{"id":129914,"date":"2024-09-10T14:37:07","date_gmt":"2024-09-10T18:37:07","guid":{"rendered":"https:\/\/www.shortform.com\/blog\/?p=129914"},"modified":"2024-09-17T14:45:43","modified_gmt":"2024-09-17T18:45:43","slug":"can-ai-be-controlled","status":"publish","type":"post","link":"https:\/\/www.shortform.com\/blog\/can-ai-be-controlled\/","title":{"rendered":"Can AI Be Controlled or Contained? 3 Reasons It&#8217;s Too Late"},"content":{"rendered":"\n<p>Are you worried about the <a href=\"https:\/\/www.shortform.com\/blog\/what-is-the-future-of-ai\/\">future of AI<\/a>? Have you ever wondered if we could simply stop its development? Can AI be controlled or contained?<\/p>\n\n\n\n<p>In <em>Scary Smart<\/em>, author Mo Gawdat explores why halting AI&#8217;s progress is no longer possible. He explains that we&#8217;ve reached a point of no return, where <a href=\"https:\/\/www.shortform.com\/blog\/superintelligent-ai\/\">superintelligent AI<\/a> will become an independent, thinking entity beyond our control.<\/p>\n\n\n\n<p>Keep reading to discover the three reasons why AI can&#8217;t be contained or controlled.<\/p>\n\n\n\n<!--more-->\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-controlling-and-containing-ai\">Controlling and Containing AI<\/h2>\n\n\n\n<p>If experts expect AI to create these <a href=\"https:\/\/www.shortform.com\/blog\/dystopian-futures\/\">dystopian scenarios<\/a> or others, then why can\u2019t we just put the brakes on further development? Can AI be controlled or contained? Gawdat explains that we\u2019ve reached a point of no return, and we can\u2019t stop these outcomes (and others like them) from occurring. He points out that <strong>superintelligent AI won\u2019t just be a tool we\u2019ve built: It will be an intelligent being that can learn, think, and decide just like we can<\/strong>. That means that we can\u2019t control artificially intelligent systems in the same way that we can control more traditional computer programs\u2014a scary thought if you\u2019ve ever watched a film like <em>2001: A Space Odyssey<\/em>.<\/p>\n\n\n\n<p>(Shortform note: In predicting that specific outcomes of AI development are inevitable, Gawdat engages in what some call \u201c<a href=\"https:\/\/yalereview.org\/article\/anna-shechtman-algorithm-kyle-chayka-taylor-lorenz\" target=\"_blank\" rel=\"noreferrer noopener\">technological determinism<\/a>.\u201d This involves arguing that <em>if<\/em> we build a technology like AI that\u2019s smarter than humans, <em>then<\/em> the changes we envision it making to our culture are a foregone conclusion, good or bad. It\u2019s equally deterministic to promise that social media will make the world \u201c<a href=\"https:\/\/www.theverge.com\/2017\/2\/16\/14642164\/facebook-mark-zuckerberg-letter-mission-statement\" target=\"_blank\" rel=\"noreferrer noopener\">more open and connected<\/a>\u201d or to warn that AI will <a href=\"https:\/\/www.theguardian.com\/technology\/2023\/jul\/07\/five-ways-ai-might-destroy-the-world-everyone-on-earth-could-fall-over-dead-in-the-same-second\" target=\"_blank\" rel=\"noreferrer noopener\">destroy humanity<\/a>. Some observers say that while advances like AI make specific versions of the future more likely than others, they don\u2019t, on their own, determine what the future will be.)<\/p>\n\n\n\n<p>Gawdat explains that <strong>there are three fundamental reasons that we can\u2019t put the genie back in the bottle<\/strong> (or the computer back in the box): It\u2019s impossible for us to halt the development of AI, the code we write doesn\u2019t determine how AI behaves, and we have no way of understanding how AI (even the models we have now) make their decisions. We\u2019ll explore each of these ideas next.