There is a lot you can do with Alexa. Here's how you can add skills. It was every Amazon Echo owner's nightmare. Alexa, the connected speaker, really, truly, was listening in on your conservations, and behind your back, passed on the recording of a private chit-chat to someone on your Echo contact list. This actually happened this week, according to Seattle TV station KIRO, which told the story of a Portland woman's privacy gone amuck.

You love the idea of buying an AI and IoT device like Google Home or Amazon Echo for the holiday season. You want to speak and have it deliver a song or weather report or sports score. However, did you ever think about the other side of this coin? Here is a nagging question we all must wrestle with: As a technological innovation increases, how to we protect our personal information and privacy? AI and IoT devices like Google Home, Amazon Echo, Microsoft Cortana, Apple HomePod and countless others that are popping up in the marketplace are simply incredible.

Every coin has two sides. We've been talking for so long about all the coming benefits of wireless smart homes, connected homes and connected offices. And it's true, there are many incredible advancements in this space. However, there is an important warning I want to discuss. Everyone must understand other side of the coin which is seldom discussed, loss of privacy.

Building on its strategy to develop innovations that drive retail transformation for brands, retailers and manufacturers, PLM leader Centric Software announces the unveiling of its first artificial intelligence-based PLM module. Centric Software provides the most innovative enterprise solutions to fashion, retail, footwear, outdoor, luxury and consumer goods companies to achieve strategic and operational digital transformation goals. The term'artificial intelligence' dates back to the 1950's when computer scientist John McCarthy coined the phrase to describe the potential'thinking machines' of the future. Today, artificial intelligence (AI) tools are systems modeled on the problem-solving abilities of the human brain, breaking complex problems down into different layers of information comprised of many smaller problems. Applications vary considerably ranging from virtual assistants like'Alexa' and'Siri' to Netflix viewing recommendations to Amazon recommending things we might like to buy.

As the buzz around data science grows every day, there is a slew of self-taught professionals who have kick-started the machine learning journey with Andrew Ng's online courses. Many enthusiasts are gravitating towards the computer science field. But if one wants to pursue a career in Machine Learning, they need to be familiar with statistics and linear algebra. With computer science and ML applications becoming more pervasive in everyday life, people from a non-technical background are also interested in joining the field. In this article, we have discussed in-depth roles a person from non-tech background can explore in the data science/AI field.

NEW YORK – Revelations that an Amazon Echo smart speaker inadvertently sent a family's private conversation to an acquaintance highlights some unexpected risks of new voice-enabled technologies. According to Amazon, the fault was an "unlikely" series of inadvertent vocal cues that triggered the speaker, caused it to begin recording and then led it to interpret subsequent conversation as a "send message" request. There is no way to eliminate these sorts of privacy risks short of unplugging entirely. Kill the mic: Most smart speakers have a physical button to disable the microphone, so a private conversation can't be recorded to begin with. You can hit that when you are having sensitive conversations.

It's every smart home speaker owner's worst nightmare: a private message recorded and sent to a recipient without their knowledge. But that's what happened to a Portland woman, who told CBS News affiliate Kiro-TV that her Amazon Echo recorded an audio clip of her conversation and sent it to a person on her contact list. The woman, who declined to provide her last name to Kiro-TV, said she was alerted to the bug when the recipient of the message -- one of her husband's employees -- called her home to alert her that she'd been "hacked." An initial investigation by an Alexa engineer turned up no leads, according to the woman. But the engineer speculated that the Echo speaker "guessed" the command to send a message via Alexa Voice Messaging without asking for verbal confirmation.

An Amazon Dot, poised to take a command. SAN FRANCISCO-- After an Oregon family described a scenario that encapsulated the worst fears about owning a smart speaker -- their Amazon Echo picked up a private conversation and then shared it with the outside world -- you might ask: How can I prevent this from happening? Aside from unplugging your smart speaker for good, there are some safeguards you can use if you're one of the 40 million people who have these voice-activated speakers from Amazon or competitors Google Home and Apple HomePod. In the Portland, Ore. family's case, one of the several Alexa devices they had in their home misinterpreted conversations happening in the house as a command to first record a conversation and then send it as a message to a contact on the father's contact list. According to Amazon, Alexa twice asked for confirmation of the requests.

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.