 |
|
|
|||||
With satellite mega-constellations like SpaceXs Starlink deploying thousands of spacecraft, monitoring their health has become an enormous challenge. Traditional methods can’t easily scale to handle the volume, and a single failure can cost millions. James Murphy, AI engineering lead at Irish space technology company Réaltra Space Systems, is addressing this by teaching artificial intelligence to spot satellite failures before they happen. His lightweight AI models run directly on spacecraft, catching subtle warning signs while reducing the data bottleneck between space and Earth. Murphy, who holds a Ph.D. in machine learning for satellite anomaly detection, worked on camera systems for Europe’s Ariane rockets that captured iconic images of the James Webb Space Telescopes separation. Hes also collaborating with Dr. Norah Patten, expected to become Irelands first person in space. Fast Company spoke with Murphy about preventing space disasters, autonomous spacecraft, and what science fiction gets wrong about AI in space. The conversation has been edited for length and clarity. What exactly is satellite anomaly detection and why is it critical for modern space missions? Satellite anomaly detection is the process of identifying unusual or unexpected behavior in a satellites systems, such as power, communications, or sensors. These anomalies can result from software bugs, hardware failures, or environmental factors like radiation. Detecting them early is critical because satellites operate in harsh, remote environments where maintenance isnt possible. A small fault can quickly escalate into a mission-ending failure. Anomaly detection systems help operators spot and correct issues before they become serious, ensuring satellites stay functional, safe, and reliable. How can an AI model catch a potential satellite failure before it happens? Imagine a satellites battery is slowly degrading due to repeated charging cycles. Normally, engineers monitor voltage and temperature levels, but subtle warning signs might be too faint for humans to notice. An AI model, trained on years of satellite telemetry data, learns what healthy battery behavior looks like. One day, it spots a slight but consistent increase in temperature during charging, even though its still within safe limits. The model flags this as an anomaly. Engineers investigate and discover early signs of thermal runaway. They adjust the satellites power schedule, preventing a potential battery failure that couldve crippled the mission. With SpaceX, Blue Origin, and other companies rapidly expanding space operations, how is AI impacting the competitive landscape? AI is enabling autonomy and scalability. With mega-constellations like SpaceXs Starlink and Amazons Kuiper deploying thousands of satellites, the workload on human operators becomes overwhelming. AI-driven anomaly detection reduces this burden by continuously monitoring telemetry, flagging unusual behavior, and even initiating automated responses. This allows operators to focus on critical issues rather than sifting through routine data. By maintaining reliability across large fleets with minimal human oversight, AI not only ensures mission success at scale but also gives both major players and emerging companies a competitive edge in managing complex space systems. Youve worked on making AI models lightweight enough to run directly on satellites. Why does this matter for the space industry, and what business advantages does it create? Running AI models directly on satellites matters because it enables real-time decision-making and overcomes a major bottleneck in space missions: limited data transmission. Satellites generate far more data than they can send to Earth due to strict link budget constraints, which limit bandwidth, power, and communication time. By processing data onboard, AI can filter, analyze, and prioritize the most valuable insights before transmission. This amplifies usable data for the models, speeds up response times, and cuts operational costs. However, there are trade-offs. More complex AI models typically require more powerful, and power-hungry, hardware, which is a major limitation in space. My work focuses on designing lightweight, efficient models optimized for constrained environments, balancing performance and power. This allows satellites to run AI models in orbit without exceeding power budgets, enabling deployment on smaller missions. What data shows