Making the artificial feel more natural is one of the goals of computer scientists these days. Last week in New York, innovators from IBM, Foursquare, Microsoft, and other companies discussed what may lie ahead in the commercial development of artificial intelligence. From baby steps in machine learning to longer strides in interacting with humans, AI has gotten better at interpreting the world--though the technology is still decades away from some of the stuff of science fiction. NYU Future Labs and ff Venture Capital co-hosted last Tuesday's forum, which led off with a keynote address from Minerva Tantoco (pictured above), senior adviser with Future\Perfect Ventures. She started her first AI company while still in college, and her early efforts in the field included getting a computer to parse a sentence in English into its grammatical parts.

We may be decades from unlocking the true power of quantum computing, but D-Wave is promising to offer a taste of the future with its significantly upgraded quantum processor. When it is released early next year, the Canadian firm's new quantum chip will be able to handle some 2,000 quantum bits (qubits), roughly double the usable number found in the processor in the existing D-Wave 2X system, and be capable of solving certain problems 1,000x faster than its predecessor. D-Wave machines are multi-million dollar computers that crunch data using "quantum transistors", tiny loops of niobium cooled to close to absolute zero by liquid helium. Only a handful of such systems are in use, run by Google and the Universities Space Research Association, Lockheed Martin and Los Alamos National Laboratory. However, D-Wave also offers access to its quantum computers via a cloud service.

We've had talking dolls since Edison's time. Now, not only do the toys talk back, but using AI and cloud-based systems, they remember, record, transcribe and even email parents. Hello Barbie is just one example of animated and "cognitized" children's toys and objects that are increasingly entering our homes. When combined with personal assistants such as Amazon's Echo and Apple's Siri, childhood interactions, general inquisitiveness and simple play are being transformed. Is this a good thing?

IBM's Project DataWorks in action: A NYC-based startup that develops clean energy projects in American inner cities used Project DataWorks to build a cognitive application that performs a comprehensive energy audit of individual properties to simulate energy savings and ultimate determine the correct mix of high-efficiency technology to reduce each customer's energy consumption. IBM's Project DataWorks uses Watson Analytics to analyze and create complex visualizations IBM's Project DataWorks uses Watson Analytics and natural language processing to analyze and create complex visualizations with one line of code – like this one, which illustrates correlations between product purchases by customers of a sporting goods store. IBM's Project DataWorks helps users access and gain insights from the 90% of unstructured data that goes untapped by organizations (according to IDC). The Console pictured here provides a snapshot that categorizes and previews an organization's data assets for easy access while also providing a full audit trail that allows users to understand who else on their team is interacting with the data and how.

Microsoft is quickly pivoting to position itself as a leader in artificial intelligence. In his second keynote on the subject this year, CEO Satya Nadella yesterday (Sept. Quartz caught up with Nadella after he hopped off stage, to talk about the progress of his quest to make machines that assist humanity in a transparent way. You started talking earlier in the year saying we need to create transparent machines, ethical machines, accountable machines. What has been done since then, what is concrete?

Starting today, Google will rely more heavily on artificial intelligence when it translates language. The new method, called Google Machine Neural Translation, cuts down errors by 80% compared to its current algorithm, and is nearly indistinguishable from human translation on standardized tests, the company said. It's a radical change from how Google translates now, which is called phrase-based translation, and is standard for the industry. Under this method, an algorithm cuts up a sentence, like one entered by a high-schooler trying to game their homework, and attempts to match words or phrases to a large dictionary. The new method takes that same large dictionary and uses it to train two neural networks.

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]