DEDHAM, MA--(Marketwired - May 17, 2016) - Rage Frameworks, a provider of knowledge-based automation technology and services, today announced that it will attend MIT Technology Review's fourth annual West Coast Emerging Digital Technologies (EmTech Digital) Conference, which brings together innovators, entrepreneurs, business leaders and venture capitalists to examine what's next for artificial intelligence across professional industries. The event will explore the latest research on artificial intelligence techniques, including deep learning and speech and image recognition, that are providing machines with valuable new capabilities and making the automation of more business decisions possible. Rage Frameworks' CEO Dr. Venkat Srinivasan will present "AI in the Enterprise" at the EmTech Digital Conference, where he will outline how artificial intelligence is already impacting daily lives and the ways in which Rage's traceable deep learning technology, RAGE AI, is helping global financial services, consumer products and manufacturing firms overcome business problems faster than ever. RAGE AI enables intelligent systems with end-to-end knowledge-based automation including contextual, traceable deep learning. Where natural language is involved, using deep linguistic parsing and proprietary linguistics-based innovations it enables the understanding of the real meaning of documents and interprets them as a human would.

According to a recent report in Futurism, US law firm Baker & Hostetler, has become the first law firm to employ the IBM Watson-powered lawyer'ROSS'- the world's first artificially intelligent attorney. ROSS will now be used in the firm's bankruptcy practice; with other firms making similar plans. According to this blog post on the IBM website, all one has to do is ask ROSS a question as a client would to his lawyer. And instead of spending countless hours on going through voluminous documents, ROSS will use a "cognitive computing system" to go through the relevant legislations and judicial pronouncements within a matter of seconds. Not only can ROSS can sort through more than a billion text documents each second, IBM claims that it learns from feedback and gets smarter over time.

Google, Uber and Tesla may be giddy about the prospect of self-driving cars, but U.S. consumers aren't convinced that the technology is safe or that they need it. Only 10 percent of people surveyed by the University of Michigan said they would have no concerns at all about riding in fully self-driving cars, while two thirds said they would be moderately or very concerned about it. The level of confidence goes up a little for cars that would be only partially self-driving. In that case, 16 percent would have no concern and 50 percent would be moderately or very concerned. The research shows that while coverage of self-driving cars and the technology behind them has increased in the media, consumers are still unsure that the cars are safe.

"Alone we can do so little and together we can do much" – a phrase from Helen Keller during 50's is a reflection of achievements and successful stories in real life scenarios from decades. Same thing applies to most of the cases from innovation with big impacts and with advanced technologies world. The machine Learning domain is also in the same race to make predictions and classification in a more accurate way using so called ensemble method and it is proved that ensemble modeling offers one of the most convincing way to build highly accurate predictive models. Ensemble methods are learning models that achieve performance by combining the opinions of multiple learners. Typically, an ensemble model is a supervised learning technique for combining multiple weak learners or models to produce a strong learner with the concept of Bagging and Boosting for data sampling.

Next up in our series on top Kagglers is the #1: Owen Zhang (Zhonghua Zhang). Owen comes from an engineering background and currently works as the Chief Product Officer at DataRobot. Back in 2011 I had just switched to analytics as a full time job (after several years working in IT), and was eager to improve my skills and to "prove myself". So it was fortuitous that Kaggle came along with the first Allstate competition. Being in the same industry I felt I had some advantage as well.

Machine learning is accomplishing Facebook's mission of connecting the environment throughout language obstacles. Fb is now serving 2 billion textual content translations for every working day. Fb can translate throughout 40 different languages in 1800 instructions like French to English. And 800 million people, pretty much 50 % of all Fb people, see translations just about every month. Alan Packer, Facebook's Director of Engineering for language technological innovation, discovered this progress nowadays at MIT's Emtech Electronic convention in San Francisco.

Taking inspiration from the way humans seem to learn, scientists have created AI software capable of picking up new knowledge in a far more efficient and sophisticated way. The new AI program can recognize a handwritten character about as accurately as a human can, after seeing just a single example. The best existing machine-learning algorithms, which employ a technique called deep learning, need to see many thousands of examples of a handwritten character in order to learn the difference between an A and a Z. The software was developed by Brenden Lake, a researcher at New York University, together with Ruslan Salakhutdinov, an assistant professor of computer science at the University of Toronto, and Joshua Tenenbaum, a professor in the Department of Brain and Cognitive Sciences at MIT. Details of the program, and the ideas behind it, are published today in the journal Science.

This is what has happened with Bitcoin mining (mostly in Tibet/China) since cost-differentiation between miners is no longer about who is using specialized hardware, but about who uses them most cheaply.

In the broadest definition, a sensor is a device whose purpose is to detect changes in the environment, and then generate a signal or data based on those changes. All living organisms contain biological sensors. Most of these are specialized cells that are sensitive to light, motion, temperature, magnetic fields, gravity, humidity, moisture, vibration, pressure, electrical fields, or sound, to name just a few. Over the years, many thousands of mechanical sensors have been developed to detect changes in their environments. Besides pressure, sensors have also been developed to measure changes in sound, vibration, chemical composition, electric current, electric potential, magnetic force, radio waves, flow, fluid velocity, ionizing radiation, subatomic particles, navigation instruments, position, angle, displacement, distance, speed, acceleration, optical, light, imaging, photon, force, density, level, thermal, heat, temperature, proximity and presence, again to name a few.

The speed of technological change is exponential. What was yesterday's hot ticket quickly becomes tomorrow's old news. We are living in the midst of a huge surge of interest and research in Artificial Intelligence (AI). It seems like every week there is a new breakthrough in the field and a new record is set in some task previously done by humans. If you don't already know what IoT, AI, VR, AR, and bots mean, you better get up to speed immediately because these technologies are changing the way data is created, collected, interpreted, and communicated.