Once the stuff of sci-fi movies and books, artificial intelligence (AI) is quickly becoming a reality. One thing is quite clear: AI has transformative potential for most if not all industries, including banking. A recent survey conducted by Synechron and TABB Group found that over 71 percent of companies believe that AI will be a hugely important technology in financial services over the next 10 years. The same survey found that 30 percent of companies already have deployments in AI. According to Gartner, by 2018, 40 percent of outsourced services will leverage smart machine technology.

As what was once mere data evolves into actionable intelligence, the context that binds that data becomes ever more essential. With no context around those four letters, you might not understand the reference or make any sort of connection. But if you add just one word to "java," such as "development," "island," or "coffee," the reference changes completely--and that's with just a single word of context. This is the type of active context and connection that the Brainspace engine provides. "Context is a very important part of what we do. When we analyze documents, we take the context into consideration," says Ravi Sathyanna, vice president of technology and product management at Brainspace.

At its annual Worldwide Developers Conference (WWDC) in San Francisco this week, Apple suggested that it was more than willing to compete toe-to-toe against Google, Amazon, and Microsoft in the artificial intelligence (A.I.) arena. As those rivals build increasingly sophisticated bots that can respond to users' natural-language commands, Apple has opened its own digital assistant, Siri, to third-party developers. If things pan out as the company expects, Siri will rapidly add more functionality, essentially becoming a butler for a growing collection of tasks. And that's just the first of what will surely be many A.I.-related initiatives on Apple's part. But how can Apple reconcile its need for personal data--the fuel of an effective A.I. platform--with its rigorous user privacy stance?

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.