The Multiple Sclerosis Society of Canada has awarded CA$1 million to a project helping doctors who treat multiple sclerosis (MS) patients make more personalized treatment decisions through the use of artificial intelligence (AI). The society awarded the five-year grant (worth about $814,800) to Douglas Arnold, MD, a neurologist with Neuro (the Montreal Neurological Institute-Hospital) at McGill University, with expertise in using magnetic resonance imaging (MRI) to assess MS and Alzheimer's disease. "We are entering a new era in which'Big Data' and increasing computer power are making it possible to develop artificial intelligence methods capable of predicting how individual MS patients will do in the future and how they will respond to specific treatments," Arnold said in a press release. "Clinicians cannot make such predictions at present," he added. "Integrating AI into the clinic will allow clinicians to adapt treatments to each individual patient's unique circumstances, to help ensure a better outcome."

The U.S. Department of Commerce's National Institute of Standards and Technology (NIST) has released a plan for prioritizing federal agency engagement in the development of standards for artificial intelligence (AI). The plan recommends that the federal government "commit to deeper, consistent, long-term engagement" in activities to help the United States speed the pace of reliable, robust and trustworthy AI technology development. "The federal government can help the U.S. maintain its leadership in AI by working closely with our experts in industry and academia, investing in research, and engaging with the international standards community," said Under Secretary of Commerce for Standards and Technology and NIST Director Walter G. Copan. "This plan provides a path to ensure the federal government supports AI standards that are flexible and inclusive--and suited for a world of rapidly changing technologies and applications." A February 2019 Executive Order directed NIST to develop a plan that would, among other objectives, "ensure that technical standards minimize vulnerability to attacks from malicious actors and reflect Federal priorities for innovation, public trust, and public confidence in systems that use AI technologies; and develop international standards to promote and protect those priorities."

Yet there's also no point in accessing richer sources of data unless you have an architecture that can consume it. An AI-ready architecture is able to address different shapes and granularities of data such as transactions, logs, geospatial information, sensors and social. In addition, real-time time-series data is key to the constant feed of input that propels data-driven devices, from smart-home appliances and health devices to self-driving cars. Make sure your AI architecture has the capability to consume different data structures in different time dimensions, especially real time. Is your organization identifying and classifying data at the point of ingestion?

We discuss a data market technique based on intrinsic (relevance and uniqueness) as well as extrinsic value (influenced by supply and demand) of data. For intrinsic value, we explain how to perform valuation of data in absolute terms (i.e just by itself), or relatively (i.e in comparison to multiple datasets) or in conditional terms (i.e valuating new data given currently existing data).

As the adoption of artificial intelligence (AI), deep learning, and big data analytics continues to grow, it is becoming increasingly important for edge computing systems to process large data sets in a timely and efficient manner. The basic compute, storage and networking capabilities are all present today at the edge, but speeds and capacity will only continue to increase and advancements like NVMe (Non Volatile Memory Express) will offer significant performance advantages and boost AI adoption at the edge. It is possible, and becoming easier, to run AI and machine learning with analytics at the edge today, depending on the size and scale of the edge site and the particular system being used. While edge site computing systems are much smaller than those found in central data centers, they have matured, and now successfully run many workloads due to an immense growth in the processing power of today's x86 commodity servers. It's quite amazing how many workloads can now run successfully at the edge.

For the most part, artificial intelligence these days is pretty sweet. Whether you want to set a timer with your voice, play a game of chess with a computer or just put a Minion's head on Superman's body, AI can help you achieve your goal. Of course, that same power can be used for things that are not so sweet, like, um, killing us all. The world's most famous physicist is warning about the risks posed by machine… Read more Read more In light of those possibilities, some of tech's biggest names launched an initiative on Tuesday to help "steer AI in a way that maximizes the benefits to society." That includes eBay founder Pierre Omidyar and LinkedIn founder Reid Hoffman, who will each donate $10 million to the Ethics and Governance of Artificial Intelligence Fund, anchoring its initial investment of $27 million.