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In the new edition of my book, The Simulation Hypothesis, released in July, Ive updated my estimate of how likely we are to be in a simulation to approximately 70%, thanks to recent AI developments. This means we are almost certainly inside a virtual reality world like that depicted in The Matrix, the most talked about film of the last year of the twentieth century. Even young people who werent born in 1999 tend to know the basic plot of this blockbuster: Neo (Keanu Reeves) thinks hes living in the real world, working in a cubicle in a mega software corporation, only to discover, with the help of Morpheus (Laurence Fishburn) and Trinity (Carrie-Anne Moss), that hes living inside a computer-generated world. The AI Factor What makes me so sure that we are living in a simulation? There are multiple reasons explored in the book, including a new way to explain quantum weirdness, the strange nature of time and space, information theory & digital physics, spiritual/religious arguments, and even an information-based way to explain glitches in the matrix. However, even while discounting these other possible reasons we may in a simulation, the main reason for my new estimate was because of the rapid advance of AI and virtual reality technology, combined with a statistical argument put forth by Oxford philosopher Nick Bostrom in 2003. In the past few years, the rise of generative AI like ChatGPT, Google’s Gemini, and Xs Grok has proceeded rapidly. We now have not just AI which has passed the Turing Test, but we already have rudimentary AI characters living in the virtual world with whom we can interact. One recent example includes prompt-generated video from Google Veo. Recently, Google has introduced the ability to create realistic-looking videos on demand, complete with virtual actors and landscapes that are completely AI generated, and speak real lines of dialogue, all based on prompts. This has led to prompt theory, a viral phenomenon of AI-generated video of realistic characters exhorting that they were definitely not generated by AI prompts. Another recent example is the release of AI companions from Grok, which combine LLMs with a virtual avatar, leading to a new level of adoption of the rising wave of AI characters that are already serving as virtual friends, therapists, teachers, and even virtual lovers. The sexy anime girl in particular has led to thousands of memes of obsession with virtual characters. The graphics fidelity and responsiveness of these characters will improveimagine the fidelity of the Google Veo videos combined with a virtual friend/boyfriend/girlfriend/assistant, who can pass what I call, the Metaverse or Virtual Turing Test (described in the new book in detail). The Simulation Point All of this means we are getting closer than ever to the simulation point, a term I coined a few years ago as a kind of technological singularity. I define this as a theoretical point at which we can create virtual worlds that are indistinguishable from physical reality, and with AI beings that are indistinguishable from biological beings. In short, when we reach the simulation point, we would be capable of building something like the Matrix ourselves, complete with realistic landscapes, avatars and AI characters. To understand why our progress in reaching this point might increase the likelihood that we are already in a simulation, we can build on the simulation argument that Bostroms proposed in his 2003 paper, “Are You Living in a Computer Simulation?” Bostrom surmised that for a technological civilization like ours, there were only three possibilities when it came to building highly realistic simulations of their past (which he called ancestor simulations). Each of these simulations would have realistic simulated minds, holding all of the information and computing power a biological brain might hold. We can think of having the capability of building these simulations as approximately similar to my definition of the simulation point. The first two possibilities, which can be combined for practical purposes, were that no civilization ever reaches the simulation point (i.e. by destroying themselves or because it isnt possible to create simulations), or that all such civilizations who reached this point decided not to build such sophisticated simulations. The term simulation hypothesis was originally meant by Bostrom to refer to the third possibility, which was that we are almost certainly living in a computer simulation. The logic underlying this third scenario was that any such advanced civilization would be able to create entirely new simulated worlds with the click of a button, each of which could have billions (or trillions) of simulated beings indistinguishable from biological beings. Thus, the number of simulated beings would vastly outnumber the tally of biological beings. Statistically, then, if you couldnt tell the difference, then you were (much) more likely to be a simulated being than a real, biological one. Bostrom himself initially declined to put a percentage on this third option compared to the other two, saying only that it was as one of three possibilities, implying a likelihood of 33.33 % (and later changed his odds for the third possibility to be around 20%). Elon Musk used a variation of Bostroms logic in 2016, when he said the chances of us being in base reality (i.e. not in a simulation) were one in billions. He was implying that there might be billions of simulated worlds, but only one physical world. Thus statistically, we are by far highly likely (99.99%+) in a simulated world. Others have weighed in on the issue, using variations of the argument, including Neil deGrasse Tyson, who put the percentage likelihood at 50%. Columbia scientist David Kipping, in a paper using Bayesian logic and Bostroms argument, came up with a similar figure, of slightly less than 50-50. Musk was relying on the improvement in video game technology and projecting it forward. This is