AI Pioneer Yuval Zukerman Honored by Space Foundation

After months of focused effort, I’m excited to finally share an update on Mission DockBot 🚀 Since October, we have been developing an Autonomous Rendezvous & Docking System designed for space missions. The goal is to enable spacecraft to intelligently approach, align, and dock with precision while accounting for real-world orbital dynamics. So far, we have successfully: • Modeled and validated the trajectory planning algorithms • Simulated environmental effects such as orbital perturbations and uncertainties • Built the core system architecture for autonomous decision-making • Ensured the project remains aligned with mission objectives and technical feasibility Working on this project has been an incredible learning experience in AI, control systems, orbital mechanics, and simulation engineering. Grateful to be part of something that contributes toward the future of intelligent space systems. Still a long way to go — but we are on the right trajectory. 🌌 Wants to know more about the or collaborate Dm me Wants to hear from ISRO - Indian Space Research Organization #AI #SpaceTech #AutonomousSystems #OrbitalMechanics #MachineLearning #Innovation #Engineering #DockingSystem

New Episode: From NASA's Mars Rover to Voice AI Savannah Cofer was one of the last people to physically touch the Mars 2020 rover before it launched. Now she's building earbuds that let you talk to your computer without anyone hearing you. In this episode, Savannah shares: - How contamination control on the Mars rover led her to machine learning - Why Voice Buds are built for speaking, not listening - The CES demo that was so loud, security showed up - Why hardware is the new moat in an AI world where software barriers are falling every week - The 150 customer interviews that shaped their product Savannah and her co-founders at Subtle Computing went from Stanford's accessibility lab to launching at CES 2026 with partnerships with Qualcomm and Nothing Technologies — proving that deep research can become a real consumer product. Whether you're a founder navigating the research-to-product journey or are interested in the future of voice-first computing, this one's worth your time. Watch the full episode https://lnkd.in/eSvtYj9h #VoiceAI #HardwareStartups #DeepTech #Accessibility #FounderStory

Deep space changes the role of AI. Onboard Orion, AI is doing what was physically impossible in the Apollo era. ● Orion's computers run 20,000 times faster than the Apollo Guidance Computer and carry 128,000 times more memory.  ● NEC's SIAT system analysed data from nearly 150,000 sensors during ground testing and established over 22 billion logical relationships between systems, a model that now underlies how future Orion vehicles are monitored.  ● In flight, Reid Wiseman sits in the left seat not to fly manually, but to oversee automated systems and step in when human judgment is required. Then there is radiation.  Beyond Earth's orbit, the planet's magnetic field no longer protects the crew. NASA is testing two models from the University of Michigan: a machine-learning model that forecasts radiation threats 24 hours ahead, and a physics-based model that simulates solar particle behaviour in the corona, where acceleration begins. NASA reserved 3,000 supercomputer nodes to run them, because a delay here is literally life-threatening. Also onboard is AVATAR. The experiment uses organ-on-a-chip devices containing bone marrow cells from the astronauts themselves, flying alongside the crew and exposed to the same deep-space radiation. After the mission, researchers will analyse how spaceflight affected those cells at the molecular level, data that will be critical for future Mars expeditions, where radiation doses will be significantly higher. This points to a central issue.  A signal takes 1.3 seconds one way to reach the Moon.  To Mars, between 3 and 22 minutes. The further from Earth, the more essential it becomes for onboard systems to decide autonomously.  Every such decision on this mission is, in effect, training AI for a future where real-time control from Earth will simply be impossible. The directions ahead are clear:  – AI monitoring of crew psychological state during long missions;  – autonomous robots preparing lunar bases before crews arrive; – real-time analysis of geological data to optimise resource extraction at the lunar south pole;  – orbital traffic management systems, developed by companies like LeoLabs and Neuraspace They are becoming critical infrastructure as orbit grows ever more crowded. Artemis II is a test not only of a rocket and a spacecraft.  It is a test of a new principle: AI not as a support tool, but as part of the crew.  And the further humanity flies from Earth, the more important that principle becomes. How much autonomy should AI really have onboard?