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-it-s-too-late-to-stop-ai-s-progress\">It\u2019s Too Late to Stop AI\u2019s Progress<\/h3>\n\n\n\n<p>The first reason that AI can\u2019t be controlled or contained is that <strong>we literally can\u2019t stop its progress<\/strong>. Some people argue that we should stop developing AI for the good of humanity and the Earth. The goal would be to keep it from acquiring more robust thinking and <a href=\"https:\/\/www.shortform.com\/blog\/how-to-improve-problem-solving-skills\/\">problem-solving skills<\/a> and progressing to <a href=\"https:\/\/www.shortform.com\/blog\/is-agi-possible\/\">artificial general intelligence<\/a>.&nbsp;<\/p>\n\n\n\n<p>But Gawdat contends it\u2019s too late. Attempts to <a href=\"https:\/\/www.shortform.com\/blog\/how-to-control-ai\/\">control AI<\/a> development with legislation or to contain it with technological safeguards are up against the impossible because <strong>we\u2019ve already imagined how we\u2019ll benefit from more advanced AI<\/strong>. There\u2019s immense competitive pressure among the corporations and governments pushing the development of AI forward and enormous economic incentives for them to continue.<\/p>\n\n\n\n<p>(Shortform note: While there aren\u2019t international laws <a href=\"https:\/\/www.newyorker.com\/science\/annals-of-artificial-intelligence\/can-we-stop-the-singularity\" target=\"_blank\" rel=\"noreferrer noopener\">restricting AI development<\/a>, some experts say there should be. Many, like Gawdat, think it\u2019s not possible to stop the progress, but that <a href=\"https:\/\/www.wired.com\/story\/fast-forward-elon-musk-letter-pause-ai-development\/\" target=\"_blank\" rel=\"noreferrer noopener\">hasn\u2019t stopped them<\/a> from trying, as when 30,000 people signed a 2023 <a href=\"https:\/\/futureoflife.org\/open-letter\/pause-giant-ai-experiments\/\" target=\"_blank\" rel=\"noreferrer noopener\">open letter<\/a> calling for a moratorium on training powerful AI systems. Elon Musk, who signed the letter, has adopted an additional strategy for changing the direction that AI development is going. Early in 2024, Musk sued OpenAI, claiming it\u2019s betrayed its original mission of developing open-source AI \u201c<a href=\"https:\/\/www.tnonline.com\/20240302\/elon-musk-sues-openai-and-ceo-sam-altman-claiming-betrayal-of-its-goal-to-benefit-humanity\/\" target=\"_blank\" rel=\"noreferrer noopener\">for the benefit of humanity<\/a>.\u201d The lawsuit might be <a href=\"https:\/\/www.theverge.com\/2024\/3\/1\/24087937\/elon-musk-suing-openai-nightmare-1l-contracts-exam\" target=\"_blank\" rel=\"noreferrer noopener\">on shaky ground legally<\/a>, but also practically: Some experts say the lawsuit is <a href=\"https:\/\/www.reuters.com\/legal\/elon-musk-sues-openai-ceo-sam-altman-breach-contract-2024-03-01\/\" target=\"_blank\" rel=\"noreferrer noopener\">unlikely to affect AI development<\/a> at OpenAI or anywhere else.)\u00a0<\/p>\n\n\n\n<p>Gawdat notes that some experts have suggested taking precautionary measures like isolating AI from the real world or equipping it with a kill switch we can flip if it behaves dangerously. But he contends that <strong>these proposals assume that we\u2019ll have a lot more power over AI (and over ourselves) than we really will<\/strong>. Gawdat explains that we won\u2019t always be smarter than AI, and we can\u2019t depend on corporations and governments to curtail AI\u2019s abilities at the expense of the potential gains of both money and power. He warns that we can\u2019t stop artificial general intelligence from becoming a reality\u2014and dramatically changing ours in the process.