At Zelros, we believe that many enterprise machine learning applications lack of a major component: the interface between the complex data workflow on one side, and the business users on the other side -- that is to say a natural way for people to interact with technology. We believe that one solution for this issue is to invent a new type of intelligent interaction, like chatbots, based on natural language and discussions. We are not the only ones. There is not a day without an article promoting benefits of conversational and invisible UI. Technically, end-to-end chatbots are constituted of several building blocks.

Cohort 3 research Orange Gao (left in image below) attended the Women in Machine Intelligence in Healthcare Dinner. RE.WORK, an all-female company which is a strong advocate for supporting female entrepreneurs and women working towards advancing technology and science, organized an evening dinner event of discussions & networking around the progress and application of machine intelligence within healthcare on Wednesday 12th October 2016 in London. Dinner and presentations commenced at 7pm and finished at approximated 10pm. RE.WORK invited 50 attendees from leading academics, industry experts and entrepreneurs. During the dinner, speakers gave wonderful and insightful presentations about the new trends and ideas around the Machine Learning in health.

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.