IBM Chief Executive Ginni Rometty (above) announced that its cognitive computing services already reach 200 million people worldwide and would reach 1 billion by the end of 2017. Healthcare and consumers are proving especially ripe targets for Watson. IBM will continue to engage data scientists, but it indicated that increasingly it will turn its attention to reaching developers. It aims to reach those developers by making Watson's machine learning tools and services as easy-to-use and cost-effective as renting cloud virtual machines. IBM Chief Executive Ginni Rometty (above) announced that its cognitive computing services already reach 200 million people worldwide and would reach 1 billion by the end of 2017.

For instance, there is legislation that governs the use of citizen data by analysts and policy makers to ensure that privacy is maintained and equal treatment is given to all. This is a crucial aspect of public service, and there have been numerous stories about the implicit bias in AI systems, whether because of biased data used to train the systems, or a bias in the algorithms behind them. For AI to thrive in public service therefore, developers need to understand these legal frameworks and how they might apply to AI based decision support.

To build OpenAI--a new artificial intelligence lab that seeks to openly share its research with the world at large--Elon Musk and Sam Altman recruited several top researchers from inside Google and Facebook. But if this unusual project is going to push AI research to new heights, it will need more than talent. It will needs enormous amounts of computing power. Google and Facebook have the resources needed to build the massive computing clusters that drive modern AI research, including vast networks of machines packed with GPU processors and other specialized chips. Google has even gone so far as to build its own AI processor.

SANTA CLARA, CA--(Marketwired - Nov 14, 2016) - NVIDIA (NASDAQ: NVDA) today announced that it is teaming up with the National Cancer Institute, the U.S. Department of Energy (DOE) and several national laboratories on an initiative to accelerate cancer research. The initiative -- known as the Cancer Moonshot, announced by President Barack Obama during his 2016 State of the Union Address, and led by Vice President Joseph Biden -- aims to deliver a decade of advances in cancer prevention, diagnosis and treatment in just five years. The research efforts include a focus on building an AI framework called CANDLE (Cancer Distributed Learning Environment), which will provide a common discovery platform that brings the power of AI to the fight against cancer. CANDLE will be the first AI framework designed to change the way we understand cancer, providing data scientists around the world with a powerful tool against this disease. Teams collaborating on CANDLE include researchers at the National Cancer Institute (NCI), Frederick National Laboratory for Cancer Research and DOE, as well as at Argonne, Oak Ridge, Livermore and Los Alamos National Laboratories.

Boston -- November 15, 2016 -- Machine learning automation leader DataRobot today announced the integration of its enterprise machine learning platform with the Alteryx, Inc. self-service data analytics platform. The Alteryx connector for DataRobot allows users of both platforms to simplify and automate the end-to-end workflow for predictive analytics, from data preparation to modeling to deployment. The connector is now available to download from the Alteryx Analytics Gallery. Predictive analytics can be challenging and complex, often requiring disconnected tools and a variety of programming environments to blend data from disparate sources, build predictive models and then seamlessly deploy them so they can drive real business value. The integration of Alteryx and DataRobot brings together self-service data preparation, blending and advanced analytics from Alteryx with intuitive, automated machine learning from DataRobot.

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively 6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer. The human brain is capable of remarkable acts of perception while consuming very little energy.

The client required a solution involving Artificial intelligence to disintermediate the process between vehicle buyers/seller and the platform. The client required a solution involving Artificial intelligence to disintermediate the process between vehicle buyers/seller and the platform. Maruti Techlabs designed a solution using SMS-Bot. In the first month of implementation, SMS-Bot dispatched 9793 initial offers and client observed 5.5% increase in the initial offers generated. Maruti Techlabs designed a solution using SMS-Bot.

What do you get if you cross aspirin with ibuprofen? Harvard chemistry professor Alán Aspuru-Guzik isn't sure, but he's trained software that could give him an answer by suggesting a molecular structure that combines properties of both drugs. The AI program could help the search for new drug compounds. Pharmaceutical research tends to rely on software that exhaustively crawls through giant pools of candidate molecules using rules written by chemists, and simulations that try to identify or predict useful structures. The former relies on humans thinking of everything, while the latter is limited by the accuracy of simulations and the computing power required.

They say what happens in Vegas stays in Vegas. But that would be a wild mistake if applied to this circumstance. If you're a business leader or entrepreneur intent on staying competitive in the years to come, you'd best pay close attention. Last month, IBM hosted the World of Watson conference in Las Vegas aimed at raising awareness and educating participants about advances in computing over the last decade. But how did we get to this point?

Level 5 self-driving tech is the highest variety, which aims for fully autonomous vehicles with no need of human steering mechanisms. AdasWorks, a Budapest-based startup launched in 2015, is charing its name to AImotive and expanding to a new U.S.-based Mountain View office to further its efforts in making Level 5 autonomy a lived reality for people and cars. AImotive's goal is to offer a full suite of self-driving enabling technologies to carmakers, via its full-stack aiDrive offering, which includes a number of AI-powered engines for learning to identify different objects organized by class, landmark-based location recognition, real-time object tracking and control of not only gas and brake, but other car features including headlights and horn. AImotive also offers aiKit, which covers collection, simulation and data testing; and aiWare, the hardware designed for use in vehicles, provided embedded computing for advanced neural networks used to drive its AI. AImotive's aim is to provide scalable Level 5 autonomy to carmakers regardless of chips used, with technology that can truly work at the global level, across different geographies, climates, environmental conditions and cultures.