All three a virtual tie, but each had questions they couldn't answer. Tune in to find out where Apple, Google and Amazon fell down. LOS ANGELES -- Apple this week had every opportunity to show off new voice skills for Siri, the personal digital assistant, and to prove that it could be competitive with Amazon and Google. Instead, it pretty much took a pass. At its annual developers conference Monday, the company spent 15 minutes on new augmented reality updates, nearly a half-hour on updates to a fall macOS software update and over an hour on the new version of iOS12, the mobile operating system upgrade for the iPhone and iPad that will be out in the fall.

Amazon's Echo Show is one of our favorite smart speakers, to the point that we described it as "the most capable mainstream smart home assistant on the market" in our 4-star review. And right now you can snag all its smarts and functionality for a killer deal on Amazon--the Echo Show is just $150, down from a list price of $230 and good for the lowest price we've ever seen. There are a lot of things to love about the Echo Show. One of the most obvious is its seven-inch color touchscreen, which allows you to see pictures and video and enables video calling with other Shows and Spots. Another cool feature is Drop In, which allows you to use multiple Echoes as an intercom system.

The Internet of Things is a major trend benefiting from the maturation of AI. Everyday examples of the IoT include wearables like the Apple Watch or Fitbit. The Amazon Echo and Google Home are racing to be the top choice for the Thing connecting all the devices powering your smart home, from Philips smart bulbs to Neato Botvacs.

While all my American and UK colleagues have had access to Amazon's Alexa for years, I've been left out of the party here in France. Those devices are now up for pre-order on Amazon at half price, with delivery set at June 13th for Prime members. It took Amazon long enough to teach Alexa French, but the assistant has reportedly gained mastery of both the language and country. "In France, Alexa is French," said Amazon VP Jorrit Van der Meulen. "We've built an entirely new experience from the ground up that honors the French language and culture, allowing customers to just ask to get music, weather, news, information, and more."

By now, everyone has heard of artificial intelligence: It populates our Facebook feeds and powers the virtual assistants we keep around our homes, including Amazon's Alexa and Apple's Siri. Perhaps you found this article through a Google search or a news aggregator app, which is also powered by AI. The average consumer interacts with AI multiple times every day -- despite a HubSpot survey that indicates that 63% of consumers don't even realize they have used AI -- but not everyone understands the full impact the technology will ultimately have on our economy. The founders of up-and-coming AI startups are poised to build multibillion-dollar enterprises. In fact, the immense value of AI technology is likely to spawn the world's first trillion-dollar companies -- a feat that tech giants like Amazon, Apple and Alibaba are racing to achieve.

In practice, Artificial Intelligence is a group of technologies that help facilitate the discovery and analysis of information for the purpose of making predictions and recommendations, support decision making, facilitate interactions, and automate certain responses. Since AI applications are continually transforming business models, the scope of traditional technology applications will scale up towards a multi-channel world with recommendation systems, virtual assistants, chatbots, and AI-managed marketing platforms. Essentially a model that helps identify patterns and associations from large amounts of data, Artificial Intelligence enhances quality control and improves operational effectiveness through digitized information assets. This allows businesses to focus time and resources on identifying new opportunities and customers, as well as different channels to market. Apart from large technology firms, the Banking and Securities sector is one of the leading industry verticals in Artificial Intelligence adoption.

Mary Meeker is a legend in Silicon Valley. Because every year, she comes out with what many think is the most complete and thorough analysis of the technology industry. Now, Mary and her team do impeccable work: her presentations are full of great graphs, her slides full of data...and you have to believe that her insights come from hours of researching the industry and listening to the thousands of entrepreneurs that come to Kleiner Perkins Caufield & Byers (Meeker's employer) for investments. There is one problem though. The research pack is long and dense.

Searching, browsing, and recommendations are common ways in which the "choice overload" faced by users in the online marketplace can be mitigated. In this paper we propose the use of hierarchical item categories, obtained from implicit feedback data, to enable efficient browsing and recommendations. We present a method of creating hierarchical item categories from implicit feedback data only i.e., without any other information on the items like name, genre etc. Categories created in this fashion are based on users' co-consumption of items. Thus, they can be more useful for users in finding interesting and relevant items while they are browsing through the hierarchy. We also show that this item hierarchy can be useful in making category based recommendations, which makes the recommendations more explainable and increases the diversity of the recommendations without compromising much on the accuracy. Item hierarchy can also be useful in the creation of an automatic item taxonomy skeleton by bypassing manual labeling and annotation. This can especially be useful for small vendors. Our data-driven hierarchical categories are based on hierarchical latent tree analysis (HLTA) which has been previously used for text analysis. We present a scaled up learning algorithm \emph{HLTA-Forest} so that HLTA can be applied to implicit feedback data.

We summarize our recent findings Ghauch et al. (2017), where we proposed a framework for learning a Kolmogorov model, for a collection of binary random variables. More specifically, we derive conditions that link outcomes of specific random variables, and extract valuable relations from the data. We also propose an algorithm for computing the model and show its first-order optimality, despite the combinatorial nature of the learning problem. We apply the proposed algorithm to recommendation systems, although it is applicable to other scenarios. We believe that the work is a significant step toward interpretable machine learning.

Apple was late releasing the HomePod. And even though the company took a few extra months to finish things up, it shipped without two key features: multi-room audio and stereo pairing. But Apple has finally released a long-awaited software update for that enables the AirPlay 2 streaming protocol -- which also lets owners pair two HomePods in stereo or group together multiple speakers across your house. With that big update out in the wild, I wanted to see how easy it was to manage multiple HomePods and hear how good a stereo pair sounded. While there are legitimate complaints with the smart speaker, the audio quality was never a problem.