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.

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. A link has been sent to your friend's email address. A link has been posted to your Facebook feed. All three a virtual tie, but each had questions they couldn't answer.

A reciprocal recommendation problem is one where the goal of learning is not just to predict a user's preference towards a passive item (e.g., a book), but to recommend the targeted user on one side another user from the other side such that a mutual interest between the two exists. The problem thus is sharply different from the more traditional items-to-users recommendation, since a good match requires meeting the preferences of both users. We initiate a rigorous theoretical investigation of the reciprocal recommendation task in a specific framework of sequential learning. We point out general limitations, formulate reasonable assumptions enabling effective learning and, under these assumptions, we design and analyze a computationally efficient algorithm that uncovers mutual likes at a pace comparable to those achieved by a clearvoyant algorithm knowing all user preferences in advance. Finally, we validate our algorithm against synthetic and real-world datasets, showing improved empirical performance over simple baselines.

It appears that Microsoft is planning to expand voice control of Xbox One consoles beyond Cortana. Windows Central reports that Xbox One support for Amazon's Alexa and the Google Assistant is on the way. While Cortana lets you control an Xbox One through either a Kinect or a headset, voice control will be much more convenient through a Home, Echo or smartphone. If and when the new feature rolls out, a "Digital Assistants" section will appear in the Kinect & Devices menu. Enable your preferred assistant on the Xbox One, install an Xbox app on your phone or voice-activated speaker and you should be good to go.

You can already use Cortana if you want to command an Xbox One with your voice, but that's not very practical now that Kinect is no longer an option for the console. Microsoft may have a simple solution: take advantage of the smart speakers you already have. A Windows Central source has leaked details of what appears to be Xbox One support for Amazon Alexa and Google Assistant in addition to Cortana. Reportedly, you have to enable the feature in the system's preferences and then install the relevant skill for your AI helper of choice. It's not certain what commands would be available, but it wouldn't be out of the question to anticipate media controls (say, launching Netflix or pausing a movie) or navigating to specific screens in the main Xbox interface. The bigger question surrounds availability, provided the feature makes the cut in the first place.

Indeed, it would almost be a waste to simply use it as a regular Bluetooth speaker or ask it about it tomorrow's weather. Apple's Siri can already do that. More and more smart-home and connected-device companies are adding support for Amazon's smart AI assistant, called Alexa, so you can control almost anything that uses electricity in your home using your voice. All you need to add Alexa and voice control to your home is Amazon's $45 Echo Dot. If you need a portable Bluetooth speaker, you can check out the $100 Amazon Tap, which comes with Alexa, and the original Echo is also $100, but it needs a wired connection to a power outlet.

Build a device that works for disorganized people. Most consumer-oriented tech expects us to engage in an organized way -- and become even more organized. Alexa is built for a much more haphazard set of use cases, in which voice helps us get our messy lives back on track, quickly and effortlessly. Get the machine's AI-generated voice just right. Listen closely to Alexa's cadence, and you won't hear the know-it-all bombast of earlier competitors.

Most industrial recommender systems rely on the popular collaborative filtering (CF) technique for providing personalized recommendations to its users. However, the very nature of CF is adversarial to the idea of user privacy, because users need to share their preferences with others in order to be grouped with like-minded people and receive accurate recommendations. Prior related work have proposed to preserve user privacy in a CF framework through different means like (i) random data obfuscation using differential privacy techniques, (ii) relying on decentralized trusted peer networks, or (iii) by adopting secured cryptographic strategies. While these approaches have been successful inasmuch as they concealed user preference information to some extent from a centralized recommender system, they have also, nevertheless, incurred significant tradeoffs in terms of privacy, scalability, and accuracy. They are also vulnerable to privacy breaches by malicious actors. In light of these observations, we propose a novel selective privacy preserving (SP2) paradigm that allows users to custom define the scope and extent of their individual privacies, by marking their personal ratings as either public (which can be shared) or private (which are never shared and stored only on the user device). Our SP2 framework works in two steps: (i) First, it builds an initial recommendation model based on the sum of all public ratings that have been shared by users and (ii) then, this public model is fine-tuned on each user's device based on the user private ratings, thus eventually learning a more accurate model. Furthermore, in this work, we introduce three different algorithms for implementing an end-to-end SP2 framework that can scale effectively from thousands to hundreds of millions of items.

In many practical problems, a learning agent may want to learn the best action in hindsight without ever taking a bad action, which is significantly worse than the default production action. In general, this is impossible because the agent has to explore unknown actions, some of which can be bad, to learn better actions. However, when the actions are combinatorial, this may be possible if the unknown action can be evaluated by interleaving it with the production action. We formalize this concept as learning in stochastic combinatorial semi-bandits with exchangeable actions. We design efficient learning algorithms for this problem, bound their n-step regret, and evaluate them on both synthetic and real-world problems. Our real-world experiments show that our algorithms can learn to recommend K most attractive movies without ever violating a strict production constraint, both overall and subject to a diversity constraint.

Artificial Intelligence is no longer just contained in science fiction films. It is a part of our everyday lives and in our classrooms. As we use tools like Siri and Amazon's Alexa, we are just beginning to see the possibilities of AI in education. And, we should expect to see more. The Artificial Intelligence Market in the US Education Sector 2017-2021 report suggests that experts expect AI in education to grow by "47.50% during the period 2017-2021."