Most people don't buy a jar of relish every week. But when they decide to buy one from Ocado--the world's largest online-only grocery retailer--they don't have to scrabble at the back of the store. Instead, they call on robots and artificial intelligence to have it delivered to their door. Ocado claims that its 350,000-square-foot warehouse in Dordon, near the U.K.'s second city of Birmingham, is more heavily automated than Amazon's warehouse facilities. The company's task is certainly more challenging in many respects: most of the 48,000 lines of goods that it sells are perishable, and many must be chilled or frozen.

Reports are out that a Chinese robot has written and published its first newspaper article. The news comes the same month as a Japanese insurance company announced it was replacing 34 workers with an artificial intelligence system. "This is absolutely a wake-up call," said Zeus Kerravala, an analyst with ZK Research, who added that it's time for people to think about their careers and if a robot or A.I. system could easily replace them. "We are the beginning of robots taking jobs," he added. But the fact is, we've had other revolutions -- like the birth of the assembly line."

A typical viral marketing model identifies influential users in a social network to maximize a single product adoption assuming unlimited user attention, campaign budgets, and time. In reality, multiple products need campaigns, users have limited attention, convincing users incurs costs, and advertisers have limited budgets and expect the adoptions to be maximized soon. Facing these user, monetary, and timing constraints, we formulate the problem as a submodular maximization task in a continuous-time diffusion model under the intersection of a matroid and multiple knapsack constraints. We propose a randomized algorithm estimating the user influence in a network ($|\mathcal{V}|$ nodes, $|\mathcal{E}|$ edges) to an accuracy of $\epsilon$ with $n=\mathcal{O}(1/\epsilon^2)$ randomizations and $\tilde{\mathcal{O}}(n|\mathcal{E}|+n|\mathcal{V}|)$ computations. By exploiting the influence estimation algorithm as a subroutine, we develop an adaptive threshold greedy algorithm achieving an approximation factor $k_a/(2+2 k)$ of the optimal when $k_a$ out of the $k$ knapsack constraints are active. Extensive experiments on networks of millions of nodes demonstrate that the proposed algorithms achieve the state-of-the-art in terms of effectiveness and scalability.

Matrix completion is a basic machine learning problem that has wide applications, especially in collaborative filtering and recommender systems. Simple non-convex optimization algorithms are popular and effective in practice. Despite recent progress in proving various non-convex algorithms converge from a good initial point, it remains unclear why random or arbitrary initialization suffices in practice. We prove that the commonly used non-convex objective function for \textit{positive semidefinite} matrix completion has no spurious local minima --- all local minima must also be global. Therefore, many popular optimization algorithms such as (stochastic) gradient descent can provably solve positive semidefinite matrix completion with \textit{arbitrary} initialization in polynomial time. The result can be generalized to the setting when the observed entries contain noise. We believe that our main proof strategy can be useful for understanding geometric properties of other statistical problems involving partial or noisy observations.

The ability to accurately forecast and control inpatient census, and thereby workloads, is a critical and longstanding problem in hospital management. Majority of current literature focuses on optimal scheduling of inpatients, but largely ignores the process of accurate estimation of the trajectory of patients throughout the treatment and recovery process. The result is that current scheduling models are optimizing based on inaccurate input data. We developed a Clustering and Scheduling Integrated (CSI) approach to capture patient flows through a network of hospital services. CSI functions by clustering patients into groups based on similarity of trajectory using a novel Semi-Markov model (SMM)-based clustering scheme proposed in this paper, as opposed to clustering by admit type or condition as in previous literature. The methodology is validated by simulation and then applied to real patient data from a partner hospital where we see it outperforms current methods. Further, we demonstrate that extant optimization methods achieve significantly better results on key hospital performance measures under CSI, compared with traditional estimation approaches, increasing elective admissions by 97% and utilization by 22% compared to 30% and 8% using traditional estimation techniques. From a theoretical standpoint, the SMM-clustering is a novel approach applicable to any temporal-spatial stochastic data that is prevalent in many industries and application areas.

Recently, I have been working on the Neural Networks for Machine Learning course offered by Coursera and taught by Geoffrey Hinton. Overall, it is a nice course and provides an introduction to some of the modern topics in deep learning. However, there are instances where the student has to do lots of extra work in order to understand the topics covered in full detail. One of the assignments in the course is to study the Neural Probabilistic Language Model (The related article can be downloaded from here). An example dataset, as well as a code written in Octave (equivalently Matlab) are provided for the assignment.

Artificial intelligence that is as accurate as human specialists at identifying skin cancer has been developed by computer scientists and dermatologists. The breakthrough was made by a team at Stanford University, who trained a deep-learning algorithm to diagnose skin cancer using a database of around 130,000 skin disease images. "We realized it was feasible, not just to do something well, but as well as a human dermatologist," said Sebastian Thrun, a professor at the Stanford Artificial Intelligence Laboratory. A woman covers herself in suncream to stress the point that people should protect themselves from the sun as part of a Cancer Research Campaign, April 8, 1998. Researchers have developed an Artificial Intelligence program that can diagnose skin lesions as accurately as any specialist.

Post written by Jorn Peters & Taco Cohen When we humans see an object we've never seen before, we are almost immediately able to recognize the same object in many different situations. For example, when a child learns about its new teddy bear, it will still recognize the teddy if you turn it upside down. In contrast, while current-generation Deep Neural Networks (DNNs) can learn to recognize the teddy bear eventually, they will need to see many examples of rotated teddy bears, each one labelled "teddy". This hunger for data, or "statistical inefficiency" is perhaps the most significant practical limitation of current deep learning technology. Many of our clients at Scyfer have problems that could be solved by deep learning, but don't have large annotated datasets.

Jeremy Elman is a partner at DLA Piper, and is head of DLA Piper Miami's Intellectual Property and Technology and Emerging Growth practices. Laws govern the conduct of humans, and sometimes the machines that humans use, such as cars. But what happens when those cars become human-like, as in artificial intelligence that can drive cars? Who is responsible for any laws that are violated by the AI? This article, written by a technologist and a lawyer, examines that future of AI law.

This article on going deeper into regression analysis with assumptions, plots & solutions, was posted by Manish Saraswat. Manish who works in marketing and Data Science at Analytics Vidhya believes that education can change this world. R, Data Science and Machine Learning keep him busy. Regression analysis marks the first step in predictive modeling. No doubt, it's fairly easy to implement.