You wouldn't think that articles on business and tech topics would make for the most beautiful music, but believe it or not, they were used by an artificial intelligence system and human composers to create an original symphony. It sounds like science fiction, but last week inside the Louvre Pyramid in Paris, a 50-piece orchestra performed "Symphonologie," a "symphonic experience" from Accenture Strategy, a business and technology consulting firm. The project aims to shed light on the tangible uses of this kind of artificial intelligence and its broad potential. "Initially, we talked about what could be a way for us to grab our clients and others in the business world? What would draw their attention? We wanted to show what's happening with digital and technology, and we thought about metaphors that cross cultures. We realized that music cuts straight through the list – there are so many different cultures and spoken languages around the world and we quickly went to music as something that transcends culture," Mark Knickrehm, group chief executive at Accenture Strategy, told CBS News. "The big question was how do you make music in the digital space using technology and artificial intelligence? It was quite creative to go from there. Words have real meaning, and they can make music."

If you are a strong-armed NFL quarterback who reads defenses like genre fiction, a movie star whose name alone can open a film in China, or a stock picker who beats Buffet every time, congratulations: you are almost as valuable as a data scientist or machine learning engineer with a PhD from Stanford, MIT, or Carnegie Mellon. At least it seems that way. Every company in Silicon Valley -- increasingly, every company everywhere -- is frantically competing for those human prizes, in a human resources version of a truffle hunt. As businesses now realize that their competitiveness relies on machine learning and artificial intelligence in general, job openings for those trained in the field well exceed all the people in the world who aren't locked up by Facebook, Google, and other superpowers. But what if you could get the benefits of AI without having to hire those hard-to-find and expensive-to-woo talents?

Optical flow or optic flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer (an eye or a camera) and the scene.[1][2] The concept of optical flow was introduced by the American psychologist James J. Gibson in the 1940s to describe the visual stimulus provided to animals moving through the world.[3] Gibson stressed the importance of optic flow for affordance perception, the ability to discern possibilities for action within the environment. Followers of Gibson and his ecological approach to psychology have further demonstrated the role of the optical flow stimulus for the perception of movement by the observer in the world; perception of the shape, distance and movement of objects in the world; and the control of locomotion.[4] The term optical flow is also used by roboticists, encompassing related techniques from image processing and control of navigation including motion detection, object segmentation, time-to-contact information, focus of expansion calculations, luminance, motion compensated encoding, and stereo disparity measurement.[5][6]

This past Saturday, September 24th, the first episode of Cyber Talk Radio hit the airwaves on 1200 WOAI and iHeartRadio streaming. To open the discussion we covered a machine learning background including mentions of TensorFlow, Watson, Tay, and many other examples of consumer facing AI as well as developer tools to build your own systems. After establishing a baseline we started to discuss the impact machine learning (aka. When the internet began you'd directly connect to it just like a big "home network" from a trust perspective. As more people connected and port scanners such as nmap became available the need for a firewall appeared.

Nine times out of ten, when you hear about deep learning breaking a new technological barrier, Convolutional Neural Networks are involved. Also called CNNs or ConvNets, these are the workhorse of the deep neural network field. They have learned to sort images into categories even better than humans in some cases. If there's one method out there that justifies the hype, it is CNNs. What's especially cool about them is that they are easy to understand, at least when you break them down into their basic parts. I'll walk you through it.

According to Tractica's research, one of the industries best positioned to leverage artificial intelligence (AI) – at least in the developed world – is agriculture. In our Artificial Intelligence for Enterprise Applications report, we forecast that spending on AI software in the agriculture industry will grow from 16.2 million to 373.7 million by 2024. Recently we sat down with Jeffrey Orrey, CEO of GeoVisual Analytics. GeoVisual is a Boulder-based startup focused on using remote sensing and big data analytics to improve and predict crop yields, better manage croplands, and improve harvests. The company's analysis is based on the properties of electromagnetic waves in the near infrared (NIR) spectrum, which are invisible to the human eye.

Last March, a computer built by a team of Google engineers beat one of the world's top players at the ancient game of Go. The match between AlphaGo and Korean grandmaster Lee Sedol was so exhilarating, so upsetting, and so unexpectedly powerful, we turned it into a cover story for the magazine. On a Friday in late April, we were about an hour away from sending this story to the printer when I got an email. According to the email, Lee had won all five matches--and all against top competition--since his loss to AlphaGo. Even as it surpasses human talents, AI can also pull humans to new heights--a theme that ran through our magazine story.

Just hours before the first US Presidential debate -- on a day when stupidity seemed to be on the top of everyone's mind -- Microsoft CEO Satya Nadella spoke passionately about artificial intelligence (AI) as a cornerstone of Microsoft's next horizon of innovation. It was a strange juxtaposition of realities: the world as we know it, grounded in the banal and the predictable, versus the world as Nadella envisions it, fueled by AI in every app, every interaction, everywhere. During the second keynote on the opening day of Microsoft's Ignite conference here, Nadella explained how his company is pursuing AI and deep learning to empower every person and every institution "to solve the most pressing problems of our society and our economy." Nadella made his remarks to a packed crowd at Philips Arena, just across the street from the Georgia World Congress Center where the Ignite conference kicked off today. About 23,000 people, primarily IT professionals, are attending the five-day event.

I'm currently working my way through the book Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian & Tom Griffiths, which deconstructs many key life decisions into algorithms that can lead to optimal decision-making. As it cogently reviews many basic concepts using a wide range of life examples, I can see many ways in which machine learning techniques could sift through mountains of clinical data on cancer patients to help guide our management decisions in ways that elude the limitations of human brains. Oncologists work with patients to weigh decisions about whether a treatment with partial benefit (limited shrinkage of a cancer or even modest progression) is good enough to continue treatment and when a stronger choice is to change treatment approaches. When is is the expected benefit of more of the same, likely with a discount from diminishing returns, less than the anticipated or unknown benefits of the next alternative therapy? A well honed algorithm should be able to follow the growth kinetics of a cancer on scans and predict when it's time to change horses.

As a native speaker (and reader and writer) of both Mandarin Chinese (both complex and traditional alphabets) and English, I've often cringed at Google Translate's output. But looking at the examples provided by Google on its blog post, I am impressed. The new system uses what the company calls Google Neural Machine Translation (GNMT), which looks at entire sentences as it decodes instead of breaking them up into words and phrases to be considered independently. The latter method often resulted in disjointed results that sometimes didn't make sense. According to the company, this new technique is better, because "it requires fewer engineering design choices than previous Phrase-Based translation systems." It still breaks up sentences into individual characters, but now considers each one in relation to those before and after it.