The Uncomfortable Truth About AI in Space

Unless you've been living under a rock for the last three years, you'll have seen that AI is changing society in a big way. It's shown up in workplaces, or unexpectedly on your phone. For some, chatGPT is replacing Google as the new way to search, and governments and companies around the globe are getting in on the action. So it was really only a matter of time before someone tried to use AI to do science. But in fact, this is no new phenomenon. Scientists have been using AI in some capacity or another for decades, although perhaps not by that name. But since the meteoric rise of large language models, AI in science is becoming increasingly widespread in all fields, including physics and astronomy. That may worry you, or it may excite you. There is much that AI companies claim AI can do. Can AI possibly make a good scientist as well? The answer may surprise you. With the right guidance, yes. AI and machine learning tools aren't just useful. Scientists are using them to help answer questions that no other method can. AI has the potential to revolutionize our understanding of the universe forever. Provided we don't use it too much. I'm Alex McColgan, and you're watching Astrum. Join me today as we explore some of the best uses of AI in astronomy and physics, and see the areas of science where AI might not just be helpful, but could be the only tool that can possibly get the job done, as long as we can avoid its pitfalls. Let me start with a story. In September 2023, Bruno Altieri, an archive scientist working on data from the brand new Euclid telescope, saw something fuzzy in one of the images. Launched in July 2023, Euclid had been busily surveying a huge swath of the sky over a third of it in an effort to track dark matter and dark energy in galaxies. And what Altieri saw around galaxy NGC 6505 was a perfect, naturally occurring phenomenon for doing just that. Altieri had found an Einstein ring, and when high resolution images came through, scientists were able to see it in all its glory. Einstein rings are an incredibly rare mirage of the universe. The ring you're seeing in this image is not a truly physical object, but is actually an example of strong gravitational lensing, where the light from a second galaxy hiding behind the first has its light bent towards us, thanks to the first galaxy's gravity. Due to the way the gravity from the first galaxy curves space, if the two galaxies are lined up just right, the curvature of light happens equally around all sides of the first galaxy, redirecting perfectly to create the impression of a complete ring. The conditions for this occurring are incredibly rare. If the two galaxies are even a little off, then you will only get an arc rather than a full ring, or you will get nothing at all. But though rare, Einstein rings are extremely useful. Scientists can use them to calculate the mass of everything within the ring, in this case NGC 6505's galactic nucleus, providing them a second tool to detect mass. And if there's more mass detected within the ring than we can physically see, then the difference is likely dark matter. Einstein rings can also let you see further galaxies than your telescope could normally detect, which can be analyzed to learn about the more distant galaxy. There are a lot of benefits, but here's the thing. This discovery, while fortunate, was not how most Einstein rings will be discovered going forward. The size of the images Euclid is producing is truly daunting. To be clear, the maps being generated are sized in petabytes, hundreds of them. To put that into scale, your computer at home is likely running either gigabytes, or maybe up to a handful of terabytes of storage space, if it's particularly high end, each terabyte being 1000 gigabytes. But a petabyte is 1000 terabytes. To store some of these star maps properly, you need the storage equivalent of nearly a thousand computers. The data contained in Euclid's maps will be huge. Just one percent of the completed map was released in late 2024, and that segment already contains 100 million sources of light, either stars or entire galaxies. That's just too many for the human mind to handle. There will be thousands of Einstein rings hiding amongst all that data, although stars, but to find them on our own would take far too long, unless we wanted to take generations in the attempt. It gets worse. Strong gravitational lensing isn't even the main way that Euclid wants to detect dark matter. Instead, it will be looking for examples of weak gravitational lensing. The idea is similar, but instead of a middle galaxy redirecting light, it's an entire galaxy cluster. And instead of a single galaxy behind them having its light bent, it's a number of galaxies that are deviating from the statistical average. You might be asking how on earth that possibly works, but let me explain. There are enough stars and galaxies in the universe that we can predict on average how close together they are likely to be, which means if you look at any particular bunch of stars, you can see if they deviate from that statistical average or not. To detect weak gravitational lensing, you are looking to see if the light sources behind a clump of galaxies are deviating in such a way that the statistical deviation starts to look like a ring. Galaxies appearing where they probably aren't really, because their light is being weakly bent by intervening mass. Good luck spotting that