Emotion recognition plays a vital role in enhancing human-computer interaction. In this study, we tackle the MER-SEMI challenge of the MER2025 competition by proposing a novel multimodal emotion recognition framework. To address the issue of data scarcity, we leverage large-scale pre-trained models to extract informative features from visual, audio, and textual modalities. Specifically, for the visual modality, we design a dual-branch visual encoder that captures both global frame-level features and localized facial representations. For the textual modality, we introduce a context-enriched method that employs large language models to enrich emotional cues within the input text. To effectively integrate these multimodal features, we propose a fusion strategy comprising two key components, i.e., self-attention mechanisms for dynamic modality weighting, and residual connections to preserve original representations. Beyond architectural design, we further refine noisy labels in the training set by a multi-source labeling strategy. Our approach achieves a substantial performance improvement over the official baseline on the MER2025-SEMI dataset, attaining a weighted F-score of 87.49% compared to 78.63%, thereby validating the effectiveness of the proposed framework.

Recent advances in diffusion models have opened new avenues for research into embodied AI agents and robotics. Despite significant achievements in complex robotic locomotion and skills, mobile manipulation-a capability that requires the coordination of navigation and manipulation-remains a challenge for generative AI techniques. This is primarily due to the high-dimensional action space, extended motion trajectories, and interactions with the surrounding environment. In this paper, we introduce M2Diffuser, a diffusion-based, scene-conditioned generative model that directly generates coordinated and efficient whole-body motion trajectories for mobile manipulation based on robot-centric 3D scans. M2Diffuser first learns trajectory-level distributions from mobile manipulation trajectories provided by an expert planner. Crucially, it incorporates an optimization module that can flexibly accommodate physical constraints and task objectives, modeled as cost and energy functions, during the inference process. This enables the reduction of physical violations and execution errors at each denoising step in a fully differentiable manner. Through benchmarking on three types of mobile manipulation tasks across over 20 scenes, we demonstrate that M2Diffuser outperforms state-of-the-art neural planners and successfully transfers the generated trajectories to a real-world robot. Our evaluations underscore the potential of generative AI to enhance the generalization of traditional planning and learning-based robotic methods, while also highlighting the critical role of enforcing physical constraints for safe and robust execution.

It might look and act like a little girl, but this creepy entity may just be the next big breakthrough in artificial intelligence (AI). Tong Tong, meaning'little girl', has been dubbed the world's first AI child after it was revealed by scientists from the Beijing Institute for General Artificial Intelligence (BIGAI). According to its creators, the AI child can assign itself tasks, learn autonomously, and explore its environment. And, although it sounds like the plot of the science-fiction movie The Creator, Tong Tong's engineers say the AI can even experience emotion. In a video, BIGAI says that Tong Tong'has her own joy, anger and sorrow'.

Although Siri and Google Assistant have the ability to schedule meetings on request, they do not have the social awareness to independently prioritise the appointments. A team of researchers has argued that the future of AI calls for the implementation of social intelligence to ensure that the growth of the technology is not stunted by a lack of social skills. The work, 'Artificial Social Intelligence: A Comparative and Holistic View,' is published in CAAI Artificial Intelligence Research. "Artificial Intelligence has changed our society and our daily life," said first author Lifeng Fan, National Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence (BIGAI). "What is the next important challenge for AI in the future? We argue that Artificial Social Intelligence (ASI) is the future of AI." ASI includes multiple siloed subfields, including social perception, Theory of Mind, and social interaction.

In 2020, Elon Musk said artificial intelligence would surpass human intelligence within five years, on its way to becoming "an immortal dictator." A new book says no way, not ever. The book, Why Machines Will Never Rule the World: Artificial Intelligence without Fear (Routledge, 2022), argues against the possibility of engineering machines that can surpass human intelligence. Coauthors are Barry Smith, professor in the philosophy department at the University at Buffalo and Jobst Landgrebe, senior research associate in the philosophy department and founder of Cognotekt, a German AI company. Machine learning and all other working software applications--the proud accomplishments of those involved in AI research--are for Smith and Landgrebe far from anything resembling the capacity of humans.

You are free to share this article under the Attribution 4.0 International license. In 2020, Elon Musk said artificial intelligence would surpass human intelligence within five years, on its way to becoming "an immortal dictator." A new book says no way, not ever. The book, Why Machines Will Never Rule the World: Artificial Intelligence without Fear (Routledge, 2022), argues against the possibility of engineering machines that can surpass human intelligence. Coauthors are Barry Smith, professor in the philosophy department at the University at Buffalo and Jobst Landgrebe, senior research associate in the philosophy department and founder of Cognotekt, a German AI company.

Barry Smith, PhD, SUNY Distinguished Professor in the Department of Philosophy in UB's College of Arts and Sciences, and Jobst Landgrebe, PhD, founder of Cognotekt, a German AI company, have co-authored "Why Machines Will Never Rule the World: Artificial Intelligence without Fear." Their book presents a powerful argument against the possibility of engineering machines that can surpass human intelligence. Machine learning and all other working software applications the proud accomplishments of those involved in AI research are for Smith and Landgrebe far from anything resembling the capacity of humans. Further, they argue that any incremental progress that's unfolding in the field of AI research will in practical terms bring it no closer to the full functioning possibility of the human brain. There cannot be a machine will, they say.

From the beginning of Artificial Intelligence, people have talked about the advent of A.G.I (Artificial General Intelligence) and also have estimated the amount of time required to achieve it. A quick search would tell you that A.G.I is far away from the current speed of innovation we are going with. There are thousands of problems that we need to tackle before we get our first A.G.I. Artificial Intelligence is branched into two(mostly) segments -- A.N.I or Artificial Narrow Intelligence and A.G.I or Artificial General Intelligence. Artificial Narrow Intelligence is when the AI is capable of doing a single performance and is nowhere near to natural intelligence.

Deepmind's Gato solves many tasks, but none of them really well. Does the new AI system nevertheless lead the way for general artificial intelligence? Hot on the heels of OpenAI's DALL-E 2, Google's PaLM, LaMDA 2, and Deepmind's Chinchilla and Flamingo, the London-based AI company is showing off another large AI model that outperforms existing systems. Yet Deepmind's Gato is different: The model can't text better, describe images better, play Atari better, control robotic arms better, or orient itself in 3D spaces better than other AI systems. But Gato can do a bit of everything. Deepmind trained the Transformer-based multi-talent with images, text, proprioception, joint moments, keystrokes, and other discrete and continuous observations and actions.

The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.