No longer the exclusive domain of data-reliant businesses like Google, Microsoft, and Amazon, Machine learning has been making its way to the masses as an essential approach to data. Today, machine learning is understood and accepted by a more mainstream audience, and has become a measurable driver for big business both on and offline. There are three key reasons why machine learning has become one ofthe top 10 strategic technology trendsthat will shape digital business opportunities through 2020. First, the volume of data companies now collect is so massive that many struggle to make sense of it. Machine learning allows companies to take advantage of the information they already have.

Technology is the future of retail. A recent Sector Insights government report (PDF) said that retailers will need to embrace the latest technology trends, such as big data, and have the skills to work with digital systems if they are to be successful in the future. The retail industry is on the whole a voracious adopter of modern data-centric technology, aping the manufacturing world by using big data analytics to streamline supply chains, and using smartphone apps and wireless beacons to harvest customer data to deliver better service. However, Robert Hetu, retail research director at analyst house Gartner, noted that, despite having the technology to collect and access large amounts of digital data, retailers fail to put it to effective use. "With the rise of digital media came explosive growth in data volumes, and inexpensive storage encouraged retention of data with no present value because of the hope that it would be fruitful in the future," he said.

Earlier this week, Amazon held a secret, invitation-only event for the machine-learning and robotics community. The three-day event, at the Parker Palm Springs resort in California, had hundreds of guests from the business, entertainment, and robotics sectors. Business Insider got photos of the event from one of the attendees. Here's what it was like:

Google may be looking to end their involvement with robots and artificial intelligence, but that's hardly stopping Amazon. The e-commerce more retailer recently held a top secret conference for robot experts and space explorers. There were even lightsabers involved. This past week, in the lovely town of Palm Springs, California, a top secret meeting took place. This invite-only conference included experts in artificial intelligence, robotics and space exploration.

Amazon.com, Inc. (NASDAQ:AMZN) organized an exclusive conference this week in Palm Springs, California, that focused on robotics, artificial intelligence (AI), space exploration, and home automation, according to Bloomberg Business and reports from attendees. The event was called MARS, which stands for Machine-Learning Automation, Robotics and Space Exploration. Some of the guests on the invite-only list came from robotics companies such as Rethink Robotics, educational institutions like MIT, research institutes like ETH Zurich, and carmakers such as Toyota. Around 130 people were invited to the cozy affair. Much of the event is still shrouded in mystery.

Online shopping and purchases are expected to rise by 78 percent this year as aggressive marketing of merchandise have risen higher than ever, a report says. "Unfazed by the slowdown, average online purchases are expected to increase by 78 percent in 2016 from 66 percent in 2015 due to attractive deals and aggressive marketing of an ever-expanding range of merchandise from clothes to jewellery, from electronics to books," said the Assocham-PwC report. With the growth of e-commerce sector, the possibilities and options for simplicity are what can bring multiple variations regardless of the prices floating in the online market. Searchub Inc., considering the prospect of the e-commerce and online shopping market developed a platform called Searchub that uses machine learning algorithms to offer users a truly personalized shopping experience. Connecting retailers with the customers through recommendation and big data, the platform performs search and displays result according to the user's taste, style and their social profile.

Robotics companies and academics descended on a resort in Palm Springs this week for an invitation-only conference organized by Amazon.com Inc. to bring together experts in artificial intelligence, robotics, home automation and space exploration. At the event, held at the Parker Palm Springs, there were robotic arms dueling with light sabers from Star Wars, seminars about imbuing machines with human values and a celebrity appearance by film director Ron Howard, known for his portrayal of Richie Cunningham in the sitcom Happy Days. Jeff Bezos mingled with attendees; one said he drank single-malt whiskey with Amazon's chief executive officer. Representatives from companies such as Rethink Robotics, Toyota Motor Corp., and others attended the conference, called MARS, short for Machine-Learning (Home) Automation, Robotics and Space Exploration.

An increasing number of people are using online social networking services (SNSs), and a significant amount of information related to experiences in consumption is shared in this new media form. Text mining is an emerging technique for mining useful information from the web. We aim at discovering in particular tweets semantic patterns in consumers' discussions on social media. Specifically, the purposes of this study are twofold: 1) finding similarity and dissimilarity between two sets of textual documents that include consumers' sentiment polarities, two forms of positive vs. negative opinions and 2) driving actual content from the textual data that has a semantic trend. The considered tweets include consumers opinions on US retail companies (e.g., Amazon, Walmart). Cosine similarity and K-means clustering methods are used to achieve the former goal, and Latent Dirichlet Allocation (LDA), a popular topic modeling algorithm, is used for the latter purpose. This is the first study which discover semantic properties of textual data in consumption context beyond sentiment analysis. In addition to major findings, we apply LDA (Latent Dirichlet Allocations) to the same data and drew latent topics that represent consumers' positive opinions and negative opinions on social media.

Clarke told the Mumsnet marketing conference Mumstock that the key to his company remaining competitive against online retailers like Amazon and discounters Aldi and Lidl was to make the shopper experience easier and more convenient - whether in store or online. "The most consistent feedback we get on the store experience is to make life as easy as possible," said Clarke. "Customers want to park easily, get in easily, find everything they want and get out early. "And online, just make the experience easy. Don't deliver things I don't want.

They took the NYC Data Science Academy 12-week full-time data science bootcamp program from Sep. 23 to Dec. 18, 2015. The post was based on their fourth in-class project (due after the 8th week of the program). Walmart uses trip type classification to segment its shoppers and their store visits to better improve the shopping experience. Walmart's trip types are created from a combination of existing customer insights and purchase history data. The purpose of the Kaggle competition is to use only the purchase data provided to derive Walmart's classification labels.