Armonk, N.Y. - 07 Apr 2017: IBM (NYSE: IBM) today announced that its scientists have been granted a patent on machine learning models to predict therapeutic indications and side effects from various drug information sources. IBM Research has implemented a cognitive association engine to identify significant linkages between predicted therapeutic indications and side effects, and a visual analytics system to support the interactive exploration of these associations. IBM Research staff member Ping Zhang (left) and Program Director, Center for Computational Health Jianying Hu (right) with their newly patented invention to help drug discovery researchers identify which drug indications are typically linked to which side effects. IBM was granted U.S. Patent 9,536,194: Method and system for exploring the associations between drug side-effects and therapeutic indications for this invention. Lack of efficacy and adverse side effects are two of the primary reasons a drug fails clinical trials, each accounting for around 30 percent of failures.

Element Data, Inc., a decision support software platform that harnesses artificial intelligence and machine learning to power decisions worldwide closed a $1.7M seed round on April 20, 2017. Archibald Cox, Jr., also known as Archie, led the seed round. Mr. Cox has more than 50 years of financial and management experience at the executive level. He served as Chief Executive Officer of Morgan Stanley International and as the Chairman of Barclays Americas and several private companies. Mr. Cox holds a Bachelor's Degree in Economics from Harvard College and an M.B.A. from the Harvard Business School.

What are tech titans Google, Amazon and Uber agitating for to further the march of machine learning technology and ultimately inject more fuel in the engines of their own dominant platforms? Specifically, they're pushing for free and liberal access to publicly funded data -- urging that this type of data continue to be "open by default," and structured in a way that supports "wider use of research data." After all, why pay to acquire data when there are vast troves of publicly funded information ripe to be squeezed for fresh economic gain? Other items on this machine learning advancement wish-list include new open standards for data (including metadata); research study design that has the "broadest consents that are ethically possible," and a stated desire to rethink the notion of "consent" as a core plank of good data governance -- to grease the pipe in favor of data access and make data holdings "fit for purpose" in the AI age. These suggestions come in a 125-page report published today by the Royal Society, aka the U.K.'s national academy of science, ostensibly aimed at fostering an environment where machine learning technology can flourish in order to unlock mooted productivity gains and economic benefits -- albeit the question of who, ultimately, benefits as more and more data gets squeezed to give up its precious insights is the overarching theme and unanswered question here.

Element Data, Inc., a decision support software platform that harnesses artificial intelligence and machine learning has acquired the technology assets and team of PV Cube, a Seattle area start-up. The acquisition expands the size of the team of existing engineers building the world's first cognitive decision engine. PV Cube's Co-Founders Vish Vadlamani and Phani Vaddadi, were most recently employed at Microsoft and led the development of Microsoft's knowledge fabric integrated within Cortana, Bing and other Microsoft products. Vadlamani and Vaddadi are named on a combined 45 awarded patents. Element Data's Chief Technology Officer Charles Davis said, "The community of sophisticated artificial intelligence and machine learning experts is in high demand. We are fortunate to have such highly regarded industry leaders on our team."

IMAGE: (a) Posteroanterior chest radiograph shows upper lobe opacities with pathologic analysis-proven active TB. OAK BROOK, Ill. - Researchers are training artificial intelligence models to identify tuberculosis (TB) on chest X-rays, which may help screening and evaluation efforts in TB-prevalent areas with limited access to radiologists, according to a new study appearing online in the journal Radiology. According to the World Health Organization, TB is one of the top 10 causes of death worldwide. In 2016, approximately 10.4 million people fell ill from TB, resulting in 1.8 million deaths. TB can be identified on chest imaging, however TB-prevalent areas typically lack the radiology interpretation expertise needed to screen and diagnose the disease. "There is a tremendous interest in artificial intelligence, both inside and outside the field of medicine," said study co-author Paras Lakhani, M.D., from Thomas Jefferson University Hospital (TJUH) in Philadelphia.

