The progression of natural language processing, deep learning algorithms and significantly improved microphones means we are beginning to see interfaces that can understand and accommodate the rigid structure of human conversation. Companies are developing personalities for their virtual assistants, which have mostly arrived as a set of female characters – embodied in phones, home assistants and navigation systems – personifying AI via voice. However, it's important to note that applying this gendered identity has ramifications, especially because the resulting impulse is to then add a "her" to every product we can. Instead, we should pay attention to the unexamined decisions we're making to avoid digitizing existing power structures under the guise of a "default" identity.

Natural language processing technologies have become quite sophisticated over the past few years. From tech giants to hobbyists, many are rushing to build rich interfaces that can analyze, understand, and respond to natural language. Amazon's Alexa, Microsoft's Cortana, Google's Google Home, and Apple's Siri all aim to change the way we interact with computers. Sentiment analysis, a subfield of natural language processing, consists of techniques that determine the tone of a text or speech. Today, with machine learning and large amounts of data harvested from social media and review sites, we can train models to identify the sentiment of a natural language passage with fair accuracy.

Everyone has the right to privacy, especially in their own home. But home assistants such as Amazon Alexa, Google Home, and Mattel Aristotle are designed to butt their noses into conversations. These devices collect ― and store ― untold amounts of data. It's unclear what the companies do with the extraneous "noise" they pick up. And if it's subpoenaed, they might have to hand it over. Say your kid jokes about terrorism or something else illegal; if there's an investigation into those activities, the companies might have to cough up the transcripts.

The interconnection of devices within the "Internet of Things" (IoT) creates new data sources. Companies can now better observe people's choices and test the effectiveness of different mechanisms to activate and retain more customers. It may also help policymakers overcome one of the most frequent problems of policy design: the lack of personalized content. We argue that the IoT not only disrupts the way we track our actions and monitor our goals, but also allows the identification of effective methods to alter our behavior. This is optimized by the combination of IoT, data analytics and behavioral science.

Gravity4 is the world's first high-frequency machine-learning marketing OS, built to enhance the advertising and SaaS industries. It collates customer experience so marketers can target a customer throughout the entire purchase journey and across all consumer touch-points, regardless of delivery channel. Its proprietary AI technology, Mona Lisa, builds a consumer persona by aggregating data across channels. The platform's fluid and constant in-stream of data is sorted into a semantic graph to form connection clusters, using the correlation variables. All through a single click, it empowers agencies and marketers to allow connected software to optimize a manually driven $200 billion global advertising market. The company's headquarters are in Miami, but it has offices in Sydney, Stockholm, Oslo, Auckland, Madrid, Singapore, Copenhagen, London, Dublin, Amsterdam, Helsinki, Hong Kong, Shanghai, Kuala Lumpur, Christchurch, Taipei and India.

I've never written one personally but I was having a discussion with the author of a great piece here on Search Engine Journal on AI and its impact on search and the question came up: Between machine learning and the limited space available for organic search, is it on its death spiral? Between machine learning, the limited space available for organic search, and the growth of both voice search and personal assistants, is it on its death spiral? To paint the picture of where this is going, let's look at just some of the changes over the past little while: I'm sure you can see the trend: Google is crafting the results layout in a way that minimizes the impact of organic results on commercially intent searchers. Not coincidentally, Google announced their personal assistant being released on all phones running Android 6.0 and above, taking us beyond running simple queries on our phone and onto more complicated communications and interactions with other systems -- all in a conversational manner.

Overall investment in automation technologies – including robotic process automation (RPA), autonomics, virtual customer service agents and personal assistants, natural language processing and machine learning – is expected to double in the next two years, the survey finds, as enterprises look to harness technologies that have the flexibility to solve more than one business problem. "Automation and artificial intelligence are top of mind for business executives and service providers alike – and with good reason," said Todd Lavieri, partner and president of ISG Americas. "Robotic process automation, autonomic systems and cognitive agents are making employees more productive by taking over routine, process-oriented tasks. At the same time, data scientists are using machine learning to find patterns and make predictions on vast troves of structured and unstructured data. These technologies, taken together, promise to usher in the next wave of enterprise growth and profitability."

In less than six months there will be 6.4 billion connected gadgetsworldwide. We use TomTom devices to avoid traffic jams, install smart water meters and orchestrate home appliances with Amazon Echo. The long-anticipated Internet of Things is finally here, but very few companies and consumers actually realize its true value. By 2018, the global Internet of Things ecosystem will annually produce 400 zettabytes of data. Provided we learn how to do data mining right, we could significantly decrease energy consumption and equipment maintenance costs, improve healthcare delivery and change the way businesses operated for decades.

If the bulb is dimmable, the Socket can control lighting intensity, too. The Socket can also be controlled with voice commands via Amazon's Alexa (Amazon Echo) and Google's Assistant (Google Home). You can set the hue, saturation, and brightness of the ring through the iDevices app. The Socket's LED color ring is the real defining characteristic and differentiation, since Philips can only do light and color at the same time in a single bulb.

Check out Crab if you haven't already. SVD will probably not work well off the bat, unless you have a way to mark "unmeasure/NA" pieces and avoid those in the SVD computation. Some sparse SVD implementations may have this, but I don't know any offhand in Python. You can still do 0/1 (2 score) rating with recommender systems, though if you have extra information (confidence) that can help. This 0/1 setup is really similar to "click through prediction", or CTR as well which is a huge field (and again, $$$ related) - check out some code that is awesome (I didn't write it, but learned a ton from it), also see the discussion in the old Kaggle competition I link to in that gist.