The market for artificial intelligence in the US is projected to grow at a CAGR of around 75% during 2016-2021. United States is the largest market for artificial intelligence solutions, globally. Continuous research and development in healthcare, autonomous vehicles, security and access control, cyber security, etc., are expected to fuel growth in the United States artificial intelligence market. Rising penetration of smart wearables, burgeoning head-up display screen market in luxury car segment and growing venture capital investments are also propelling adoption of artificial intelligence solutions in the country. United States government is also playing a proactive role in the country's artificial intelligence market by organizing workshops in association with leading solution providers.

Tech giant Microsoft has acquired artificial intelligence (AI)-based scheduling service Genee that simplifies the scheduling and rescheduling of large group meetings for companies. Microsoft will integrate the AI technology into Office 365 before shutting down Genee. Co-founders Ben Cheung and Charles Lee, who plan to join Microsoft, started Genee in 2014 to simplify the time-consuming task of scheduling (and rescheduling) meetings. "It is especially useful for large groups and for when you don't have access to someone's calendar," Jha added. Genee uses natural language processing and optimised decision-making algorithms so that interacting with a virtual assistant is just like interacting with a human one.

Earlier this year, Mark Zuckerberg revealed he is dedicating his year to making a home AI butler. Now, he has revealed the project is already coming to fruition - and promised to reveal it next month. While at a Facebook'town hall' event in Rome, he told an audience'I'm making progress - I hope to have a demo next month.' Pope Francis meets Facebook founder and CEO Mark Zuckerberg, second from left, and his wife Priscilla Chan, at the Santa Marta residence, the guest house in Vatican City. However, Zuckerberg later revealed his wife doesn't have access to his home AI system.

Facebook's Mark Zuckerberg hopes to show off his Jarvis-like home AI next month Microsoft's new Catapult v2 server design is targeted at AI Facebook's AI plans to analyse your photographs to check your health Mark Zuckerberg is building a futuristic AI assistant for his home, and he'll unveil it in September

First there was big data – extremely large data sets that made it possible to use data analytics to reveal patterns and trends, allowing businesses to improve customer relations and production efficiency. Then came fast data analytics – the application of big data analytics in real-time to help solve issues with customer relations, security, and other challenges before they became problems. Now, with machine learning, the concepts of big data and fast data analytics can be used in combination with Artificial Intelligence (AI) to avoid these problems and challenges in the first place. So what is machine learning, and how can it help your business? Machine learning is a subset of AI that lets computers'learn' without explicitly being programmed.

The mystery of how Parkinson's disease progresses could be cracked thanks to researchers at the Australian National University (ANU) and machine learning. Deborah Apthorp of the ANU Research School of Psychology has won funding for a study that will track early symptoms with the aim of finding possible indicators of progression, using machine learning. This research has received 138,930 over 5 years from the Perpetual Impact Philanthropy Grant. As it stands, the type of Parkinson's a diagnosed patient has or how quickly it will progress is hard to determine. Apthorp noted in an ANU report that some individuals can be fine for quite a while while others can experience a more rapid progression.

UC Berkeley artificial intelligence (AI) expert Stuart Russell will lead a new Center for Human-Compatible Artificial Intelligence, launched this week. BRETT, the Berkeley Robot for the Elimination of Tedious Tasks, ties a knot after watching others demonstrate it. Russell, a UC Berkeley professor of electrical engineering and computer sciences and the Smith-Zadeh Professor in Engineering, is co-author of Artificial Intelligence: A Modern Approach, which is considered the standard text in the field of artificial intelligence, and has been an advocate for incorporating human values into the design of AI. The primary focus of the new center is to ensure that AI systems are beneficial to humans, he said. The co-principal investigators for the new center include computer scientists Pieter Abbeel and Anca Dragan and cognitive scientist Tom Griffiths from UC Berkeley; computer scientists Bart Selman and Joseph Halpern from Cornell University; and AI experts Michael Wellman and Satinder Singh Baveja from the University of Michigan.

Last summer, I was at a conference having lunch with Hal Daume III when we got to talking about how "Bayesian" can be a funny and ambiguous term. It seems like the definition should be straightforward: "following the work of English mathematician Rev. Thomas Bayes," perhaps, or even "uses Bayes' theorem." But many methods bearing the reverend's name or using his theorem aren't even considered "Bayesian" by his most religious followers. Why is it that Bayesian networks, for example, aren't considered… y'know… Bayesian? As I've read more outside the fields of machine learning and natural language processing -- from psychometrics and environmental biology to hackers who dabble in data science -- I've noticed three distinct uses of the term "Bayesian."

For the casual internet user, a quick Google search is often all it takes to find plenty of information on any particular topic. But for specialized financial research, analysts often find themselves laboriously searching proprietary databases, regulatory filings, and paywalled sources that aren't even indexed by the big search engines, says Jack Kokko, the founder and CEO of financial search engine company AlphaSense. That's why he and cofounder and CTO Raj Neervannan created AlphaSense, which applies natural language processing and machine learning techniques to let users find relevant information in financial documents. "It started from my first job out of college as an analyst at Morgan Stanley, where I was, as every analyst, going through these huge piles of paper on my desk and trying to find information very manually--nights and days spent toiling through that information and still fearing that I'm missing a lot," Kokko says. The San Francisco-based company takes in information from thousands of licensed data sources, as well as public web sources like news reports, and automatically processes them to extract meaning on a sentence-by-sentence level.

It seems we are all part of a big experiment. We're testing our data-driven consciousness and determining how much information we can digest and at what speed. And if we continue at our current pace, we will soon see sensors and ambient computing infuse our personal and professional lives with a myriad of interactive things. Even items such as coffee mugs may be connected. In such a digital-driven, hyperconnected world, our perceptions will heavily depend on virtual experiences and our biological view.