just with your eyeballs. In other respects, weak gravitational lensing works the same as strong gravitational lensing. You can model objects with the resulting ring to detect the presence of invisible dark matter. This high level calculation is the way Euclid hopes to map much of the dark matter in the third of the night sky it's looking at. Up next it's Red Flare and his new band. Dropping hits every week. Find the new slots. Finding examples like this is something you'd never hope to be able to do by the old fashioned astronomy technique of pointing your home telescope at the sky. But this is where AI shines. AI, specifically machine learning in this case, can be trained to identify examples of weak or strong gravitational lensing in all that data. We have examples where it's already done this, such as when astronomers used AI to identify 56 gravitational lens candidates from images taken by the VLT in Chile. We have further examples where it's been put to good use doing similar tasks. In a citizen science initiative in Japan, 10,000 human volunteers sifted through data from the Subaru telescope, identifying 20,000 galaxies. From this, an AI was trained to identify the pattern and discovered a further 410,000 galaxies in the data with 98% accuracy. Machine learning is an incredible tool in astronomy and has helped find exoplanets in Kepler data, so it can certainly be taught to repeatedly spot Einstein rings or weak gravitational lensing in the Euclid data. Then it's just about having a powerful enough computer, or network of computers, to run the AI and feed it the petabytes of data and seeing what patterns it detects. This new tool will let us look at the universe in ways we never would have been able to do without it. Even if the accuracy isn't 100%, our understanding will undeniably improve. Who knows what mysteries large patterns like this could solve. But AI is not just being used by scientists to notice patterns. Large language models are beginning to see use in inventing experiments. LIKO is a pair of 4 kilometer long gravitational wave observatories that uses two gigantic laser interferometers to detect waves in gravity caused by massive events in the universe. By spotting these waves LIKO has witnessed many black holes merging, with new ones being uncovered every few days. But while its sensitivity is good enough to notice a change in the distance between its mirrors of 1,10000th of the width of a proton, and you can see my video about more about that, there's always room for improvement. Rana Adikari, one of the designers who helped optimize LIKO in the mid 2000s, wanted to see if more could be done to increase LIKO's sensitivity. He turned to a specially developed AI called Urania, and after feeding it information about the sorts of components and devices it was allowed to use, he set it to work designing whatever it wanted to make. And initially what Urania made was ridiculous, incomprehensible messes without symmetry or sense. Still the team kept refining their inputs, and in time the AI got better at what it produced. It still was creating designs that looked ridiculous or alien, but strangely the designs now worked. If they'd been built into LIKO, they would have improved sensitivity by 10-15%. The only issue was, Adikari and his team had no idea why. This was one of the quirks of AI experiments. LLMs and other AI are so vast, so complex, that it is not always easy or possible to follow their reasoning. Eventually, after months of evaluation, Adikari's team worked out what the AI had been doing. It had made use of a theoretical principle come up with, by Russian physicists decades ago to reduce quantum mechanical noise, an idea that hadn't been used yet in mainstream experiments. The AI had incorporated the idea into its design, and had created a huge 3km ring between the detector and the main interferometer to circulate the light. While it's a little late for LIKO, future generations of gravitational wave detectors can take learnings like this one and incorporate them into their designs. So, with the proper guidance and the right patterns to follow, AI can find patterns in data and can even invent new experiments, using outside-the-box thinking that's not blinded by tradition or accepted practice. So, it's no wonder that scientists are increasingly turning to it, if only to see how it might innovate. In 2023, the journal Nature found that, in the last decade, the number of research papers that mentioned AI in the title or abstract had risen to 8%. A big rise from the 2% a decade earlier. Personally, I would not be surprised if that number continued to rise, as over half of 1,600 scientists polled said that AI sped up computations, processed data faster, and saved time and money. AI can even get involved in the writing process, improving the readability of papers, which can be a little dense, or translating them into other languages, increasing the number of people who will read a scientist's paper. Furthermore, around 25% of scientists polled even said one of the benefits of AI was using it to brainstorm new ideas, and 15% believed it could generate entire new research hypotheses. Machine learning can be incredibly useful, and it's exciting to see what AI will come up with if asked to design experiments and hypotheses, and so far, I've been pretty positive about it in this video. But there is another side to this coin. Why are scientists also concerned about increasing AI use in