Machine learning is a field that threatens to both augment and undermine exactly what it means to be human, and it's becoming increasingly important that you--yes, you--actually understand it. I don't think you should need to have a technical background to know what machine learning is or how it's done. Too much of the discussion about this field is either too technical or too uninformed, and, through this blog, I hope to level the playing field. This is for smart, ambitious people who want to know more about machine learning but who don't care about the esoteric statistical and computational details underlying the field. You don't need to know any math, statistics, or computer science to read and understand it.

CHICAGO--The overarching theme of RSNA 2016 has been deep learning and machine intelligence. Both are designed to help you with your workflow and ability to provide optimal patient care. But, questions still exist about what these tools are and how you can implement them. To answer this question, Vlado Menkovski, a former research scientist with vendor Philips, discussed the differences between these two tools, highlighting how they can be used. "This technology has provided breakthroughs," he said.

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Keen Footwear sells its iconic boots, shoes and sandals through thousands of retailers worldwide. But the Oregon manufacturer, which is working hard to honor its commitment to become "American Built," does not have the manpower to support a dedicated information security staff. With a team of six information technology professionals -- all but two focused on handling the day-to-day client issues of its 450 employees -- the IT staff would fall behind in triaging incidents the company's security software flagged. "We fit squarely in the realm that we have the problems of all the big players, but we don't have the resources of a large enterprise," said Clark Flannery, Keen's director of IT in Portland. To solve the problem, Flannery augmented his IT staff with machines.

China's government is preparing for a war of sorts with the United States to claim the vantage point to define the technological trend for the next generation. At the annual meeting of China's parliament this week, the usual Communist Party agenda of economic growth, social welfare, jobs, health care and pension made way for an unusual addition: a clarion call by some of China's most influential business and technology leaders for the government to set policies to define what they consider the Next Big Thing. They include the founder of the largest Chinese internet search engine Baidu, the owner of smartphone maker Xiaomi, and the founder of Geely Automobile, which bought Volvo. They are tabling motions and proposals for the government to take the lead in getting Chinese enterprises to collaborate on artificial intelligence (AI) research, and facilitate the industrialising of the technology. AI made its way into Premier Li Keqiang's March 5 work report, a signal that it has caught the attention of China's top decision makers.

Data for the enterprise is now a currency of its own, yet many companies and institutions are still trying to navigate the moving of large volumes of data from on-premise to the cloud in an effort to capitalize on the value of data stored in many locations. "I think longer-term the economic advantage of using cloud environments are undeniable. The cost advantages of hosting information in the cloud, the benefits that come from the scalability of those environments is far surpassing capabilities that organizations can invest in themselves or their own data centers," said Paul Scott-Murphy, vice president of product management, big data/cloud, at WANdisco Inc. During the Google Cloud Next event, Scott-Murphy spoke with Stu Miniman (@stu), host of theCUBE, SiliconANGLE Media's mobile live streaming studio, at SiliconANGLE's Palo Alto, CA, studio to discuss the trends WANdisco is seeing with its customers, as well as news from Google Cloud Next. WANdisco's enterprise and institutional customers are all facing similar problem: The availability of data and the combination of where it is stored makes it difficult to access and derive any benefits for them.

Watson captured the public imagination about artificial intelligence after defeating two world champions of Jeopardy in 2011 and bringing home a $1 million prize. Since then, Watson has gained new cognitive capabilities through APIs like Alchemy (for sentiment analysis), Tone Analyzer (for personality and emotional analysis), and Conversation (a chatbot builder) and has been embedded in hundreds of applications across financial services, healthcare, retail, and digital. "Chances are, you've interacted with Watson without realizing it," says Alyssa Simpson, Program Director at IBM Watson. "Many companies hide their use of the technology for competitive reasons. They don't want a competitor to buy it too."

This is the second of three stories in a mini-series on how artificial intelligence is affecting the work that agencies do. The next installment will publish on Friday. As marketer interest in artificial Intelligence (AI) grows, Publicis.Sapient sees opportunity to provide guidance. It has a dedicated unit that provides AI-related advice for 30 clients, including Patrón and Dove soap. The AI practice informally launched about four years ago and has seen an uptick in spend over the past year and a half, said Josh Sutton, global head of AI at Publicis.Sapient.

Fintech startups looking to disrupt payday lending are using artificial intelligence to make loans with rates as low as 6% and with default rates of 7% or less. AI can make a difference on several fronts, the startups say. It can process enormous amounts of data that traditional analytics programs can't handle, including data scraped constantly off the borrower's phone. It can find patterns of creditworthiness or lack thereof on its own, without having to be told of every clue and correlation, startups like Branch.co And the cost savings of eliminating the need for loan officers lets these companies make the loans at a profit.

AI's role in Morgan, and numerous other creative endeavors, shows how far AI has come. Using techniques such as deep learning has enabled tremendous progress, but AI remains relegated to an assistant role--for now. "What's interesting is that, compared to a lot of other machine learning techniques, deep learning technology is what's called a'generative model,' meaning that it learns how to mimic the data it's been trained on," explains Jason Toy, CEO of Somatic, a start-up focused on developing deep learning applications. "If you feed it thousands of paintings and pictures, all of a sudden you have this mathematical system where you can tweak the parameters or the vectors and get brand new creative things similar to what it was trained on." But even highly touted AI techniques have their limitations.