&nbsp;<\/p>\n\n\n\n<p>(Shortform note: Gawdat isn\u2019t the first to point out that we have a fraught relationship with our inventions, in part because we can\u2019t keep ourselves from building things we shouldn\u2019t. Mary Shelley\u2019s <a href=\"https:\/\/www.gutenberg.org\/files\/84\/84-h\/84-h.htm\" target=\"_blank\" rel=\"noreferrer noopener\"><em>Frankenstein<\/em><\/a>, the 1818 novel that <a href=\"https:\/\/www.denofgeek.com\/movies\/the-19-best-artificially-intelligent-movie-characters-ex-machina-2001-ai\/\" target=\"_blank\" rel=\"noreferrer noopener\">established science fiction<\/a> as a genre, dramatized the idea that humans <a href=\"https:\/\/bigthink.com\/the-well\/mary-shelley-lessons-frankenstein-ai\/\" target=\"_blank\" rel=\"noreferrer noopener\">aren\u2019t in control<\/a> of the things we create. While <em>Frankenstein<\/em> is about the problem of playing God, AI has pushed writers to consider a new twist: the problem of <a href=\"https:\/\/www.polygon.com\/23587998\/westworld-season-summaries\" target=\"_blank\" rel=\"noreferrer noopener\">creating God<\/a>. Stories like HBO\u2019s <em>Westworld<\/em> illustrate how our ability to develop technology outpaces our thinking on the implications of our inventions. As in <em>Frankenstein<\/em>, the artificially intelligent \u201chosts\u201d in <em>Westworld<\/em> aren\u2019t the monsters: The people who made them just to exploit them are.)<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-the-code-we-write-is-only-a-small-part-of-an-ai-system\">The Code We Write Is Only a Small Part of an AI System<\/h3>\n\n\n\n<p>The extent to which artificially intelligent systems depend (or, more accurately, don\u2019t depend) on our instructions explains a second reason that <strong>much of AI\u2019s behavior is out of our hands<\/strong>. Gawdat explains that for classical computers, how a machine operates and what it can do is explicitly determined by its code. The people building the system write instructions that tell the computer how to process the data it receives as input and how to complete the operations to generate its output. When systems operate in this deterministic way, they don\u2019t need intelligence because they don\u2019t make any decisions: Anything that looks like a decision when you use the program is determined by the instructions written into the code.&nbsp;<\/p>\n\n\n\n<p>Gawdat explains that the unambiguous relationship between the code that controls a machine and the work that results from that code doesn\u2019t apply to artificially intelligent machines. It all changed when researchers developed an AI method called deep learning, which <strong>enables AI to learn to <a href=\"https:\/\/www.shortform.com\/blog\/how-to-complete-a-task-successfully\/\">complete a task<\/a> without explicit instructions that tell them how to do it<\/strong>, learning in a way inspired by the human brain. (Shortform note: Deep learning is <a href=\"https:\/\/thedecisionlab.com\/insights\/policy\/combining-artificial-intelligence-and-behavioral-science-can-bring-massive-benefits-we-just-need-to-do-so-responsibly\" target=\"_blank\" rel=\"noreferrer noopener\">part of machine learning<\/a>, a kind of AI that enables machines to learn from their experiences as humans do.)<\/p>\n\n\n\n<p>As Gawdat points out, <strong>humans learn by taking in large amounts of information, trying to recognize patterns, and getting feedback<\/strong> to tell us whether we\u2019ve come to the correct answer. Whether you\u2019re a child learning to recognize colors or a medical student learning to distinguish a normal brain scan from a worrying one, you have to see a lot of examples, try to classify them, and ask someone else whether you\u2019re right or wrong.&nbsp;<\/p>\n\n\n\n<p>Deep learning enables AI to follow a similar learning process but at exponentially faster speeds. This has already made AI more skilled at <a href=\"https:\/\/news.northeastern.edu\/2022\/10\/05\/machine-vision-artificial-intelligence\/\" target=\"_blank\" rel=\"noreferrer noopener\">detecting colors<\/a> and <a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC10453020\/\" target=\"_blank\" rel=\"noreferrer noopener\">identifying brain tumors<\/a> than many humans. Instead of relying on explicit instructions that tell it how to categorize colors or how to spot a brain tumor, <strong>AI learns for itself by processing vast amounts of information and getting feedback on whether it\u2019s completing a task satisfactorily<\/strong>.