Machine learning and artificial intelligence already are and will continue to be beneficial in health care. However, serious pitfalls dot the health care landscape -- while failed experiments like Twitter chatbots may be amusing, mistakes in a medical context can have serious consequences. This talk will demystify the basic concepts of machine learning and several of the freely available tools that are making this technology more and more accessible, as well as explore the pros and cons of machine learning's expansion across the health care industry.

It was a heady mix of business and technical sessions on how to take advantage of the power of analytics. Of course there are innumerable applications for analytics, from fraud detection to supply chain optimization to garden variety business intelligence. For this post I'll focus mainly on customer-related applications, and some of the more advanced SAS capabilities discussed at the conference. Let's start with what SAS announced: SAS Viya -- billed as a new open, "cloud-ready" analytics platform. The idea is to bring together the SAS portfolio, built over some 40 years, into one platform that can be used by data scientists and other analytics professionals; business users and IT management; and senior management.

Someone sent me this article by psychology professor Susan Fiske, scheduled to appear in the APS Observer, a magazine of the Association for Psychological Science. The article made me a little bit sad, and I was inclined to just keep my response short and sweet, but then it seemed worth the trouble to give some context. I'll first share the article with you, then give my take on what I see as the larger issues. The title and headings of this post allude to the fact that the replication crisis has redrawn the topography of science, especially in social psychology, and I can see that to people such as Fiske who'd adapted to the earlier lay of the land, these changes can feel catastrophic. I will not be giving any sort of point-by-point refutation of Fiske's piece, because it's pretty much all about internal goings-on within the field of psychology (careers, tenure, smear tactics, people trying to protect their labs, public-speaking sponsors, career-stage vulnerability), and I don't know anything about this, as I'm an outsider to psychology and I've seen very little of this sort of thing in statistics or political science. As I don't know enough about the academic politics of psychology to comment on most of what Fiske writes about, so what I'll mostly be talking about is how her attitudes, distasteful as I find them both in substance and in expression, can be understood in light of the recent history of psychology and its replication crisis. In short, Fiske doesn't like when people use social media to publish negative comments on published research. She's implicitly following what I've sometimes called the research incumbency rule: that, once an article is published in some approved venue, it should be taken as truth.

I had the opportunity to co-present with Su Doyle from Checkpoint Systems at the NRF Big Show last week. It was a fun an effective collaboration that helped us highlight the benefits of capturing in-store and supply chain data using the RFID capabilities from Checkpoint and combining it with a variety of public, purchased and proprietary data to build Advanced Analytics models using Azure Machine Learning. It's not enough to just capture the data; it's also important to understand what business questions the data will be used to answer. After data is captured and processed, machine learning techniques can be applied to the data to better understand patterns, correlations and to answer business questions.

Object recognition is one of the most exciting areas in machine learning right now. Computers have been able to recognize objects like faces or cats reliably for quite a while, but recognizing arbitrary objects within a larger image has been the Holy Grail of artificial intelligence. Maybe the real surprise is that human brains recognize objects so well. We effortlessly convert photons bouncing off objects at slightly different frequencies into a spectacularly rich set of information about the world around us. Machine learning still struggles with these simple tasks, but in the past few years, it's gotten much better.

When you see or hear something happen, you can instantly describe it: "a girl in a blue shirt caught a ball thrown by a baseball player," or "a dog runs along the beach." It's a simple task for us, but an immensely hard one for computers -- fortunately, IBM and MIT are partnering up to see what they can do about making it a little easier. The new IBM-MIT Laboratory for Brain-inspired Multimedia Machine Comprehension -- we'll just call it BM3C -- is a multi-year collaboration between the two organizations that will be looking specifically at the problem of computer vision and audition. Note: This article previously stated that "no money is changing hands," but this is not quite the case. While IBM declined to provide specific financial details, these academic partnerships do involve varying amounts of funding and sharing access to resources and personnel on both sides.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Denon put wireless speaker leader Sonos in its crosshairs when it launched its HEOS wireless audio platform in 2014. This past month, Denon showed a major commitment to HEOS by integrating the multi-room audio technology into its new receiver models. Denon upped the ante again yesterday, announcing it will integrate Amazon's Alexa digital assistant into HEOS products. We should see Alexa-powered HEOS speakers and components sometime in the first quarter of 2017. With digital assistants gaining greater integration in smart devices, computers, and gaming consoles, audio is the next logical battleground.

How could health care be more like Uber? What could it learn from Airbnb? Sitting in the heart of Silicon Valley, Medicine X would hardly be complete without a panel mentioning such companies. Luckily, Jonathan Bush's Saturday morning keynote embraced the questions and discussed bringing "the network effect" to health care, with a rollicking sense of humor to boot.Bush, founder and CEO of athenahealth, extolled the network as the principle by which supply and demand can be re-calibrated in real time, just like Uber does with auto transport and Airbnb does with temporary housing. Who is looking for what?

Google's decision to disable end-to-end encryption by default in its new #Allo chat app is dangerous, and makes it unsafe.