Meta, the owner of Facebook and Instagram, announced on Friday it had built a new artificial intelligence model called Movie Gen that can create realistic-seeming video and audio clips in response to user prompts, claiming it can rival tools from leading media generation startups like OpenAI and ElevenLabs. Samples of Movie Gen's creations provided by Meta showed videos of animals swimming and surfing, as well as clips using people's real photos to depict them performing actions like painting on a canvas. Movie Gen also can generate background music and sound effects synced to the content of the videos, Meta said in a blogpost. Users can also edit existing videos with the model. In one such video, Meta had the tool insert pompoms into the hands of a man running by himself in the desert, while in another it changed a parking lot on which a man was skateboarding from dry ground into one covered by a splashing puddle.

Third-year student Jack Quinlan was confident he knew what I was going to ask before we conducted our interview. He wasn't psychic, and I hadn't fed him questions – he'd just done a trial run on ChatGPT. Prior to our meeting, the software engineering and neuroscience undergraduate logged on to the program to generate the kinds of questions a "professional journalist at the Guardian" would ask a student about artificial intelligence at universities. "What prompted your university to begin using generative AI tools in education?" the software version of me began. "How have students and educators at your university responded to the introduction of generative AI? Have there been any challenges and concerns raised?"

Meta just announced its own media-focused AI model, called Movie Gen, that can be used to generate realistic video and audioclips. The company shared multiple 10-second clips generated with Movie Gen, including a Moo Deng-esque baby hippo swimming around, to demonstrate its capabilities. While the tool is not yet available for use, this Movie Gen announcement comes shortly after its Meta Connect event, which showcased new and refreshed hardware and the latest version of its large language model, Llama 3.2. Going beyond the generation of straightforward text-to-video clips, the Movie Gen model can make targeted edits to an existing clip, like adding an object into someone's hands or changing the appearance of a surface. In one of the example videos from Meta, a woman wearing a VR headset was transformed to look like she was wearing steampunk binoculars.

This week, Microsoft started rolling out the Windows 11 2024 update, but it quickly became clear that the company was far more eager to unveil new features for its Copilot AI and Copilot AI PCs. In this episode, Devindra and Cherlynn chat about Microsoft's current AI priorities, and what it means for people with older PCs. Also, we discuss the death of HoloLens and Microsoft giving up on AR as Meta, Apple and even Snap build for an augmented reality future. Listen below or subscribe on your podcast app of choice. If you've got suggestions or topics you'd like covered on the show, be sure to email us or drop a note in the comments! And be sure to check out our other podcast, Engadget News! Tech debt led to Sonos' disastrous app relaunch, will they be able to win users back? Google is making Gmail summaries more useful and adding a "happening soon" tab to your inbox – 41:11 Harvard students hack together facial recognition for Meta's smart glasses that instantly doxes strangers – 44:00 ...

When used as a surrogate objective for maximum likelihood estimation in latent variable models, the evidence lower bound (ELBO) produces state-of-the-art results. Inspired by this, we consider the extension of the ELBO to a family of lower bounds defined by a particle filter's estimator of the marginal likelihood, the filtering variational objectives (FIVOs). FIVOs take the same arguments as the ELBO, but can exploit a model's sequential structure to form tighter bounds. We present results that relate the tightness of FIVO's bound to the variance of the particle filter's estimator by considering the generic case of bounds defined as log-transformed likelihood estimators. Experimentally, we show that training with FIVO results in substantial improvements over training the same model architecture with the ELBO on sequential data.

In early September, Warner Bros. released a teaser for A Minecraft Movie, the studio's new film based on Mojang's nearly 15-year-old sandbox game. Directed by Napoleon Dynamite helmer Jared Hess, it was, frankly, very goofy. Jack Black was Steve; Jason Momoa was sporting maybe the worst hairdo he's ever had. Everyone involved, even the animated creatures, seemed to think they were in a different movie. But that wasn't what the trolls latched onto.

A gated recurrent unit (GRU) is a successful recurrent neural network architecture for time-series data. The GRU is typically trained using a gradient-based method, which is subject to the exploding gradient problem in which the gradient increases significantly. This problem is caused by an abrupt change in the dynamics of the GRU due to a small variation in the parameters. In this paper, we find a condition under which the dynamics of the GRU changes drastically and propose a learning method to address the exploding gradient problem. Our method constrains the dynamics of the GRU so that it does not drastically change. We evaluated our method in experiments on language modeling and polyphonic music modeling. Our experiments showed that our method can prevent the exploding gradient problem and improve modeling accuracy.

Neural Information Processing Systems http://nips.cc/

As the bitterly contested U.S. election campaign enters its final stretch, misinformation researchers have raised the alarm over threats artificial intelligence and foreign influence pose -- but voters appear more concerned about falsehoods from a more familiar source: politicians. The United States is battling a firehose of misinformation before the Nov. 5 vote -- from fake "news" sites that researchers say were created by Russian and Iranian actors, to manipulated images generated by AI tools that have blurred the boundaries between reality and fiction. More concerning for voters, however, is misinformation spreading the good-old-fashioned way, through politicians sowing falsehoods, with researchers saying they face almost no legal consequences for distorting the truth.

Point processes are powerful tools to model user activities and have a plethora of applications in social sciences. Predicting user activities based on point processes is a central problem. However, existing works are mostly problem specific, use heuristics, or simplify the stochastic nature of point processes. In this paper, we propose a framework that provides an efficient estimator of the probability mass function of point processes. In particular, we design a key reformulation of the prediction problem, and further derive a differential-difference equation to compute a conditional probability mass function. Our framework is applicable to general point processes and prediction tasks, and achieves superb predictive and efficiency performance in diverse real-world applications compared to the state of the art.