We show that dispersive propagation of light followed by phase detection has properties that can be exploited for extracting features from the waveforms. This discovery is spearheading development of a new class of physics-inspired algorithms for feature extraction from digital images with unique properties and superior dynamic range compared to conventional algorithms. In certain cases, these algorithms have the potential to be an energy efficient and scalable substitute to synthetically fashioned computational techniques in practice today.

Amazon's best-known side ventures have been consumer electronics devices like the Echo and Fire, but the online retailer is potentially looking into a redo of a product at which it has failed: smartphones. Amazon is developing a smartphone series named Ice, Gadgets360 reports. The series will be powered by Google Android and intended to be a low-cost model for developing markets like India. Internally, the phone's specifications are expected to fit this mold. As Gadgets360 noted, one of the phones potentially will include a 5.2-inch to 5.5-inch display.

Machine learning (ML) has received a lot of attention recently, and not without good reason. It has already revolutionized fields from image recognition to healthcare to transportation. "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." Not very clear, is it? This post, the first in a series of ML tutorials, aims to make machine learning accessible to anyone willing to learn.

Deep learning, a computing construct based loosely on the architecture of the human brain, has emerged as one of the most promising enabling technologies in the world of artificial intelligence (AI). Although many of the concepts underlying AI and technological biomimicry of human intelligence are over 50 years old, deep learning's growth today is the result of a rather sudden convergence of three key trends: big, even colossal, data generation; advancements in hardware capabilities; and improvements in algorithms. According to a new report from Tractica, deep learning has been commercialized into more than 100 distinct use cases to date, touching virtually every industry. Tractica forecasts that deep learning software revenue will grow from $655 million in 2016 to $34.9 million worldwide by 2025. "Businesses around the world are beginning to harness deep learning due to its ability to drive efficiencies in the form of speed, accuracy, agility, and access in several key areas," says principal analyst Jessica Groopman.

The internet of things has been a main focus among tech companies in recent years as more manufacturers have figured out ways to integrate online connectivity into devices like cars and home appliances. But a report from the Pew Research Center and Elon University's Imagining the Internet Center finds many industry analysts are taking a largely pessimistic view toward the growth of the IoT industry. In the survey, researchers polled around 1,200 analysts, academics and industry members to get their views on the growth of IoT. While 15 percent of those polled said significant numbers of people would choose to disconnect from the IoT, the remaining 85 percent of experts said users would likely choose to further integrate into the IoT thanks both to the convenience of IoT and the difficulty in disconnecting from it. Many of those polled agreed on several broad IoT development points.

Organizations around the world are producing accurate data-based predictions and benefitting from insightful analysis in a fraction of the time required by conventional tools and methods. This is the power of machine learning automation. In this webinar, learn how DataRobot automates predictive modeling, and how our platform can deliver these same types of insights and a substantial productivity boost to your machine learning endeavors. Built for speed and scalability, DataRobot radically reduces the time required to complete a data science project. From data to deployment, with DataRobot you can deliver highly-accurated predictions faster, react quickly to rapidly changing market conditions, and speed the transformation of your business.

In late February, the UN-Secretary General held a press conference, highlighting the risk of starvation in East Africa and the necessity to raise funds to address the emergency situations in Somalia and South Sudan. Drought has been back in these countries and their neighbours since 2016, leading to a huge current food crisis. While governments are trying to handle the situation, how could technology innovations help prevent starvation and improve agriculture management in the future? We met with Laurent Rivière, a French 30 years-old entrepreneur, who shared with us his view on the subject with a combination of engineer pragmatism and changemaker idealism . Founder and CEO at Sunbirds for two years, he explained to us how his "bird of the sun," his solar drone, is addressing the agriculture challenges of the 21st century.

The companies had poured $8.6 million into a campaign to keep fingerprinting, which can be expensive and time-consuming, out of driver checks. The vote came after the City Council passed an ordinance in December that, among other rules for ride-sharing companies, required their drivers to undergo fingerprint-based background checks by February 1, 2017. Uber and Lyft announced after the results of Saturday's vote that they were set to suspend operations in Austin, the capital city of Texas, on the morning of May 9, 2016.

Intelligence amplification (IA) (also referred to as cognitive augmentation and machine augmented intelligence) refers to the effective use of information technology in augmenting human intelligence. The idea was first proposed in the 1950s and 1960s by cybernetics and early computer pioneers. IA is sometimes contrasted with AI (artificial intelligence), that is, the project of building a human-like intelligence in the form of an autonomous technological system such as a computer or robot. AI has encountered many fundamental obstacles, practical as well as theoretical, which for IA seem moot, as it needs technology merely as an extra support for an autonomous intelligence that has already proven to function. Moreover, IA has a long history of success, since all forms of information technology, from the abacus to writing to the Internet, have been developed basically to extend the information processing capabilities of the human mind (see extended mind and distributed cognition).

Ryan Hagemann for the Niskanen Center: The fears over artificial intelligence, while at a very early stage, are an expected feature of the "techno-panics" associated with emerging technologies. As described in a paper from the Information Technology and Innovation Foundation's Daniel Castro and Alan McQuinn, these panics are part of a broader privacy panic cycle ... The cycle is composed of four stages: trusted beginnings, rising panic, deflating fears and moving on. So where are we with fears over AI? Based on the cross-ideological concerns, Mercatus senior research fellow Adam Thierer noted ... that we're still in the "rising panic" stage of the current AI hysteria. Unfortunately, we haven't yet reached peak hysteria. That boiling-over point, however, is probably coming sooner than a lot of people expect -- legislators, regulators, researchers and those techno-optimists who tout this technology's benefits should be prepared.