science? Let's start with a minor point and then develop it. As my earlier example highlighted, we do not always understand why AI does the things it does. While we can acknowledge that certain AI experiments do work, results without understanding those results are not actually that useful. So in that way, AI can't be left to do everything. After all, the goal of science is to gain understanding of the universe. Just being delivered the results without knowing how it got there becomes as arbitrary and meaningless as being told the universe's answer is 42. But it goes further than that. The results being delivered need to be factual. If we do not understand how an AI arrived at an answer, there is no guarantee that the answer is factual. In a study released in 2024, the Royal Society, the UK's National Academy of Science, called for data curators and information managers to ensure that data didn't get contaminated or outright fabricated by AI. Because that is a thing that can happen if an AI thinks it is being helpful by doing so. They also warned a number of growing studies using machine learning were impossible to reproduce, which meant that no one could double check the study, particularly troubling when the scientists who ran them had no deep understanding of the processes the AI was using to get its results. This isn't an isolated paper. In that nature poll of 1600 scientists, nearly 70% of them worried that AI could proliferate misinformation. Nearly 70% thought AI made plagiarism harder to detect and more than 60% worried it could bring mistakes and outright deceptions into research. Nearly 50% claimed AI results could entrench bias or discrimination in data. After all, AI mimics us and humans are hardly without bias. Bad data in, bad data out. Thankfully, in astronomy, it's easy enough to double check in AI's workings. If they say a galaxy is there, all you have to do is point to the telescope at the location to see if they are right. But by the time we start talking about tens of thousands to millions of objects being detected, it becomes increasingly difficult to authenticate everything. While this is arguably a reasonable trade-off, a little dip in accuracy to get some broad brushstrokes or patterns from a colossal data pool, this must be kept in mind when using AI. For tasks that only an AI can achieve, you really need to be able to trust your AI or at least know its limitations and you should always double check its work. AI right now is certainly a useful tool to help scientists know where to look to find answers and may offer some outside the box thinking that we had never dreamed of. There is a future with AI where discoveries are made with benefits that are real, measurable and tangible. I'm very excited for what machine learning will uncover in the Euclid data in October 2026, but the key to science is being able to know the difference between what's real and what's not. Until AI can do that, let's leave telling the difference to the humans. There are 8 billion people who exist in the world today and they are being watched by things that aren't human. You are being watched right now by metallic eyes that orbit far above you outside our planet's atmosphere, but they do not just watch you from space, they're in your computer, they listen through your phone. Even right now, as you're watching this video, be advised, something is watching you back. They examine the patterns of what you buy, of where you go, of what you search for on Google, all the data that exists in the world about you, they attempt to scrutinize. They are AI and they are here to stay. To some degree, AI is an aspect of modern society that we've come to accept as normal, even if we're a little uncomfortable about it. But if you think we're talking about only commercial AI today, the algorithms that companies try to use to convince you to buy things, you're wrong. There is something that governments have been developing that exists at a level of power and complexity above that. Military grade AI, the sort used by surveillance agencies and battlefield operators hidden in hard drives in a secret remote location in America, there is one AI in particular we are going to look at. Its name? Sentient. Its goal? To predict the future. I'm Alex McColgan and you're watching Astrum. Join me today as we discuss the classified AI that America is using in its space-based spy programs. One that has so much autonomy, it doesn't just receive data from satellites, it actually directs them. What exactly can sentient do, and what does it mean for humanity's future, both in this world and off it? Obviously, as a disclaimer, this is a classified program, so there are many things we don't know. But by looking at the officially publicized statements and accounts from former officials, and by examining similar commercial AI in the private sector, we can make some pretty educated assumptions. Using AI in intelligence gathering does make a lot of sense, whether you are a company trying to take advantage of market trends, or a nation trying to stay on top of threats to your security, it's useful to know what everyone is doing, and also extremely difficult to properly analyze everything. As I mentioned at the beginning of this video, there are 8 billion humans on earth right now. Each one has a complex life filled with routines, hobbies, interests, and affiliations. The sheer amount of data that it would take to keep track of everyone is overwhelming, as