🚀 An AI just autonomously drove a robot on Mars. No human planned the route. And the way it worked will blow your mind. For 28 years, every Mars rover drive has been manually planned by NASA engineers. Every meter carefully calculated: terrain risk, wheel stress, energy usage, sand traps, boulders — because one wrong move could strand a $2.7B rover. Then in late 2025, something changed. An AI system (Claude, integrated with NASA workflows) was tasked with generating a navigation plan for Perseverance rover in Jezero Crater. But this wasn’t a chatbot suggesting ideas. It was generating Rover Markup Language (RML) — the actual command instructions sent to a robot on another planet. Here’s what makes it fascinating: 🛰️ It analyzed HiRISE orbital images like human planners do 🧭 It identified hazards: bedrock, dunes, boulder fields 📍 It broke a 456-meter journey into safe, structured waypoints ⚙️ It validated each step against rover constraints (turn radius, power, wheel load) 🧪 NASA then tested the full plan in a digital twin simulation before sending it to Mars The result? The AI-generated route worked. Only minor human adjustments were needed. Perseverance successfully drove 689 feet on Sol 1707, followed by another 807 feet later, following AI-designed paths across Martian terrain. No joystick control. No human-drawn route. Just autonomous planning → simulation → execution… 140 million miles away. And the deeper shift is this: Mars already has a ~20-minute communication delay. Beyond Mars — Europa, Titan, outer solar system — real-time human driving becomes impossible. Autonomous navigation isn’t optional anymore. It’s the only scalable path forward. What’s striking isn’t just that AI planned a rover path. It’s that the same systems used for coding, reasoning, and analysis on Earth are now being trusted to navigate another planet. So the question becomes: If AI can safely plan movement on Mars… what else can it autonomously operate beyond human supervision? Source: NASA JPL — https://lnkd.in/g-dkbgUU #ArtificialIntelligence #NASA #SpaceExploration #AIInnovation #MachineLearning #Mars #LLM #Anthropic #FutureTech #AIAgents #MLOps #LLMOps

NASA scientist Vandi Verma, leading Mars’ first AI-driven rover operations, marks a breakthrough in how we explore and interact with space environments. Unlike traditional missions that rely heavily on human instructions from Earth, AI-powered systems enable rovers to make real-time decisions, navigate autonomously, and adapt to unpredictable terrain, all while operating millions of miles away. This shift dramatically improves mission efficiency and opens the door to more complex and ambitious space exploration. What makes this milestone powerful is not just the technology, but the transition from remote control to intelligent autonomy, where machines can sense, decide, and act independently in extreme conditions. This is a glimpse into a future where AI doesn’t just assist exploration, it leads it. Agentic AI systems move from support roles to autonomous execution, driving faster decisions and smarter outcomes. The future of AI isn’t just intelligent. It’s autonomous, adaptive, and boundary-breaking. Visit: snsihub.ai NASA - National Aeronautics and Space Administration SpaceX Google Microsoft NVIDIA IBM #businessgrowth #scalability #enterpriseai #digitaltransformation #innovation #artificialintelligence #agenticai #machinelearning #fintech #digitaltransformation #enterpriseai #predictiveanalytics #innovation #datadriven #futureofwork#futureofwork #automation #decisionintelligence#ai #spaceai #nasa #autonomoussystems #innovation #futureofai #techinnovation #agenticai #snssquare

Day 26 of #0to100xEngineer AI just drove a rover on Mars. And I need a moment. NASA's Perseverance rover completed its first ever Mars drives planned entirely by AI. It used Claude's vision-language models to analyse orbital imagery and terrain data and autonomously generate safe waypoints. Two drives. 456 metres. Zero human planning. Yahoo Finance A task human operators had done manually for 28 years. Gone. Let that sink in for a second. What actually happened The AI looked at images from orbit. Understood the terrain. Decided the safest path. Sent the instructions. The rover moved. No human in the loop for the navigation decision. Just an AI model reading a planet from space and choosing where to go. Why this hit different for me 25 days ago I was learning what a diffusion model was. Today I am reading about the same category of AI technology being used to navigate another planet autonomously. We talk about Gen AI for content, for ads, for code. But the same foundation — vision models, language models, multi-modal reasoning — is now making decisions on Mars. What this means for engineers learning AI today The engineers who built this did not just know how to use AI tools. They understood the fundamentals deeply enough to trust an AI model with a 456 metre drive on terrain 140 million miles away. That level of understanding starts exactly where we started. One concept at a time. Day 26 done. If AI can navigate Mars autonomously what do you think it will do in your industry in the next 3 years? #GenAI #0to100xEngineer #NASA #Mars #Claude #AIEngineering #LearningInPublic #BuildInPublic #MachineLearning #FutureOfAI