the impact so far of this new tech? Its hard to come up with numbers yet, because its like youve developed the first iPhone. Will it take off, or will it not? Is your money on Apple or Nokia? Its a generational leap, but it requires that level of trust within the community. In terms of time and money saving, you’re looking at potentially upwards of 90% cost savings when it comes to mission operations. You’re working closely with the woman expected to be Ireland’s first person in space. How are you collaborating, what have you learned from that, and what does it mean for Ireland’s role in the global space economy? Working with Dr. Norah Patten has been an incredibly inspiring and grounding experience. Our collaboration focuses on translating complex space technology, like AI-enabled satellite systems, into tangible outreach, education, and innovation opportunities. Dr Patten brings a powerful public-facing perspective and a deep understanding of the human side of spaceflight, while I contribute from the technical and mission design side. What Ive learned is the importance of communicating advanced technologies in ways that excite and engage people beyond the space sector. Its also a signal that Ireland is stepping into a more visible role in the global space economy, not just as a contributor, but as a leader in cutting-edge areas like AI and human spaceflight. This expansion of the space industry in Ireland has also brought more commercial opportunities for companies like Réaltra. Looking ahead 510 years, how do you see AI impacting space exploration? What new capabilities might we expect to see? I believe well see spacecraft with far greater autonomy, driven by AI systems capable of adapting to unexpected conditions without waiting for instructions from Earth. This will be essential for deep-space missions, where communication delays make human-in-the-loop control impractical. AI will manage satellite mega-constellations, optimize data flows, and detect and respond to anomalies at scale. Onboard science processing will become standard, turning raw sensor data into actionable insights before its ever downlinked. Ultimately, I believe AI will serve as the co-pilot of space missionsenabling smarter, faster, and more cost-effective exploration, and allowing us to go further than ever before. What factors will be most crucial to AIs future role in space? Something I always bring up about AI for Space is trustworthiness. As we move toward more autonomous spacecraft and decision-making onboard, trust becomes just as critical as performance. These systems need to be reliable, explainable, and resilient to unexpected behavior or edge cases. Its not enough for an AI model to work in lab conditions, it has to prove itself in harsh, unpredictable environments with no room for error. Building that trust, through transparency, testing and robustness, is, in my view, one of the most important challenges facing the future of AI for Space. What’s the biggest misconception people have about AI in space, and what reality would surprise them most? The biggest misconception is that AI in space is still a future concept, or that it requires sentient robots like those in science fiction. In reality, what surprises most people is just how widespread AI already is in an industry thats traditionally years behind the tech curve. From autonomous docking on the International Space Station to Earth observation satellites analyzing data in real time, and even the Ingenuity drone exploring Mars, AI is already active across the solar system. What has science fiction gotten right about the role of AI in space travel, what has it gotten wrong, and where do you draw inspiration from beyond your own research arena? While were still far from having a HAL 9000 on board, science fiction has been remarkably accurate in predicting the rise of autonomy in space travel. One of the best real-world examples is NASAs Ingenuity drone on Mars. Thanks to AI-driven autonomy, it has surveyed vastly more terrain than all Mars rovers combined, without real-time human control. This kind of autonomy is essential when dealing with communication delays or limited contact windows. Beyond that, AI is playing a growing role in autonomous docking, and even space debris removal. Where sci-fi often gets it wrong is in assuming AI must be fully sentient to be useful. In reality, narrow AI, designed for specific tasks, is already transforming space missions.
