Scientists are rethinking what cattle are capable of after an Austrian cow named Veronika was found to use tools with impressive skill. The discovery, reported by researchers in Vienna, suggests cows may have far greater cognitive abilities than previously assumed. Veronika, a cow living in a mountain village in the Austrian countryside, has spent years perfecting the art of scratching herself using sticks, rakes, and brooms. Word of her behaviour eventually reached animal intelligence specialists in Vienna, who found Veronika used both ends of the same object for different tasks. If it were her back or another tough area that warranted a good scratch, she would use the bristle end of a broom.

Building self-improving AI systems remains a fundamental challenge in the AI domain. We present NNGPT, an open-source framework that turns a large language model (LLM) into a self-improving AutoML engine for neural network development, primarily for computer vision. Unlike previous frameworks, NNGPT extends the dataset of neural networks by generating new models, enabling continuous fine-tuning of LLMs based on closed-loop system of generation, assessment, and self-improvement. It integrates within one unified workflow five synergistic LLM-based pipelines: zero-shot architecture synthesis, hyperparameter optimization (HPO), code-aware accuracy/early-stop prediction, retrieval-augmented synthesis of scope-closed PyTorch blocks (NN-RAG), and reinforcement learning. Built on the LEMUR dataset as an audited corpus with reproducible metrics, NNGPT emits from a single prompt and validates network architecture, preprocessing code, and hyperparameters, executes them end-to-end, and learns from result. The PyTorch adapter makes NNGPT framework-agnostic, enabling strong performance: NN-RAG achieves 73% executability on 1,289 targets, 3-shot prompting boosts accuracy on common datasets, and hash-based deduplication saves hundreds of runs. One-shot prediction matches search-based AutoML, reducing the need for numerous trials. HPO on LEMUR achieves RMSE 0.60, outperforming Optuna (0.64), while the code-aware predictor reaches RMSE 0.14 with Pearson r=0.78. The system has already generated over 5K validated models, proving NNGPT as an autonomous AutoML engine. Upon acceptance, the code, prompts, and checkpoints will be released for public access to enable reproducibility and facilitate community usage.

'Something special is happening on Madagascar.' Breakthroughs, discoveries, and DIY tips sent every weekday. Lemurs first arrived on the island of Madagascar 53.2 million years ago, probably hitching a ride on a vegetation raft from mainland Africa. The island was predator free, and the lemurs evolved into an abundance of species to thrive in its various habitats--an expansion that hasn't stopped since. Scientists typically expect such rapid species growth to eventually slow down. However, in a study recently published in the journal a team of researchers presents evidence that lemurs defy this evolutionary principle.

While elephants have the reputation as animals who never forget, they may have some competition from some primates. Lemurs use their long-term memory in combination with smell and social cues to find hidden fruit. This technique may have deep evolutionary roots, according to a study published in the International Journal of Primatology. "Our study provides evidence that lemurs can integrate sensory information with ecological and social knowledge, which demonstrates their ability to consider multiple aspects of a problem," study co-author and New York University anthropologist Elena Cunningham said in a statement. Cunningham is a clinical professor of molecular pathobiology at NYU College of Dentistry.

The demonstrated code-understanding capability of LLMs raises the question of whether they can be used for automated program verification, a task that often demands high-level abstract reasoning about program properties, which is challenging for verification tools. We propose a general methodology to combine the power of LLMs and automated reasoners for automated program verification. We formally describe this methodology as a set of derivation rules and prove its soundness. We instantiate the calculus as a sound automated verification procedure, which led to practical improvements on a set of synthetic and competition benchmarks.

This paper presents LEMURS, an algorithm for learning scalable multi-robot control policies from cooperative task demonstrations. We propose a port-Hamiltonian description of the multi-robot system to exploit universal physical constraints in interconnected systems and achieve closed-loop stability. We represent a multi-robot control policy using an architecture that combines self-attention mechanisms and neural ordinary differential equations. The former handles time-varying communication in the robot team, while the latter respects the continuous-time robot dynamics. Our representation is distributed by construction, enabling the learned control policies to be deployed in robot teams of different sizes. We demonstrate that LEMURS can learn interactions and cooperative behaviors from demonstrations of multi-agent navigation and flocking tasks.

Madagascar's critically endangered'singing' lemurs -- Indri indri -- have a natural ability to keep a beat, just like us humans do, a study has concluded. Researchers from the Max Planck Institute for Psycholinguistics and the University of Turin studied the songs of indri in the rainforests of the island country. They found that the lemurs' strange, wailing songs have the same kinds of universal, categorical rhythms found across human musical cultures. Outside of humans, having rhythm is a rare trait in mammals -- although it can be found elsewhere in the animal kingdom, perhaps most notably in songbirds. Madagascar's critically endangered'singing' lemurs -- Indri indri -- have a natural ability to keep a beat, just like us humans do, a study has concluded.

As you've probably noticed, Machine Learning and Artificial Intelligence are here to stay and will continue to disrupt the market. Many products have inherently integrated AI functions (i.e., Netflix's suggestions, Facebook's auto-tagging, Google's question answering), and by 2024, 69% of the manager's routine workload, will be automated, as Gartner forecasts. A lot of work has been done around designing products that make AI accessible for users, but what about designing a product that improves the AI model? How does UX approach the development of better AI? I've always been very excited about AI, and for the past couple of months, I've been working on the Product Management and UX of several highly technical and advanced AI products. In my experience, bridging the gap between the science behind Machine Learning(ML) and the end-user is a real challenge, but it's crucial and valuable.

When it comes to exploring far off planets, robots need to be able to tackle all sorts of challenges, so NASA has been working on a series of climbing robots to take on different tasks in inhospitable environments. First up is LEMUR (Limbed Excursion Mechanical Utility Robot) which can climb rock walls using hundreds of fishhooks in its fingers. It uses A.I. to navigate around obstacles that it cannot climb, and is one of NASA's first generation of climbing robots. It was developed to perform repair tasks aboard the International Space Station, and below you can see it in a field test in Death Valley, California. Then there's the somewhat terrifying-looking Ice Worm, which was adapted from one of LEMUR's limbs.

Robots can drive on the plains and craters of Mars, but what if we could explore cliffs, polar caps and other hard-to-reach places on the Red Planet and beyond? Designed by engineers at NASA's Jet Propulsion Laboratory in Pasadena, California, a four-limbed robot named LEMUR (Limbed Excursion Mechanical Utility Robot) can scale rock walls, gripping with hundreds of tiny fishhooks in each of its 16 fingers and using artificial intelligence (AI) to find its way around obstacles. In its last field test in Death Valley, California, in early 2019, LEMUR chose a route up a cliff while scanning the rock for ancient fossils from the sea that once filled the area. For Climbing Robots, the Sky's the Limit: The climbing robot LEMUR rests after scaling a cliff in Death Valley, California. The robot uses special gripping technology that has helped lead to a series of new, off-roading robots that can explore other worlds.Credit: NASA/JPL-Caltech LEMUR was originally conceived as a repair robot for the International Space Station.