The release of ChatGPT has uncovered a range of possibilities whereby large language models (LLMs) can substitute human intelligence. In this paper, we seek to understand whether ChatGPT has the potential to reproduce human-generated label annotations in social computing tasks. Such an achievement could significantly reduce the cost and complexity of social computing research. As such, we use ChatGPT to relabel five seminal datasets covering stance detection (2x), sentiment analysis, hate speech, and bot detection. Our results highlight that ChatGPT does have the potential to handle these data annotation tasks, although a number of challenges remain. ChatGPT obtains an average accuracy 0.609. Performance is highest for the sentiment analysis dataset, with ChatGPT correctly annotating 64.9% of tweets. Yet, we show that performance varies substantially across individual labels. We believe this work can open up new lines of analysis and act as a basis for future research into the exploitation of ChatGPT for human annotation tasks.

The spread of misinformation in social media outlets has become a prevalent societal problem and is the cause of many kinds of social unrest. Curtailing its prevalence is of great importance and machine learning has shown significant promise. However, there are two main challenges when applying machine learning to this problem. First, while much too prevalent in one respect, misinformation, actually, represents only a minor proportion of all the postings seen on social media. Second, labeling the massive amount of data necessary to train a useful classifier becomes impractical. Considering these challenges, we propose a simple semi-supervised learning framework in order to deal with extreme class imbalances that has the advantage, over other approaches, of using actual rather than simulated data to inflate the minority class. We tested our framework on two sets of Covid-related Twitter data and obtained significant improvement in F1-measure on extremely imbalanced scenarios, as compared to simple classical and deep-learning data generation methods such as SMOTE, ADASYN, or GAN-based data generation.

Phil Plait, creator of the popular astronomy blog Bad Astronomy, credits his interest in outer space partly to his childhood love of science fiction movies such as Angry Red Planet and Robinson Crusoe on Mars. "I'm a huge science fiction dork," Plait says in Episode 541 of the Geek's Guide to the Galaxy podcast. "I've watched every TV show, just about, and movies and everything, read tons of books. In his new book, Under Alien Skies, Plait explores what various cosmic vistas would look like for a person who was physically present, studying them with ordinary human eyesight. "I open each chapter with a short vignette, basically a fictional tale," he says. So I say'You are at this planet,' 'You are standing on the bridge of your starship,' 'You are standing there watching a dust storm approach you on Mars.' Plait hopes that the book will serve as a valuable resource for filmmakers and science fiction authors looking to inject an extra dose of reality into their speculative visions. "I've actually done some consulting for movies and TV shows, and even a couple of video games," he says. "So I kind of know that process of advising writers, or other folks who are involved in the entertainment business, of what the real science is." As much as Plait enjoys seeing science fiction that incorporates real science, he recognizes that the ultimate aim of any book or movie is to tell a good story. "Even if they don't get the science correct, it's OK, because you're still inspiring people," he says. "And if they get the science right?

Existing machine learning (ML) models used to detect online misinformation are less effective when matched against content created by ChatGPT or other large language models (LLMs), according to new research from Georgia Tech. Current ML models designed for, and trained on, human-written content have significant performance discrepancies in detecting paired human-generated misinformation and misinformation generated by artificial intelligence (AI) systems, said Jiawei Zhou, a PhD student in Georgia Tech's School of Interactive Computing. Zhou's paper detailing the findings has received a best paper honorable mention award at the 2023 ACM CHI Conference on Human Factors in Computing Systems. Advised by Associate Professor Munmun De Choudhury, Zhou's research demonstrates that LLMs can manipulate tone and linguistics to allow AI-generated misinformation to slip through the cracks. "We found the AI-generated misinformation carried more emotions and cognitive processing expressions than its human-created counterparts," Zhou said.

In 2023, people who want to reside in Georgetown, Texas, may have the opportunity to live in a large, 3D-printed neighborhood. Homebuyers interested in relocating to Georgetown, Texas, may have the opportunity to live in a large, 3D-printed neighborhood. ICON, a construction tech company, along with Lennar, a home construction company and Bjarke Ingels Group (BIG), an architecture company, are teaming up to develop Wolf Ranch -- a 100-home, 3D-printed community. "Wolf Ranch marks the largest community of its kind in development in the world and in partnership with one of the largest home builders in the country, Lennar," Dmitri Julius, chief of special projects at ICON, told Fox News Digital. The partnership between Austin-based ICON and Lennar "offers a promising path toward an alternate method of delivering technology-driven homes that meet rising demand in desirable communities," Julius added.

MAN VS MACHINE - Inside the Pentagon's battle to keep artificial intelligence under human control. 'ACTIVE ROLE' - Ex-CIA testifies Biden official orchestrated controversial Hunter Biden laptop letter. HARD TIME - Former cop Kim Potter convicted in Daunte Wright death to be freed from prison. SPACE JAM - Footage of UFOs over conflict zones seen for first time: 'This is devastating'. BROKEN FAMILIES - Permissive parenting is the left's newest, totalitarian way to separate the family unit, writes Karol Markowicz.

Days before his SpaceX Starship blew up after takeoff and he removed the blue check marks from Twitter users, he appeared in a prerecorded interview on Fox News' "Tucker Carlson Tonight." As if he didn't have his hands full enough with SpaceX, Twitter and Tesla -- which disappointed investors with its results this week, causing the stock to fall around 10 percent Thursday -- Musk talked about his plans for "TruthGPT," an AI product he plans to deploy to compete with Microsoft and Google.

Reddit announced it will start charging companies to use its huge, ever-growing trove of text to train A.I. chatbots. It's another expense for the fledgling tech and another knock against the "open internet" ideals that Reddit once embodied. If you enjoy this show, please consider signing up for Slate Plus. Slate Plus members get benefits like zero ads on any Slate podcast, bonus episodes of shows like Slow Burn and Dear Prudence--and you'll be supporting the work we do here on What Next TBD. Sign up now at slate.com/whatnextplus to help support our work.

CTA Thematic Programs Director Brian Comiskey and tech strategist, author David Espindola discuss how artificial intelligence will continue to change how businesses operate and the impact on consumers. Artificial intelligence will be capable of producing higher-quality "digital humans" and could use more energy than the entire global workforce by 2025, according to experts. Brian Comiskey, the Director of Thematic Programs for the Consumer Technology Association, revealed the trade association had developed an artificial intelligence working group to navigate the new technology. With his time in thematic indexing, Comiskey noted that "responsible AI" is a growing buzzword in the financial space and has become an increasing priority for leaders across various industries. By 2025, without sustainable AI practices, AI will consume more energy than the human workforce, significantly offsetting carbon-zero gains, according to Gartner's research for their 2023 strategic predictions.

Fox News correspondent Douglas Kennedy has the details on software that could'transform society' on'America's Newsroom.' The use of facial recognition technology, a form of biometric artificial intelligence, is growing across the U.S. as an efficient security system that can identify people based on measuring facial features, but has been hit with some notable criticisms. Police departments, the health care industry, and companies looking to fight back against cyber fraud have rolled out the technology in recent years to bolster security measures. The tech is far from new, with its roots stretching back to the mid-1960s, when researchers in Palo Alto pioneered training computers to recognize faces, and has exploded in use since around 2010. Today, machine learning algorithms - a subset of artificial intelligence that uses data and algorithms to mimic how humans learn - has fine-tuned the technology. The tech can measure and identify facial measurements in a photo or video, and cross-analyze whether two photos or videos show the same person, or even pick a person out in a crowd of people, Amazon Web Services explains.