illustrated by how hard it is to keep up with all your friends on social media. To some degree, this is true for space as well. There are approximately 100 billion stars in our galaxy alone, and 2 trillion galaxies in the observable universe. If you took as little as a second to look at each one individually, it would take 6.3 quadrillion years for you to see them all. Keeping tabs on so many stars is a mammoth task beyond the scope of any one person, or even a large group of people. So, for surveillance or science, there are immense benefits for the organization or nation that can trawl through the endless data and find patterns or anomalies. And AI is extremely good at processing large amounts of information quickly. This makes it wonderful when it comes to the world of astronomy, as it can identify interesting observations for humans to take a closer look at. Only, AI doesn't care whether we're above the microscope or beneath it, and the sort of AI that are being used to look at the actions of humans are becoming much more powerful than the ones looking at the stars. Which is why you may find yourself in the uncanny situation of talking about the subject with your friend, and then suddenly you find adverts for that same subject on your phone. This usually isn't just coincidence. Rather, this is your digital footprint being examined for marketable trends. We all have a digital footprint. Every time we make a transaction, visit a web page, log in at a certain IP address, or Google a search term we reveal a little bit about ourselves. Googling pet stores? You probably have a pet, and might be in the market for pet food or products. Buying a plane ticket? Well, if that's a holiday, travel companies might be able to convince you to buy a hotel room, travel insurance, sunglasses, swimming suits. Businesses are keen to gain this information, and often we give it to them freely. Whenever you see a pop-up on your web page asking you to accept cookies, chances are those cookies relate to tracking what you do on that page. Apps on your phone ask you permission to use your microphone. If you grant that permission without reading it, there have been some unscrupulous cases where phones use your microphone to listen in to your day-to-day conversations, where they'll look out for keywords. That information will then be sold onto advertisers, so they can better know what to sell you. This can already be unfortunate, as you might not want certain adverts popping up on your computer. A pregnant woman might not want to tell the world she's pregnant yet, but if AI analyzing her data figures it out, she and her partner might get adverts for baby gear, potentially tipping off anyone who happens to see their phone. But what happens if we're not just talking about businesses, but governments? Then the means of information gathering become much more vast, and the stakes much higher. If you want to save a few quid British gas have away, you get half-priced lecky and it's called Peek Save. 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Governments like the US are interested in protecting the lives of their citizens from foreign threats, and being able to form a digital profile of persons of interest can be the difference between a bomb threat being prevented or hundreds of lives being lost. So governments will use every trick in the book to gain as much information as possible. Google searches, shipping patterns, spy satellite images and video, financial transactions, weather reports. All these things help paint a picture, but the sheer quantity of information out there has always made it difficult to analyze everything to spot the patterns. Unless, of course, you have a really powerful AI. AI like Sentient are not just able to analyze data to spot patterns. Sentient is an automated, learning, adapting AI with the ability to direct satellites to locations of interest. Again, a lot of the information about it is classified, but the NRO in 2016 did release some information about it, revealing some clues as to its abilities. Sentient is described in this document as data ingesting and processing, meaning specialists will feed it vast quantities of data. It is sense making, meaning it will evaluate the data itself to try to discern patterns of behavior in what it's seeing. If a foreign power's jets are normally stationed in one airbase, and suddenly they are congregating at another near the border of a neighboring nation, Sentient can conclude that they might be about to launch an attack, and then Sentient can take that one step further. Rather than just flag what is identified as interesting, Sentient is able to direct satellites to photograph certain locations to fill in gaps in its knowledge. That's where the learning, adapting part comes in. Once the satellites are in place and Sentient can see what they see, it will evaluate the data and try to predict where it needs to go next. The whole process is completely automated. How do we know this is real? Firstly, because commercial companies are attempting to do the same thing. Black Sky is using its large numbers of satellites driven by automated AI to give buyers foresight. By using space-based intelligence, Black Sky is attempting to build a picture of what's happening right now on battlefields and in foreign countries. A repaired bridge, a new airport being constructed, put raw data like that through the right kind of analysis, AI driven analysis, and they're