Compared with traditional machine learning models, neural networks are computationally more complex and introduce many additional parameters. This often prevents machine learning engineers and data scientists from getting the best performance from their models. In some cases, it might even dissuade data scientists from using neural networks. In this post, we show how to tune a Convolutional Neural Network (CNN) for a Natural Language Processing (NLP) task 400 times faster than with traditional random search on a CPU. Additionally, this method also achieves greater accuracy.

This week Amazon took the wraps off a new incarnation of its Alexa voice assistant, giving the AI an eye so it can see as well as speak and hear. The Echo Look also contains a depth sensor that's being used, in the first instance, to create a bokeh effect for a hands-free style selfies feature that Amazon is hoping will sell the device to fashion lovers, by making their outfits pop out against the bedroom wallpaper, and making them more eager to socially share. The Echo Look app is where users can view the style selfies (and videos) they've asked Alexa to record for them (she indefinitely stores a copy for Amazon too). But the flagship feature of the app is a fashion feedback service, called Style Check, which Amazon says will utilize machine learning to rate fashion choices and help users choose between outfit pairs. And ultimately, presumably, give their entire wardrobe a score.

Editor's Note: This article reflects the opinions of the author and does not necessarily represent the view of Star Cloud Services. With 2017 nearly half over, we thought it would be a good idea to review some of the top retail trends we are seeing so far this year. Retail technology is featuring high on the list of what independent and local retailers can now do to improve their in-store offers. We provide mobile coupons, digital receipts and in the future customizable receipt promos with our unique cloud-connected printers. As such, we're on the leading edge of the intersection of retail and the Internet of Things, with a unique ability to turn the paper and digital receipts into loyalty-marketing and monetizable advertising that produces ROI.

Spark's popularity means the field is expanding in terms of both use and capability. Faster than Hadoop and MapReduce, but compatible with Java, Scala, Python, and R, this open source clustering framework is becoming a must-have skill. Spark: Big Data Cluster Computing in Production goes beyond the basics to show you how to bring Spark to real-world production environments. With expert instruction, real-life use cases, and frank discussion, this guide helps you move past the challenges and bring proof-of-concept Spark applications live.

If you want to get a lot of retailers nodding their heads, ask them this: "Are you pursuing a personalization strategy?" You'll get a pretty resounding "yes!". In fact, personalization already drives a lot of activity on eCommerce sites. It often influences the products or offers featured on the home page, the order of products you see on a category or search results page, and makes product recommendations both on the site and in the digital marketing (email, retargeting) that follow. If you've ever been browsing a site and suddenly had the order of products change around on a product listing page, that was probably a decision driven by personalization.

Alibaba Group Holding Ltd. Chairman Jack Ma said society should prepare for decades of pain as the internet disrupts the economy. The world must change education systems and establish how to work with robots to help soften the blow caused by automation and the internet economy, Ma said in a speech to an entrepreneurship conference in Zhengzhou, China. "In the next 30 years, the world will see much more pain than happiness," Ma said of job disruptions caused by the internet. "Social conflicts in the next three decades will have an impact on all sorts of industries and walks of life." It was an unusual speech for the Alibaba co-founder, who tends to embrace his role as visionary and extol the promise of the future.

Igor Kononenko studied computer science at the University of Ljubliana, Slovenia, receiving his BSc in 1982, MSc in 1985 and PhD in 1990. He is now professor at the Faculty of Computer and Information Science there, teaching courses in Programming Languages, Algorithms and Data Structures; Introduction to Algorithms and Data Structures; Knowledge Engineering, Machine Learning and Knowledge Discovery in Databases. He is the head of the Laboratory for Cognitive Modelling and a member of the Artificial Intelligence Department at the same faculty. His research interests include artificial intelligence, machine learning, neural networks and cognitive modelling. He is the (co) author of 170 scientific papers in these fields and 10 textbooks.

In this paper we study the setting where features are added or change interpretation over time, which has applications in multiple domains such as retail, manufacturing, finance. In particular, we propose an approach to provably determine the time instant from which the new/changed features start becoming relevant with respect to an output variable in an agnostic (supervised) learning setting. We also suggest an efficient version of our approach which has the same asymptotic performance. Moreover, our theory also applies when we have more than one such change point. Independent post analysis of a change point identified by our method for a large retailer revealed that it corresponded in time with certain unflattering news stories about a brand that resulted in the change in customer behavior. We also applied our method to data from an advanced manufacturing plant identifying the time instant from which downstream features became relevant. To the best of our knowledge this is the first work that formally studies change point detection in a distribution independent agnostic setting, where the change point is based on the changing relationship between input and output.

Smart speakers are getting increasingly popular and tech giants Google and Apple could not have been left behind in the race. While Amazon was the first to operate in the segment, Google jumped onto the bandwagon last year and now Apple is ready to give it competition. According to Apple tipster Sonny Dickson, Apple is all set to release its smart speakers soon. Dickson tweeted Thursday saying: "Apple is currently finalizing designs for their Alexa competitor, expected to be marketed as a Siri/AirPlay device." He further added that the device would be using Beats technology, which the company uses for some iPhone accessories and will run on iOS.

With the Artificial Intelligence (AI) market predicted to grow at a CAGR of 53.65% from 2015 to 2020, momentum for AI is undoubtedly surging. AI, which is essentially the capability of a machine to imitate intelligent human behaviour (such as visual perception, speech recognition, etc.) and carry out multiple tasks, is proven to drive greater efficiency, and is on course to play a key role in propelling the growth of the retail sector. As consumers continue to demand greater speed and easier access to product and service information, fuelled by our connected world and the internet of things, AI will dramatically enhance the customer experience through improving responsiveness, personalisation and productivity. The potential of AI and its anticipated impact on the future of retail is massive, and by embracing it early on, brands and retailers have the opportunity to gain a major competitive edge. In a world where a growing number of customers now check their smartphones while in store and browse consumer-generated content (CGC), such as product ratings, reviews and Q&As, photos and videos, retailers and brands need to evolve and react to demands that blur the lines between online and offline shopper journeys.