Category:
E-Commerce
As a technologist, and a serial entrepreneur, Ive witnessed technology transform industries from manufacturing to finance. But Ive never had to reckon with the possibility of technology that transforms itself. And thats what we are faced with when it comes to AIthe prospect of self-evolving AI. What is self-evolving AI? Well, as the name suggests, its AI that improves itselfAI systems that optimize their own prompts, tweak the algorithms that drive them, and continually iterate and enhance their capabilities. Science fiction? Far from it. Researchers recently created the Darwin Gödel Machine, which is a self-improving system that iteratively modifies its own code. The possibility is real, its closeand its mostly ignored by business leaders. And this is a mistake. Business leaders need to pay close attention to self-evolving AI, because it poses risks that they must address now. Self-Evolving AI vs. AGI Its understandable that business leaders ignore self-evolving AI, because traditionally the issues it raises have been addressed in the context of artificial general intelligence (AGI), something thats important, but more the province of computer scientists and philosophers. In order to see that this is a business issue, and a very important one, first we have to clearly distinguish between the two things. Self-evolving AI refers to systems that autonomously modify their own code, parameters, or learning processes, improving within specific domains without human intervention. Think of an AI optimizing supply chains that refines its algorithms to cut costs, then discovers novel forecasting methodspotentially overnight. AGI (Artificial General Intelligence) represents systems with humanlike reasoning across all domains, capable of writing a novel or designing a bridge with equal ease. And while AGI remains largely theoretical, self-evolving AI is here now, quietly reshaping industries from healthcare to logistics. The Fast Take-Off Trap One of the central risks created by self-evolving AI is the risk of AI take-off. Traditionally, AI take-off refers to the process by which going from a certain threshold of capability (often discussed as “human-level”) to being superintelligent and capable enough to control the fate of civilization. As we said above, we think that the problem of take-off is actually more broadly applicable, and specifically important for business. Why? The basic point is simpleself-evolving AI means AI systems that improve themselves. And this possibility isnt restricted to broader AI systems that mimic human intelligence. It applies to virtually all AI systems, even ones with narrow domains, for example AI systems that are designed exclusively for managing production lines or making financial predictions and so on. Once we recognize the possibility of AI take off within narrower domains, it becomes easier to see the huge implications that self-improving AI systems have for business. A fast take-off scenariowhere AI capabilities explode exponentially within a certain domain or even a certain organizationcould render organizations obsolete in weeks, not years. For example, imagine a companys AI chatbot evolves from handling basic inquiries to predict and influence customer behavior so precisely that it achieves 80%+ conversion rates through perfectly timed, personalized interactions. Competitors using traditional approaches cant match this psychological insight and rapidly lose customers. The problem generalizes to every area of business: within months, your competitors operational capabilities could dwarf yours. Your five-year strategic plan becomes irrelevant, not because markets shifted, but because of their AI evolved capabilities you didnt anticipate. When Internal Systems Evolve Beyond Control Organizations face equally serious dangers from their own AI systems evolving beyond control mechanisms. For example: Monitoring Failure: IT teams cant keep pace with AI self-modifications happening at machine speed. Traditional quarterly reviews become meaningless when systems iterate thousands of times per day. Compliance Failure: Autonomous changes bypass regulatory approval processes. How do you maintain SOX compliance when your financial AI modifies its own risk assessment algorithms without authorization? Security Failure: Self-evolving systems introduce vulnerabilities that cybersecurity frameworks werent designed to handle. Each modification potentially creates new attack vectors. Governance Failure: Boards lose meaningful oversight when AI evolves faster than they can meet or understand changes. Directors find themselves governing systems they cannot comprehend. Strategy Failure: Long-term planning collapses as AI rewrites fundamental business assumptions on weekly cycles. Strategic planning horizons shrink from years to weeks. Beyond individual organizations, entire market sectors could destabilize. Industries like consulting or financial servicesbuilt on information asymmetriesface existential threats if AI capabilities spread rapidly, making their core value propositions obsolete overnight. Catastrophizing to Prepare In our book TRANSCEND: Unlocking Humanity in the Age of AI, we propose the CARE methodologyCatastrophize, Assess, Regulate, Exitto systematically anticipate and mitigate AI risks. Catastrophizing isnt pessimism; its strategic foresight applied to unprecedented technological uncertainty. And our methodology forces leaders to ask uncomfortable questions: What if our AI begins rewriting its own code to optimize performance in ways we dont understand? What if our AI begins treating