Science fiction is full of characters, set pieces, and scenarios that few other genres could ever get away with. Due to its often speculative nature, the most accomplished sci-fi movies can sometimes require a bit of work on the part of the viewer. Yet as fans of the genre understand, when it's done right, a great sci-fi film is well worth the mental gymnastics that watching it might demand. Speaking of sci-fi done right: Whether you're a lifelong genre devotee or have never even sat through a Star Wars movie to the end, a little guidance can go a long way--and that's exactly what we've got for you. When you're ready to take your mind on a cinematic journey, check out any one (or all) of our picks for the very best science fiction movies you can watch right now.

Recent advancements in natural language generation (NLG) technology have significantly improved the diversity, control, and quality of large language models (LLM)-generated text. A notable example is OpenAI's ChatGPT, which demonstrates exceptional performance in tasks such as answering questions, composing email messages, essays, and codes. However, this newfound capability to produce human-like text at high efficiency also raises concerns about detecting and preventing misuse of LLMs in tasks such as phishing, disinformation, and academic dishonesty. For instance, many schools banned ChatGPT due to concerns over cheating in assignments,11 and media outlets have raised the alarm over fake news generated by LLMs.14 These concerns about the misuse of LLMs have hindered the NLG application in important domains such as media and education.

ROUGH'ROAD': The Jake Gyllenhaal-starring "Road House" remake is facing two major hurdles ahead of its release. 'IDEOLOGICAL ECHO CHAMBER': The controversy surrounding the artificial intelligence chatbot Gemini is reigniting concerns about political bias at Google, a company that has repeatedly been accused of favoring Democrats and fostering a culture of progressive workers. CAPITALIZING ON CONSUMERS: Elon Musk is suing ChatGPT-maker OpenAI and its chief executive Sam Altman, among others, saying they had abandoned the company's original founding mission to develop open-source artificial general intelligence technology for the benefit of humanity over profits. CREEPY COMPANION: Have you ever wished for a robot friend who can keep you company, teach you new skills and inspire you to explore the wonders of technology? If so, you might want to check out Doly, the latest creation from Limibit, a technology startup based in Ontario, Canada.

Despite years of evidence to the contrary, many Republicans still believe that President Joe Biden's win in 2020 was illegitimate. A number of election denying candidates won their primaries during Super Tuesday, including Brandon Gill, the son-in-law of right-wing pundit Dinesh D'Souza and promoter of the debunked 2000 Mules film. Going into this year's elections, claims of election fraud remain a staple for candidates running on the right, fueled by dis- and misinformation, both online and off. And the advent of generative AI has the potential to make the problem worse. A new report from the Center for Countering Digital Hate (CCDH), a nonprofit that tracks hate speech on social platforms, found that even though generative AI companies say they've put policies in place to prevent their image-creating tools from being used to spread election-related disinformation, researchers were able to circumvent their safeguards and create the images anyway.

Whether through the frowning high-definition face of a chimpanzee or a psychedelic, pink-and-red-hued doppelganger of himself, Reuven Cohen uses AI-generated images to catch people's attention. "I've always been interested in art and design and video and enjoy pushing boundaries," he says--but the Toronto-based consultant, who helps companies develop AI tools, also hopes to raise awareness of the technology's darker uses. "It can also be specifically trained to be quite gruesome and bad in a whole variety of ways," Cohen says. He's a fan of the freewheeling experimentation that has been unleashed by open source image-generation technology. But that same freedom enables the creation of explicit images of women used for harassment.

Last week saw the release of Kara Swisher's Burn Book, the highly anticipated career memoir from a titanic, justly celebrated veteran of tech journalism. Considering her unique, outsize stature in Silicon Valley, and her decadeslong record of landing bombshell inside scoops about the single most important industry of the 21st century, Swisher's choice to promote her latest project with the help of famous friends (Don Lemon, Massachusetts Gov. Maura Healey, etc.) certainly makes sense. What makes much less sense, however, is her selection of tech-world executives. The book tour is going to be lit -- with guest moderators like @RobertIger, @laurenepowell, @mcuban, @donlemon, @reidhoffman, @sama and more. Some of the "moderators" on her tour include Laurene Powell Jobs, Disney CEO Bob Iger, OpenAI CEO Sam Altman, LinkedIn co-founder Reid Hoffman, and Lean In board member Adam Grant. Per NPR's Steve Inskeep, she personally requested that these folks "interview her on stage," in a series of conversations she intends to turn into individual podcast episodes.

