Adobe is rolling out AI enhancements to the Experience platform, its customer experience management service. The creative software giant said it's adding more features that draw upon its Sensei AI framework, including advanced customer personalization, product recommendations and streamlined activation. Adobe's Data Science Workspace aims to help enterprises leverage real-time data at scale. The tool lets data scientists access large datasets in the Experience platform to create, train and optimize custom AI and machine learning models using Sensei. For enterprise retailers, Data Science Workspace is also pitched as a way to analyze omnichannel data easier and more quickly.

Data science features significantly when you talk about high paying future jobs. To understand what's driving this trend, which experts say will only grow bigger with time, you have to know how the technology landscape has experienced tremendous growth and undergone a sea change over the last few years, thanks to a high degree of reliability on computer programs, data-based analysis, and digital technology. Working with huge sets of data and making data-driven decisions, in particular, have become a norm in most sectors -- from banks and fintech to transportation, IT, and more. After all, almost each interaction with technology has data at its core. Thus, be it your online purchase at marketplaces like Amazon, streaming recommendations for you on Netflix or Amazon Prime, your Facebook feed, or even the facial recognition necessary to sign into your phone, almost everything leverages data to perform certain operations.

Is reinforcement learning practical at this point for industry work? The most prominent examples we see are from DeepMind (AlphaStar, AlphaGo), but the team are world-class researchers (over 40 of them) who also worked closely with expert Starcraft 2 players with a ton of computing resources. As someone who hasn't had much experience in RL, I see potential applications but am unsure of the amount of work or practicality of it. For example, one potential application for RL is to learn fraudulent behavior in an online retailer system (i.e. Amazon, EBay) and proactively find methods of fraud before they happen.

The problem with merchandising today is obvious – no two stores are alike, and treating them alike requires an acceptance of the notion of acceptable financial loss,

Here is the brief story of this evolution of self-service. Not that long-ago, shop clerks pulled merchandise for us from high shelves behind a counter. Today, we push squeaky, wheeled, metal baskets around the well-lit warehouses we call supermarkets. Innovation in retail shopping--as with all service businesses--happens through shifts in business processes and technologies. Automated self-service fuses process and technology so customers can serve themselves.

Accenture has a rich history of helping customers all over the world build artificial intelligence (AI) and machine learning (ML) powered solutions with AWS services. In doing so, they always look for new and engaging ways to develop their teams with the appropriate level of enablement and hands-on training. Accenture's next ML initiative is rolling out their version of an AWS DeepRacer League, which is the world's first global autonomous racing league launched by AWS at re:Invent in 2018. Accenture's league spans 30 global locations and 17 countries, with each location featuring both a physical and virtual track to compete on for the title of Accenture AWS DeepRacer Champion. Machine learning is one of the fastest growing areas in the market.

Retailers are launching AI tools at a relatively high rate, but there is still room to improve when it comes to serving hyper-personalized experiences and understanding the customer intent. They are also struggling with a slew of issues during high-demand times, such as the holiday season, ranging from site performance to serving up personalized recommendations -- both key factors in a consumer's journey when shopping online. Those are top findings from a Lucidworks Retail TouchPoints best practices survey that outlines retailers' top concerns, such as performance, product "find-ability" and customer experience. Nearly three-quarters (73%) of retailer cite downtime, degraded site performance, and poor customer experience, as top worries during peak shopping periods, according to a press release. "Retailers put endless pressure on themselves to create the perfect shopping experience for customers," said Diane Burley, Lucidworks vice president of content, in the release.

Customers can now use a new version of the SageMaker XGBoost algorithm that is based on version 0.90 of the open-sourced XGBoost framework. XGBoost is a highly efficient and flexible algorithm for problems in regression, classification, and ranking.

Matloff delivers a well-balanced book for advanced beginners. Besides the mathematical formulas, he also presents many chunks of R code, and if the reader is able to read R code, the formulas and calculations become clearer. Due to the computational R code, the well-written Appendix, and an overall clear English, the book will help students and autodidacts. Matloff has written a textbook of the best kind for such a broad topic." ". . . the book is well suitable for a wide audience: For practitioners interested in applying the methodology, for students in statistics as well as economics/social sciences and computer science.

Convenience stores across Colorado will soon roll-out cannabidiol dispensing robots for patrons to purchase products via a touchscreen and in under three minutes. Boulder and Denver are the first cities to receive the CBD-dispensing robots, which were designed to also entertain and educate customers on the benefits of the cannabis-based products. CBD, or cannabidiol, is certainly trendy right now, and its sellers insist it may be very useful in treating things like pain, anxiety and epilepsy. The AI-powered robots were developed by Greenbox Robotics and allow consumers to purchase products via a touchscreen – saving them a trip to the dispensary. Tetrahydrocannabinol (THC) and cannabidiol (CBD) are both derived from the cannabis plant.