The recommendation engine is one of the biggest martech innovations of the last few years, and it's shaping our entire digital experience. For example, let's say I visit the Harvard Business Review website and read about manufacturing marketing tactics (as one does). Soon, I'm getting retargeted marketing emails recommending the latest manufacturing supply chain management articles and other related content, as well as customized recommendations the next time I visit the site. When I fire up Netflix, I know that my experience is going to be managed in part because Netflix knows I've been binging on the show Haven and it's going to suggest other supernatural crime fighting series. The development of recommendation engine technology has brought digital personalization to a whole new level.

The goal of a recommendation system is to predict the interest of a user in a given item by exploiting the existing set of ratings as well as certain user/item features. A standard approach to modeling this problem is Inductive Matrix Completion where the predicted rating is modeled as an inner product of the user and the item features projected onto a latent space. In order to learn the parameters effectively from a small number of observed ratings, the latent space is constrained to be low-dimensional which implies that the parameter matrix is constrained to be low-rank. However, such bilinear modeling of the ratings can be limiting in practice and non-linear prediction functions can lead to significant improvements. A natural approach to introducing non-linearity in the prediction function is to apply a non-linear activation function on top of the projected user/item features. Imposition of non-linearities further complicates an already challenging problem that has two sources of non-convexity: a) low-rank structure of the parameter matrix, and b) non-linear activation function. We show that one can still solve the non-linear Inductive Matrix Completion problem using gradient descent type methods as long as the solution is initialized well. That is, close to the optima, the optimization function is strongly convex and hence admits standard optimization techniques, at least for certain activation functions, such as Sigmoid and tanh. We also highlight the importance of the activation function and show how ReLU can behave significantly differently than say a sigmoid function. Finally, we apply our proposed technique to recommendation systems and semi-supervised clustering, and show that our method can lead to much better performance than standard linear Inductive Matrix Completion methods.

Since last year I've had a smart speaker in my living room--an Echo Dot. My family uses it mostly to ask Amazon's digital assistant, Alexa, to play music. But after I saw a report that an Alexa-enabled speaker owned by a family in Portland, Oregon, had recorded a conversation and sent it to a contact, I started wondering: what is it picking up on at my house when we're not talking to it directly? So I checked my Alexa history (you can do that through the "settings" portion of the Amazon Alexa smartphone app) to see what kinds of things it recorded without my knowledge. That's when the hairs on the back of my neck started to stand up.

Amazon's Alexa recently made headlines for one of the strangest consumer AI mistakes we've ever heard of: A family in Portland, Oregon claims that the company's virtual assistant recorded a conversation and sent it to a seemingly random person in the husband's contact list. Alexa didn't just make one slip-up -- it made several that, when combined, led to a pretty remarkable breach of privacy. The company's explanation, provided to news outlets yesterday, makes clear just how unlikely this whole situation was: "Echo woke up due to a word in background conversation sounding like'Alexa,'" the statement reads. "Then, the subsequent conversation was heard as a "send message" request. At which point, Alexa said out loud "To whom?" At which point, the background conversation was interpreted as a name in the customers contact list. Alexa then asked out loud, "[contact name], right?" Alexa then interpreted background conversation as'right'."

An Amazon Echo multimedia smart speaker, taken on November 28, 2016. Voice speakers such as the Amazon Echo have already shown themselves to be less than trustworthy. Only a few months ago, consumers complained about sinister laughter coming from the Amazon Alexa assistant. Meanwhile, the always listening devices have come under fire amid accusations that they could enable snooping by law enforcement. This week, public fear has ramped up a level after a US couple's conversation was recorded and sent to an acquaintance without their knowledge.

A couple in Portland, Ore., discovered that their Amazon Echo had recorded their conversation and sent it to one of their contacts. A couple in Portland, Ore., discovered that their Amazon Echo had recorded their conversation and sent it to one of their contacts. As secret recordings go, the Portland couple's conversation was pretty mundane: They were talking about hardwood floors. But their Amazon Echo was listening and recording their discussion. The device then sent the recording to someone in their contacts -- without the couple's knowledge.

One of the top conversations that we have with businesses all over the world is how digital transformation is impacting every part of their operations. This wave of transformation is being driven by the ongoing advances in computing building blocks of the last few decades: compute, storage, and networks. At the same time as companies are continuing down the path of digital transformation, there is a new generation of software building blocks that will drive even greater transformation for businesses in the future of which AI is one of the core drivers. AI will help to transform all industries, including transportation, manufacturing, retail, agriculture, and more and create opportunities we have yet to consider. In this post I will talk about how Microsoft is helping businesses transform with AI.

For the 7,000 people in the audience for Google's (GOOG, GOOGL) I/O keynote last week, the Google Duplex demo was a mind-fryer. CEO Sundar Pichai had said to his phone, "OK Google, book me a haircut appointment on Tuesday between 10 a.m. and noon." And then, silently and invisibly (to him), Google Assistant had made a phone call to a human receptionist at the salon and had held a conversation, flawlessly impersonating an actual person, complete with "umms" and "ahhs." The receptionist never knew she'd been talking to AI. "That the many in Google did not erupt in utter panic and disgust at the first suggestion of this is incredible to me," tweeted Zeynep Tufekci, a University of North Carolina professor. "This is horrible and so obviously wrong. And on "CBS This Morning," Salesforce (CRM) CEO Marc Benioff spoke about it in the context of his call for a new, national privacy law. "That was the most amazing AI technology I've seen.

In this video from Intel AI DevCon 2018, Andrew Ng from Deeplearning.ai and Landing.ai When you ask Siri for directions, peruse Netflix's recommendations or get a fraud alert from your bank, these interactions are led by computer systems using large amounts of data to predict your needs. The market is only going to grow. By 2020, the research firm IDC predicts that AI will help drive worldwide revenues to over $47 billion, up from $8 billion in 2016. Still, Andrew NG says fears that AI will replace humans are misplaced: "Despite all the hype and excitement about AI, it's still extremely limited today relative to what human intelligence is."

It is a device normally used to check the weather forecast or order more toilet paper. But Alexa can now summon a prayer to say grace before your evening meal or give thanks to God before going to bed at night. Amazon's virtual assistant has been picked up by the Church of England to make being a practising Christian easier. In response to a simple command, its app gets Alexa to read out the Lord's Prayer or the Ten Commandments, for those concerned about wrongdoing. Along with other Google searches, users can ask how to become a Christian, how to pray and who God is.