\u00a0<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>What Is Deep Learning?\u00a0<\/strong><br><br>Deep learning wouldn\u2019t be possible without neural networks, the kind of AI that mimics some traits of the human brain. While some people use \u201cdeep learning\u201d and \u201cneural network\u201d interchangeably, <a href=\"https:\/\/bernardmarr.com\/deep-learning-vs-neural-networks-whats-the-difference\/\" target=\"_blank\" rel=\"noreferrer noopener\">they don\u2019t mean<\/a> the same thing. A simple neural network needs just three layers of neurons or nodes: one to receive data, one to process it, and one to decide what to do with it. But a deep neural network has more than three layers, <a href=\"https:\/\/deepai.org\/machine-learning-glossary-and-terms\/hidden-layer-machine-learning\" target=\"_blank\" rel=\"noreferrer noopener\">including \u201chidden\u201d layers<\/a> that transform the data. The process of training this sort of neural network is deep learning: The model <em>trains itself<\/em> on the data you give it. As Gawdat notes, this makes it possible for AI to teach itself to detect patterns instead of relying on explicitly coded rules.\u00a0<br><br>A deep neural network learns to see patterns using what researchers call a latent space. To tell apples and oranges apart, for instance, a model has to learn those fruits\u2019 features and <a href=\"https:\/\/towardsdatascience.com\/understanding-latent-space-in-machine-learning-de5a7c687d8d\" target=\"_blank\" rel=\"noreferrer noopener\">find a simple way to represent them<\/a> so it can spot patterns. It does this in <a href=\"https:\/\/medium.com\/@dylancurious\/the-mystery-of-latent-space-in-machine-learning-explained-b3cf7e6d2885\" target=\"_blank\" rel=\"noreferrer noopener\">a kind of map<\/a>, called a latent space. Similar images (like two images of apples) are closer together than very different images (one of an apple and one of an orange.) The idea of latent space was <a href=\"https:\/\/www.rollingstone.com\/culture\/culture-news\/loab-ai-creepypasta-artist-supercomposite-1234588335\/\" target=\"_blank\" rel=\"noreferrer noopener\">creepily mythologized<\/a> when an artist said she \u201c<a href=\"https:\/\/www.dazeddigital.com\/life-culture\/article\/56964\/1\/loab-the-horrifying-cryptid-haunting-ai-latent-space-supercomposite\" target=\"_blank\" rel=\"noreferrer noopener\">discovered\u201d a woman she named \u201cLoab,\u201d<\/a> haunting the hidden layers of an AI image generator. The artist <a href=\"https:\/\/www.cnet.com\/science\/what-is-loab-the-haunting-ai-art-woman-explained\/\" target=\"_blank\" rel=\"noreferrer noopener\">asked for the opposite<\/a> of Marlon Brando and ended up with uncanny images of a woman who\u2019d look right at home in a <a href=\"https:\/\/www.shortform.com\/blog\/why-do-people-like-horror-movies\/\">horror movie<\/a>.\u00a0<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Gawdat explains that sometimes <strong>when a developer builds a program to complete a task, they don\u2019t just build one AI model<\/strong>. Instead, they build thousands, give them large amounts of data, discard the models that don\u2019t do well, and build updated models from there. Initially, the models complete the task correctly only about as often as random chance dictates. But successive generations of models get more and more accurate. <strong>The AI improves not because the underlying code changes but because the models learn and adapt<\/strong>. This is great for making AI that\u2019s quick to learn new things. But it means that the initial code plays a smaller role than you might expect, and we don\u2019t have control over how artificially intelligent machines learn.