Former Deputy Administrator of NASA says collaboration is the key to space exploration. The STS-92 crew exits the Operations and Checkout Building on their way to the Astrovan and Launch Pad 39A for a simulated countdown in 2000. Breakthroughs, discoveries, and DIY tips sent every weekday. Pamela Ann Melroy's life has been defined by challenges that continually pushed the boundaries of what was possible. She is a veteran astronaut who flew on three Space Shuttle missions and played a leading role in building the International Space Station. She later served as the Deputy Administrator of NASA.

--As Deep Neural Networks (DNNs) continue to drive advancements in artificial intelligence, the design of hardware accelerators faces growing concerns over embodied carbon footprint due to complex fabrication processes. In this work, we propose a carbon-efficient design methodology for 3D DNN accelerators, leveraging approximate computing and genetic algorithm-based design space exploration to optimize Carbon Delay Product (CDP). By integrating area-efficient approximate multipliers into Multiply-Accumulate (MAC) units, our approach effectively reduces silicon area and fabrication overhead while maintaining high computational accuracy. Experimental evaluations across three technology nodes (45nm, 14nm, and 7nm) show that our method reduces embodied carbon by up to 30% with negligible accuracy drop. The rapid growth of Artificial Intelligence (AI) has resulted in the wide adoption of Deep Neural Networks (DNNs) as a fundamental component of modern computing systems. To efficiently support the computational demands of DNNs, specialized hardware accelerators have been developed, offering significant improvements in throughput and energy efficiency. These accelerators have enabled AI deployment across a wide range of environments, from large-scale data centers to resource-constrained edge devices.

Watermarking AI-generated text is critical for combating misuse. Yet recent theoretical work argues that any watermark can be erased via random walk attacks that perturb text while preserving quality. However, such attacks rely on two key assumptions: (1) rapid mixing (watermarks dissolve quickly under perturbations) and (2) reliable quality preservation (automated quality oracles perfectly guide edits). Through large-scale experiments and human-validated assessments, we find mixing is slow: 100% of perturbed texts retain traces of their origin after hundreds of edits, defying rapid mixing. Oracles falter, as state-of-the-art quality detectors misjudge edits (77% accuracy), compounding errors during attacks. Ultimately, attacks underperform: automated walks remove watermarks just 26% of the time -- dropping to 10% under human quality review. These findings challenge the inevitability of watermark removal. Instead, practical barriers -- slow mixing and imperfect quality control -- reveal watermarking to be far more robust than theoretical models suggest. The gap between idealized attacks and real-world feasibility underscores the need for stronger watermarking methods and more realistic attack models.

Recent Large Language Models (LLMs) such as OpenAI o3-mini and DeepSeek-R1 use enhanced reasoning through Chain-of-Thought (CoT). Their potential in hardware design, which relies on expert-driven iterative optimization, remains unexplored. This paper investigates whether reasoning LLMs can address challenges in High-Level Synthesis (HLS) design space exploration and optimization. During HLS, engineers manually define pragmas/directives to balance performance and resource constraints. We propose an LLM-based optimization agentic framework that automatically restructures code, inserts pragmas, and identifies optimal design points via feedback from HLs tools and access to integer-linear programming (ILP) solvers. Experiments compare reasoning models against conventional LLMs on benchmarks using success rate, efficiency, and design quality (area/latency) metrics, and provide the first-ever glimpse into the CoTs produced by a powerful open-source reasoning model like DeepSeek-R1.

Moon exploration is undergoing a potentially transformative moment. Over the course of six weeks, three different lunar landers began a rocket-fueled space journey to learn more about Earth's nearest neighbor. All three landers are operated by private, and relatively newly-formed companies. That's a marked shift away from space exploration of the 20th century, which was dominated by state-backed, public institutions like NASA. If they complete their missions, these space upstarts could help pave the way for future planned human moon missions, and possibly, even a not-too distant lunar economy.

