It was almost a flat year for Intel (NASDAQ:INTC) in 2016, as the stock was up roughly 5% over the year. In comparison, most of the Intel's competitors such as Advanced Micro Devices (AMD), Qualcomm (QCOM) and NVIDIA (NVDA) delivered huge returns to stockholders. However, that does not means Intel is out of the game, as it still holds 80% market share in the worldwide microprocessor market mainly due to its leading position in the PC industry. The company has been generating huge profits by selling high-end desktop processing chips to data centers. It is highly likely that CPUs will continue to see reasonable growth in data centers, as the company requires huge computation power to make use of the big data they have gathered. According to marketsandmarkets.com, the data center market is projected to reach $32.30 billion by 2020 from $18.56 billion in 2015, which represents a compound annual growth rate of 11.7%.

We bring together top technologists, researchers, startups, media/brand executives, creators and investors with the purpose of exploring how visual technologies leveraging computer vision, machine learning and artificial intelligence are revolutionizing how humans communicate and do business.

The general idea here is that the work done by a Machine Learning engineer can be automated, thus freeing potential users from the tyranny of needing to have specific expertise. Presumably, the ultimate goal of such automations is to make Machine Learning accessible to more people. After all, if a thing can be done automatically, that means anyone who can press a button can do it, right? I'm going to make a three-part argument here that "Machine Learning Automation" is really just a poor proxy for the true goal of making Machine Learning useable by anyone with data. Furthermore, I think the more direct path to that goal is via the combination of automation and interactivity that we often refer to in the software world as "abstraction". By understanding what constitutes a powerful Machine Learning abstraction, we'll be in a better position to think about the innovations that will really make Machine Learning more accessible.

My mother began to lose her hearing while I was away at college. I would return home to share what I'd learned, and she would lean in to hear. Soon it became difficult for her to hold a conversation if more than one person spoke at a time. Now, even with a hearing aid, she struggles to distinguish the sounds of each voice. When my family visits for dinner, she still pleads with us to speak in turn. My mother's hardship reflects a classic problem for hearing aid manufacturers.

In recent years we've seen new, disruptive innovations in the world of wearable technology; advances that will potentially transform life, business, and the global economy. Products like Google Glass, Apple Watch, and Oculus Rift promise not only to change the way we approach information, but also our long established patterns of social interaction. Indeed, we are witnessing the advent of entirely new genre of interface mechanisms that brings with it a fundamental paradigm shift in how we view and interact with technology. Recognizing, understanding, and effectively leveraging today's growing landscape of wearables is likely to be increasingly essential to the success of a wide array of businesses. In this article, we discuss the ways in which effective interface design will need to adapt, in some ways dramatically, to address the new psychology of wearable technology.

Closed Domain Question with Retrieval Responses: This is a low hanging fruit area for chatbots to provide business value. We process over 1.5 billion calls a year for IVR business, which is the function that is occurring in Square 1. Chatbots could mirror the IVR functionality with an improved UI for a much better customer experience. We are also using this Square 1 approach to develop content delivery for mobile self-service capabilities when our My:Time digital engagement solution includes a mobile app.

AI has improved manifold in last few years. It Seems like soon the Robotics & Automation will be things of past and AI will take over. Most of the people are aware, about AI used by Google for search and other applications, Apple for SIRI, Amazon for Alexa, Facebook for Image recognition, Netflix for content recommendation etc. Now we are reaching a stage where AI tools like Watson implementation has started and some companies have started claiming the benefits. These successful implementations will follow a surge in implementations in similar fields and definitely loss of few jobs.

These principles were developed in conjunction with the 2017 Asilomar conference (videos here), through the process described here. Artificial intelligence has already provided beneficial tools that are used every day by people around the world. Its continued development, guided by the following principles, will offer amazing opportunities to help and empower people in the decades and centuries ahead. Please help build momentum by emailing the Principles link, futureoflife.org/ai-principles, To date, the Principles have been signed by AI/Robotics researchers and others. Find the full list of signatories here.

The American elections have been a hot topic in the office as we contemplate expanding our presence to the US. Since its debut in March, we have been entertained by the senseless tweets of DeepDrumpf, a Twitter bot created by Bradley Hayes, a postdoc at MIT. DeepDrumpf was trained on a few hours worth of transcripts of victory speeches and debates from the president elect using deep learning techniques. The tweets were constructed character by character and inspired by recurrent neural network models that had been previously employed to mimic Shakespearean speech. Although not the most sophisticated use of deep learning that we've seen, we must hand it to him for originality and capturing the zeitgeist.

The air rescue of a victim who fell 100 feet down seaside cliffs in Pacifica was abandoned Friday night after authorities say a local man flew his drone into the area to watch what was happening, forcing a helicopter crew to withdraw. The rescue played out about 10:15 p.m. on the rocky shoreline below 320 Esplanade Ave., Pacifica police said. First responders determined the person could not make it up the cliff by foot and called in the California Highway Patrol to help, authorities said. The CHP helicopter crew lowered a medic to the victim and prepared him to be hoisted up when other rescuers spotted the drone. "Once the helicopter crew found out there was a drone on scene, they had to suspend the rescue operation and gain altitude to avoid a collision," police said in a statement.