The competition among Siri, Cortana, Google Now, Alexa, and Facebook's M (still in its early stages), has kept the spotlight on virtual assistants for the last couple of years. The assistants and the AI technologies that power them have piqued the interest of venture capitalists as well. "Having written a bad version of ELIZA [an early-stage natural language processing program] as one of my earliest programs, I have been excited about the prospect of conversing with machines for a long time. So I will be watching this development closely…" Union Square Ventures' Albert Wenger wrote in his blog. We've identified 21 startups working in the virtual assistant space or providing underlying technology to power digital assistants that either received either Series B, Series A, or seed/angel funds since 2015.

If you've been out on the streets of Silicon Valley or New York City in the past nine months, there's a good chance that your bad driving habits have already been profiled by Nexar. This U.S.-Israeli startup is aiming to build what it calls "an air traffic control system" for driving, and has just raised an extra 10.5 million in venture capital financing. Since Nexar launched its dashcam app last year, smartphones running it have captured, analyzed, and recorded over 5 million miles of driving in San Francisco, New York, and Tel Aviv. The company's algorithms have now automatically profiled the driving behavior of over 7 million cars, including more than 45 percent of all registered vehicles in the Bay Area, and over 30 percent of those in Manhattan. Using the smartphone's camera, machine vision, and AI algorithms, Nexar recognizes the license plates of the vehicles around it, and tracks their location, velocity, and trajectory.

Data science, and knowledge discovery, are among the most "brain-like" operations that a company does, and its practitioners have a unique vantage point into the utility of artificial intelligence. With the emergence of deep learning now upending AI, it is worth exploring how this powerful class of techniques relates to knowledge and understanding, using our own brain as a gold standard for how information is stored for synthesis and insight. Is there a general "process" by which data can be turned into knowledge, or a "rule" for learning rules? Most neuroscientists think so, and so do deep learning researchers. They comprise two search parties, looking for the self-organizing logic that is the magic key for turning data into knowledge.

What if a simple camera capturing data for machine learning could tell you the threat level of an individual approaching a fence? What if the same combination of camera and computer could classify the behavior of shoppers in a grocery store isle and judge things like intent to purchase, presence of decision paralysis, and ease of identifying desired products? Fueled by advances in image recognition and processing power, smart-cameras that can classify human behavior rather than simply observe it may be the next step for IoT. Nanit is one of the first companies in this space. Dr. Assaf Glazer, a parent himself, and his team are working to take the pain out of one of the most strenuous tasks of any parent, making sure their baby gets a good night's sleep.

Apple Inc.'s new artificial-intelligence capabilities may help it protect its software and services businesses from incursions by Alphabet Inc.'s Google -- and cut the internet giant out of lucrative search roles on its devices. At Apple's Worldwide Developer Conference this week, the company unveiled a crop of new AI-powered features: Its Siri voice-based digital assistant expanded from iPhones and iPads to its Mac laptop and desktop computers; natural-language processing brought new automated emoji creation to the iPhone maker's iMessage service; and image-recognition will give its gadgets the ability to automatically understand and organize photos stored on them. The capabilities are, in part, a natural riposte to recent AI-infused Google offerings, such as a mobile keyboard, a popular picture-organizing app named Photos, and a planned digital assistant that will live inside a new mobile messaging system called Allo. The Gboard keyboard was specifically designed for iPhones and is a way for Google to get its search service used in new ways on those devices. The Photos app works on iOS devices and Google's own Android mobile operating system and represents a twist on Google's search capabilities.

This post is to comment on the representation of emerging technology and its application to recruitment, and it's not my intention to speculate on a possible future of robots replacing humans, but there's an algorithmic future that's being neatly swept under the carpet by those who are "pro-robot". Research from Harvard University found that ads for arrest records were significantly more likely to show up on searches for distinctively black names or a historically black fraternity. Research from the University of Washington found that a Google Images search for "C.E.O." produced 11 percent women, even though 27 percent of United States chief executives are women. Google's AdWords system showed an ad for high-income jobs to men much more often than it showed the ad to women, a new study by Carnegie Mellon University researchers found. Those who advocate a perfect future will have to confront this research and much more like it.

There has been a dramatically increasing interest in Artificial Intelligence (AI) in recent years. AI has been successfully applied to societal challenge problems and it has a great potential to provide tremendous social good in the future. In this workshop, we discussed the successful deployments and the potential use of AI in various topics that are essential for social good, including but not limited to urban computing, health, environmental sustainability, and public welfare. The videos below were recorded at the workshop and are divided by topic area and panel group.

The Weka machine learning workbench is a powerful and yet easy to use platform for predictive modeling. In this post you will discover how you can install Weka on your workstation fast, and get started with machine learning. How to Download and Install the Weka Machine Learning Workbench Photo by Nicholas A. Tonelli, some rights reserved. All versions of Weka can be downloaded from the Weka download webpage. Select the version of Weka that you would like to install then visit the Weka download page to locate and download your preferred version of Weka.

There's no better way to express mock sympathy than by playing the "world's smallest violin," but the sad song you mime can now actually be heard. Enterprising minds at creative studio Design I/O have combined machine learning, radar and other technologies so that when you rub your thumb and forefinger together, it actually produces the sound of a violin. Google's Project Soli plays a key role in the magic. Soli is a purpose-built interaction sensor that uses miniature radar to track the motion of the human hand. The sensor and antenna array are packed into an ultra-compact 8mm x 10mm chip.

In a blog post, Amy Nicholson, technical evangelist at Microsoft, explains how she used the Cortana Intelligence Gallery, together with Azure ML Studio, to help her start building her own ML (machine learning) models "as well as my knowledge about this space." Azure ML is a fully managed cloud service on Microsoft Azure that helps you easily build, deploy and share predictive analytics solutions, Nicholson writes. All of this in a simple drag-and-drop interface to go from an idea to the deployment of a ML API in a matter of clicks." To learn more, visit the Cortana Intelligence and Machine Learning Blog.