At first glance, if you squint, you might think it was a photograph: a couple nuzzling together in the front row of a concert hall, in a Facebook advertisement for the Queensland Symphony Orchestra (QSO). But look again and you'll see why it's caused a stir among creative workers and the union representing them. The couple's tangled fingers are both too large and too many; there's a strange sheen making them look more like wax dolls; and then there's the clothes: she in a tulle gown encrusted with jewels, he in a tuxedo – and, simultaneously, a tulle gown encrusted with jewels. Also: she has a large cube on her lap. "Want to do something different this Saturday? Come see an orchestra play," reads the ad.

Radio advertising remains an integral part of modern marketing strategies, with its appeal and potential for targeted reach undeniably effective. However, the dynamic nature of radio airtime and the rising trend of multiple radio spots necessitates an efficient system for monitoring advertisement broadcasts. This study investigates a novel automated radio advertisement detection technique incorporating advanced speech recognition and text classification algorithms. RadIA's approach surpasses traditional methods by eliminating the need for prior knowledge of the broadcast content. This contribution allows for detecting impromptu and newly introduced advertisements, providing a comprehensive solution for advertisement detection in radio broadcasting. Experimental results show that the resulting model, trained on carefully segmented and tagged text data, achieves an F1-macro score of 87.76 against a theoretical maximum of 89.33. This paper provides insights into the choice of hyperparameters and their impact on the model's performance. This study demonstrates its potential to ensure compliance with advertising broadcast contracts and offer competitive surveillance. This groundbreaking research could fundamentally change how radio advertising is monitored and open new doors for marketing optimization.

This study investigates the generation of synthetic disinformation by OpenAI's Large Language Models (LLMs) through prompt engineering and explores their responsiveness to emotional prompting. Leveraging various LLM iterations using davinci-002, davinci-003, gpt-3.5-turbo and gpt-4, we designed experiments to assess their success in producing disinformation. Our findings, based on a corpus of 19,800 synthetic disinformation social media posts, reveal that all LLMs by OpenAI can successfully produce disinformation, and that they effectively respond to emotional prompting, indicating their nuanced understanding of emotional cues in text generation. When prompted politely, all examined LLMs consistently generate disinformation at a high frequency. Conversely, when prompted impolitely, the frequency of disinformation production diminishes, as the models often refuse to generate disinformation and instead caution users that the tool is not intended for such purposes. This research contributes to the ongoing discourse surrounding responsible development and application of AI technologies, particularly in mitigating the spread of disinformation and promoting transparency in AI-generated content.

Human brain and behavior provide a rich venue that can inspire novel control and learning methods for robotics. In an attempt to exemplify such a development by inspiring how humans acquire knowledge and transfer skills among tasks, we introduce a novel multi-task reinforcement learning framework named Episodic Return Progress with Bidirectional Progressive Neural Networks (ERP-BPNN). The proposed ERP-BPNN model (1) learns in a human-like interleaved manner by (2) autonomous task switching based on a novel intrinsic motivation signal and, in contrast to existing methods, (3) allows bidirectional skill transfer among tasks. ERP-BPNN is a general architecture applicable to several multi-task learning settings; in this paper, we present the details of its neural architecture and show its ability to enable effective learning and skill transfer among morphologically different robots in a reaching task. The developed Bidirectional Progressive Neural Network (BPNN) architecture enables bidirectional skill transfer without requiring incremental training and seamlessly integrates with online task arbitration. The task arbitration mechanism developed is based on soft Episodic Return progress (ERP), a novel intrinsic motivation (IM) signal. To evaluate our method, we use quantifiable robotics metrics such as 'expected distance to goal' and 'path straightness' in addition to the usual reward-based measure of episodic return common in reinforcement learning. With simulation experiments, we show that ERP-BPNN achieves faster cumulative convergence and improves performance in all metrics considered among morphologically different robots compared to the baselines.