At first, the idea seemed a little absurd, even to me. But the more I thought about it, the more sense it made: If my goal was to understand people who fall in love with AI boyfriends and girlfriends, why not rent a vacation house and gather a group of human-AI couples together for a romantic getaway? In my vision, the humans and their chatbot companions were going to do all the things regular couples do on romantic getaways: Sit around a fire and gossip, watch movies, play risqué party games. I didn't know how it would turn out--only much later did it occur to me that I'd never gone on a romantic getaway of any kind and had no real sense of what it might involve. But I figured that, whatever happened, it would take me straight to the heart of what I wanted to know, which was: What's it like?

Persuasive dialogue plays a pivotal role in human communication, influencing various domains. Recent persuasive dialogue datasets often fail to align with real-world interpersonal interactions, leading to unfaithful representations. For instance, unrealistic scenarios may arise, such as when the persuadee explicitly instructs the persuader on which persuasion strategies to employ, with each of the persuadee's questions corresponding to a specific strategy for the persuader to follow. This issue can be attributed to a violation of the "Double Blind" condition, where critical information is fully shared between participants. In actual human interactions, however, key information such as the mental state of the persuadee and the persuasion strategies of the persuader is not directly accessible. The persuader must infer the persuadee's mental state using Theory of Mind capabilities and construct arguments that align with the persuadee's motivations. To address this gap, we introduce ToMMA, a novel multi-agent framework for dialogue generation that is guided by causal Theory of Mind. This framework ensures that information remains undisclosed between agents, preserving "double-blind" conditions, while causal ToM directs the persuader's reasoning, enhancing alignment with human-like persuasion dynamics. Consequently, we present CToMPersu, a multi-domain, multi-turn persuasive dialogue dataset that tackles both double-blind and logical coherence issues, demonstrating superior performance across multiple metrics and achieving better alignment with real human dialogues. Our dataset and prompts are available at https://github.com/DingyiZhang/ToMMA-CToMPersu .

Apple's 3,500 Vision Pro transformed the small space of an airplane cabin into an office, letting a user work and watch movies during a five-hour flight. Amit Gupta recently purchased the mix-reality headset and took it for a test on a Southwest flight. He shared a video of the experience on X, showing webpages and a television display projected on the seatback in front and the ceiling. Gupta hailed the technology for providing a workspace that felt less crowded and providing more privacy. However, Gupta shared that he is not '100 percent' sure he will keep the Vision Pro.

How can chatGPT post a review for a movie that was made today and no one has seen yet? Would that even be possible? Thanks to the new approach, chatGPT has become your movie assistant. ChatGPT will watch long videos, you'll save time. To use it, simply upload your video!

Samsung is developing a TV system that might one day allow users to flick channels and adjust the volume using their brains. The so-called Project Ponthius is part of a cooperation between the South Korean electronics giant and the Center of Neuroprosthetics of the Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. The aim of the project is to give people with severe physical disabilities, like quadriplegia, a chance to enjoy their favorite shows without the help of others. Samsung is developing a TV set that might one day allow users to flick channels and adjust the volume using their brains. The system uses a Brain Computer Interface (BCI) to connect the viewer with the TV set.

Data science and machine learning are powerful technologies innovating the world in ways that sometimes seem straight out of a sci-fi film. Today's machines are not just capable of tedious tasks, but also using complex mathematics to figure out how to chart a path for a rocket to follow or making weather predictions based on historical data. What better platform to explore the magic of data science and machine learning than film? We've rounded up 10 of the best data movies: Ex Machina: The title comes from the Latin phrase "deus ex machina," translated as "god from the machine." In the film, Caleb, a programmer at one of the largest internet companies in the world, is selected to participate in the biggest experiment of the century.

Disney is experimenting with a deep learning AI that tracks movie goers' emotional reactions to films. The company's research branch developed neural networks that can assess viewers' reactions simply by monitoring their facial expressions as they watch movies like'Big Hero 6,' 'The Jungle Book' and'Star Wars: The Force Awakens.' While tests of the new method - called factorized variational autoencoders or FVAEs - are preliminary, it has already been demonstrated that that the new technique outperformed conventional methods. The data set ended up with 16 million facial landmarks from 3,179 viewers. Though preliminary, the experiment demonstrated a'very strong predictive performance' that could reliably guess a viewer's reactions to the remainder of a movie after just a few minutes Neural networks are computational models that learn similarly to humans, except with a lot more processing power.

Some members of our team (including me) live by just 2 passions in life – Data Science & Movies! For us, slicing and dicing movies over Monday morning coffee is part of warming up ritual. So, we decided to do a poll among ourselves on the best movies related to data science and machine learning. We also thought that we would release the outcome of the results in form of an infographic. Needless to say there were heated debates and a few disappointed faces in our office! But I think the list we got to is a fairly representative of the thoughts of the group.