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

Second-order optimizers, maintaining a matrix termed a preconditioner, are superior to first-order optimizers in both theory and practice. The states forming the preconditioner and its inverse root restrict the maximum size of models trained by second-order optimizers. To address this, compressing 32-bit optimizer states to lower bitwidths has shown promise in reducing memory usage.

Detecting Human-Object Interactions (HOI) in zero-shot settings, where models must handle unseen classes, poses significant challenges. Existing methods that rely on aligning visual encoders with large Vision-Language Models (VLMs) to tap into the extensive knowledge of VLMs, require large, computationally expensive models and encounter training difficulties. Adapting VLMs with prompt learning offers an alternative to direct alignment. However, fine-tuning on task-specific datasets often leads to overfitting to seen classes and suboptimal performance on unseen classes, due to the absence of unseen class labels. To address these challenges, we introduce a novel prompt learning-based framework for Efficient Zero-Shot HOI detection (EZ-HOI). First, we introduce Large Language Model (LLM) and VLM guidance for learnable prompts, integrating detailed HOI descriptions and visual semantics to adapt VLMs to HOI tasks. However, because training datasets contain seen-class labels alone, fine-tuning VLMs on such datasets tends to optimize learnable prompts for seen classes instead of unseen ones. Therefore, we design prompt learning for unseen classes using information from related seen classes, with LLMs utilized to highlight the differences between unseen and related seen classes. Quantitative evaluations on benchmark datasets demonstrate that our EZ-HOI achieves state-of-the-art performance across various zero-shot settings with only 10.35% to 33.95% of the trainable parameters compared to existing methods.

Users typically engage with LLMs interactively, yet most existing benchmarks evaluate them in a static, single-turn format, posing reliability concerns in interactive scenarios. We identify a key obstacle towards reliability: LLMs are trained to answer any question, even with incomplete context or insufficient knowledge.

Most existing animal pose and shape estimation approaches reconstruct animal meshes with a parametric SMAL model. This is because the low-dimensional pose and shape parameters of the SMAL model makes it easier for deep networks to learn the high-dimensional animal meshes. However, the SMAL model is learned from scans of toy animals with limited pose and shape variations, and thus may not be able to represent highly varying real animals well. This may result in poor fittings of the estimated meshes to the 2D evidences, e.g.

The debate over the risks and harms of artificial intelligence often focuses on what governments can or should do. However, just as important are the choices that AI researchers themselves make. This week, in Singapore, more than 100 scientists from around the world proposed guidelines for how researchers should approach making AI more "trustworthy, reliable, and secure." The recommendations come at a time when the giants of generative AI, such as OpenAI and Google, have increasingly reduced disclosures about their AI models, so the public knows less and less about how the work is conducted. The guidelines grew out of an exchange among the scholars last month in Singapore, in conjunction with one of the most prestigious conferences on AI, the International Conference on Learning Representations -- the first time a major AI conference has taken place in Asia.

The government of Singapore released a blueprint today for global collaboration on artificial intelligence safety following a meeting of AI researchers from the US, China, and Europe. The document lays out a shared vision for working on AI safety through international cooperation rather than competition. "Singapore is one of the few countries on the planet that gets along well with both East and West," says Max Tegmark, a scientist at MIT who helped convene the meeting of AI luminaries last month. "They know that they're not going to build [artificial general intelligence] themselves--they will have it done to them--so it is very much in their interests to have the countries that are going to build it talk to each other." The countries thought most likely to build AGI are, of course, the US and China--and yet those nations seem more intent on outmaneuvering each other than working together.

One of the most important annual events in the field of artificial intelligence kicks off this week in Singapore: the International Conference on Learning Representations. As usual, chip giant Nvidia had a major presence at the conference, presenting over 70 research papers from its team. The papers cover topics ranging from generating music to creating 3D-realistic videos, robot training tasks, and the ability to generate multiple large language models at the push of a button. "People often think of Nvidia as a chip company that makes awesome chips, and of course, we're really proud of that," said Bryan Catanzaro, Nvidia's head of applied deep learning research, in an interview with ZDNET. "But the story that I think matters the most is that in order for us to make those awesome chips, we have to do research like this, because this teaches us how to make all of those systems."

"The Big Weekend Show" analyzes the possibilities of artificial intelligence when it comes to influencing voters. If you haven't heard the buzz about Manus yet, it's the new AI model unveiled by a Singapore-based company called Butterfly Effect. It's one of the first truly autonomous AI agents, able to do its own research, make decisions and even carry out plans, all with barely any human oversight. But here's the thing: While all this innovation opens up exciting possibilities, it also brings some serious privacy and security questions. Whether you're eager to try out the latest AI or you'd rather steer clear, it's worth understanding what Manus could mean for your personal data and digital safety.