Differently, our unsupervised multi-body task requires the model's ability to handle part-level local equivariance, Figure 1: Structure of our feature extractor based on EPN. "EPNConv" is the SE(3)-equivariant convolution proposed in the vanilla EPN network. Part-level SE(3)-equivariance is desirable for motion analysis, especially rotation estimation. Song and Y ang utilized the methodology proposed by Choy et al . All other objects were considered part of the background.

Science Biology Evolution Neanderthals used'crayons' to color Ancient ochre pigment fragments show that our cousins had an artistic flair. Breakthroughs, discoveries, and DIY tips sent every weekday. Neanderthals are getting a well-deserved scientific rewrite. A growing body of paleoarchaeological evidence indicates that our extinct cousins were far from the lumbering oafs we initially believed them to be. Recent discoveries show the one-time Homo sapien competitors were creative enough to craft stone multitools and even collect small trinkets.

Differently, our unsupervised multi-body task requires the model's ability to handle part-level local equivariance, Figure 1: Structure of our feature extractor based on EPN. "EPNConv" is the SE(3)-equivariant convolution proposed in the vanilla EPN network. Part-level SE(3)-equivariance is desirable for motion analysis, especially rotation estimation. Song and Y ang utilized the methodology proposed by Choy et al . All other objects were considered part of the background.

Imitation learning for robotic manipulation often suffers from limited generalization and data scarcity, especially in complex, long-horizon tasks. In this work, we introduce a hierarchical framework that leverages code-generating vision-language models (VLMs) in combination with low-level diffusion policies to effectively imitate and generalize robotic behavior. Our key insight is to treat open-source robotic APIs not only as execution interfaces but also as sources of structured supervision: the associated subtask functions - when exposed - can serve as modular, semantically meaningful labels. We train a VLM to decompose task descriptions into executable subroutines, which are then grounded through a diffusion policy trained to imitate the corresponding robot behavior. To handle the non-Markovian nature of both code execution and certain real-world tasks, such as object swapping, our architecture incorporates a memory mechanism that maintains subtask context across time. We find that this design enables interpretable policy decomposition, improves generalization when compared to flat policies and enables separate evaluation of high-level planning and low-level control.

Neanderthals bred with our human ancestors 100,000 years earlier than previously thought, according to a new study. Experts have discovered that a five–year–old child who lived 140,000 years ago had parents from both species. Their fossil – likely a female – was first unearthed 90 years ago in the Skhul Cave on Mount Carmel in what is now northern Israel. A team from Tel Aviv University and the French Centre for Scientific Research conducted a series of advanced tests on the remaining bones, including a CT scan of the skull. 'Genetic studies over the past decade have shown that these two groups exchanged genes,' said lead author Professor Israel Hershkovitz.

What jumps to mind when you think about the impending AI apocalypse? If you're partial to sci-fi movie cliches, you may envisage killer robots (with or without thick Austrian accents) rising up to terminate their hubristic creators. Or perhaps, a la The Matrix, you'll go for scary machines sucking energy out of our bodies as they distract us with a simulated reality. For Yuval Noah Harari, who has spent a lot of time worrying about AI over the past decade, the threat is less fantastical and more insidious. "In order to manipulate humans, there is no need to physically hook brains to computers," he writes in his engrossing new book Nexus.

This article was featured in the One Story to Read Today newsletter. "About 14 billion years ago, matter, energy, time and space came into being." So begins Sapiens: A Brief History of Humankind (2011), by the Israeli historian Yuval Noah Harari, and so began one of the 21st century's most astonishing academic careers. Sapiens has sold more than 25 million copies in various languages. Since then, Harari has published several other books, which have also sold millions. He now employs some 15 people to organize his affairs and promote his ideas. Check out more from this issue and find your next story to read. Harari might be, after the Dalai Lama, the figure of global renown who is least online.

Abstract--In this demo paper, we introduce SAPIEN, a platform for high-fidelity virtual agents driven by large language models that can hold open domain conversations with users in 13 different languages, and display emotions through facial expressions and voice. The platform allows users to customize their virtual agent's personality, background, and conversation premise, thus providing a rich, immersive interaction experience. Furthermore, after the virtual meeting, the user can choose to get the conversation analyzed and receive actionable feedback on their communication skills. This paper illustrates an overview of the platform and discusses the various application domains of this technology, ranging from entertainment to mental health, communication training, language learning, education, healthcare, and beyond. Additionally, we consider the ethical implications of such realistic virtual agent representations and the potential challenges in ensuring responsible use.

Human culture relies on innovation: our ability to continuously explore how existing elements can be combined to create new ones. Innovation is not solitary, it relies on collective search and accumulation. Reinforcement learning (RL) approaches commonly assume that fully-connected groups are best suited for innovation. However, human laboratory and field studies have shown that hierarchical innovation is more robustly achieved by dynamic social network structures. In dynamic settings, humans oscillate between innovating individually or in small clusters, and then sharing outcomes with others. To our knowledge, the role of social network structure on innovation has not been systematically studied in RL. Here, we use a multi-level problem setting (WordCraft), with three different innovation tasks to test the hypothesis that the social network structure affects the performance of distributed RL algorithms. We systematically design networks of DQNs sharing experiences from their replay buffers in varying structures (fully-connected, small world, dynamic, ring) and introduce a set of behavioral and mnemonic metrics that extend the classical reward-focused evaluation framework of RL. Comparing the level of innovation achieved by different social network structures across different tasks shows that, first, consistent with human findings, experience sharing within a dynamic structure achieves the highest level of innovation in tasks with a deceptive nature and large search spaces. Second, experience sharing is not as helpful when there is a single clear path to innovation. Third, the metrics we propose, can help understand the success of different social network structures on different tasks, with the diversity of experiences on an individual and group level lending crucial insights.

In 2017 president Putin discussed the importance of AI in the future while speaking with students. He said, "Whichever nation will lead in AI will be the ruler of the world." Further, he discussed that the future of humanity is in AI, which comes with advantages and threats. AI is now gradually becoming a part of our lives, whether we notice it or not. According to Statista, the global revenue for AI has reached 327.5 billion USD, and companies are investing dozens of billions into the AI sector.