Recently, tech leaders including Tesla CEO Elon Musk and billionaire Bill Gates warned about robots taking over jobs. However, a new report shows people are not that concerned about job automation. The report The Robots Are Coming, But Not For Me, is based on an online survey of more than 2,000 adults commissioned by consumer engagements firm LivePerson last month. Although experts agree robots, artificial intelligence and automated technology will have at least some effect on global economies, the majority of people think they'll be alright but say other professionals will be affected by mass layoffs, the report found. Among those surveyed, a high 88 percent said they were not very worried about losing their job after they were told about an Oxford University study that said 47 percent of US jobs were "highly likely" to be replaced by technology.

SOUTH OF MOSUL, IRAQ – Closely supported by the U.S.-led international coalition, Iraqi forces secured a series of cautious advances on Thursday, pushing into a sprawling military base outside of Mosul and onto the grounds of the city's airport, where they took control of the runway. The three-pronged attack began just after sunrise, with three convoys of Iraqi forces snaking north across Nineveh's hilly desert on Mosul's southern approach. Iraq's special forces joined federal police and rapid response units in the push -- part of a major assault that started earlier this week to drive IS from the western half of Iraq's second-largest city. By afternoon they had entered the Ghazlani military base south of the city, as well as the airport. Iraqi helicopters circled above Mosul firing down onto the city's southwestern edge. Coalition and Iraqi airstrikes that hit targets inside Mosul sent plumes of white smoke into the air on the horizon.

Human-robot interaction (HRI) is an emerging field of research with the aim to integrate robots into human social environments. One of the biggest challenges in the development of social robots is to understand human social norms [1]. It is therefore essential for social robots to possess deep models of social cognition, and be able to learn and adapt in accordance with their shared experiences with human partners. Most of the social robots to date are either preprogrammed, or are controlled by teleoperation or semiautonomous teleoperation [2], and do not possess the ability to learn and update themselves. Designing an adaptable and autonomous sociable robot is particularly challenging, as the robot needs to correctly interpret human behaviors as well as respond appropriately to them.

In this paper, we propose a fundamentally new approach to Datalog evaluation. Given a linear Datalog program DB written using N constants and binary predicates, we first translate if-and-only-if completions of clauses in DB into a set Eq(DB) of matrix equations with a non-linear operation where relations in M_DB, the least Herbrand model of DB, are encoded as adjacency matrices. We then translate Eq(DB) into another, but purely linear matrix equations tilde_Eq(DB). It is proved that the least solution of tilde_Eq(DB) in the sense of matrix ordering is converted to the least solution of Eq(DB) and the latter gives M_DB as a set of adjacency matrices. Hence computing the least solution of tilde_Eq(DB) is equivalent to computing M_DB specified by DB. For a class of tail recursive programs and for some other types of programs, our approach achieves O(N^3) time complexity irrespective of the number of variables in a clause since only matrix operations costing O(N^3) or less are used. We conducted two experiments that compute the least Herbrand models of linear Datalog programs. The first experiment computes transitive closure of artificial data and real network data taken from the Koblenz Network Collection. The second one compared the proposed approach with the state-of-the-art symbolic systems including two Prolog systems and two ASP systems, in terms of computation time for a transitive closure program and the same generation program. In the experiment, it is observed that our linear algebraic approach runs 10^1 ~ 10^4 times faster than the symbolic systems when data is not sparse. To appear in Theory and Practice of Logic Programming (TPLP).

Facial recognition systems -- computers that can verify a person's identity from a digital image or video still -- have been around for a few years now. Following a recent explosion in big data analytics and artificial intelligence, facial recognition software is about to spread well beyond security cameras and criminal databases. Computers are becoming so good at recognizing faces that soon enough consumers will be able to use them to make purchases, book tickets and unlock doors simply by looking into a camera lens and letting the software make rapid simultaneous measurements of the face that's as distinct as a fingerprint. This far more secure, AI-powered facial recognition software would be more convenient and create new exciting apps and hardware, but it could also let security forces track and identify people with far more precision. "It's a little creepy, but undeniably impressive," wrote Will Knight, a reporter for MIT Technology Review, which published a piece on Wednesday about some of the latest facial recognition technology. The article is part of a series of reports on the latest breakthrough technologies poised to change the way we live and interact with the world.

PDF: We made a fancy PDF of this post for printing and offline viewing. Note: The reason this post took three weeks to finish is that as I dug into research on Artificial Intelligence, I could not believe what I was reading. It hit me pretty quickly that what's happening in the world of AI is not just an important topic, but by far THE most important topic for our future. So I wanted to learn as much as I could about it, and once I did that, I wanted to make sure I wrote a post that really explained this whole situation and why it matters so much. Not shockingly, that became outrageously long, so I broke it into two parts. This is Part 1--Part 2 is here. We are on the edge of change comparable to the rise of human life on Earth. It seems like a pretty intense place to be standing--but then you have to remember something about what it's like to stand on a time graph: you can't see what's to your right. So here's how it actually feels to stand there: Imagine taking a time machine back to 1750--a time when the world was in a permanent power outage, long-distance communication meant either yelling loudly or firing a cannon in the air, and all transportation ran on hay. When you get there, you retrieve a dude, bring him to 2015, and then walk him around and watch him react to everything. It's impossible for us to understand what it would be like for him to see shiny capsules racing by on a highway, talk to people who had been on the other side of the ocean earlier in the day, watch sports that were being played 1,000 miles away, hear a musical performance that happened 50 years ago, and play with my magical wizard rectangle that he could use to capture a real-life image or record a living moment, generate a map with a paranormal moving blue dot that shows him where he is, look at someone's face and chat with them even though they're on the other side of the country, and worlds of other inconceivable sorcery.

The workers would not have been able to rescue the dog without the help of the robotic arm,

For many it is no more than the first port of call when a niggling question raises its head. Found on its pages are answers to mysteries from the fate of male anglerfish, the joys of dorodango, and the improbable death of Aeschylus. But beneath the surface of Wikipedia lies a murky world of enduring conflict. A new study from computer scientists has found that the online encyclopedia is a battleground where silent wars have raged for years. Since Wikipedia launched in 2001, its millions of articles have been ranged over by software robots, or simply "bots", that are built to mend errors, add links to other pages, and perform other basic housekeeping tasks.

This post originally appeared on the Yhat blog. Yhat is a Brooklyn based company whose goal is to make data science applicable for developers, data scientists, and businesses alike. Yhat provides a software platform for deploying and managing predictive algorithms as REST APIs, while eliminating the painful engineering obstacles associated with production environments like testing, versioning, scaling and security. SVM is a supervised machine learning algorithm which can be used for classification or regression problems. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs.

Shortly after walking through the door at Face, a Chinese startup valued at roughly a billion dollars, I see my face, unshaven and looking a bit jet-lagged, flash up on a large screen near the entrance. Having been added to a database, my face now provides automatic access to the building. It can also be used to monitor my movements through each room inside. As I tour the offices of Face (pronounced "face plus plus"), located in a suburb of Beijing, I see it appear on several more screens, automatically captured from countless angles by the company's software. On one screen a video shows the software tracking 83 different points on my face simultaneously.