what I do in detail in my book where I lay out the 10 stages of getting to the simulation point, including virtual reality (VR), augmented reality (AR), BCIs (Brain Computer Interfaces), AI, and more. It was the progress in these areas over the past few years that gives me the conviction that we are getting closer to the simulation point than ever before. The Equation In my new book, I argue that the percentage likelihood we are in a simulation is based almost entirely on whether we can reach the simulation point. If we can never reach this point, then the chances are basically zero that we are in a sim that was already developed by anyone else. If we can reach this point, then the chances of being in a simulation simply boil down to how far from this theoretically point we are, minus some uncertainty factor. If we have already reached that point, then we can be 99% confident about being in a simulation. Even if we havent reached the simulation point (we havent, at least not yet), then the likelihood of the simlation hypothesis, Psim , basically simplifies down to Psimpoint, the confidence level we have that we can reach this point, minus some small extra uncertainty factor (pu). Psim Psimpoint pu If we are 100% confident we can reach the simulation point, and the small factor pu is 1, then the likelihood of being in a simulation jumps up to 99%. Why? Per the earlier argument, if we can reach this point, then it is very likely that another civilization has already reached this point, and that we are inside one of their (many) simulations. pu is likely to be small because we have already built uncertainty into our Psimpoint for any value less than 100%. So, in the end, it doesnt matter when we reach this point, its a matter of capabilities. And the more we develop our AI, video game, and virtual reality technology, the more likely it is that at some point soon, we will be able to reach the simulation point. Are we there yet? So how close are we? In the new book, I go through each of the 10 stages and estimate that we are more than two-thirds of the way there, and I am fairly certain that we will be able to get there eventually. This means that todays AI developments have convinced me we are at least 67% likely to be able to reach the simulation point and possibly more than 70%. If I add in factors from digital and quantum physics detailed in the book, and if we take the trip reports of mystics of old and todays near-death experiencers and psychonauts (who expand their awareness using DMT, for example) at face value, we can be even more confident that our physical reality is not the ultimate reality. Those who report such trips are like Platos philosopher who not only broke his chains, but also left Platos allegorical cave. If you read Platos full allegory, it ends with the philosopher returning to the cave to describe what he had seen in the world outside to the other residents, who didnt believe him and were content to continue watching shadows on the wall. Because most scientists are loath to accept these reports and are likely to dismiss this evidence, I wont include them in my own percentage estimation, though as I explain in the book, this brings my confidence level that we are in a virtual, rather than a physical reality even higher. Which brings us back to the inescapable realization that if we will eventually be able to create something like the Matrix, someone has likely already done it. While we can debate what is outside our cave, its our own rapid progress with AI that makes it more likely than ever that we are already inside something virtual like the Matrix.
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Lush forests and crisp mountain air have drawn people to New Yorks Adirondack Mountains for centuries. In the late 1800s, these forests were a haven for tuberculosis patients seeking the cool, fresh air. Today, the region is still a sanctuary where families vacation and hikers roam pristine trails. However, hidden health dangers have been accumulating in these mountains since industrialization began. Tiny metal particulates released into the air from factories, power plants and vehicles across the Midwest and Canada can travel thousands of miles on the wind and fall with rain. Among them are microscopic pollutants such as lead and cadmium, known for their toxic effects on human health and wildlife. For decades, factories released this pollution without controls. By the 1960s and 1970s, their pollution was causing acid rain that killed trees in forests across the eastern U.S., while airborne metals were accumulating in even the most remote lakes in the Adirondacks. In the early 1900s, sanatoriums such as the New York State Hospital at Ray Brook, near Saranac Lake, were built to house tuberculosis patients. The crisp mountain air was believed to help their recovery. [Photo: Detroit Publishing Company Photograph Collection/Library of Congress] As paleolimnologists, we study the history of the environment using sediment cores from lake bottoms, where layers of mud, leaves, and pollen pile up over time, documenting environmental and chemical changes. In a recent study, we looked at two big questions: Have lakes in the Northeast U.S. recovered from the era of industrial metal pollution, and did the Clean Air Act, written to help stop the pollution, work? Digging up time capsules On multiple summer trips between 2021 and 2024, we hiked into the Adirondacks backcountry with 60-pound inflatable boats, a GPS and piles of long, heavy metal tubes in tow. We focused on four pondsRat, Challis, Black and Little Hope. In each, we dropped cylindrical tubes that plunge into the darkness of the lake bottom. The tubes suction up the mud in a way that preserves the accumulated layers like a history book. Back in the lab, we sliced these cores millimeter by millimeter, extracting metals such as lead, zinc and arsenic to analyze the concentrations over time. An illustration of the authors shows how lake sediment cores capture the history