I'd love to talk more about Flowcast, but I'm still not able to shake the image of you making a robotic submarine run by San Diego poolside (laughs). As a STEM enthusiast, I have been in awe of IBM Watson's capabilities. And I feel it's an honor to be talking to someone who has contributed to its capabilities. Winnie: Flowcast came about with my friend and co-founder Ken So. We met back when I was at MIT and he was doing his MBA at Berkeley.

Has AI gone too far? This might seem like a nonsensical question to data scientists who strive every day to expand the capabilities of AI until you read the headlines created by this just released peer reviewed scientific paper: Automated Inference on Criminality Using Face Images (Xiaolin Wu, McMaster Univ. That's right, shades of The Minority Report (movie in which criminals are arrested before the crime occurs) and the 19th century studies of phrenology. These researchers claim 89.51% accuracy in making this classification on several sets of unlabeled validation images, each of about 1,500 facial images. I hope this has really taken your breath away.

This article is not about smoothing ore into gems though your may find a few gems herein. Systematic Pattern and Random Noise In "Components of Time Series Data", I discussed the components of time series data. In time series analysis, we assume that the data consist of a systematic pattern (usually a set of identifiable components) and random noise (error), which often makes the pattern difficult to identify. Most time series analysis techniques involve some form of filtering out noise to make the pattern more noticeable. Two General Aspects of Time Series Patterns Though I have discussed other components of time series data, we can describe most time series patterns in terms of two basic classes of components: trend and seasonality.

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Computer processor maker Advanced Micro Devices (AMD) announced that Google will start using its compute accelerators on its cloud platform. Google plans to start rolling out the AMD hardware in 2017. It will use AMD's single-precision dual GPU compute accelerators, Radeon-based AMD FirePro S9300 x2 Server GPUs, to help accelerate Google Compute Engine and Google Cloud Machine Learning services. The GPUs can handle highly parallel calculations, including complex medical and financial simulations, seismic and subsurface exploration, machine learning, video rendering and transcoding, and scientific analysis. "Google is building up its GPU-based infrastructure, and they want to ensure they offer AMD's architecture," said Raja Koduri, senior vice president and chief architect at AMD, in an interview with Forbes.

Las Vegas: Marvin is just a few inches tall, but he has a big mouth. "What do you think of that, human?" he sneers as he ties the scores 1-1. "Is that the best you can do?" he jeers as he takes a 2-1 lead. "Oh no, IBM will fire me if I lose," he says, as the score is tied 2-2. The decisive move comes up, and Marvin is beaten.

There is a lot of buzz these days on artificial intelligence (AI) in business intelligence products. At a recent tweetchat of my #BIWisdom group of users, vendors and consultants, a participant asked: Is AI becoming a part of everything we do, the way analytics have been added to apps of all shapes and sizes across the business and personal space? Although companies tend to overreact to emerging technologies, AI is not a new technology. One of the tweetchat participants commented that AI is crossing a chasm, either in awareness or approach and that perhaps its evolution to this point is due to the prevalence of data scientists. Another member of the group agreed, tweeting that the data scientists craze has helped more organizations embrace machine learning and AI to get deeper insights.

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The radiology world has been abuzz with discussions of machine learning and what artificial intelligence may mean for the future of the field. The goal is for the technology to quickly scan medical images and prioritize abnormal results, allowing doctors to spend their time on the more difficult cases. The machines would also provide a check on human error. Companies are jumping on board. IBM Watson Health, which acquired enterprise imaging software company Merge Healthcare in 2015, plans to put its Watson supercomputer to work analyzing medical images.

Humanity's relentless march towards a future where machines rule everything gained some ground today, as Google revealed that one of its fancy artificial brains is now better at diagnosing some medical conditions than human doctors are. This should prove extremely valuable for whenever the inevitable robot uprising results in a swift demise for the human race. Google's announced its findings in the Journal of the American Medical Association in a paper detailing the company's work with deep learning algorithms and how they can be utilized for medical purposes. In this particular case, Google set its sights on diagnosing diabetic retinopathy via retina photographs. The neural network Google used for the project was fed over 128,000 images to train it in detecting the condition.