"A.I. is happening rapidly, and we didn't help people cope with globalization and technological change very well over a 30- and 40-year period," Mr. Hubbard explained. "We're probably not going to do it again." Policymakers across Washington generally agree that A.I. portends generational change, with vast implications for everything from medical research to warfare. That has helped spark an investment boom in computing, and a burst of new growth for the broader economy, which Mr. Trump has tried to maximize. Through a series of executive orders, signed over the last 11 months, Mr. Trump has moved to eliminate regulatory guardrails and make it easier for tech companies to build data centers, power their operations, sell computer chips and source critical materials.

Price posting is a common selling mechanism across various corners of e-commerce.

Nearly everywhere on earth, when you're stuck in traffic, you're still surrounded by the usual sea of heads attached to shoulders attached to arms attached to steering wheels. But in a tiny handful of places--in Los Angeles, Phoenix, San Francisco, and Wuhan, China--you can find yourself flanked by taxis with no one in the drivers' seats, picking up passengers, unsupervised by any human. And if you live in one of those cities, the sight probably doesn't even prompt a double-take anymore. It's like the future is suspended between those who've never encountered it--and those who are already a little blind to it. Granted, practically everyone has been numbed by the hype cycle.

Drones are becoming versatile in a myriad of applications. This has led to the use of drones for spying and intruding into the restricted or private air spaces. Such foul use of drone technology is dangerous for the safety and security of many critical infrastructures. In addition, due to the varied low-cost design and agility of the drones, it is a challenging task to identify and track them using the conventional radar systems. In this paper, we propose a reinforcement learning based approach for identifying and tracking any intruder drone using a chaser drone. Our proposed solution uses computer vision techniques interleaved with the policy learning framework of reinforcement learning to learn a control policy for chasing the intruder drone. The whole system has been implemented using ROS and Gazebo along with the Ardupilot based flight controller. The results show that the reinforcement learning based policy converges to identify and track the intruder drone. Further, the learnt policy is robust with respect to the change in speed or orientation of the intruder drone.

Ever wondered what it would be like for a machine to observe, perceive and interact with its immediate environment and the world at large just like humans do? That is exactly what Artificial General Intelligence (AGI), the penultimate frontier of AI and a precursor to the God-like concept of Superintelligence, can enable machines and software applications to do. Artificial General Intelligence, a concept that has not yet been brought to life, will, for all purposes, think and carry out complex tasks such as corporate restructuring and HR management using characteristic human qualities such as logical reasoning, empathy and human-centeredness while retaining the expected computational speed, accuracy and big data analytics associated with standard AI applications. Although the abilities of the updated technology are somewhat well-known, one would need to know the exact differences between it and AI to then understand why we may not see AGI in the near future (or ever). Often, people confuse AGI with AI, which is loosely used nowadays by marketers and businesses to describe run-of-the-mill machine learning applications and even normal automation tools.

One of the most dangerous aspects looming the computer world is security threats. It is estimated that around three trillion dollars are lost in cyber crimes every year. This figure is expected to double by 2021. With all of these threats lurking around, it is difficult to track and eliminate every threat, especially as the number of users is rising exponentially. The most popular among the existing cyber threats now is the distributed denial of service (DDoS) attack.

For all the progress that has been made in developing artificial intelligence technologies in recent years, the software "brains" that power machines are still nowhere near the sophistication of human minds. And advancing the field will require bold, new ideas, says A.I. researcher David Cox. Cox (pictured) has spent the past 11 years at Harvard University--first as a research fellow, and later as an associate professor--trying to enhance machine intelligence by studying the intersection of neuroscience and computer science. Now, he'll continue that work about two miles down the road at IBM's Kendall Square office in Cambridge, MA. This month, IBM named Cox the director of its efforts in a new, joint A.I. research lab being run in partnership with MIT.