On October 4th, roughly one year after the introduction of its branded line of hardware products, Google unveiled a second iteration of "Made by Google" hardware. This was a major product launch, but more than that, the presenters repeatedly hammered home Google's AI first messaging mantra with proof points in the form of a second generation branded product line built around AI and machine learning. The company's hardware strategy is clear. Google believes it is uniquely positioned to blend AI Software Hardware to deliver innovative products that will win in the marketplace, even if they are late to market. This second generation of Google hardware provides abundant proof that the company can bring uniquely differentiated features to existing product categories, and maybe even create some new ones.

If you're concerned (or super excited) about machine learning (ML) becoming mainstream, a recent survey by Oxford Economics on behalf of human resources (HR) and IT asset management company ServiceNow should pique your interest. The report, which surveyed 500 Chief Information Officers (CIOs) in 11 countries and across 25 industries, found that 49 percent of the companies are already using ML to improve traditional business processes. Of the 500 CIOs surveyed, 200 said they're already beyond the pilot stage and have begun deploying ML in some capacity. CIOs are hoping to limit user error and errors in judgement by introducing automation. Almost 70 percent of CIOs said decisions made by machines will be more accurate than those made by humans.

When US troops return home from a tour of duty, each person finds their own way to resume their daily lives. But they also, every one, complete a written survey called the Post-Deployment Health Assessment. It's designed to evaluate service members' psychiatric health and ferret out symptoms of conditions like depression and post-traumatic stress, so common among veterans. But the survey, designed to give the military insight into the mental health of its personnel, can wind up distorting it. Thing is, the PDHA isn't anonymous, and the results go on service members' records--which can deter them from opening up.

Welcome to the Semantic Web - Chris Skinner's blog. Vincent Fournier/Gallerystock By Toby Walsh However you look at it, the future appears bleak. The world is under immense stress environmentally, economically and politically. The novelist who inspired Elon Musk. Elon Musk, the world's most restless entrepreneur, has embarked on yet another venture.

At the bottom of the background page, there is a more extensive list of resources on AI safety.

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.

In machine learning and data mining, linear models have been widely used to model the response as parametric linear functions of the predictors. To relax such stringent assumptions made by parametric linear models, additive models consider the response to be a summation of unknown transformations applied on the predictors; in particular, additive isotonic models (AIMs) assume the unknown transformations to be monotone. In this paper, we introduce sparse linear isotonic models (SLIMs) for highdimensional problems by hybridizing ideas in parametric sparse linear models and AIMs, which enjoy a few appealing advantages over both. In the high-dimensional setting, a two-step algorithm is proposed for estimating the sparse parameters as well as the monotone functions over predictors. Under mild statistical assumptions, we show that the algorithm can accurately estimate the parameters. Promising preliminary experiments are presented to support the theoretical results.

Any form of classification is supervised and not unsupervised[1][2]. You are probably interested in unsupervised image segmentation, where the algorithm attempts to determine which pixels are related and groups them into certain categories. This can be done by using traditional partitional clustering algorithms, such as K-means/EM[3], or advanced deep learning methods such as convolutional autoencoders[4], bayesian methods[5] and so on. You may read this survey research paper on the evaluation of such techniques - Image segmentation evaluation: A survey of unsupervised methods.

Lear what artificial intelligence is and how it can benefit content.

A recent survey compiled by MIT Technology Review and Google Cloud suggests that machine learning (ML) is being adopted by businesses at a rapid pace. According to data collected, 60 percent of respondents indicated they have already implemented ML strategies, with almost a third attesting they were at the "mature stage" of those efforts. The survey, which was conducted in 2016, gathered responses from 375 businesses of all sizes. Companies ranged from one-person shops to those with more than 3,000 employees. About half of the companies surveyed employed less than 50 people.