of the region going back thousands of years. [Image: Sky Hooler] The changes in the levels of metals we found in different layers of the cores paint a dramatic picture of the pristine nature of these lakes before European settlers arrived in the area, and what happened as factories began going up across the country. A century plagued by contamination Starting in the early 1900s, coal burning in power plants and factories, smelting and the growing use of leaded gasoline began releasing pollutants that blew into the region. We found that manganese, arsenic, iron, zinc, lead, cadmium, nickel, chromium, copper, and cobalt began to appear in greater concentrations in the lakes and rose rapidly. At the same time, acid rain, formed from sulfur and nitrogen oxides from coal and gasoline, acted like chemical shovels, freeing more metals naturally held in the bedrock and forest soils. Acid rain damaged trees in several states over the decades, leaving ghostly patches in forests. [Photo: Will & Deni McIntyre/Getty Images] The result was a cascade of metal pollution that washed down the slopes with the rain, winding through creeks and seeping into lakes. All of this is captured in the lake sediment cores. As extensive logging and massive fires stripped away vegetation and topsoil, the exposed landscapes created express lanes for metals to wash downhill. When acidification met these disturbed lands, the result was extraordinary: Metal levels didnt just increase, they skyrocketed. In some cases, we found that lead levels in the sediment reached 328 parts per million, 109 times higher than natural preindustrial levels. That lead would have first been in the air, where people were exposed, and then in the wildlife and fish that people consume. These particles are so small that they can enter a persons lungs and bloodstream, infiltrate food webs, and accumulate in ecosystems. A wind map shows how pollution moves from the Midwest, reaching the Adirondacks. The colors show the average wind speed, in meters per second, and arrows show the wind direction about 3,000 meters above ground from 1948 to 2023. Average calculated using NCEP/NCAR reanalysis data. [Image: Sky Hooler] Then, suddenly, the increase stopped. A public outcry over acid rain, which was stripping needles from trees and poisoning fish, led to major environmental legislation, including the initiation of the Clean Air Act in 1963. The law and subsequent amendments in the following decades began reducing sulfur dioxide emissions and other toxic pollutants. To comply, industries installed scrubbers to remove pollutants at the smokestack rather than releasing them into the air. Catalytic converters reduced vehicle exhaust, and lead was removed from gasoline. The air grew cleaner, the rain became less acidic, and our sediment cores show that the lakes began to heal through natural biogeochemical processes, although slowly. By 1996, atmospheric lead levels measured at Whiteface Mountain in the Adirondacks had declined by 90%. National levels were down 94%. But in the lakes, lead had decreased only by about half. Only in the past five years, since about 2020, have we seen metal concentrations within the lakes fall to less than 10% of their levels at the height of pollution in the region. Our study is the first documented case of a full recovery in Northeast U.S. lakes that reflects the recovery seen in the atmosphere. Its a powerful success story and proof that environmental policy works. Looking forward But the Adirondacks arent entirely in the clear. Legacy pollution lingers in the soils, ready to be remobilized by future disturbances from land development or logging. And there are new concerns. We are now tracking the rise of microplastics and the growing pressures of climate change on lake ecosystems. Recovery is not a finish line; its an ongoing process. The Clean Air Act and water monitoring are still important for keeping the regions air and water clean. Though our findings come from just a few lakes, the implications extend across the entire Northeast U.S. Many studies from past decades documented declining metal deposition in lakes, and research has confirmed continued reductions in metal pollutants in both soils and rivers. In the layers of lake mud, we see not only a record of damage but also a testament to natures resilience, a reminder that with good legislation and timely intervention, recovery is possible. Sky Hooler is a PhD student in environmental science at the University at Albany, State University of New York. Aubrey Hillman is an associate professor of environmental sciences at the University at Albany, State University of New York. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Generative AI platforms have sent shock waves through the K-12 education sector since the public release of ChatGPT nearly three years ago. The technology is taking hold under the belief that students and teachers need to be proficient in these powerful tools, even though many concerns remain around equity, privacy, bias, and degradation of critical thinking among students. As a professor who teaches future educators and is part of an AI-focused working group, I have observed the potential for artificial intelligence to transform teaching and learning practices in K-12 schools. The trends I am seeingand that I encourageare for K-12 educators to use AI to shift from memorization and rote learning to instead emphasize critical thinking and creativity. Jumping in the deep end After the public release of ChatGPT in late 2022, some large school districts initially banned the use of AI due to concerns about cheating. Surveys also reflected worries about chatbots fabricating information, such as references for school papers, in addition to concerns about misinformation and biases existing in AI responses to prompts. Students, on the other hand, tended to jump into the deep end of the AI pool. Common Sense Media, which offers recommendations on childrens media consumption, published a report in 2024 showing