NASA just launched humans orbiting the Moon again after 54 years — and they’re snapping pics with iPhones in space! Welcome to AI News in a Minute, brought to you by Tavus, the future of interactive video. I'm Jerome W Dewald. This is Sunday, April 5, 2026. NASA's Artemis II launched on April 1, marking the first crewed lunar mission in more than half a century. The crew will orbit the Moon to test spacecraft systems without landing. Surprisingly, astronauts are capturing space photos using NASA-qualified iPhone 17 Pro Max devices. Gene therapy just shook up medicine with a small trial reversing deafness. Injecting the OTOF gene into inner ears of 10 patients drastically improved hearing, cutting average sound detection from 106 to 52 decibels. This breakthrough paves the way for tackling other deafness genes. Rutgers researchers created a biometric login system using unique skull vibrations from breathing and heartbeat. Tested with 52 users over 10 months, it achieved over 95% accuracy. This tech promises seamless, secure logins for VR and AR headsets. In the UK, an AI-powered 'Litter Cam' spots drivers tossing rubbish from cars and automatically issues fines. This smart system tackles anonymous littering with AI automation, aiming to keep streets cleaner without human intervention. A portable clip for smartphones tests water contamination in under a minute using a glowing test strip. Perfect for emergency responders and communities in need, this rapid tech detects harmful water markers fast and on the go. That was your AI News in a Minute with Jerome W Dewald. Stay ahead in the AI race! Leave your thoughts about Which breakthrough excites you most? in the comments, give this video a like, and hit subscribe so you don't miss tomorrow's AI News in a Minute! https://lnkd.in/eWfVQUjU

NASA just launched a telescope 100 times more powerful than Hubble. The Nancy Grace Roman Space Telescope will map billions of galaxies, measure dark energy, and discover thousands of exoplanets. All from 1.5 million kilometres away. But here's what caught our attention: the technology that makes this possible isn't new. It's the same infrared imaging, data processing, and pattern recognition that's been quietly transforming industries on Earth. Including property. The same AI that helps astronomers identify patterns in cosmic data is helping developers identify patterns in buyer behaviour. Which units get the most attention. Which price points trigger engagement. Which marketing channels actually convert. The difference? NASA spent $4.3 billion building their data machine. Developers can access similar analytical power for a fraction of that, and most still choose to rely on gut feel and spreadsheets. We're living in an era where we can map the observable universe in unprecedented detail. Surely we can do better than guessing which apartment floor plan will sell. #Space #AI #PropTech #DataDriven #PropertyDevelopment

Yesterday, I had the opportunity to present PitIA at the European Space AI Workshop (ESAW 2026), representing GMV and our work on AI‑driven spacecraft operations. PitIA is an unsupervised anomaly detection system designed to tackle one of the key challenges in modern space missions: the reliable and scalable monitoring of large volumes of multivariate telemetry, without requiring labeled data or continuous operator intervention. During the presentation, I shared how PitIA: - Leverages Multivariate Statistical Process Control (MSPC) and PCA, adapted from industrial process monitoring to real spacecraft telemetry - Was evaluated on the official ESA anomaly detection benchmark, using real mission data and operationally relevant metrics - Achieves competitive results in precision, early detection, and contextual accuracy, particularly in high‑dimensional telemetry scenarios and Mission 2 - Supports a realistic operational deployment, with continuous monthly retraining, low computational cost, and fully autonomous operation 📄 The results presented are detailed in the ESA publication (doi: 10.2760/2119408, pp. 57–60), demonstrating PitIA’s suitability for unattended use in mission control environments. Many thanks to European Space Agency - ESA and the ESAW community for the opportunity to share our work and exchange ideas. At GMV, we continue working to bring robust, explainable, and operationally grounded AI from research into real mission operations. #ESAW2026 #PitIA #AnomalyDetection #SpaceOperations #Telemetry #AIForSpace #UnsupervisedLearning #GMV

A robot on Mars just fired its human drivers. Not metaphorically. NASA's Perseverance rover completed the first Mars drives ever planned entirely by AI last week. No human in the loop. It analyzed orbital photos, picked safe routes, generated waypoints, and drove 456 meters across Martian terrain on its own. The AI doing the navigation? Anthropic's Claude. Yes. The same family of models people use to summarize meeting notes is now trusted to steer a $2.7 billion rover across another planet. Sit with that for a second. We spent years debating if AI could reliably write a for-loop. Meanwhile, NASA handed it the keys to one of humanity's most expensive machines — on a planet where "oops, rollback" is not an option. This is what actually matters about AI right now. Not benchmark scores. Not who raised the most money. But the moment when someone decides the technology is reliable enough for zero-margin-of-error work. Most teams I talk to are still in "let's pilot this internally" mode. Running safe experiments. Waiting for the models to get better. NASA just deployed it 225 million kilometers from the nearest debugger. The gap between "interesting demo" and "mission-critical production" closed while we were all arguing about AGI timelines on Twitter. What's stopping your team from trusting AI with the hard stuff?