A shot of Amazon's automated store Amazon Go Right from Google's predictive search, Apple's Siri to Facebook's automatic friend tagging and Uber using artificial intelligence (AI) applications, AI is making big noise and has got attention of the world in a massive way. While AI is increasingly everywhere what you probably don't hear much about is AI in retail. While it may not sound as sexy as AI applications empowering recommendation engines for Google and Netflix, AI in retail will affect how we all shop, be it online or offline. AI and deep learning uses software programs to perform complex tasks without active participation or insight from humans making the task at hand a hundred times easier and faster to complete. Armed with the ability to research a retail brand online, customers are today more knowledgeable than ever before.

If you're a Dish subscriber, it just got ridiculously easy to control your TV. The satellite provider has introduced an Alexa skill that lets control any Hopper DVR or Wally using an Amazon Echo. You can ask the set-top box to tune into a specific channel, search for shows (including on Netflix) or directly control playback without touching a thing -- you just need your voice. You can't record shows, alas, but this could be worthwhile if you want to change channels from the kitchen. The Amazon tie-ins aren't done, either.

A week ago, Microsoft held its Build developer conference in its backyard in Seattle. This week, Google did the same in an amphitheater right next to its Mountain View campus. While Microsoft's event felt like it embodied the resurgence of the company under the leadership of Satya Nadella, Google I/O -- and especially its various, somewhat scattershot keynotes -- fell flat this year. The two companies have long been rivals, of course, but now -- maybe more than ever -- they are on a collision course that has them compete in cloud computing, machine learning and artificial intelligence, productivity applications and virtual and augmented reality. Both opened their respective shows. But while Pichai used his time mostly to announce new stats and a new product or two, Nadella instead used his time on stage to talk about the opportunities and risks of the inevitable march of technological progress that went way beyond saying that his company is now'AI first.' "Let us use technology to bring more empowerment to more people," Nadella said of one of the core principles of what he wants his company to focus on.

First it was the mobile revolution that disrupted the banking system, now artificial intelligence (AI) is looking to make its mark. Finn.ai, a Vancouver, BC-based fintech company, is looking to digitally transform financial institutions and make banking easier for consumers by offering an AI-powered personal banking and financial management assistant. With Finn.ai's smart virtual assistant, banks can provide a personalized wealth manager for every single client at zero marginal cost, and through any conversational channel, such as text, Facebook Messenger and other Android or iOS apps. "Our AI-powered day-to-day banking concierge will help banks evolve from a utility to a value-added service," explains Ruby Lin, the company's growth and partnerships lead. "What banks can traditionally do through a web/mobile app, Finn.ai can do through a conversational interface: pay bills, transfer money to friends, check balances, find ATMs, find product information and customer support through a mix of AI and human interaction."

Apple locks down iOS, so you can't just launch right into Google Now like you can by saying "Hey Siri," but it's worth the effort of opening the app. Plus, you can add a widget to Google, so the assistant is always just one swipe away. You can use Siri to launch apps or do things like silence your phone -- things you can't do with Google Assistant -- but I've never used Siri in that manner. I think those functions are helpful but not really a measurement of a machine's intelligence. Google Assistant also lets you type in queries, so you don't have to speak out in public if you don't want to, and you can make sure every query is dead-accurate.

A week ago, Microsoft held its Build developer conference in its backyard in Seattle. This week, Google did the same in an amphitheater right next to its Mountain View campus. While Microsoft's event felt like it embodied the resurgence of the company under the leadership of Satya Nadella, Google I/O -- and especially its various, somewhat scattershot keynotes -- fell flat this year. The two companies have long been rivals, of course, but now -- maybe more than ever -- they are on a collision course that has them compete in cloud computing, machine learning and artificial intelligence, productivity applications and virtual and augmented reality. Both opened their respective shows. But while Pichai used his time mostly to announce new stats and a new product or two, Nadella instead used his time on stage to talk about the opportunities and risks of the inevitable march of technological progress that went way beyond saying that his company is now'AI first.' "Let us use technology to bring more empowerment to more people," Nadella said of one of the core principles of what he wants his company to focus on.

In many distributed learning problems, the heterogeneous loading of computing machines may harm the overall performance of synchronous strategies. In this paper, we propose an effective asynchronous distributed framework for the minimization of a sum of smooth functions, where each machine performs iterations in parallel on its local function and updates a shared parameter asynchronously. In this way, all machines can continuously work even though they do not have the latest version of the shared parameter. We prove the convergence of the consistency of this general distributed asynchronous method for gradient iterations then show its efficiency on the matrix factorization problem for recommender systems and on binary classification.

Connectivity has improved by leaps and bounds since the dawn of the 21st century, and that has in turn enabled the growth of many industries, analytics included. However, as much as bandwidth and latency may have improved across large distances, there is still no real substitute for the response time of local hardware, and when it comes to analyzing big data, sometimes that's really important. With masses of on board sensors and cameras, there is a ton of data to process at any given moment, helping the passengers avoid obstacles and stay within the bounds of the roadway. Sending that information to the cloud is doable, but in moments of danger where every millisecond counts, it's far better to make those decisions locally. Relying on a remote connection would also require such vehicles to remain in strong coverage areas such as major population centers, where the improved infrastructure of traditional transport networks makes driverless transit less useful than it might in somewhere more remote.

Spotify has announced the acquisition of Niland, a machine learning startup based out of Paris. The music company made the announcement itself this week, explaining that Niland shares its'passion for surfacing the right content to the right user at the right time.' Spotify plans to use the company's technology and know-how to improves its own recommendation abilities, doing so with the power of artificial intelligence behind it. Spotify announced the acquisition on Wednesday, saying that the Niland team will be joining the music company's own team in its New York office. The terms of the deal weren't revealed, such as how much Spotify paid for the company or when the deal was finalized. We do know, however, that personalized recommendations on Spotify are about to get much better than to the startup's work in machine learning and artificial intelligence.

Since the days of punch cards, the human-computer interaction landscape has undergone great developments. Scientists are constantly trying to find new ways to bridge the gap between man and machine. The effort has led to the invention of keyboards, mice and touch screens, which made computational power more accessible. Computers themselves have undergone great changes. We gradually moved from mainframe computers to PCs, laptops, smartphones and beyond.