IMAGE: Philip Thomas at UMass Amherst, with colleagues there and at Stanford, says they say they hope that machine learning researchers will go on to develop new and more sophisticated... view more Guaranteeing safe and fair machine behavior is still an issue today, says machine learning researcher and lead author Philip Thomas at the University of Massachusetts Amherst. "When someone applies a machine learning algorithm, it's hard to control its behavior," he points out. This risks undesirable outcomes from algorithms that direct everything from self-driving vehicles to insulin pumps to criminal sentencing, say he and co-authors. Writing in Science, Thomas and his colleagues Yuriy Brun, Andrew Barto and graduate student Stephen Giguere at UMass Amherst, Bruno Castro da Silva at the Federal University of Rio Grande del Sol, Brazil, and Emma Brunskill at Stanford University this week introduce a new framework for designing machine learning algorithms that make it easier for users of the algorithm to specify safety and fairness constraints. "We call algorithms created with our new framework'Seldonian' after Asimov's character Hari Seldon," Thomas explains.

AI and ML solutions are already being used by thousands of companies with the goal of improving the healthcare experience. For example, Babylon Health is changing the way we manage and better understand health. Founder, Ali Parsa developed the app in 2013 with a mission of providing accessible and affordable healthcare to every individual on earth. Babylon's AI system has been designed to understand and recognise the way humans express their medical symptoms and it can interpret symptoms and medical questions through a chatbot interface and match them to the most appropriate service. It can recognise most healthcare issues seen in primary care and provide information on next steps to take.

Nearly all of the UK's core industries is set to invest heavily into artificial intelligence (AI) in the coming years, but they will also invest in the human workforce and don't expect a significant reduction in headcount as a result of AI investments. This is according to a new international study conducted by IFS. Polling 600 enterprise decision-makers, the report states that 99 per cent of UK's respondents confirmed plans to invest in AI. This puts the UK firmly ahead of North America and the rest of Europe and puts it on course to becoming an AI powerhouse. For Enterprise Resource Planning (ERP), Enterprise Asset Management (EAM), and Field Service Management (FSM) industries, AI would be used to increase the productivity among the current workforce (60 per cent), and to add extra value to products and services (48 per cent).

Researchers have developed stretchable, pocket-sized robots which could crawl up walls and across ceiling to clean them, for environmental monitoring and deployment in hazardous environments. Published in the journal Soft Robotics, the study from University of Bristol in the UK describe how a robot made from the skin, called "ElectroSkin", can be scrunched up, put in one's pocket and then later pulled out and thrown on a surface where it moves. ElectroSkin is a new fundamental building block for a range of soft next-generation robots. "ElectroSkin is an important step toward soft robots that can be easily transported, deployed and even worn. The combination of electrical artificial muscles and electrical gripping replicated the movements of animals like slugs and snails, and where they can go, so could our robots," said study researcher Jonathan Rossiter, Professor at University of Bristol.

Artificial Intelligence (AI) has increasingly become a dentists' best friend in improving productivity and make the best out of the $33 billion global dentistry market. Though machines are still learning to address certain dental anomalies creatively, the market is already blooming with AI-powered dental diagnostics products. It is projected that the AI tools, as of now can increase clinics' revenue by 25%. This could happen as a result of software program enhancing the quality of chair time of dentists by slicing down the time wasted on analyzing reports. Cavity or caries is among the most common dental issues.

Artificial intelligence (AI) is competent to have a revolutionary impact on businesses globally. Talking about the information technology sector, it is no longer merely about codifying business logic. Insight is indeed the modern currency, and the pace with which we all can scale that insight is the fundamental of value creation. As per a report by Gartner, AI is going to be one of the top investment preferences for over 30% of CIOs worldwide by 2020. A lot of corporations are yet in their initial phase in comprehending that how AI is scalable enough to transform their businesses.

After decades of living in a common internal market, many people seem to have forgotten how important it is, and how easy. But when you travel to for example the United States, and you forget your adapter, you can't even charge your mobile phone - a problem that never occurs when you travel on the European continent. The internal market is not only about the free movement of goods and services. That's why worldwide people talk about "European standards", as a label of global quality. It is the task of the IMCO committee to make sure these European standards are upheld in every single space in Europe.

Throughout its history, Machine Learning (ML) has coexisted with Statistics uneasily, like an ex-boyfriend accidentally seated with the groom's family at a wedding reception: both uncertain where to lead the conversation, but painfully aware of the potential for awkwardness. This is caused in part by the fact that Machine Learning has adopted many of Statistics' methods, but was never intended to replace statistics, or even to have a statistical basis originally. Nevertheless, Statisticians and ML practitioners have often ended up working together, or working on similar tasks, and wondering what each was about. The question, "What's the difference between Machine Learning and Statistics?" has been asked now for decades. Machine Learning is largely a hybrid field, taking its inspiration and techniques from all manner of sources. It has changed directions throughout its history and often seemed like an enigma to those outside of it.1

Say what you will about Kim Kardashian--at least she's a human. The next generation of the famous-for-being-famous are being engineered from scratch. They're synthetic stars--algorithmically generated characters who have millions of Instagram followers, show up in glossy magazines, and have songs on Spotify. She models for the likes of Prada and Calvin Klein, her first single came out last year, and she has sponsorship deals with companies like Samsung. Among her pals: Bermuda, a rule-breaking bad girl who models and touts brands, and Blawko, an L.A.-based Gen-Zer who likes fast cars and Absolut vodka, and who is never seen without his trademark scarf covering his nose and mouth.

Our society relies heavily on digital devices and channels, and with that the concept of identity has quickly become the foundation of every customer interaction--particularly within the digital advertising ecosystem. In response to the emerging strategic importance of identity, Experian today announced a new innovative solution that uses the fusion of data and artificial intelligence, to help marketers connect Mobile Ad IDs (MAIDs) with digital and offline identity attributes to better understand their target audiences. Powered by Experian's vast data assets and identity platform, the new solution incorporates machine-learning algorithms, as well as deterministic and probabilistic techniques, to sift and connect billions of advanced identity signals and data elements, including MAIDs, from a wide variety of internal and external sources. The outcome of this process allows brand marketers to implement more effective analytics, audience segmentation and activation, and measurement capabilities. "Experian has always been a leader in identity resolution, helping brand marketers more accurately identify and understand customers, while also keeping customers at the heart of every marketing strategy," said Kevin Dean, Experian's president and general manager of Marketing Services, North America.