A group of prominent academics and tech executives fear that the U.K.'s exit from the European Union could jeopardize the U.K.'s lead in the development of machine learning technologies. British researchers have played a critical role in advances in machine learning -– a kind of artificial intelligence in which software learns from experience or data. But as demand for related expertise proliferates across industries, the country faces a "substantial skill shortage in this area," concluded a report published by Tuesday by The Royal Society, one of the world's oldest and most well-known scientific organizations. Although the report doesn't mention Brexit specifically, it implies that the U.K.'s decision to leave the European Union could exacerbate this skills gap. "As it considers its future approach to immigration policy, the U.K. must ensure that research and innovation systems continue to be able to access the skills they need," the report said.

Welcome to the Schindler Conference Call on key figures for the First Quarter 2017. I'm here together with Erich Ammann, our CFO we will go into all financial details later during the call. As an introduction to the remark it is fair to say that we continued our successful plan of the last year as we kept our direction towards top-line growth and also higher profitability. Let's have a closer look on our highlights of the first quarter 2017 on slide two. We made further operational and strategic progress. First, we were able to confirm our growth path. Orders received increased by 5.9% in local currencies and also operating revenue rose by 3.8% in local currencies. Operating revenue was therefore within our guidance of 3% to 5% growth in 2017. Our investments into our geographic diversification mainly into our strategic markets were paying off. Second, we also continued to improve our profitability. The EBIT margin increased to 11.5% and even 11.7% before restructuring costs. Net profit stayed flat at CHF179 million due to some temporary booking losses on the ALSO exchangeable bond. Third, we made further progress in our strategic initiatives. We are on track with our globally harmonized modular product platforms, but it is still a long way to go to finalize this, and we were also able to successfully launch our new Internet of Elevator and Escalator Solutions, Schindler Ahead. Yesterday, we launched officially our new Schindler Ahead initiative and I would like to stay a little bit with that topic. As you can see on slide number 3, we will create significant customer benefits in the future. We increased the uptime of our equipment with predictive maintenance, we offer comprehensive insights about all type of information of the equipment for a better building, maintenance and management, and we generate convenience with superior customer service by interactive and personalized passenger experience. On slide four, you find the solution concept of our enhanced service offerings. There are four elements to be mentioned. First the Cube, The Cube enables machine intelligence, all relevant machine data are collected, filtered and transmitted to the cloud platform. The Cube is an intelligent device, not only a transmitter or a gateway, as we can run apps and stream multimedia content and handle emergency calls. The second topic is the cloud platform. The cloud platform creates real time insights.

People in Britain are more scared of the artificial intelligence embedded in household devices and self-driving cars than in systems used for predictive policing or diagnosing diseases. That's according to a survey commissioned by the Royal Society, which is billed as the first in-depth look at how the public perceives the risks and benefits associated with machine learning, a key AI technique. Participants in the survey were most worried by the notion that a robot, acting on conclusions derived by machine learning, would cause them physical harm. Accordingly, machines with close proximity to their users, such as those in the home and self-driving cars, were viewed as very risky. The notion of a robot animated by AI is known as "embodiment."

There has been a lot of interest in artificial intelligence and predictive learning systems – and with good reason. The systems provide a fast, powerful method to handle data analysis, as well as handoff routine decisions to something that can research and collate information far quicker than a human. AI can perform tasks as basic as splitting data into two general piles or as complex as driving a car across the country without a human in the vehicle. This kind of processing ability is a boon to industries that rely on huge amounts of data, such as marketing. Ad agencies work with constantly updating information that they need to track, such as staying up-to-date on news from influencers and media developments or analyzing website traffic results.

Almost everyone who went to college had to go to the process of applying for a job. When you graduate, you will obviously look for a job. Part of this process is to perform an interview with the recruiter of the company to convince him that you are the right person for the job. This interview can be stressful for some people and easy-going for others. Sometime, recruiters can be biased by personal opinions they may have about you or they might try to trick you.