that students were using AI-supported search and chatbots for homework and to stave off boredom as well as other personal reasons, including creating content as a joke, planning activities, and seeking health advice. Most of the teachers and parents of the students in the study were unaware that students were using the technology. In my work at Drexel University teaching graduate students who are aspiring school principals or superintendents, I found that in 2023, K-12 students were afraid of using AI due to the policies implemented in their districts banning it. However, it quickly became apparent that students were able to mask their use of AI by instructing AI to insert some mistakes into their assignments. Meanwhile, despite teachers initial concerns about AI, approximately 60% of K-12 teachers now admit to using AI to plan lessons, communicate with parents, and assist with grading. Concerns over students cheating still exist, but time-strapped teachers are finding that using AI can save them time while improving their teaching. A recent Walton Foundation and Gallup study revealed that teachers who used AI tools weekly saved an average of 5.9 hours per week, which they reallocated to providing students more nuanced feedback, creating individualized lessons, writing emails, and getting home to their families in a more reasonable time. Opening up new ways of teaching I recommend that my graduate students use AI because I think ignoring emerging trends in education is not wise. I believe the benefits outweigh the negatives if students are taught ethical use of the technology and guardrails are put in place, such as requiring that AI be cited as a source if students use it in coursework. Advocates say AI is changing teaching for the better, since it forces teachers to identify additional ways for students to demonstrate their understanding of content. Some strategies for students who rely too heavily on AI include oral presentations, project-based learning, and building portfolios of a students best work. One practice could involve students showing evidence of something they created, implemented, or developed to address a challenge. Evidence could include constructing a small bridge to demonstrate how forces act on structures, pictures, or a video of students using a water sampling device to check for pollution, or students designing and planting a community garden. AI might produce the steps needed to construct the project, but students would actually have to do the work. Teachers can also use AI to create lessons tailored to students interests, quickly translate text to multiple languages, and recognize speech for students with hearing difficulties. AI can be used as a tutor to individualize instruction, provide immediate feedback and identify gaps in students learning. When I was a school superintendent, I always asked applicants for teaching positions how they connected their classroom lessons to the real world. Most of them struggled to come up with concrete examples. On the other hand, I have found AI is helpful in this regard, providing answers to students perennial question of why they need to learn what is being taught. Thought partner Teachers in K-12 schools are using AI to help students develop their empathetic skills. One example is prompting an AI to redesign the first-day experience for a relocated student entering a new middle school. AI created the action steps and the essential questions necessary for refining students initial solutions. In my own classroom, Ive used AI to boost my graduate students critical thinking skills. I had my students imagine that they were college presidents facing the loss of essential federal funding unless they implemented policies limiting public criticism of federal agencies on campus. This proposed restriction, framed as a requirement to maintain institutional neutrality, requires students to develop a plan of action based on their knowledge of systems and design thinking. After each team developed their solution, I used AI to create questions and counterpoints to their proposed solution. In this way, AI becomes a critical thought partner to probe intended and unintended outcomes, gaps in students thinking, and potential solutions that might have been overlooked. AI researcher Ethan Mollick encourages educators to use AI as a springboard, similar to jazz msicians improvising, as a way to unleash new possibilities. Mollick advises people to partner with AI as co-intelligence, be the human in the loop, treat AI as a coworker, albeit one that needs to be prodded for evidence, and to learn to use it well. I concur. Changing perspectives on AI Some early studies on the effects of using AI in education have raised concerns that the convenience of generative AI will degrade students learning and erode their critical thinking skills. I think that further studies are needed, but I have found in my own work and in the work of my graduate students that AI can enhance human-produced work. For example, AI-powered teaching assistants, like Khanmigo or Beghetto Bots, use AI to help students solve problems and come up with innovative solutions without giving away the answers. My experiences with other educators on the front lines show me that they are beginning to change their perspectives toward students using AI, particularly as teachers realize the benefit of AI in their own work. For example, one of my graduate students said his district is employing a committee of educators, students and outside experts to explore how AI can be used ethically and in a way that wont erode students critical thinking skills. Educators are starting to realize that AI isnt going away anytime soonand that its better to teach their students how to use it, rather than leave them to their own devices. Michael G. Kozak is an associate clinical professor of educational administration and leadership at Drexel University. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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