A team from the Houston Methodist Research Institute says they have developed artificial intelligence software capable of analyzing mammograms for breast cancer with 99 percent accuracy. The technique involves scanning patient charts and cross-checking them with results from mammogram X-rays and clinical reports. "The imaging characteristics of breast cancer subtypes have been described previously, but without standardization of parameters for data mining," according to the study published in Cancer. But their algorithm allows for a more comprehensive and accurate analysis that helps avoid false positives -- a very common incident. "We figured out you can mine a clinical report for additional information," said lead researcher Stephen Wong. "Most of the clinical reports are not in a structured format, they are in free form text. So if we can run an AI program to extract the medical information and build a risk assessment model we can score the information and reduce unnecessary biopsies."

It was December 2012, and Doug Burger was standing in front of Steve Ballmer, trying to predict the future. Ballmer, the big, bald, boisterous CEO of Microsoft, sat in the lecture room on the ground floor of Building 99, home base for the company's blue-sky R&D lab just outside Seattle. The tables curved around the outside of the room in a U-shape, and Ballmer was surrounded by his top lieutenants, his laptop open. Burger, a computer chip researcher who had joined the company four years earlier, was pitching a new idea to the execs. He called it Project Catapult. The tech world, Burger explained, was moving into a new orbit.

When Google first introduced Project Loon, its internet balloons used static algorithms to change altitude and stay in position. While clever, they were limited -- Google couldn't do much to adapt to unexpected weather patterns, which are quite common tens of thousands of feet in the air. The Project Loon team has revealed that it's using artificial intelligence technology (specifically, machine learning) to alter balloons' behavior and keep them in position for much longer. One test balloon stayed in the Peruvian stratosphere for 98 days, adapting to tricky wind conditions that might have sent it drifting away. As Wired notes, the algorithms now comb over large amounts of data and learn from it.

Hi! I am back again with the transcript of a new-- episode of Capgemini's "Data and The Hunch" podcast series. I am a Principal Analyst for VINT, the Sogeti Trend Lab, and work on anything Analytics related to the Connected Service Experience. My Twitter handle is @BLO2M – B-L-O-Numeric2-M – so if you like, feel free to follow me. Today, Fenny has joined me in the studio. Hi Fen, how can I help you?

MIT researchers from have developed "EQ-Radio," a device that can detect a person's emotions using wireless signals. By measuring subtle changes in breathing and heart rhythms, EQ-Radio is 87 percent accurate at detecting if a person is excited, happy, angry or sad -- and can do so without on-body sensors, according to the researchers. MIT professor and project lead Dina Katabi of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) envisions the system being used in health care and testing viewers' reactions to ads or movies in real time. Using wireless signals reflected off people's bodies, the device measures heartbeats as accurately as an ECG monitor, with a margin of error of approximately 0.3 percent, according to the researchers. It then studies the waveforms within each heartbeat to match a person's behavior to how they previously acted in one of the four emotion-states.

From time to time, scientific equations appear in the media and claim to distil the perfect way to make a cup of tea or identify the most miserable day of the year. Not according to the critics who line up on social media and blogs to complain about the pseudoscience and the commercial interests of those often involved. Some of that scrutiny deserves a more important target. In a short space of time, the equations of big-data algorithms have permeated almost every aspect of our lives. A massive industry has grown up to comb and combine huge data sets -- documenting, for example, Internet habits -- to generate profiles of individuals.

One of the biggest challenges will be to advance pattern recognition and prediction. A human can easily describe what they saw happen in an event and predict what happens next, IBM says, but that's virtually "impossible" for current AI. That ability to quickly summarize and foresee events could be useful for everything from health care workers taking care of the elderly to repairing complicated machines, among other examples. There's no guarantee that IBM and MIT will crack a problem that has daunted Google, Facebook and countless academics. However, it's rare that scientists get access to this kind of technology.

As many noted during the AI Now Experts' Workshop, the means to create and train AI systems are expensive and limited to a handful of large actors. Or, put simply, it's not possible to DIY AI without significant resources. Training AI models requires a huge amount of data – the more the better. It also requires significant computing power, which is expensive. This limits fundamental research to those who can afford such access, and thus limits the possibility of democratically creating AI systems that serve the goals of diverse populations.

Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started, build accurate models and confidently complete predictive modeling machine learning projects using Python in 14 days. This is a big and important post. You might want to bookmark it. Python Machine Learning Mini-Course Photo by Dave Young, some rights reserved.

Volkswagen made international headlines this year when the company had to shell out 15 billion after a high-profile emissions scandal rocked the automaker and left its future uncertain. It's a prime example of the type of situation David Copps is trying to prevent with his Dallas-based AI business, Brainspace. "If they had known who said what and when early on in terms of all the emissions problems that they're having, they potentially could've saved 12 billion," Copps says. That's where he sets his sights with Brainspace. The company uses AI and machine learning to enable organizations to garner insights from the data they accumulate each business day, faster than ever before.