Both things can be true: I spent my first three hours playing Monster Hunter Rise screaming, "I hate this game!" at my Nintendo Switch screen. Since then, I have logged more hours in Rise than in any other game this year, and I've successfully convinced several friends to pick the game up as well. For some further context, I'd never played a Monster Hunter game before. I knew that the title did most of the explaining as to what the franchise is about--you hunt big monsters--but that was about it. Rise's debut marks the sixth main Monster Hunter game (the games have been popular enough to spawn several spinoffs, as well as a recent blockbuster adaptation starring Milla Jovovich), but that success has been a mixed blessing, as the series has become notorious for having a steep learning curve.
Artificial intelligence is one of the most revolutionary technologies of our time, which is advancing as each day goes by. AI labs contribute to these advancements by housing scientists and researchers under one roof to study this disruptive technology for further developments. While there are quite a few AI labs across the globe, artificial intelligence researchers go perplexed when people ask them to rate the top labs in the world. And rightfully so, because they're all unique in the way they work. While every lab focuses on different domains of artificial intelligence, commercial AI labs like Google, Facebook, Amazon, Apple, and Microsoft, the U.S Big Tech, have set up dedicated AI labs too.
A new machine-learning program accurately identifies COVID-19-related conspiracy theories on social media and models how they evolved over time--a tool that could someday help public health officials combat misinformation online. A lot of machine-learning studies related to misinformation on social media focus on identifying different kinds of conspiracy theories. Instead, we wanted to create a more cohesive understanding of how misinformation changes as it spreads. Because people tend to believe the first message they encounter, public health officials could someday monitor which conspiracy theories are gaining traction on social media and craft factual public information campaigns to preempt widespread acceptance of falsehoods. The study, anonymized Twitter data to characterize four COVID-19 conspiracy theory themes and provide context for each through the first five months of the pandemic.
Scientists have developed a new machine learning tool that can identify Covid-19-related conspiracy theories on social media and predict how they evolved over time, an advance which may lead to better ways for public health officials to fight misinformation online. The study, published in the Journal of Medical Internet Research, analysed anonymised Twitter data to characterise four Covid-19 conspiracy theory themes – such as one that erroneously claims the Bill and Melinda Gates Foundation engineered or has malicious intent related to the pandemic. Using the AI tool's analysis of more than 1.8 million tweets that contained Covid-19 keywords, the scientists from the Los Alamos National Laboratory in the US categorised the posts as misinformation or not, and provided context for each of these conspiracy theories through the first five months of the pandemic. "From this body of data, we identified subsets that matched the four conspiracy theories using pattern filtering, and hand labeled several hundred tweets in each conspiracy theory category to construct training sets," explained Dax Gerts, a computer scientist and co-author of the study from the Los Alamos National Laboratory. The four major themes examined in the study were that 5G cell towers spread the virus; that the Bill and Melinda Gates Foundation engineered or have "malicious intent" related to Covid-19; that the novel coronavirus was bioengineered or was developed in a laboratory; and that vaccines for Covid-19, which were still in development during the study period, would be dangerous.
No longer content to simply provide a platform for keeping up with long-lost cousins and spreading conspiracy theories, Facebook has branched out into developing artificial intelligence to help treat complex diseases. The social media giant's AI research department and the Helmholtz Zentrum München, a research center in Germany focused on environmental health, unveiled an open-source AI model designed to determine the viability of repurposing existing drugs into new pharmaceutical cocktails. Researchers and biologists now have free access to the Compositional Perturbation Autoencoder, or CPA, which evaluates the effects of drug combinations in varying dosages--a complicated task, as the number of possibilities can accelerate exponentially into the billions as more medicines are thrown into the mix. The model predicts not only how the drugs interact with one another, but also how they might work together to attack specific cell types and interrupt diseases. The researchers trained the machine learning model on single-cell RNA sequencing data, to help it gauge the effects of drug cocktails on individual cells without requiring drug- or cell-specific programming.
Ever wish you could easily export all your Facebook posts and notes onto a completely different platform? On Monday, Facebook announced a few new data portability options that allow you to seamlessly transition the content you've written on the social network onto platforms made for writing. Specifically, Facebook has built in an option to transfer your posts and notes into Google Docs as well as two popular blogging platforms, WordPress.com To give people more control and choice over their data, today we're announcing that Facebook posts and notes can be directly transferred to @GoogleDocs, @Blogger and @WordPress via our Transfer Your Information tool:https://t.co/ksHO0oeYq5 Facebook already offers options to export your data to your local hard drive.
Video is on an exponential growth trajectory, and it's not just Netflix originals and HBO docs and new films on Amazon Prime. In today's world, when people aren't eating or sleeping (or perhaps even when they are), they are likely viewing a video. Each day, people watch over 1 billion hours of YouTube. Creating and delivering movies, news and other compelling visual content is no longer just for the Hollywood elite. In fact, some of today's most prolific storytellers are doing so with little resources and amateur tools.
More than 70 advocacy groups have called on the Department of Homeland Security to stop using Clearview AI's facial recognition software. In a letter addressed to DHS Secretary Alejandro Mayorkas and Susan Rice, the director of the White House's Domestic Policy Council, the American Civil Liberties Union, Electronic Frontier Foundation, OpenMedia and other organizations argue "the use of Clearview AI by federal immigration authorities has not been subject to sufficient oversight or transparency." The letter points to a recent BuzzFeed News report that found employees from 1,803 government bodies, including police departments and public schools, have been using the software without many of their bosses knowing about it. The company has given out free trials to individual employees at those organizations hoping that they'll advocate for their agency to sign up for it. Besides the lack of oversight, the letter points to issues like racial bias in facial recognition software and the fact Clearview built its database by scraping websites like Facebook, Twitter and YouTube.
Two men died near Houston, Texas, on Saturday while riding in a 2019 Tesla Model S that, according to local authorities, was speeding into a turn and ended up going off the road and crashing into a tree. It took first responders four hours and more than 30,000 gallons of water to put out the resulting fire, which kept reigniting; when damaged, the lithium ion batteries in electric cars can cause fires that are very difficult to extinguish because of how they store energy. Authorities reportedly attempted to ask Tesla for advice on how to put out the fire, but it's unclear whether they ended up getting any help. Besides the fire, there was something especially disturbing about the crash: No one was in the driver's seat. One of the men was in the passenger seat and the other in the rear.