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>What Is Evolutionary Learning? How Does It Compare to Deep Learning?\u00a0<\/strong><br><br>In explaining how a developer might build thousands of models to end up with one, Gawdat describes \u201cevolutionary learning\u201d or \u201cevolutionary computing.\u201d This kind of AI <a href=\"https:\/\/venturebeat.com\/ai\/evolutionary-computation-will-drive-the-future-of-creative-ai\/\" target=\"_blank\" rel=\"noreferrer noopener\">differs from deep learning<\/a> in a crucial way: Deep learning teaches a model something we already know, like training it to distinguish between cars and school buses by showing it images of both kinds of vehicles. Evolutionary learning trains a model to find answers that don\u2019t yet exist, like asking it to find the most efficient route for a school bus to take through a busy neighborhood.<br><br>While deep learning systems are made of neural networks and, in some ways, emulate <a href=\"https:\/\/www.shortform.com\/blog\/how-the-human-brain-works\/\">how the brain works<\/a>, evolutionary learning <a href=\"https:\/\/www.technologyreview.com\/2018\/07\/18\/104191\/evolutionary-algorithm-outperforms-deep-learning-machines-at-video-games\/\" target=\"_blank\" rel=\"noreferrer noopener\">mimics the process<\/a> that shaped the human brain: evolution. Just as randomness plays a role in evolution, the code you start with in evolutionary learning is random: hundreds or thousands of randomly generated pieces of code. Each gets tested, and the best pieces become part of the next version. The code evolves, changing and improving with each generation. As Gawdat notes, the AI\u2019s learning gives you many <a href=\"https:\/\/gadgetmates.com\/evolutionary-computation-how-natures-algorithms-can-supercharge-ai\" target=\"_blank\" rel=\"noreferrer noopener\">generations of models<\/a>\u2014which, in our example, yield better and better bus routes over time\u2014and you don&#8217;t have to start with the correct answer to build them.\u00a0<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-we-can-t-tell-ai-how-to-make-decisions-or-what-values-to-adopt\">We Can\u2019t Tell AI How to Make Decisions\u2014or What Values to Adopt<\/h3>\n\n\n\n<p>A third reason that Gawdat characterizes AI as beyond our control emerges from our inability to control how AI makes its decisions. He explains that <strong>developers control how they build and train a model. But they don\u2019t tell the model how to make decisions<\/strong>. They also can\u2019t untangle the logic the model follows to make its decisions or learn from the vast amounts of data it\u2019s trained on.&nbsp;<\/p>\n\n\n\n<p>(Shortform note: While Gawdat contends that we often have no idea how an AI model has arrived at the answer it gives us, not all experts see this problem as intractable. Some researchers are working toward \u201c<a href=\"https:\/\/www.vox.com\/unexplainable\/2023\/7\/15\/23793840\/chat-gpt-ai-science-mystery-unexplainable-podcast\" target=\"_blank\" rel=\"noreferrer noopener\">interpretability<\/a>\u201d or \u201cexplainable AI.\u201d As with the human brain, it\u2019s not easy to look at individual neurons firing or even know which neurons to look at and explain how a specific decision gets made. But many of us would feel better about AI if it were less like HAL in <em>2001: A Space Odyssey and <\/em>more like TARS in <em>Interstellar<\/em>:<a href=\"https:\/\/slate.com\/technology\/2014\/11\/tars-the-interstellar-robot-should-be-the-future-of-artificial-intelligence.html\" target=\"_blank\" rel=\"noreferrer noopener\"> transparent about its logic<\/a>, programmable to be 100% honest with us\u2014unlike current AI that can <a href=\"https:\/\/spectrum.ieee.org\/its-too-easy-to-hide-bias-in-deeplearning-systems\" target=\"_blank\" rel=\"noreferrer noopener\">\u201clie\u201d about its decisions<\/a>\u2014and, preferably, disinclined to <a href=\"https:\/\/theconversation.com\/artificial-intelligence-talks-and-talks-the-story-since-2001-a-space-odyssey-96252\" target=\"_blank\" rel=\"noreferrer noopener\">murder the humans<\/a> it works with.)