Machine learning is a mechanism to provide popular functionality like recommendation engines, predictive maintenance, and is a cornerstone of future IoT workflows. Cloudera customers are delivering strategic functionality with machine learning at its core. As an example, a popular dating site Zoosk uses Cloudera Enterprise for creating successful matches and reduce user churn using big data and machine learning. MMO gaming company Wargaming uses machine learning on Cloudera to elevate customer experience and engage users in advanced gameplay. And one of America's leading financial services companies Transamerica uses Cloudera to test and validate data models at a much faster scale.

Apple is joining fellow tech giants Google, Microsoft, Amazon, Facebook and IBM as the sixth founding member of the recently-formed artificial intelligence research group - the Partnership on AI. The non-profit said Apple has been "involved and collaborating with the Partnership since before it was announced" in September and is "thrilled to formalize its membership." "We glad to see the industry engaging on some of the larger opportunities and concerns created with the advance of machine learning and AI," Tom Gruber, Apple's head of development for its intelligence personal assistant Siri, said in a statement on the Partnership's website. "We believe it's beneficial to Apple, our customers, and the industry to play an active role in its development and look forward to collaborating with the group to help drive discussion on how to advance AI while protecting the privacy and security of consumers." The Partnership also announced that six new non-industry trustees will be joining its board including Jason Furman, former top economic adviser for Barack Obama; Carol Rose, executive director of the American Civil Liberties Union of Massachusetts and Subbarao Kambhampati, president of the Association for the Advancement of AI.

The next version of Apple's mobile platform will include an opt-in for iOS users to share their iCloud data in order to help the company improve software products, such as its voice-powered virtual assistant, Siri. The iOS 10.3 beta was released earlier this week. A note about the forthcoming change, under the heading "iCloud Analytics & Privacy", says any user data shared with Apple via this opt in will undergo "privacy preserving techniques" -- continuing its privacy-first approach to stepping up its AI efforts. One of the most interesting changes in 10.3 – Apple will use differential privacy on iCloud user data to improve services (opt-in) pic.twitter.com/NKqrTee8Fq The company has generally lagged behind data-mining rivals such as Google in developing machine learning powered technologies and embedding them into its software and services to offer a more personalized and/or predictive experience, in no small part because it has prioritized (and championed) user privacy -- meaning, unlike its major rivals, Apple does not routinely suck up users' personal data in the clear.

Unless you've been on another planet for the last year or so, you'll almost certainly have noticed that chatbots (and conversational agents in general) became quite popular during the course of 2016. It seems that every day a new start up or bot framework was launched, no doubt fuelled at least in part by a growth in the application of data science to language data, combined with a growing awareness in machine learning and AI techniques more generally. So it's not surprising that we now see on a daily basis all manner of commentary on various aspects of chatbots, from marketing to design, development, commercialisation, etc. But one topic that doesn't seem to have received quite as much attention is that of evaluation. It seems that in our collective haste to join the chatbot party, we risk overlooking a key question: how do we know when the efforts we have invested in design and development have actually succeeded?

Anti-virus software has a hard time keeping up. Piles of new viruses come out each week, so cybersecurity unicorn Cylance is taking what it claims to be a completely new approach: artificial intelligence that learns to recognize malicious code based on an analysis of viruses from the past. It calls the new product CylancePROTECT. In an AMA on Reddit today, the company's head of reseach, Jon Miller, wrote: "Cylance was the first AI built to statically analyze and convict malware pre-execution. We definitely didn't invent AI, but we were the first to use it this way to deliver pre-execution protection. Many other products have been using machine learning, it's just that it was used to support legacy methodologies of protection/detection, using ML to identify trends so static signatures could be built, which in a world where attackers are creating individual pieces of malware to avoid signatures, results in a severe lack of efficacy, thats the problem Cylance was built to solve."

Analysis Huawei's upcoming launch of a smartphone incorporating Amazon's Alexa virtual assistant will mark a new phase in one of the most important battles for the modern internet experience. Voice-activated assistants - which use powerful AI engines to deliver detailed, context-aware and personalized answers to users' questions – are the way in which web giants hope to place themselves at the heart of a user's whole range of activities, whether they are in their smart home, connected car, at work or on the phone. Apple Siri kicked off the race, though the company seems to have lost its early momentum in voice interfaces – rapidly taking over from touch/text for many uses. Google Now and Microsoft Cortana added new levels of AI to the digital assistant category, but then Amazon launched its Fire Phone, whose defining technology was the retailer's own AI-driven assistant, Alexa. However, the Fire Phone flopped, and it seemed Alexa might have died with it, until the technology reappeared in Amazon's Echo home hub, which has unexpectedly seized the initiative back from its rivals, leaving Apple and Google to announce hasty moves into the home hub market last year.

Identifying or authenticating people based on how they type is not a new idea, but thanks to advances in artificial intelligence it can now be done with a very high level of accuracy, making it a viable replacement for other forms of biometrics. Research in the field of keystroke dynamics, also known as keyboard or typing biometrics, spans back over 20 years. The technique has already been used for various applications that need to differentiate among computer users, but its widespread adoption as a method of authentication has been held back by insufficient levels of accuracy. Keystroke dynamics relies on unique patterns derived from the timing between key presses and releases during a person's normal keyboard use. The accuracy for matching such typing-based "fingerprints" to individual persons by using traditional statistical analysis and mathematical equations varies around 60 percent to 70 percent, according to Raul Popa, CEO and data scientist at Romanian startup firm TypingDNA.

As the Global Legal Post reported today that the Law Society urges firms to harness the power of automation, the following article was the cover story published in the Leeds & Yorkshire Lawyer (the official journal of Leeds Law Society) titled AI and the rise of the robot on 20th December 2016 and is reproduced with kind permission. I was delighted to be interviewed and share some of my thoughts about AI in the legal eco-system and the benefits for lawyers, entrepreneurs, business people and consumers... Artificial intelligence (AI) is slowly changing how law firms work. Should lawyers be worried about their futures? Technology has been forcing subtle changes to the practice of law for decades now. Whether lawyers have welcomed all of the alterations to the way in which they work has almost been immaterial.

When the Partnership on Artificial Intelligence to Benefit People and Society was announced in September, it was with the stated goal of educating the public on artificial intelligence, studying AI's potential impact on the world, and establishing industry best practices. Now, how those goals will actually be achieved is becoming clearer. This week, the Partnership brought on new members that include representatives from the American Civil Liberties Union, the MacArthur Foundation, OpenAI, the Association for the Advancement of Artificial Intelligence, Arizona State University, and the University of California, Berkeley. The organizations themselves are not officially affiliated yet--that process is still underway--but the Partnership's board selected these candidates based on their expertise in civil rights, economics, and open research, according to interim co-chair Eric Horvitz, who is also director of Microsoft Research. The Partnership also added Apple as a "founding member," putting the tech giant in good company: Amazon, Microsoft, IBM, Google, and Facebook are already on board.

When you imagine the ideal chatbot, what do you picture? The bot in your mind's eye may resemble something along the lines of blockbuster movie favorites like Baymax from Big Hero 6, Wall-E's EVE, or my personal hero, C-3PO. You probably didn't imagine a small tower that lives on your shelf, like Alexa; a virtual being that resides in the cloud and is accessed through your smartphone, like Siri; or even a pop-up chat window on your computer screen that can answer questions about your favorite products. Besides their looks, there's another major difference between today's artificial intelligence (AI) and our movie favorites: Our most beloved fictional chatbots all possess emotional intelligence (EI). As we move into the future, AI needs humanizing qualities to improve the way it interacts with us, meets our needs for information, and even controls the other technology around us.