In this paper, a Wide Learning architecture is proposed that attempts to automate the feature engineering portion of the machine learning (ML) pipeline. Feature engineering is widely considered as the most time consuming and expert knowledge demanding portion of any ML task. The proposed feature recommendation approach is tested on 3 healthcare datasets: a) PhysioNet Challenge 2016 dataset of phonocardiogram (PCG) signals, b) MIMIC II blood pressure classification dataset of photoplethysmogram (PPG) signals and c) an emotion classification dataset of PPG signals. While the proposed method beats the state of the art techniques for 2nd and 3rd dataset, it reaches 94.38% of the accuracy level of the winner of PhysioNet Challenge 2016. In all cases, the effort to reach a satisfactory performance was drastically less (a few days) than manual feature engineering.

Several methods exist to infer causal networks from massive volumes of observational data. However, almost all existing methods require a considerable length of time series data to capture cause and effect relationships. In contrast, memory-less transition networks or Markov Chain data, which refers to one-step transitions to and from an event, have not been explored for causality inference even though such data is widely available. We find that causal network can be inferred from characteristics of four unique distribution zones around each event. We call this Composition of Transitions and show that cause, effect, and random events exhibit different behavior in their compositions. We applied machine learning models to learn these different behaviors and to infer causality. We name this new method Causality Inference using Composition of Transitions (CICT). To evaluate CICT, we used an administrative inpatient healthcare dataset to set up a network of patients transitions between different diagnoses. We show that CICT is highly accurate in inferring whether the transition between a pair of events is causal or random and performs well in identifying the direction of causality in a bi-directional association.

We consider the problem of online active learning to collect data for regression modeling. Specifically, we consider a decision maker with a limited experimentation budget who must efficiently learn an underlying linear population model. Our main contribution is a novel threshold-based algorithm for selection of most informative observations; we characterize its performance and fundamental lower bounds. We extend the algorithm and its guarantees to sparse linear regression in high-dimensional settings. Simulations suggest the algorithm is remarkably robust: it provides significant benefits over passive random sampling in real-world datasets that exhibit high nonlinearity and high dimensionality --- significantly reducing both the mean and variance of the squared error.

There are lots of economic opportunities coming thanks to gains in artificial intelligence, the White House said in a report today, but that same report warns that millions of jobs could be displaced while the technology improves. Artificial intelligence, the report notes, accelerates trends seen since the industrial revolution, as people lose jobs to automation and are forced to learn new skills to find new career paths. How fast we'll see those impacts is the question. The report notes that researchers' estimates about jobs threatened ranges widely from 9 to 47 percent, and notes that because "AI is not a single technology, but rather a collection of technologies that are applied to specific tasks, the effects of AI will be unevenly felt throughout the economy." That said, the general assessment is that the jobs hardest hit are those that are more easily automated, which disproportionately impacts people with less educational attainment.

Sure, Amazon made its first drone delivery last week, but 7-Eleven already has it beat. Today, the convenience store company announced that it has already made a total of 77 deliveries by drone in the state of Nevada. Of course the caveat here is that 7-Eleven relied on Flirtey, a drone delivery service company that's already made a name for itself by delivering Domino's in New Zealand and textbooks in Australia. It also made the first FAA-approved urban drone delivery earlier this year. Though the deliveries kicked off in July, it was in November when the company started making regular weekend deliveries from a 7-Eleven store to about a dozen customers.

Bayesian Reasoning and Machine Learning by David Barber has a chapter on Approximate Sampling Christophe Andrieu et al. have written an introductory tutorial (pdf) on MCMC methods that covers most of the MCMC algorithms Dr. Daphne Koller offers an online course on Coursera, Probabilistic Graphical Models, which also covers the Gibbs Sampler and the Metropolis-Hastings Algorithm Dr. A. Taylan Cemgil has prepared very useful lecture notes (pdf) for his Monte Carlo methods course

Artificial intelligence (or AI) has permeated most facets of our lives. Algorithms suggest our social media mates. But could the arrival of the robots be applied to education? Jozef Misik, managing director of Knowble, a language tech start-up whose products are built on AI, believes so: "Most educational technology products will have an AI or deep learning component in future," he says. Already, AI is able to address common learning challenges.

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As the race to digital accelerates, and as organizations continue to differentiate their products services and interactions, the need for smart and speedy human resources becomes significant. Business complexity has grown from managing simple standard processes and applications to complex, dynamic, and adaptable interactions, processes, and services for better outcomes. AI will make all resources smarter to deal with the increased speed of changes reflected in dynamic goals. This research note reviews five key Cognitive Artificial Intelligence predictions for 2017 and beyond (see Note 1).

Seventy-seven percent of business-to-business marketers say that artificial intelligence is going to be "the next big thing," while 23% dismiss it as "just a lot of hype," Demandbase and Wakefield Research report. Ninety percent say that they're not currently employing the tactic, citing barriers such as integration with current systems and the need to train employees.