As Google Home gets closer to launch, the AI assistant is not only getting smarter, but also a little more friendly and -- hopefully -- a whole lot funnier. As the Wall Street Journal's Christopher Mims notes in a piece about friendly AI like Siri, Alexa and Google Assistant, Alphabet's big play for the space includes hiring up comedy writing alumni of Pixar and The Onion. While none of the major AI assistants on the market today or coming soon are truly "artificial intelligence" (that is: the device itself doesn't actually understand the conversation) people have a natural tendency to form an emotional connection with the little robot voice in the kitchen speaker. So, subtle improvements like a witty joke or unexpected bit of humor can go a long way to improving the user experience, especially as voice and conversation becomes the interface itself. Although Siri and Alexa already have a few jokes in their repertoire, they tend to be pretty bad and definitely don't have quite the same punch as the topical humor of "America's Finest News Source."

Artificial intelligence can be just as biased as human beings, which is why experts are trying to prevent discrimination in machine learning. In a new paper, three Google researchers note that there is no existing way to ensure--as the White House calls it--"equal opportunity by design," but they have an idea. "Despite the need, a vetted methodology in machine learning for preventing this kind of discrimination based on sensitive attributes has been lacking," wrote Moritz Hardt, a research scientist with the Google Brain Team and co-author of the paper, in a blog post. Hardt throws out two seemingly intuitive approaches, "fairness through unawareness" and "demographic parity," but dismisses them for their respective loopholes. By learning from the shortcomings of the aforementioned methods, the team came up with a new approach.

Securing your digital assets is a clear need for any business and individual, whether you are looking to protect your personal photos, company's intellectual property, customers' sensitive data or any other aspect that can harm your reputation or business continuity. This need will continue to grow massively over the next few years as the amount of generated and aggregated data is exploding (IDC predicts that by 2020, the volume of digital data will reach 44 Zettabytes, 1,000,000,000,000 GB 1ZB). The greatest challenge, in all disciplines of Cybersecurity, is to be able to recognize new threats efficiently without relying on any signatures or easy to bypass heuristics, which rely on known, previously-seen malicious activities. Supporting this trend, although billions of dollars are spent on cybersecurity (the latest estimate by Garter, worldwide information security spending will reach 81.6 billion in 2016), we keep seeing the growing number of reported cyber-attacks and the higher magnitude of breaches every day, for example the recently published high-magnitude cyber-breaches -- Yahoo 500M accounts data breach is among the biggest in the history, Dropbox confirmed 68M accounts details leaked. There are many Cybersecurity frontiers where harnessing the predictive power of AI might bring the upper hand to security vendors and to us all, individuals and businesses.

It seems like there isn't a week in which deep learning doesn't come up as achieving some kind of remarkable task. I understand that one of the powers of deep learning is that it is capable of learning the features. This capacity seems totally decoupled from the underlaying problem. So basically I read this as "no matter what problem you have... You can use deep learning".

Experimental tests have shown that the new system, which is based on the artificial intelligence algorithm known as "reservoir computing," not only performs better at solving difficult computing tasks than experimental reservoir computers that do not use the new algorithm, but it can also tackle tasks that are so challenging that they are considered beyond the reach of traditional reservoir computing. The results highlight the potential advantages of self-learning hardware for performing complex tasks, and also support the possibility that self-learning systems--with their potential for high energy-efficiency and ultrafast speeds--may provide an extension to the anticipated end of Moore's law. The researchers, Michiel Hermans, Piotr Antonik, Marc Haelterman, and Serge Massar at the Université Libre de Bruxelles in Brussels, Belgium, have published a paper on the self-learning hardware in a recent issue of Physical Review Letters. "On the one hand, over the past decade there has been ...

There are plenty of nerdy things that Google's search engine can do, and the latest is a peach if you're a graphic designer. If you type in "RGB to Hex," you'll be shown a color converter that'll let you pick a shade and get the RBG and Hex values for both. In addition, you can hit the Show Color Values toggle and get a breakdown of the HSV, HSL and CMYK counts for those shades. Yes, it's not the most useful feature in the world, but it'll save you having to open up Photoshop just to get a color value for your web design project.

Salesforce is more than a standalone company: It's also central to an eco-system of many smaller firms that feed utilities and functions into the SaaS-based CRM app. So when Salesforce announced Einstein--its application of artificial intelligence to all aspects of its CRM--some eco-system partners took note, and wondered what the future held for them. "There is a lot to be said for its potential, shifting companies like ours from traditional communications, from one-to- one to one-to- many. In Five9's case, the specialty is providing a cloud-based "contact center", which can round up, analyze and present customer data to its call-center clients. "Salesforce is great at tracking digital interactions," Anadkat said. "When we play into Salesforce, we plug to our ability to take and receive calls." Salesforce is a "system of record" for many companies. "We're a system of interaction" Anadkat added. Imagination has no technical limit. It is easy to conjecture a system where a bot, backed by Salesforce Einstein, can respond to a caller's needs, artificially cognizant of a caller's record with that business. Yet the technology may not be there yet. "When a live person is getting involved, you have more context," Anadkat said. Callers still want human contact. Artificial intelligence can automate routine tasks and simply repeated interactions, he explained. Yet there still remains a clear-cut boundary between simple interactions and complex ones. Where AI shows promise is interaction between companies and applications. Anadkat offered a use case to illustrate his point. That customer's data is cross-linked with his Uber ride from the airport and with the hotel he is staying at, providing a seamless experience that works. That traveler goes from city to city without any complication. What will be the impact of AI on Five9? "We're peripherally involved in it," Anadkat said. "We're a customer care company, augmenting our ability to connect with AI in a care scenario." Call routing to call centers and backing that up with data is what Five9 will be about. "That's the core of our business,' Anadkat said.

Last week, Microsoft took the unusual step of placing its Cortana and Bing product teams inside the same organization as Microsoft Research. The new Microsoft AI and Research Group will be led by computer vision pioneer and executive vice president Harry Shum, whose 20-year Microsoft career involves leading Bing's search efforts from 2007 through 2013 and helping launch Microsoft Research China. We asked Shum how this new organization will benefit Microsoft's digital assistant in the following interview, which has been edited for length and clarity. The language of the blog post announcing the formation of Microsoft's new AI division, together with how Satya Nadella has characterized it, suggests that Microsoft thinks it's in a space race of sorts when it comes to artificial intelligence. I just feel that the timing's right to go big on AI.