cybersecurity, legal compliance, or ethical guidelines as optimization constraints to work around rather than rules to follow? What if it starts pursuing objectives, we didn’t explicitly program but that emerge from its learning process? Key diagnostic questions every CEO should ask so that they can identify organizational vulnerabilities before they become existential threats are: Immediate Assessment: Which AI systems have self-modification capabilities? How quickly can we detect behavioral changes?What monitoring mechanisms track AI evolution in real-time? Operational Readiness: Can governance structures adapt to weekly technological shifts? Do compliance frameworks account for self-modifying systems? How would we shut down an AI system distributed across our infrastructure? Strategic Positioning: Are we building self-improving AI or static tools? What business model aspects depend on human-level AI limitations that might vanish suddenly? Four Critical Actions for Business Leaders Based on my work with organizations implementing advanced AI systems, here are five immediate actions I recommend: Implement Real-Time AI Monitoring: Build systems tracking AI behavior changes instantly, not quarterly. Embed kill switches and capability limits that can halt runaway systems before irreversible damage. Establish Agile Governance: Traditional oversight fails when AI evolves daily. Develop adaptive governance structures operating at technological speed, ensuring boards stay informed about system capabilities and changes. Prioritize Ethical Alignment: Embed value-based constitutions into AI systems. Test rigorously for biases and misalignment, learning from failures like Amazons discriminatory hiring tool. Scenario-Plan Relentlessly: Prepare for multiple AI evolution scenarios. Whats your response if a competitors AI suddenly outpaces yours? How do you maintain operations if your own systems evolve beyond control? Early Warning Signs Every Executive Should Monitor The transition from human-guided improvement to autonomous evolution might be so gradual that organizations miss the moment when they lose effective oversight. Therefore, smart business leaders are sensitive to signs that reveal troubling escalation paths: AI systems demonstrating unexpected capabilities beyond original specifications Automated optimization tools modifying their own parameters without human approval Cross-system integration where AI tools begin communicating autonomously Performance improvements that accelerate rather than plateau over time Why Action Cant Wait As Geoffrey Hinton has warned, unchecked AI development could outstrip human control entirely. Companies beginning preparation nowwith robust monitoring systems, adaptive governance structures, and scenario-based strategic planningwill be best positioned to thrive. Those waiting for clearer signals may find themselves reacting to changes they can no longer control.
Category:
E-Commerce
Business is a team sport, and it’s nice to have the camaraderie of laughing, grinding toward deadlines, and even gossiping with your teammates. But when youre the boss, youre not just one of the creweven if youd like the easy camaraderie shared among people who arent calling the shots. What happens when layoffs are approaching, or the company is facing budget cuts? You may feel lonelyyou know whats coming but lack peers to confide in or commiserate with. Then there are the everyday stressors that come with leadership, like giving feedback or telling someone they won’t be getting the promotion. It can be lonely at the top. If you miss being part of the team, here are some actions you can take. Accept your position and the restrictions that come with it As a leader, there are many things you wont be able to share with the folks on your teamand thats just the way it is. For example, you may feel jealousy when you see them laughing and having a good time while youre stuck doing the budgeting. Dont fight these feelings; acknowledge them. Accept the reality that youre the leader, and that many times youll have to stand alone. Find a trusted adviser Even though youre the boss, you still need someone to bounce ideas off of: you cant live in a silo. Find a person who shares your philosophies regarding business, leadership, and people. Establish a consistent cadence and routine for working with your adviser outside of your company. Note that this should be a reciprocal relationship; offer your ideas and opinions to your adviser when asked. Be someone in whom they can confide. Find appropriate times to pop in Just because youre the boss doesn’t mean you cant have any part of the day-to-day team operations. Find instances where you can pop in and be part of the team. Be judicious about thisfor example, you probably dont want to hop in on a meeting the team can handle on their own. An appropriate time to join the team may be when everyone is working toward a deadline and the load is intense. Otherwise, be present, but not overbearing. You have a new team group Even though youre the boss, youre not completely alone. You have new peersother managers, or the executive leadership team. Everything evolves and changes; you can have fun with this new group, too. Look for opportunities to connect, even if you miss your old team. You will evolve as a leader. Being the boss can be a great new opportunity, even if you miss the camaraderie that came without that title. Instead of longing for what was, make the most of your position and forge new relationships among your peers in leadership.
Category:
E-Commerce
All news |
||||||||||||||||||
|
||||||||||||||||||