We investigate the online version of Principle Component Analysis (PCA), where in each trial t the learning algorithm chooses a k -dimensional subspace, and upon receiving the next instance vector \x_t, suffers the compression loss, which is the squared Euclidean distance between this instance and its projection into the chosen subspace. When viewed in the right parameterization, this compression loss is linear, i.e. it can be rewritten as \text{tr}(\mathbf{W}_t\x_t\x_t \top), where \mathbf{W}_t is the parameter of the algorithm and the outer product \x_t\x_t \top (with \ \x_t\ \le 1) is the instance matrix. In this paper generalize PCA to arbitrary positive definite instance matrices \mathbf{X}_t with the linear loss \text{tr}(\mathbf{W}_t\X_t) . We evaluate online algorithms in terms of their worst-case regret, which is a bound on the additional total loss of the online algorithm on all instances matrices over the compression loss of the best k -dimensional subspace (chosen in hindsight). We focus on two popular online algorithms for generalized PCA: the Gradient Descent (GD) and Matrix Exponentiated Gradient (MEG) algorithms.

KARIYA, Japan & TOKYO--(BUSINESS WIRE)--DENSO Corporation and Toshiba Corporation have reached a basic agreement to jointly develop an artificial intelligence technology called Deep Neural Network-Intellectual Property (DNN-IP), which will be used in image recognition systems which have been independently developed by the two companies to help achieve advanced driver assistance and automated driving technologies. DNN, an algorithm modeled after the neural networks of the human brain, is expected to perform recognition processing as accurately as, or even better than the human brain. To achieve automated driving, automotive computers need to be able to identify different road traffic situations including a variety of obstacles and road markings, availability of road space for driving, and potentially dangerous situations. In image recognition based on conventional pattern recognition and machine learning, objects that need to be recognized by computers must be characterized and extracted in advance. In DNN-based image recognition, computers can extract and learn the characteristics of objects on their own, thus significantly improving the accuracy of detection and identification of a wide range of objects.

Artificial Intelligence, with the help of IBM Watson, Yoast and Moz, has helped Mash'n Learn to produce one of the helping tool for all e-Commerce Merchants. Whatever the industry and the platform, copywriting great contents for Google search engine is a complex and long task. You can get Content Copywriters on the Market for a cheap cost, but you will still have to wait for quite a long time before they produce description for more than 100 products. Most of the successful Merchants have more than ten thousands SKU references. We estimated with our Content provider that thirty thousands descriptions of 300 words would take more than three months to produce.

The iPhone 7's AirPods wireless headphones have divided analyst opinion: Is the W1 chipset – Apple's first-ever wireless chip – a game changer or does it lack originality? "The new AirPods are a wholly new product," says Paul Erickson, IHS Markit Senior Analyst for the Connected Home, citing the W1 chipset and its groundbreaking functionality. The AirPods' W1 chipset is important, because it enables Apple to move iOS-based accessory purchases from third parties and industry standards to Apple's product families. But the larger importance of the AirPods could be in how they help accelerate Apple's artificial intelligence (AI) push. "With respect to Apple's push into AI, I think their strategy is part of a larger industry shift towards ubiquitous computing," says IHS Principal Analyst for wireless communications, Wayne Lam.

SalesForce built the best database of customers-sales interaction in the industry, and they are now increasing its value by cataloging, analyzing it and extracting insights to improve the sales process for both salespeople and customers. Indeed, thanks to that early commitment to analytics, they are in position to create AI to better match sales representatives with prospects, predict customers' questions to help providing the right information at the right time, drive the sales discussion in the most efficient way, etc.

When we use our muscles, they produce heat as a byproduct. When we use them a lot, we need to actively cool them, which is why we sweat. By sweating, we pump water out of our bodies, and as that water evaporates, it cools us down. Robots, especially dynamic robots like humanoids that place near-constant high torque demands on their motors, generate enough heat that it regularly becomes a major constraint on their performance. In this paper we propose a novel method to utilize the skeletal structure not only for supporting force but can also cool by using latent heat.

Since years, recruiters and talent acquisition experts have had to depend on archaic methods and "Boolean search" techniques when hunting for the best talents. This meant that recruitment professionals wasted endless hours on verifying the data and following up with potential candidates, who might not make the perfect fit for their organisations. Nevertheless, a breed of new-age and advanced technologies is changing the game of recruitment for HR and talent acquisition professionals. There has been a downpour of cutting-edge tech trends in human resources such as Big Data, Deep Learning and Data Analytics, but one particular tech trend that is gaining a decent traction in the HR space is the use of Artificial Intelligence (AI) for recruitment and people management. Does it mean death of the recruiter or the human touch from recruitment?

I've been thinking about bots lately (who hasn't?) and it occurred to me that the landscape is fairly confusing. Sure Microsoft, Facebook, Slack, and Kik have launched bot platforms, but a platform doesn't answer the questions: What type of bot is good for my business? And do I even need to build a bot at all? Luckily, with some help from a presentation by Josh Jacobs, the President of Kik,--one of the largest bot-platforms on the market--I was able to codify the types of bots that you'll be seeing a lot more of in 2017. Think of them as a non-fleshy friend that you can text back-and-forth with. Here's an example: rather than going to your favorite flight-aggregator app to find a sweet airline deal, savvy texters are asking travel bots to find them the cheapest flights from the comfort of their favorite messaging platform.

Apple CEO Tim Cook hinted that Japan has a big part to play in the company's ongoing development of AI, as he continued his travels around the country via bullet train on Monday. Speaking to Nikkei Asian Review, Cook revealed that Apple's new advanced R&D center in Yokohama, currently scheduled for completion in December, would focus on "deep engineering" and would be "very different" from the R&D base Apple is building in China, without giving away specifics. Photo via Tim Cook However, Cook intimated that one of its main focuses would be on developing Apple's artificial intelligence services – which recently drew some criticism in the press – and emphasized that the company wanted to leverage specifically Japanese expertise in the field. "AI is horizontal in nature, running across all products, and is used in ways that most people don't even think about," said Cook. "We want the AI to increase your battery life, to recommend music to Apple Music subscribers... [to] help you remember where you parked your car."Japan's robotics heritage is legendary, coming on the back of years of successfully building industrial robots, however the state of its AI research remains ambiguous, given its relative lack of investment in deep learning from large amounts of analyzed data, which U.S. companies like Facebook and Google are already heavily researching. To improve on this front, Japan has just opened a Center for Advanced Integrated Intelligence Research in Tokyo (RIKEN), which specifically aims to develop systems of AI that will be able to solve problems using "Big Data".

The University of Cambridge has opened a 10 million research centre to explore the impact of artificial intelligence, Wired reports. The Leverhulme Centre for the Future of Intelligence -- first announced last December and funded with a research grant from the Leverhulme Trust -- will study the impacts of this "potentially epoch-making technological development, both short and long term." The centre's new website details a list of projects that its researchers will look at. The centre also writes on its website that its aim is to build a new interdisciplinary community of researchers, with strong links to technologists and the policy world. Led by Cambridge philosophy professor Huw Price, the centre, which opened on Monday, will work in conjunction with the university's Centre for the Study of Existential Risk (CSER), which is funded by Skype cofounder Jaan Tallinn and looks at emerging risks to humanity's future including climate change, disease, warfare, and artificial intelligence.