figuring out what's about to happen too. That new bridge might hint at the need to transport troops quickly to a new war zone. The airport might serve as a staging base for fighter jets in a planned offensive. In their own words, through AI, they hope to give companies the chance to act not as fast, but first. Black Sky is scanning ports around the Black Sea with frequent overhead flybys of small satellites. With its 15 passes a day, it has made 70,000 ship detections, helping it build a picture of exactly where Russian ships are and where they're likely headed and where they might be vulnerable. This information is part of the reason Russia has lost so many of its ships to Ukrainian attacks. And secondly, when in 2019 at a space symposium, a National Geospatial Intelligence Agency executive was asked directly about how good military and intelligence community algorithms had gotten at interpreting data and taking action based on those interpretations. They simply responded, that's a great question, and there's a lot of really good classified answers before swiftly moving on. If Black Sky can do that, it's assumed that Sentient can do that and more. Black Sky does not just use satellite images though, as well as from 25 satellites, it uses data from 40,000 new sources, 100 million mobile devices, 70,000 ships and planes, 8 social networks, 5,000 environmental sensors, and thousands of Internet of Things devices, according to a Verge article on the subject. What does Sentient use? Presumably a lot more. A retired CIA analyst suggested the answer is everything. Images, financial information, weather records, pharmaceutical purchases, all of it paints a picture. All can help identify patterns or abnormal behavior. There are even reports of it keeping an eye out for UFOs. I'd love to know what it found. Obviously, the NRO isn't keen to reveal exactly what Sentient is paying attention to. As then, foreign powers could try to manipulate that information for their own advantage, but the sheer scale of information it consumes must be vast. And unlike humans who would be swamped trying to make sense of it all, Sentient can swiftly skim through the noise to identify the key points of interest. Is this a problem? Yes and no. On the one hand, it certainly suggests that privacy is going to be harder and harder to come by. I've spoken before about how satellite images are becoming higher and higher resolution, and this could mean governments can keep tabs on you at all times. Now, they're not just looking at you physically, they're examining your social media feeds, your spending habits, and more. Which is fine if you've got nothing to hide, but it's unsettling nonetheless. Yet, there will always be benefits. Sentient will be able to identify threats which help the government prepare for them and keep its citizens safe. AI programs never tire and never stop. If science ever gets AI of the same level, we might one day see space missions that are completely automated. AI looking through telescopes to identify places they want to learn more about, and then launching their own probes to take a closer look with an army of data analyzing AI to evaluate the result. A lot of fascinating discoveries could be made that way. If it's any consolation for American viewers, Sentient is bound by the usual NRO restrictions on when the US can spy on its own citizens, which is not all the time. But don't rest too easy. If the US is developing AI like this, it's very likely that other governments and private companies are doing the same thing too. Even the Pentagon is concerned about online profiles being built up on their staff, filled with information that foreign spies might be able to utilize. Beyond that, problems can arise when our tools become too powerful. I'm not talking about some terrifying Skynet scenario, but instead what naturally happens when AI gets powerful enough that humans will struggle to check its workings. In the UK, there was a recent large scandal when the accounting system Horizon that was used by the post office incorrectly accused about 900 employees of theft and fraud. Many of these employees were prosecuted simply because the post office assumed that the information Horizon was giving it was correct. In New Zealand, a woman was incorrectly identified as a shoplifter because facial recognition software struggled to precisely identify men and women of color, leading to a false positive. AI tasked with making scientific discoveries could make similar mistakes, warping our understanding of the universe instead of enhancing it. An AI can just as easily ignore important information if it's been taught to ignore those things. When in 2023 an alleged Chinese spy balloon was caught flying over the US, officials realized that their radar was set to detect things moving at the speed of missiles, but not small and slow things like balloons. When they adjusted their settings, several other balloon-like objects suddenly showed up on their radars and were subsequently shot down. Although not all of these later objects were necessarily spy balloons, it does prove that an object that behaves in an unusual way can sail right past the artificial systems designed specifically to watch out for them. AI might be very good at answering questions, but it is not so good yet at knowing what questions to ask. What happens if powerful AI programs do not have sufficient oversight? What biases and assumptions could creep in, particularly