To the editor: I was dismayed by the article on Google DeepMind's computer. It was further evidence of how the media's naivete regarding the term "artificial intelligence," or AI, has totally corrupted its meaning. DeepMind, as well as IBM's legendary Jeopardy super-champion Watson and numerous other cited AI systems all have the intelligence of a rock. The intelligence of these systems lies in the human intelligence of the programmers that created the systems, not in the systems themselves. The generally accepted test for true AI is the Lovelace test, which was created, in partnership, by David Ferrucci, who not incidentally was the head of IBM's Watson development team.

Deep learning is a field within machine learning which uses algorithms that contain many layers of processing and transformations. A RNN makes predictions based on sequential data. When a RNN is trained on sequences of words, it learns to represent each word as a high dimensional vector which encodes the model's understanding of that word. By projecting these high dimensional vectors into a two dimensional space, it's possible to visualize their relationships and glean insights into the concepts that the model has learned. In the above visualization the position of the word is determined by the two dimensional projection of its word vector.

Proponents of automated cargo handling at U.S. ports have a rule: where megaships call, robots soon follow. So far, Southern California appears to be the destination of choice for both. Over the next decade, marine terminals at the Ports of Los Angeles and Long Beach are expected to lead the way in adopting robotic cargo-handling capabilities in response to the arrival of more supersize ships. Unloading and organizing tens of thousands of containers--and readying an equal number to ship back out--requires coordination on a scale that is best left to machines, said Ashebir Jacob, a port planner and engineer with Moffatt & Nichol, a maritime infrastructure advisory firm. "As we start to receive bigger and bigger vessels on the West Coast, [automation] becomes really critical," he said.

By now you've likely heard the story of Tay, Microsoft's social AI experiment that went from "friendly millennial girl" to genocidal misogynist in less than a day. While Tay promised to learn from her interactions with people online, Microsoft apparently hasn't learned anything from the countless headlines about how Twitter users like to talk to visible women – everything from gleefully anarchic trolling to threats and abuse – otherwise it would have seen this coming. At first, Tay's story seems like a fun one for anyone who's interested in cautionary sci-fi. What does it mean for the future of artificial intelligence if a bot can embody the worst aspects of digital culture after just 16 hours online? If any AI is given the vastness of human creation to study at lightning speed, will it inevitably turn evil?

The machine was born in a sandbox. Stumbling for meaning, its five parents watched as it learned the rules of the world around it. Everything is built of cubes, which can be climbed one at a time. There is light, and there is darkness. "Up" has a meaning different from "down."

QyFuse is an artificial intelligence domain technology that uses information sensed from complex situations to perform ongoing learning for decision making. Beginning with the physical integration of multiple sensors producing hundreds of parameters, it fuses these inputs into an overall integrated landscape which it then can analyze and make decisions on. QyFuse algorithms continuously analyze local data as well as data from other systems, in process of adaptive learning. QyFuse intelligent analysis produces continually evolving, customer-specific logic models that provide our systems with the highest level, situation-relevant decisions, a new level of user experiences and optimized operational efficiencies.

ML builds heavily on statistics. For example, when we train our machine to learn, we have to give it a statistically significant random sample as training data. If the training set is not random, we run the risk of the machine learning patterns that aren't actually there. And if the training set is too small (see law of large numbers), we won't learn enough and may even reach inaccurate conclusions. For example, attempting to predict company-wide satisfaction patterns based on data from upper management alone would likely be error-prone.

To me, photography is the simultaneous recognition, in a fraction of a second, of the significance of an event. As a child I waited anxiously for the arrival of each new issue of National Geographic Magazine. The magazine had amazing stories from around the world, but the stunningly beautiful photographs were more important to me. The colors, shadows and composition intrigued and wowed me, and there was a cohesion of visual arrangement and storytelling. This childhood fascination with photographs aroused in me a curiosity to understand the behavior, nuances and semantics embedded inside them.

Estrous synchronization (ES) and artificial insemination (AI) are reproductive management tools that have been available to beef producers for over 50 years. Synchronization of the estrous cycle has the potential to shorten the calving season, increase calf uniformity, and enhance the possibilities for utilizing AI. Artificial insemination allows producers the opportunity to infuse superior genetics into their operations at costs far below the cost of purchasing a herd sire of similar standards. These tools remain the most important and widely applicable reproductive bio-technologies available for beef cattle operations (Seidel, 1995). However, beef producers have been slow to utilize or adopt these technologies into their production systems.

With a vibrant startup ecosystem and fewer legacy systems, India will play a key role in Google's strategy as it looks to take on Amazon's AWS and Microsoft's Azure in the global enterprise cloud services space. "India is a pretty exciting place because there are so many companies growing so quickly over there and the fact that these companies do not have [legacy systems] is pretty exciting. And they can just start in the cloud. You have seen Sundar [Pichai] take a deep interest in India and so, Google overall is very interested in India," Google Cloud chief Diane Greene said, adding that there is a pool of talented manpower as well and, overall, "its vibrant and it's important to be there [in India]." Google is investing heavily in creating business tools and products, driven by open source technology and machine learning to help companies use affordable fast computing.