Google said Friday it will provide a new stream of automated insights in its Google Analytics mobile app, allowing marketers to see key data metrics in a matter of minutes. Previously, it could take marketers several hours to discover the same data points, Google said. The new feature is available today on both Android and iOS. Other companies offer analytics products to their customers through machine learning -- IBM and its Watson Analytics comes to mind -- but few, if any, offer similar features on a mobile app. This can be especially useful for marketers who are on the go, about to head into a meeting or unfamiliar with analytics products. "I think what Google is realizing is people tend to exhibit the same behaviors when they pull reports from Analytics," said Kevin Lee, executive chairman and co-founder of Didit, a full-service digital agency that specializes in search.

LONDON – 25 August 2016 – IP EXPO Europe, Europe's number one enterprise IT event, has today announced the addition of several influential industry speakers to this year's keynote and seminar programme. Attendees will have the opportunity to hear how key IT issues are affecting businesses and humanity alike, from Author & Founding Director of Oxford University's Future of Humanity Institute: Nick Bostrom; the'Father of Java': James Gosling; the creator of the'MySpace worm' and now an Independent Security Researcher: Samy Kamkar; and Independent Cyber Security Consultant Dr Jessica Barker. These additions to the 2016 IP EXPO Europe program are the latest in a list which already includes some of the world's most renowned technology innovators, from the likes of HPE, Microsoft, and Amazon Web Services. With his work influencing the likes of Bill Gates, Professor Stephen Hawking, and Elon Musk, keynote speaker Nick Bostrom, Author & Founding Director of Oxford University's Future of Humanity Institute, is one of the world's foremost authorities on Artificial Intelligence (AI). Opening Day One at this year's IP EXPO Europe, Bostrom will be discussing the impact that AI and intelligent machines will have on business and society, and sets out to answer the question: 'Will AI bring about the end of humanity?'

SAN FRANCISCO • For years, science fiction moviemakers have been making us fear the bad things that artificially intelligent machines might do to their human creators. But for the next decade or two, our biggest concern is more likely to be that robots will take away our jobs or bump into us on the highway. Now, five of the world's largest tech companies are trying to create a standard of ethics around the creation of artificial intelligence (AI). While science fiction has focused on the existential threat of AI to humans, researchers at Google's parent company, Alphabet, and those from Amazon, Facebook, IBM and Microsoft have been meeting to discuss more tangible issues, such as the impact of AI on jobs, transportation and even warfare. Tech companies have long over-promised what artificially intelligent machines can do.

For centuries, physicians and healers focused primarily on treating acute problems such as broken bones, wounds, and infections. "If you had an infectious disease, you went to the doctor, the doctor treated you, and then you went home," says Balaji Krishnapuram, director and distinguished engineer at IBM Watson Health. Today, the majority of healthcare revolves around treating chronic conditions such as heart disease, diabetes, and asthma. Treating chronic ailments often requires multiple visits to healthcare providers, over extended periods of time. In modern societies, "the old ways of delivering care will not work," says Krishnapuram. "We need to enable patients to take care of themselves to a far greater degree than before, and we need to move more treatment from the doctor's office or hospital to an outpatient setting or to the patient's home." Unlike traditional healthcare, which tends to be labor-intensive, emerging models of healthcare are knowledge-driven and data-intensive. Many of the newer healthcare delivery models will depend on a new generation of user-friendly, real-time big data analytics and artificial intelligence/machine learning (AI/ML) tools. Identifying risks, determining who is at risk, and identifying interventions that will reduce risk. Supporting and enabling customized self-care treatment plans for individual patients, monitoring patient health in real time, adjusting doses of medication, and providing incentives for behavioral changes leading to improved health. Optimizing healthcare processes (everything from medical treatment itself to the various ways insurers reimburse providers) through rigorous data analysis to improve outcomes and quality of care while reducing costs.

Amazon lures away eBay's artificial intelligence chief Amazon poaches eBay A.I. chief, continues ramping up machine learning operations Many of today's martech companies that espouse machine learning capabilities simply offer a ...

In our quarterly analysis of companies pursuing healthcare-focused applications of AI, we reported that deals leapt from less than 10 in 2011 to 60 in 2015. So far this year (as of 8/23/2016), companies in this space have raised over 55 equity funding rounds. Some of the recent deals include a 25M Series A round raised by London-based health services startup, Babylon Health, backed by investors including Kinnevik and Google-owned DeepMind Technologies (Babylon will reportedly roll out a Siri-like voice recognition interface this year), and a 154M Series A round raised by China-based iCarbonX. "By 2025, AI systems could be involved in everything from population health management, to digital avatars capable of answering specific patient queries." We identified over 90 companies that are applying machine learning algorithms and predictive analytics to reduce drug discovery times, provide virtual assistance to patients, and diagnose ailments by processing medical images, among other things.

Loyal readers will recall that last spring we conspired with artificial intelligence expert Randal Olson to develop the ultimate U.S. road trip. The map Olson came up with -- he did all the work, really -- optimized the best way to drive by car to 50 major U.S. landmarks, using machine learning algorithms and Google Maps. We're happy to report that Olson is back at it, just in time for summer road tripping. By leveraging the power of genetic algorithms and other artificial intelligence technology, Olsen's optimized loop route will get you across the country and back in a little over eight days -- starting in Concord, N.H., and dropping you back in Boston, Mass. How did Olson generate his road trip map?

In 2014, Stanford University launched the One Hundred Year Study, a long-term look into the future of artificial intelligence set to publish a paper every five years. Just two years in, the team released its first report Sept. 1, Artificial Intelligence and Life in 2030. The document outlines the history of AI and where its being currently applied, like transportation for self-driving cars and healthcare with surgical robots. It's an important document not only for the research community, but for policymakers grappling to understand technology that existing laws could be unequipped to handle. The report says evil AI isn't what people need to anticipate--it's the unintended consequences of otherwise helpful things AI gives, like the erosion of privacy or displacement of labor.

PoolParty Semantic Suite is integrating improved machine learning capabilities to support the most critical knowledge engineering tasks. PoolParty is a semantic technology platform developed, owned and licensed by the Semantic Web Company GmbH (SWC), a provider of graph-based metadata, search, and analytic solutions. PoolParty 5.5 now represents a complete stack of standards-based technologies to be used as an enterprise middleware for cognitive computing platforms, according to Andreas Blumauer, PoolParty CEO. The product is focused entirely on supporting organizations to enter the era of semantic and cognitive computing as smoothly as possible, says Blumauer. New features include semi-automatic creation of knowledge graphs; options for integration with SharePoint, Elasticsearch, and Stardog; and the ability to maintain context aware knowledge models.

"Machine Learning" (ML) methods have been around for ages but Big Data revolution and plummeting cost of computing power are now making them truly excellent and practical analytical tools in banking, across a variety of use cases, including credit risk. ML algorithms may sound complex and futuristic but the way they work is quite simple. Essentially they combine a massive set of decision trees (i.e., a decision-making model that breaks out individual decisions and possible consequences, as known as "learners") to create an accurate model. By churning through these learners at high speeds, ML models are able to find "hidden" patterns, particularly in unstructured data that common statistical tools miss. Overfitting (the analytical description of random errors instead of underlying relationships) of the model is a typical concern that comes up in regards to ML. Overfitting of ML models can be avoided by carefully choosing input variables and the specific algorithm used.