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"Open AI researchers said that while it was "impossible to reliably detect all AI-written text", good classifiers could pick up signs that text was written by AI. The tool could be useful in cases where AI was used for "academic dishonesty" and when AI chatbots were positioned as humans, they said."

"But with all of the successes of AI, it's also important to pay attention to when, and how, it can go wrong, in order to prevent future errors. A recent paper by Roman Yampolskiy, director of the Cybersecurity Lab at the University of Louisville, outlines a history of AI failures which are "directly related to the mistakes produced by the intelligence such systems are designed to exhibit." According to Yampolskiy, these types of failures can be attributed to mistakes during the learning phase or mistakes in the performance phase of the AI system."

"Wikipedia lives and dies by its references, the links to sources that back up information in the online encyclopaedia. But sometimes, those references are flawed - pointing to broken websites, erroneous information or non-reputable sources. A study published on 19 October in Nature Machine Intelligence1 suggests that artificial intelligence (AI) can help to clean up inaccurate or incomplete reference lists in Wikipedia entries, improving their quality and reliability."

"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."

"It shows rapid technological advances are allowing governments and political actors to distort reality, and fake content is easier to publish than ever before. "The difference is being blurred between true and false, real and artificial, facts and artifices, jeopardising the right to information," the report said. "The unprecedented ability to tamper with content is being used to undermine those who embody quality journalism and weaken journalism itself." Artificial intelligence was "wreaking further havoc on the media world", the report said, with AI tools "digesting content and regurgitating it in the form of syntheses that flout the principles of rigour and reliability". This is not just written AI content but visual, too. High-definition images that appear to show real people can be generated in seconds."

"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."

"But even cleverest AI can be fooled with the simplest of hacks. If you write out the word "iPod" on a sticky label and paste it over the apple, Clip does something odd: it decides, with near certainty, that it is looking at a mid-00s piece of consumer electronics. In another test, pasting dollar signs over a picture of a dog caused it to be recognised as a piggy bank."

"The Open AI researchers were intrigued by a claim that self-driving cars would be intrinsically hard to fool (tricking them into sudden braking maneuvers, say), because "they capture images from multiple scales, angles, perspectives, and the like.""

"But researchers at New York University's AI Now Institute have issued a strong warning against not only ubiquitous facial recognition, but its more sinister cousin: so-called affect recognition, technology that claims it can find hidden meaning in the shape of your nose, the contours of your mouth, and the way you smile. If that sounds like something dredged up from the 19th century, that's because it sort of is."

"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

"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."

"In October, American teachers prevailed in a lawsuit with their school district over a computer program that assessed their performance. The system rated teachers in Houston by comparing their students' test scores against state averages. Those with high ratings won praise and even bonuses. Those who fared poorly faced the sack. The program did not please everyone. Some teachers felt that the system marked them down without good reason. But they had no way of checking if the program was fair or faulty: the company that built the software, the SAS Institute, regards its algorithm a trade secret and would not disclose its workings."

"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."

"Automated decision-making is appealing to businesses as it can save time and eliminate human emotional volatility. "People have a bad day and it then colors their perception of the world and they make different decisions. In a hedge fund that's a big deal," he added."

"Concerns have been growing about AI's so-called "white guy problem" and now scientists have devised a way to test whether an algorithm is introducing gender or racial biases into decision-making."

"Computers can classify skin cancers as successfully as human experts, according to the latest research attempting to apply artificial intelligence to health."

" 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."