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"FOLLOW US Twitter / Facebook / RSS Machine learning image classifiers use context clues to help understand the contents of a room, for example, if they manage to identify a dining-room table with a high degree of confidence, that can help resolve ambiguity about other objects nearby, identifying them as chairs. The downside of this powerful approach is that it means machine learning classifiers can be confounded by confusing, out-of-context elements in a scene, as is demonstrated in The Elephant in the Room, a paper from a trio of Toronto-based computer science academics."

" Critics have pointed out that the old computing adage "garbage in, garbage out" also applies to ML. If the data from which a machine "learns" is biased, then the outputs will reflect those biases. And this could become generalised: we may have created a technology that - however good it is at recommending films you might like - may actually morph into a powerful amplifier of social, economic and cultural inequalities."

"Drowning in literature? Scientists often must manage research, teaching, and acquiring funding, and more. It can be hard to find time to read new papers in the field. It can also help non-specialists who are reading complicated papers and struggling to find the gist. Using this tool, you can enter a paper's abstract. The site will then generate a short summary. "The free tool, which creates what the team calls TLDRs (the common Internet acronym for 'Too long, didn't read'), was activated this week for search results at Semantic Scholar, a search engine created by the non-profit Allen Institute for Artificial Intelligence (AI2) in Seattle, Washington."

"Unskilled manual laborers have felt the pressure of automation for a long time - but, increasingly, they're not alone. The last few years have been a bonanza of advances in artificial intelligence. As our software gets smarter, it can tackle harder problems, which means white-collar and pink-collar workers are at risk as well. Here are eight jobs expected to be automated (partially or entirely) in the coming decades. Call Center Employees call-center Telemarketing used to happen in a crowded call center, with a group of representatives cold-calling hundreds of prospects every day. Of those, maybe a few dozen could be persuaded to buy the product in question. Today, the idea is largely the same, but the methods are far more efficient. Many of today's telemarketers are not human. In some cases, as you've probably experienced, there's nothing but a recording on the other end of the line. It may prompt you to "press '1' for more information," but nothing you say has any impact on the call - and, usually, that's clear to you. But in other cases, you may get a sales call and have no idea that you're actually speaking to a computer. Everything you say gets an appropriate response - the voice may even laugh. How is that possible? Well, in some cases, there is a human being on the other side, and they're just pressing buttons on a keyboard to walk you through a pre-recorded but highly interactive marketing pitch. It's a more practical version of those funny soundboards that used to be all the rage for prank calls. Using soundboard-assisted calling - regardless of what it says about the state of human interaction - has the potential to make individual call center employees far more productive: in some cases, a single worker will run two or even three calls at the same time. In the not too distant future, computers will be able to man the phones by themselves. At the intersection of big data, artificial intelligence, and advanced

"It is also self-improving. The 10-year-old grading software leverages deep learning algorithms to "compare notes" with human teachers' scores, suggestions, and comments. An engineer involved in the project compared its capabilities to those of AlphaGo, the record-breaking AI Go player developed by Google subsidiary DeepMind."

"Neural networks can find telltale patterns in a person's gait that can be used to recognize and identify them with almost perfect accuracy, according to new research published in IEEE Transactions on Pattern Analysis and Machine Intelligence. The new system, called SfootBD, is nearly 380 times more accurate than previous methods, and it doesn't require a person to go barefoot in order to work. It's less invasive than other behavioral biometric verification systems, such as retinal scanners or fingerprinting, but its passive nature could make it a bigger privacy concern, since it could be used covertly."

"The platform's automated tools may have mistaken the visuals of the burning building for 9/11 footage, according to Vagelis Papalexakis, an assistant professor of computer science and engineering at the University of California, Riverside who studies machine learning used in similar systems."

"That can include statistical or machine learning algorithms that rely on police records detailing the time, location, and nature of past crimes in a bid to predict if, when, where, and who may commit future infractions. In theory, this should help authorities use resources more wisely and spend more time policing certain neighborhoods that they think will yield higher crime rates."

"Cellebrite, the Israeli-founded and now Japanese-owned company behind some of the software, claims a wider rollout would solve problems over failures to disclose crucial digital evidence that have led to the collapse of a series of rape trials and other prosecutions in the past year. However, the move by police has prompted concerns over privacy and the potential for software to introduce bias into processing of criminal evidence."

"We need to look at the nose to tail production of artificial intelligence. The seeds of the data problem were planted in the 1980s, when it became common to use data sets without close knowledge of what was inside, or concern for privacy. It was just "raw" material, reused across thousands of projects."

"The point of creating this vast portfolio of digital gun art is to feed an algorithm made to detect a firearm as soon as a security camera catches it being drawn by synthetically creating tens of thousands of ways each gun may appear. Arcarithm is one of several companies developing automated active shooter detection technology in the hopes of selling it to schools, hotels, entertainment venues and the owners of any location that could be the site of one of America's 15,000 annual gun murders and 29,000 gun injuries."

"This time the model could look at both the surrounding words and the content of the image to fill in the blank. Through millions of repetitions, it could then discover not just the patterns among the words but also the relationships between the words and the elements in each image."

"The performance of LipNet compares incredibly favorably to human lipreading experts on GRID corpus, the largest publicly-available sentence-level lipreading dataset. In fact, where human experts got just 52 percent, LipNet scored 93 percent. Its sentence-based approach to lip-reading also smashed the best previous attempt by a machine, which managed 79.6 percent accuracy on the same dataset."

"In Neural Photo Editing With Introspective Adversarial Networks, a group of University of Edinburgh engineers and a private research colleague describe a method for using "introspective adversarial networks" to edit images in realtime, which they demonstrate in an open project called "Neural Photo Editor" that "enhances" photos by predicting what should be under your brush."

"Then came the algorithm, which used automatic speech recognition to detect specific features and patterns in each of the 48 recordings. It was clear from examining the waveforms of the cries that each category had a specific pattern."

"The unique approach to 'deception detection' analyses the micro-gestures of travellers to figure out if the interviewee is lying."

"To date, the presence of Babylon's GP surgery in London has forced the NHS to reallocate millions of pounds in funding to mitigate for the disruption it has caused. Its expansion plans have been blocked before being allowed to proceed. But concerns linger with clinicians questioning the GP surgery's impact and the effectiveness of its much-hyped artificial intelligence platform."

"Activists from Fight for the Future prowled the halls of Congress in "jumpsuits with phone strapped to their heads conducting live facial recognition surveillance" to "show why this tech should be banned.""