Since the 1970s, we've sent a lot of big things to Mars. But when NASA successfully sent twin Mars Cube One spacecraft, the size of cereal boxes, in November 2018, it was the first time we'd ever sent something so small. Just making it this far heralded a new age in space exploration. NASA and the community of planetary science researchers caught a glimpse of a future long sought: a pathway to much more affordable space exploration using smaller, cheaper spacecraft. RIP Roberta Flack, one of the realest to ever do it.

Robot locomotion learning using reinforcement learning suffers from training sample inefficiency and exhibits the non-understandable/black-box nature. Thus, this work presents a novel SME-AGOL to address such problems. Firstly, Sequential Motion Executor (SME) is a three-layer interpretable neural network, where the first produces the sequentially propagating hidden states, the second constructs the corresponding triangular bases with minor non-neighbor interference, and the third maps the bases to the motor commands. Secondly, the Adaptable Gradient-weighting Online Learning (AGOL) algorithm prioritizes the update of the parameters with high relevance score, allowing the learning to focus more on the highly relevant ones. Thus, these two components lead to an analyzable framework, where each sequential hidden state/basis represents the learned key poses/robot configuration. Compared to state-of-the-art methods, the SME-AGOL requires 40% fewer samples and receives 150% higher final reward/locomotion performance on a simulated hexapod robot, while taking merely 10 minutes of learning time from scratch on a physical hexapod robot. Taken together, this work not only proposes the SME-AGOL for sample efficient and understandable locomotion learning but also emphasizes the potential exploitation of interpretability for improving sample efficiency and learning performance.

Although wheeled robots have been predominant for planetary exploration, their geometry limits their capabilities when traveling over steep slopes, through rocky terrains, and in microgravity. Legged robots equipped with grippers are a viable alternative to overcome these obstacles. This paper proposes a gripping system that can provide legged space-explorer robots a reliable anchor on uneven rocky terrain. This gripper provides the benefits of soft gripping technology by using segmented tendon-driven fingers to adapt to the target shape, and creates a strong adhesion to rocky surfaces with the help of microspines. The gripping performances are showcased, and multiple experiments demonstrate the impact of the pulling angle, target shape, spine configuration, and actuation power on the performances. The results show that the proposed gripper can be a suitable solution for advanced space exploration, including climbing, lunar caves, or exploration of the surface of asteroids.

A few months ago, I flew to Houston to visit a small startup called Lunar Resources, which aspires to build the largest telescope in the solar system--not on Earth but on the far side of the moon. Houston is nicknamed Space City; on the ride from the airport, I passed the ballpark where the Astros play, and, outside a McDonald's on East NASA Parkway, I saw a giant sculpture of an astronaut holding French fries. I found Lunar Resources in a boxy building where the company leases square footage from the aerospace contractor Lockheed Martin. Elliot Carol, the C.E.O. and co-founder of Lunar Resources, is thirty-three, with a cherubic face and curly hair speckled with gray. Although he grew up in Connecticut and previously worked as a hedge-fund manager, he was wearing black cowboy boots.

In the field of space exploration, floating platforms play a crucial role in scientific investigations and technological advancements. However, controlling these platforms in zero-gravity environments presents unique challenges, including uncertainties and disturbances. This paper introduces an innovative approach that combines Proximal Policy Optimization (PPO) with Model Predictive Control (MPC) in the zero-gravity laboratory (Zero-G Lab) at the University of Luxembourg. This approach leverages PPO's reinforcement learning power and MPC's precision to navigate the complex control dynamics of floating platforms. Unlike traditional control methods, this PPO-MPC approach learns from MPC predictions, adapting to unmodeled dynamics and disturbances, resulting in a resilient control framework tailored to the zero-gravity environment. Simulations and experiments in the Zero-G Lab validate this approach, showcasing the adaptability of the PPO agent. This research opens new possibilities for controlling floating platforms in zero-gravity settings, promising advancements in space exploration.