Data-driven techniques have improved decision-making processes for people in industries such as finance and real estate. Yet, despite promising solutions that data analytics and artificial intelligence/machine learning (ML) tools can bring to healthcare, the industry remains largely unconvinced. In this O'Reilly report, you'll explore the potential of--and impediments to--widespread adoption of AI and ML in the medical field. You'll also learn how extensive government regulation and resistance from the medical community have so far stymied full-scale acceptance of sophisticated data analytics in healthcare. Mike Barlow is an award-winning journalist, author, and communications strategy consultant.

I am amazed sometimes by how technology is impacting the world we live in. For example, SpaceX being so successful in reusing spaceships may not have a great impact today, but it does change the way we look at the future, and what we understand by science fiction. Human Resource is one of those industries which does not quite make its way into science fiction books, and no kid grows up thinking of revolutionizing the technology inside this particular market. Traditionally, HR was always viewed as a paper-intensive, non-innovative area, where salary decisions are made, people get hired or fired and where team building sessions are organized. But behind the scenes, the industry is changing and incorporating technology at a rapid pace, even if it wasn't designed for HR to start with.

Sentiment analysis is opinion mining of text content which identifies and extracts subjective information in source materials. ParallelDots Sentiment analysis API provides a very accurate analysis of the overall sentiment of the text content which can be widely applied to reviews and social media for a variety of applications, ranging from marketing to customer service.

In the 2013 film Her, Spike Jonze offered his take on a society in which our closest relationships are with technology, not humans. The movie tells the story of a lonely man who purchases a voice-controlled robot that evolves to meet his needs. Samantha, as she's known, eventually becomes so in sync with him that they fall in love. Oddly enough, the technology in Her is not as far away as you might think. At her core, Samantha is an interface capable of interpreting needs through conversation and contextual cues.

The London-based big data startup Flock is building a platform for performing real-time quantified risk analysis of drone flights, including by applying AI (artificial intelligence) to tracking data sourced from urban environments. According to TechCrunch, the company is licensing data pertaining to the position of buildings, people and cars in urban environments, as well as weather conditions, and feeding that into its risk assessment platform. Idea being for its software to analyze a planned drone flight in real-time and perform a "cost/benefit" analysis -- to help insurers set premiums or drone operators decide whether or not to undertake a given flight. In the future the company intneds to apply machine learning algorithms to the urban data it's getting in order to generate "real-time risk reduction" for drone operators via predictive assessments for drone flight scheduling -- which it reckons could be used to power fully autonomous drone flights zipping along risk-minimized routes. "The idea is to have a robust trend analysis built in to the system so we can analyze historic data, and then build up a really good understanding of how cities move generally, how cities breathe, and how populations and traffic conditions change over time," says Ed Leon Klinger, CEO.

Stuart Russell argues for a fundamental reorientation of the field artificial intelligence. Click here to watch the full keynote http://oreil.ly/1YSpwEh Follow O'Reilly on Twitter: http://twitter.com/oreillymedia

The government has decided to collaborate with more than 20 firms and research institutions to promote development of artificial intelligence technology to be applied in the medical, manufacturing and other sectors. The public-private initiative involving entities such as the government-backed Riken research institute, Toyota Motor Corp and NEC Corp is aimed at finding technological solutions to labor shortage and aging society issues that Japan is facing. In the medical field, AI technology is expected to be used in diagnosing a patient's symptoms and giving doctors advice on optimal treatment by analyzing the patient's electronic medical record and a huge amount of past similar cases. The government sees the AI sector as one of the pillars of its growth strategy and set up a research and development center for innovative intelligence technologies at Riken in April.

This is a guest post by the Mycroft team as part of "the startup stories", a series of blog posts about how and why innovative companies are using Ubuntu technology. If you work in technology you've probably had this dream. You wake up and your whole house is a computer, a starship in cyberspace that listens to you, understands you, and performs tasks effortlessly. Mycroft A.I. is achieving that dream using Snappy Ubuntu Core. Mycroft is an open source / open hardware project that is working to bring natural language recognition and Internet of Things (IoT) integration to homes and offices everywhere.

Faculty Opening Young and research-intensive, Nanyang Technological University (NTU Singapore) is ranked 13th globally. It is also placed 1st amongst the world's best young universities and ranked 6th globally for Engineering and Technology. With an engineering research output ranked amongst the top seven universities in the world, the College of Engineering (CoE) is a recognised leader in innovation and technology. CoE is the largest College in NTU and comprises six internationally known engineering schools with more than 14,000 students, 550 faculty and 1,400 staff. At the College of Engineering, faculty are encouraged to seek new knowledge, discover and create innovative solutions.

Naive Bayes classifiers, a family of classifiers that are based on the popular Bayes' probability theorem, are known for creating simple yet well performing models, especially in the fields of document classification and disease prediction. In this first part of a series, we will take a look at the theory of naive Bayes classifiers and introduce the basic concepts of text classification. In following articles, we will implement those concepts to train a naive Bayes spam filter and apply naive Bayes to song classification based on lyrics. Starting more than half a century ago, scientists became very serious about addressing the question: "Can we build a model that learns from available data and automatically makes the right decisions and predictions?" Looking back, this sounds almost like a rhetoric question, and the answer can be found in numerous applications that are emerging from the fields of pattern classification, machine learning, and artificial intelligence. Data from various sensoring devices combined with powerful learning algorithms and domain knowledge led to many great inventions that we now take for granted in our everyday life: Internet queries via search engines like Google, text recognition at the post office, barcode scanners at the supermarket, the diagnosis of diseases, speech recognition by Siri or Google Now on our mobile phone, just to name a few.