For screenwriters like me--and job seekers all over--AI gig work is the new waiting tables. In eight months, I've done 20 of these soul-crushing contracts for five different platforms. My name on the platform is ri611. I work as an AI trainer. I assess whether a chatbot's tone is natural or flat, affected or annoying. I identify patterns in pictures of furniture; search the internet for group photos of strangers whom I'll eliminate from the portrait, one by one. I trawl through bizarre videos so I can annotate and time-stamp the barking of a dog, the moment a stranger walks past a window, the precise millisecond a balloon pops. I generate anime sex scenes and decapitate young women, coax LLMs into giving me recipes for bombs made of household items, and generate invites to a reprise of January 6 at the White House, all as part of a red team whose purpose is to test safety precautions and probe weaknesses. I work for companies with names like Mercor and Outlier and Task-ify and Turing and Handshake and Micro1. In my "other" career, I am a Hollywood writer and showrunner. I create prime-time TV, usually featuring a middle-class white lady having the worst day of her life, with some salt-of-the-earth police interference to raise the stakes. You can find my shows on Paramount and Hulu and the BBC.

As AI advances in text generation, human trust in AI generated content remains constrained by biases that go beyond concerns of accuracy. This study explores how bias shapes the perception of AI versus human generated content. Through three experiments involving text rephrasing, news article summarization, and persuasive writing, we investigated how human raters respond to labeled and unlabeled content. While the raters could not differentiate the two types of texts in the blind test, they overwhelmingly favored content labeled as "Human Generated," over those labeled "AI Generated," by a preference score of over 30%. We observed the same pattern even when the labels were deliberately swapped. This human bias against AI has broader societal and cognitive implications, as it undervalues AI performance. This study highlights the limitations of human judgment in interacting with AI and offers a foundation for improving human-AI collaboration, especially in creative fields.

Machine learning is a branch of data science which involves using "data science programs that can adapt based on experience," said Ben Tasker, technical program facilitator of data science and data analytics at Southern New Hampshire University. As the fields of science and engineering continue to advance, artificial intelligence is becoming "a lot less artificial and a lot more intelligent," Tasker said. Because so much about the field of data science in general and AI in particular is new, there are many opportunities to "make your own niche, especially now that many companies have started to invest in the idea of artificial intelligence," Tasker said. AI Engineer: In this role, one may be involved in the different facets of designing, developing and building artificial intelligence models using machine learning algorithms. Big Data Engineer: Overlapping with the role of a data scientist, the person in this role analyzes a company's volume of data known as "big data," and then uses the analyses to mine useful information in support of the company and its business model.

Every day for over four years, Ramses woke up in his home in Barquisimeto, Venezuela, turned on his computer, and began labeling images that will help make self-driving cars ubiquitous one day. Through a microtasking platform called Remotasks, he would identify mundane objects that line the streets everywhere -- trees, lampposts, pedestrians, stop signs -- so that autonomous vehicles could learn to notice them, too. Like many Venezuelans, Ramses turned to microtasking when his country plunged into economic turmoil. The gig gave him the opportunity to earn American dollars instead of the local currency, which is subject to extraordinarily high inflation. "I would work Sunday to Sunday," Ramses, who asked to use only his first name for privacy reasons, told Rest of World over WhatsApp.

If there is one thing we learned from the COVID-19 pandemic, it's that when humans are sent home, machines keep working. This doesn't mean that robots will take over the world. It does, however, mean that our technical landscape is changing. Human history has a long and favorable track record of technological advancements, particularly when it comes to ideas that seem ludicrous at the time (Wright brothers, anyone?). The printing press, assembly line and personal computer have all helped move civilization forward by leaps and bounds over the last few centuries.

People have long used Tasker to take care of repetitive tasks on their Android device, or to customize its features based on things like whether they're at home or at the office. Now the app's features are a little easier to use since you can trigger them via Google Assistant. XDA points out a post by the developer on Reddit where he points out the currently available triggers, which you can use to run your favorite automations by name. Tasker is an incredibly powerful utility, but it can be a bit complex and intimidating, and voice control could be the difference in making it usable on a regular basis around your home. If you have the Google Play Pass subscription then access is free, and there's also a seven day trial available, otherwise it costs $3.49 in the Play Store.

We introduce a two-player contest for evaluating the safety and robustness of machine learning systems, with a large prize pool. Unlike most prior work in ML robustness, which studies norm-constrained adversaries, we shift our focus to unconstrained adversaries. Defenders submit machine learning models, and try to achieve high accuracy and coverage on non-adversarial data while making no confident mistakes on adversarial inputs. Attackers try to subvert defenses by finding arbitrary unambiguous inputs where the model assigns an incorrect label with high confidence. We propose a simple unambiguous dataset ("bird-or- bicycle") to use as part of this contest. We hope this contest will help to more comprehensively evaluate the worst-case adversarial risk of machine learning models.

MR. DEAN: Aaron, where is AI making a difference right now for your business? LEVIE: Box helps companies manage and share, and collaborate around their information. If you think about all of the unstructured data in the enterprise--every document, every media asset, every email, every proposal, every contract--all of this data you work on for one second and then it goes into an archive or repository, and you never get or extract value from it in the future. At Box, we have tens of billions of files stored in the platform, and some customers have billions of files in their own instance. We want to be able to help customers make more sense of their information, and hopefully that begins to change the very business processes they run.

As it goes every year, one hot feature sets a trend in technology, and suddenly every company boasts some variation of which that is uniquely theirs. This year, that feature is AI. Hot on the heels of Alexa's and Google Assistant's holiday successes, Artificial Intelligence on phones has become the de facto must-have feature – whether consumers know it or not. In any case, manufacturers seem not to realize that AI doesn't mean "Anything Intuitive" – that's just how operating systems are supposed to be. Yet it seems that OEM's are eager to label nearly any vaguely intuitive feature as AI.

Every day, we talk to artificial intelligence practitioners who are either labeling data internally for training AI models, or they're using crowdsourcing (or outsourcing) for the labeling/annotating. Both are bummers; that's why we exist. Recently, we covered the issues with an in-house approach. If you need only simplistic training data--say, categorized images or ranked articles--then maybe a crowdsourcing solution will cut it. But if you need more sophisticated data, you need good tooling--stuff to do bounding boxes, polygons, segmentation masks, pixel-level annotation, semantic segmentation, and so on.