. Some robotics companies say their machines can move gadgets, toys and consumer products 50% faster than human workers. Retailers and logistics companies are counting on the new advances to help them keep pace with explosive growth in online sales and pressure to ship faster.