Artificial intelligence, which can generate astonishingly realistic false images and videos, is increasingly being used to detect them. Distinguishing between fact and fakery has become an everyday part of our online lives. During the U.S. election campaign, a manipulated video appearing to show Joe Biden forget which state he was in went viral, receiving more than a million views before it was debunked. The doctoring of visual material for political mischief-making is nothing new. Josef Stalin notoriously erased undesirable companions from photographs during the Great Purge in 1930s Russia.
Quantum computers offer great promise for cryptography and optimization problems. ZDNet explores what quantum computers will and won't be able to do, and the challenges we still face. In 1936, some 2.4 million members of the Literary Digest magazine's mailing list responded to its publisher by mail, in the broadest presidential candidates' opinion poll conducted in the United States to that time. By a margin of 57 to 43, those readers reported they favored the Republican governor of Kansas, Alf Landon, over the incumbent Democrat, Franklin D. Roosevelt. The week after the election, the magazine's cover announced in bold, black letters the message, "Is Our Face Red!" Also: Could quantum computers fix political polls? The following January, Oxford University's Public Opinion Quarterly published an essay that examined how a seemingly much smaller survey of only 50,000 participants, conducted by a fellow named George Gallup, yielded a far more accurate result than did Literary Digest. Gallup's poll was "scientific," and Oxford wanted to explain what that meant, and why opinion polling deserved that lofty moniker. For the first time, the Oxford publication explained a concept called selection bias. Specifically, if you don't ask people for enough facts about themselves, you never attain the information you need to estimate whether the people around them think and act in similar ways.
It would be the harbinger of an entirely new medium of calculation, harnessing the powers of subatomic particles to obliterate the barriers of time in solving incalculable problems. You and I are being continually surveyed. We reveal information about ourselves with astonishingly little resistance. Social media has made many of us into veritable slot machines for our own personal data. We're fed a little token of encouragement that someone may yet like us, our arm is gently pulled, and we disgorge something we hope people will find valuable enough for commencing small talk. What personal facts, real or trivial, we do end up disclosing -- perhaps unwittingly -- immediately undergo unceasing analysis. The inferences these analyses draw about us as people are being aggregated, baselined, composited, deliberated, and profiled.
Darryl Richardson was delighted when he landed a job as a "picker" at the Amazon warehouse in Bessemer, Alabama. "I thought, 'Wow, I'm going to work for Amazon, work for the richest man around," he said. "I thought it would be a nice facility that would treat you right." Richardson, a sturdily built 51-year-old with a short, charcoal beard, took a job at the gargantuan warehouse after the auto parts plant where he worked for nine years closed. Now he is strongly supporting the ambitious effort to unionize its 5,800 workers because, he says, the job is so demanding and working for Amazon has fallen far below his expectations. Last August, five months after the warehouse opened, Richardson began pushing for a union in what is not only the first effort to organize an entire Amazon warehouse in the United States, but also the biggest private-sector union drive in the south in years. "I thought the opportunities for moving up would be better. I thought safety at the plant would be better," Richardson said. "And when it comes to letting people go for no reason – job security – I thought it would be different."
Seeking to call into question the mental acuity of his opponent, Donald Trump looked across the presidential debate stage at Joseph Biden and said, "So you said you went to Delaware State, but you forgot the name of your college. Biden chuckled, but viewers may have been left wondering: did the former vice president misstate where he went to school? Those who viewed the debate live on an app from the London-based company Logically were quickly served an answer: the president's assertion was false. A brief write-up posted on the company's website the next morning provided links to other fact-checks from National Public Radio and the Delaware News Journal on the same claim, which explain that Biden actually said his first Senate campaign received a boost from students at the school. Logically is one of a number of efforts, both commercial and academic, to apply techniques of artificial intelligence (AI), including machine learning and natural language processing (NLP), to identify false ...
Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. The articles listed below represent a small fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Links to GitHub repos are provided when available. Especially relevant articles are marked with a "thumbs up" icon.
In general, text pre-processing should include lowercasing all words, removing punctuation and stop words, and stemming or lemmatization. When working with tweets, in addition to the normal text-preprocessing tasks we also have to consider hashtags, acronyms, re-tweet syntax ('RT @scrapfishies:…'), emojis, and other elements. Should hashtags be be segmented (divided into their unique words) or kept as a single concatenated string? Well, I'd argue that it depends on the hashtag. As an example, the #blacklivesmatter hashtag was used frequently in this corpus -- segmenting would give us 3 distinct tokens: 'black', 'lives', and'matter'.
Some applications of facial recognition that can lead to discrimination should be banned altogether, according to Europe's human rights watchdog, following months of deliberation on how to best regulate the technology. The Council of Europe has published new guidelines to be followed by governments and private companies that are considering the deployment of facial recognition technologies. For example, workplaces that use digital tools to gauge worker engagement based on their facial expressions, or insurance companies using the technology to determine customers' health or social status could all be affected by the new guidelines. The watchdog effectively advises that where the technology is used exclusively to determine an individual's skin color, religious belief, sex, ethnic origin, age, health or social status, the use of facial recognition should be prohibited, unless it can be shown that its deployment is necessary and proportionate. Under the same conditions, the ban should also apply to some of the digital tools that can recognize emotions, detect personality traits or mental health conditions, and which can be used unfairly in hiring processes or to determine access to insurance and education.
Andrew Yang will not forestall the robot apocalypse from the Oval Office, but he may get to do it from New York City Hall. In the 2020 Democratic presidential primary, the former entrepreneur's quirky campaign found a surprisingly robust audience, attracted by Yang's warnings about automation and his promise to mail every American a "freedom dividend" (or, at least, by his math jokes and laid-back, open collar). In the end, the Yang Gang only got their guy as far as the New Hampshire primary. But thanks in part to the name recognition and national network of donors he accrued during that race, Yang is actually leading the polls this year's contest to be the Democratic candidate for New York City mayor. On Friday, Henry Grabar and Jordan Weissmann, two of Slate's native New Yorkers, convened to debate whether this is a good thing. Their debate has been edited and condensed for clarity.
The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data. Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis. ML has applications in various fields. This review focuses on some of the fields and applications such as education, healthcare, network security, banking and finance, and social media. Within these fields, there are multiple unique challenges that exist. However, ML can provide solutions to these challenges, as well as create further research opportunities. Accordingly, this work surveys some of the challenges facing the aforementioned fields and presents some of the previous literature works that tackled them. Moreover, it suggests several research opportunities that benefit from the use of ML to address these challenges.