Porous materials exhibit vast structural diversity and support critical applications in gas storage, separations, and catalysis. However, predictive modeling remains challenging due to the multiscale nature of structure-property relationships, where performance is governed by both local chemical environments and global pore-network topology. These complexities, combined with sparse and unevenly distributed labeled data, hinder generalization across material families. We propose the Interaction Topological Transformer (ITT), a unified data-efficient framework that leverages novel interaction topology to capture materials information across multiple scales and multiple levels, including structural, elemental, atomic, and pairwise-elemental organization. ITT extracts scale-aware features that reflect both compositional and relational structure within complex porous frameworks, and integrates them through a built-in Transformer architecture that supports joint reasoning across scales. Trained using a two-stage strategy, i.e., self-supervised pretraining on 0.6 million unlabeled structures followed by supervised fine-tuning, ITT achieves state-of-the-art, accurate, and transferable predictions for adsorption, transport, and stability properties. This framework provides a principled and scalable path for learning-guided discovery in structurally and chemically diverse porous materials.

Reinforcement learning (RL) has demonstrated compelling performance in robotic tasks, but its success often hinges on the design of complex, ad hoc reward functions. Researchers have explored how Large Language Models (LLMs) could enable non-expert users to specify reward functions more easily. However, LLMs struggle to balance the importance of different features, generalize poorly to out-of-distribution robotic tasks, and cannot represent the problem properly with only text-based descriptions. To address these challenges, we propose ELEMENTAL (intEractive LEarning froM dEmoNstraTion And Language), a novel framework that combines natural language guidance with visual user demonstrations to align robot behavior with user intentions better. By incorporating visual inputs, ELEMENTAL overcomes the limitations of text-only task specifications, while leveraging inverse reinforcement learning (IRL) to balance feature weights and match the demonstrated behaviors optimally. ELEMENTAL also introduces an iterative feedback-loop through self-reflection to improve feature, reward, and policy learning. Our experiment results demonstrate that ELEMENTAL outperforms prior work by 42.3% on task success, and achieves 41.3% better generalization in out-of-distribution tasks, highlighting its robustness in LfD.

It had a great new idea for a movie--Elemental, based on characters from The Good Dinosaur's director Peter Sohn--but actually animating the film's titular elements was proving to be a problem. After all, it's one thing to draw a crumbling mound of sentient dirt, but how do you capture the ethereal nature of fire onscreen, and how would a corporeal body made of water even work? Can you see through it? Do the eyes just float around? While some of those questions could be answered with good old-fashioned suspension of disbelief, Pixar's animators thought the fire issue was a real conundrum, especially considering that one of their movie's leads, Ember, was actually supposed to be made of the stuff. They had tools to make a flame effect from years of previous animations, but when you actually tried to shape it into a character, the results were pretty terrifying, a cross between Studio Ghibli's Calcifer and Nicolas Cage's Ghost Rider, but somehow harsher.

With billions of books, news stories, and documents online, there's never been a better time to be reading -- if you have time to sift through all the options. "There's a ton of text on the internet," says Justin Solomon, an assistant professor at MIT. "Anything to help cut through all that material is extremely useful." With the MIT-IBM Watson AI Lab and his Geometric Data Processing Group at MIT, Solomon recently presented a new technique for cutting through massive amounts of text at the Conference on Neural Information Processing Systems (NeurIPS). Their method combines three popular text-analysis tools -- topic modeling, word embeddings, and optimal transport -- to deliver better, faster results than competing methods on a popular benchmark for classifying documents. If an algorithm knows what you liked in the past, it can scan the millions of possibilities for something similar.

Tech giants including Apple and Amazon have hit back at claims by Bloomberg their servers may have been fitted with tiny microchips placed there by Chinese spies. The chips, which were'not much bigger than a grain of rice,' would have given China unprecedented backdoor access to computers and data, according to Bloomberg. Apple, Amazon and Super Micro, the Chinese motherboard manufacturer believed to have introduced the chips, have all issued statements denying the report. An Apple spokesman strongly denied the report in a statement, saying: 'On this we can be very clear: Apple has never found malicious chips, 'hardware manipulations' or vulnerabilities purposely planted in any server. Apple never had any contact with the FBI or any other agency about such an incident.

The Robot Show at the Museum of Art and History in Lancaster, Calif., is comprised of eight exhibitions exploring the place robots, and other forms of artificial intelligence, have in a contemporary social landscape – from popular culture to nature and spirituality. Featured in the Main Gallery at MOAH is a retrospective of Emmy-nominated artist and animator, Dave Pressler. The exhibition is on view through September 26, 2018. Robert Nelson is encouraging viewers' Awakening in his new exhibition, a part of The Robot Show. Using a vivid palette, mixing pop and surrealist styles, Nelson juxtaposes images that play with deep, edgy ideas of technology.