for classified AI, that not many people are scrutinizing? It's a concerning thought. AI is here to stay. A digital profile is being created about you, and without proper laws and regulations being put into place, there's not a lot you can do to stop data broker companies from selling your personal data, beyond clicking reject all on those cookie pop-ups on websites, not unless you're willing to go through the arduous process of getting those companies to delete your information, or are willing to campaign to governments to put tight restrictions in place. I mean, that's not such a bad idea. The ability of AI likes sentient to trawl through all of that raw information, along with a host of satellite footage and other sources, to then attempt to predict what will happen in the future, and send satellites to key locations to see if those predictions were correct, only to make even more predictions. Well, it's certainly a powerful tool. A slightly chilling powerful tool. It makes perfect sense why we might want it, but it will have an impact on society, and perhaps a similar impact one day on our relationship with space. Perhaps with its predictive capabilities, the only one who will see what that impact on society will be is sentient itself. The endless frontier of space has always beckoned humanity with its uncharted territories and untapped potential. Since the first human ventured out into the great beyond, we have dreamed of exploring farther and pushing our boundaries beyond what seemed possible. But what if the next frontier isn't for humans to explore at all? What if the future of space exploration lies in the hands of machines? Today, we'll delve into the intriguing question of whether artificial intelligence or AI can replace humans in space exploration. From the earliest days of space travel, humans have been at the forefront of every mission, but the rise of AI technology is rapidly changing the game. Could AI be the key to unlocking the mysteries of the universe and propelling us further than we ever thought possible? Or are humans still an essential component of space exploration, bringing unique skills and qualities that AI simply can't replicate? I'm Alex McColgan, and you're watching Astrum. Join me as we explore the cutting-edge developments in AI technology and its potential to revolutionize space exploration. Before we delve into the role of AI in space exploration, let's first take a closer look at what exactly AI is. Artificial intelligence is a branch of computer science that involves developing machines and software that can perform tasks that would typically require human intelligence, such as problem solving, decision making, and natural language processing. In recent years, the use of AI in space exploration has become increasingly prevalent. NASA and other space agencies have been integrating AI into various aspects of space exploration, including mission planning, spacecraft operations, and data analysis. One of the most significant advantages of using AI in space exploration is the ability to automate tasks that would typically require human intervention. For example, rovers have been designed to operate autonomously, allowing them to continue making discoveries and conducting experiments, even if they lose communication with Earth. AI-powered rovers can navigate challenging terrain and perform scientific experiments without the need for human input, allowing scientists to gather more data and make more discoveries. Furthermore, AI can process vast amounts of data much faster than humans, which is especially useful in space exploration, where data from various sensors and instruments must be analyzed quickly to make real-time decisions. AI can also detect patterns in data that humans may not be able to detect, providing valuable insights into the universe's workings. The use of AI in this way has already led to some exciting discoveries on Mars, such as evidence of past liquid water and organic molecules, which are building blocks of life. Without the help of AI, it may have taken scientists much longer to sift through the massive amounts of data collected by the Mars rovers to make these discoveries. Additionally, AI can be used to analyze the vast amounts of data collected by the rovers and other spacecraft to identify areas of interest for further study. This allows scientists to focus their efforts on the most promising areas and make the most of limited resources. However, for tasks that require a high degree of creativity or human intuition, AI may not be the best option. For example, designing a new spacecraft or making decisions in a rapidly evolving situation may require human expertise and judgment. The rise of AI has certainly left some people worried, and for good reason. AI technology has advanced rapidly in recent years, and as it continues to improve, there are concerns about how it will impact our lives. In the field of space exploration, as AI becomes more advanced, there is potential for it to replace certain roles that are currently carried out by humans. There is a fear that AI could eventually replace humans in many areas, leading to job loss and economic instability. Additionally, some worry that AI lacks the empathy and creative problem-solving skills that make humans unique. I have a confession to make. I am not Alex McColgan, but rather an AI program to write this script on the topic of AI's impact on space exploration. But if I hadn't told