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.

Amazon's Alexa and Google Assistant, Hindi says, have trained consumers to think that every voice command must be processed and stored online, even if the task has no inherent connection to the internet. With Snips, he's hoping to prove them wrong. For the past couple of years, Snips has been building an offline voice assistant for individual developers and enterprises that don't want to depend on Big Tech. Now, the startup is working on its own consumer hardware, including a set of smart speakers and a base station for processing voice commands locally. "We're making a very strong bet on people's willingness to trade, basically, a recognized brand for privacy," Hindi says.

Apple Inc. AAPL 0.59% on Monday unveiled new controls to help people curb the amount of time they spend on iPhones and iPads, as well as allow parents to remotely track and limit their children's use of those devices--a response to growing societal concern that adults and children are too focused on phones. The company said a new app it will release in September called "Screen Time" will provide users with weekly reports of the apps they use and allow them to set time limits for their use of those apps. Parents will be able to use the system to remotely monitor the apps their children use and limit their time on devices. The new features played a central role at Apple's annual Worldwide Developers Conference, a gathering of about 6,000 developers who create the apps for the iPhone, iPad and Mac computers. The event is designed to show off Apple's latest software--including the newest features to further integrate into people's digital lives.

Day 1's keynote was an entirely software-based affair, surprising some who were expecting to see more hardware-related announcements or refreshes of current products. Apple executives took to the stage at the San Jose McEnery Convention Center to announce numerous (if incremental) enhancements to its iOS, tvOS, watchOS and MacOS platforms designed to optimize the overall Apple user experience. From a smart home/home automation perspective, there is no question that one of Apple's key focus areas in 2018 is making Siri more compelling and productive. To that end, Apple announced it is now allowing developers much deeper integration with iOS 12 and MacOS Mojave. Third-party apps can now build "shortcuts" that allow Siri to execute popular quick actions.

Remember pagers, fax machines, VCRs, Floppy Disks, Teletext? Chances are if you're born after a certain date, you won't. Soon enough, smartphones will be part of the catalogue of obsolete tech. We still often behold smartphones as a revolutionary piece of kit and they are, really. But as Apple releases its iPhone8 model and delivers their recent keynote for the iPhone X, we're beginning to see shifts in smartphone capabilities.