Artificial intelligence (AI) is a thrilling prospect for most people, even if some have a nagging feeling in the back of their minds that robots will ultimately replace them. And, given the number of jobs that have already become automated, that fear is not unwarranted. Technology is certainly changing everything, and those who can't adapt will likely be pushed out of the way. For business leaders, though, staying on top of how AI is progressing today can be the key to mapping out their companies' future. Right now, AI is being used to gather and interpret business metrics.

We live in a world where consumer attention span is getting shorter and shorter: 40 percent of people abandon a website that takes more than three seconds to load, and the average shopping cart is abandoned more than 68 percent of the time. Software platforms that drive ecommerce websites are creating visual search capabilities which allow consumers to upload an image and find similar/complementary products. The offline to online experience requires minimal steps to shop and purchase, providing a sense of autonomy to the consumer. Brands are creating more interactive shopping experiences to provide product recommendations based on natural conversation and cognitive data derived from AI.

I don't want to tell data scientists and AI developers what to do in any given situation. I want to give scientists and engineers tools for thinking about problems. We surely can't predict all the problems and ethical issues in advance; we need to be the kind of people who can have effective discussions about these issues as we anticipate and discover them. What are some of the ethical questions that AI developers and researchers should be thinking about? Even though we're still in the earliest days of AI, we're already seeing important issues rise to the surface: issues about the kinds of people we want to be, and the kind of future we want to build.

For the last decade or so, most of the'new' legal challenges facing the IT industry have been driven by the Internet, mobile and cloud computing technologies that have altered almost every aspect of society. One of the key areas is in the field of privacy and data security. For larger technology companies, securing and protecting data are key competitive advantages, and privacy and data security are now commonplace on the corporate risk agenda. These issues have created friction with existing laws and regulations, and have highlighted the fact that many laws/regulations have not kept pace with technological development. Further, with the advancement of robotics and artificial intelligence (AI), the questions and issues will become far more complex and challenging.

Autonomous AI agents have begun to take over entire tasks start to finish. And this is just the beginning. We expect to see a plethora of such agents in the next half decade, and these will take on all sorts of tasks that humans currently perform, if unhappily. In many ways the machines will be better at these tasks than we are. Self-driving cars have superhuman sensors, reaction time and true multitasking, and they will always apply their full attention to the task at hand, driving!

Today, you're used to getting information in app form. In the near future, more of your interaction with computers might be managed by an A.I. interface that acts like a person. His California design agency is loaded up with work. They don't do U.I. design as you think of it today. Drawing on the arts, including acting, theater, and screenwriting, this agency designs personalities for artificial intelligence.

Few technologies have the transformative potential to reshape how we live, move, and work. Electricity and the Internet were two technologies that fundamentally transformed life in the 20th century. Artificial intelligence (AI) is the 21st century equivalent of electricity and the Internet. According to a new white paper from Tractica, AI is expected to bring massive shifts in how people perceive and interact with technology, with machines performing a wider range of tasks, in many cases doing a better job than humans. Tractica's white paper analyzes 10 key trends that are influencing the development of the global artificial intelligence market, and is available for free download on the firm's website.

Google Cloud Platform solidified its partnership with Intel and announced plans to create customized silicon chips for tasks like machine learning, security, container orchestration and the Internet of Things, while bringing on a new executive to help the company flush out its strategy. The Cloud Foundry Foundation announced that its former executive director, Sam Ramji has joined Google in a leadership position, but his new title is not yet know. Cloud Foundry Foundation is the home of the open source application development platform of the same name. Companies like Pivotal and IBM base their PaaS off CF. Google cloud is also the preferred public cloud for Pivotal's hosted CF version. For Intel, it reinforces the dominate silicon vendor as a major arms dealer for Google's data centers.

Researchers at the University of Toronto say they have developed an algorithm that can learn directly from human instructions rather than a set of examples. Researchers usually provide neural networks with labeled data and teach the system how to make decisions based on the samples. With the new heuristic training model, humans program the algorithm with instructions that are used to classify training samples. Researchers Parham Aarabi and Wenzhi Guo trained their algorithm to identify people's hair in photographs. "Our algorithm learned to correctly classify difficult, borderline cases -- distinguishing the texture of hair versus the texture of the background," says Aarabi.

REDWOOD CITY, CA – November 14, 2016– Numerous proposals have been offered for how intelligent machines might learn sequences of patterns, which is believed to be an essential component of any intelligent system. Researchers at Numenta Inc. have published a new study, "Continuous Online Sequence Learning with an Unsupervised Neural Network Model," which compares their biologically-derived HTM sequence memory to traditional machine learning algorithms. The paper has been published in MIT Press Journal's Neural Computation 28, 2474-2504 (2016). You can read and download the paper here. Authored by Numenta researchers Yuwei Cui, Subutai Ahmad, and Jeff Hawkins, the new paper serves as a companion piece to Numenta's breakthrough research offered in "Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex," which appeared in Frontiers in Neural Circuits, in March 2016.