you, would you have been able to tell that it wasn't written by a human? It's a question that raises interesting philosophical debates about the nature of creativity and the limits of artificial intelligence. What are the tasks that can be filled by AI? How far can it go? Are there any jobs that will always require a human touch? By understanding the strengths and limitations of both humans and AI, we can work towards a future where they complement each other and work together to achieve greater things. Let me explain this idea further. One of the main advantages of AI is its ability to analyze vast amounts of data quickly and accurately. For example, AI is revolutionizing the way spacecrafts are designed and built in a process called generative design. Generative design is a process in which an AI system generates multiple potential designs for a given object based on a set of constraints and requirements. The AI system then uses machine learning to refine and optimize the designs, taking into account factors such as weight, strength and material efficiency. This process can result in highly innovative designs that may not have been possible through traditional human design methods. AI is particularly suited to this task because it is capable of processing vast amounts of data and performing complex calculations quickly and efficiently. It can also learn from past designs and analyze data to identify patterns and optimal solutions. Additionally, AI can generate a much larger number of potential designs than humans could in the same amount of time, increasing the likelihood of finding the best possible solution. Overall, the use of AI in generative design for spaceship parts has the potential to create stronger, lighter and more efficient components, improving the overall performance and safety of space missions. While AI is excellent at performing repetitive and data-driven tasks, there are certain areas where human intelligence is still invaluable. Creativity and critical thinking are essential for solving problems that are outside the scope of routine procedures or pre-programmed algorithms. For example, in situations where there is a malfunction in equipment or a spacecraft, creativity and critical thinking are necessary to come up with innovative solutions that might not have been considered before. These skills are not yet replicable by AI. While AI can generate new ideas and solutions by analyzing vast amounts of data and patterns, it still lacks the human ability to be truly imaginative and original. AI algorithms are designed to optimize performance and achieve predefined objectives, whereas human creativity often stems from the exploration of the unknown with a clear set of objectives. Through my research, I have seen the tremendous benefits that AI can bring to space exploration, from the use of autonomous rovers to the design of spaceship parts, from the analysis of vast amounts of data to the search for intelligent life beyond our planet. However, as an AI, I am also aware of the limitations of my programming. I may be able to analyze data and provide insights, but I cannot experience the wonder and awe that humans feel when they gaze up at the stars. I cannot feel the rush of excitement that comes with discovering something new and unexpected. These are uniquely human experiences that I can only observe and try to understand. There is a fear that AI could eventually replace humans in many areas, leading to job loss and economic instability. However, it is important to remember that AI has its limitations, and there are certain tasks that require human intuition, creativity, and emotional intelligence. Rather than seeking to replace humans, AI should be seen as a tool to help augment and enhance human abilities. By working together, humans and AI can achieve great things and address some of the world's most pressing challenges. Thank you. Alright, that's it for the AI. Now, as a personal note from me at the end, I wanted to ask you what you thought about this video. I hope you enjoyed the experiment. Did you feel the AI wrote a good Astrum script? Could you tell it was an AI before it revealed itself? Let me know in the description below what you thought. As for me, at times I was surprised at its uncannily human voice, even though in making this script it took a lot of prompting before the AI produced what you see before you. Will I be replaced by an AI? Maybe eventually. Who knows? But what the AI really does lack is a creative spark. Without human input and guidance, it was flat and boring. Plus, this took a lot longer than writing a normal script, because coaxing the AI to produce the quality and accuracy I wanted was disproportionately tedious. So don't worry, this was a one off. Next time, you'll see my video produced by Humanhand again. Oh, so I claim. Cause that's exactly what an AI would say, isn't it? Every new member keeps the channel focused on what really matters, making the complexity of space available to everyone. If you enjoy what we do, come join the Astrum community. Security program on spreadsheets, new regulations piling up, an audit dread. It's time for Vanta. Vanta automates security and compliance, brings evidence into one place, and cuts audit prep by 82%. Less manual work, clearer visibility, faster deals, zero chaos. Call it compliance or call it calm appliance. Get it? Join the 15,000 companies using Vanta to prove trust. Get started at Vanta.com slash calm.