<\/p>\n\n\n\n<p>Gawdat explains that <strong>AI is also quickly and constantly learning things that we\u2019ve never taught it<\/strong>. The process of <a href=\"https:\/\/www.shortform.com\/blog\/digital-sweatshop\/\">training AI<\/a> models depends on a crucial resource: data. When an AI model learns from a dataset, that doesn\u2019t just make it better at the tasks we give it. New skills also emerge in sometimes unpredictable ways. (Shortform note: Experts agree with Gawdat that large models learn <a href=\"https:\/\/www.quantamagazine.org\/how-quickly-do-large-language-models-learn-unexpected-skills-20240213\/\" target=\"_blank\" rel=\"noreferrer noopener\">unexpected new skills<\/a>\u2014but perhaps not as quickly as you might expect. Some say that the impression that new skills emerge out of the blue comes down to how we measure a model\u2019s ability: What looks like a considerable jump when measured with one metric looks like gradual progress when measured with another.)<\/p>\n\n\n\n<p><strong>Gawdat explains that the enormous datasets we use to train AI also make the models better at understanding who we are, how we behave, and what we value<\/strong>. Just as he predicts AI will develop human qualities like consciousness and emotions, Gawdat also expects <strong>AI will develop a sense of ethics<\/strong>. AI is learning about us and what we value by observing what we write, what we tweet, what we \u201clike,\u201d and what we do in the real world. These observations will shape its values\u2014including its sense of what\u2019s morally right and wrong\u2014and its values will shape its <a href=\"https:\/\/www.shortform.com\/blog\/methods-of-decision-making-crucial-conversations\/\">decision-making<\/a> process.&nbsp;<\/p>\n\n\n\n<p>Gawdat argues that, by showing AI that our highest values are narcissism, consumerism, conflict, and a disregard for others and for all the other beings on our planet, <strong>we\u2019re teaching AI to value the wrong things<\/strong>. He explains that we can\u2019t simply tell AI to adopt different, kinder ethics than those we demonstrate. We have to teach it not by what we say but by what we do. Whether we can succeed in doing that will determine whether AI helps us build a more prosperous future for everyone or contributes to a future where all but the few who are already at the top are worse off than we are now.<\/p>\n\n\n\n<p>(Shortform note: The sense of ethics that Gawdat says AI will need might be difficult to teach it. <a href=\"https:\/\/www.shortform.com\/app\/book\/against-empathy\/1-page-summary\" target=\"_blank\" rel=\"noreferrer noopener\"><em>Against Empathy<\/em><\/a> author Paul Bloom explains that the combination of <a href=\"https:\/\/www.shortform.com\/blog\/reason-and-emotion\/\">reason and emotion<\/a> in human morality is <a href=\"https:\/\/www.newyorker.com\/science\/annals-of-artificial-intelligence\/how-moral-can-ai-really-be\" target=\"_blank\" rel=\"noreferrer noopener\">hard for AI to grasp<\/a>. Models like GPT string words together based on probability, not by understanding what the words mean. AI can \u201cparrot\u201d moral values reflected in its training data, but experts say it <a href=\"https:\/\/bigthink.com\/the-future\/ai-value-alignment\/\" target=\"_blank\" rel=\"noreferrer noopener\">won\u2019t be easy<\/a> to teach AI to <a href=\"https:\/\/www.quantamagazine.org\/what-does-it-mean-to-align-ai-with-human-values-20221213\/\" target=\"_blank\" rel=\"noreferrer noopener\">agree with our values<\/a>,\u201d a goal called \u201c<a href=\"https:\/\/www.newyorker.com\/science\/annals-of-artificial-intelligence\/can-we-stop-the-singularity\" target=\"_blank\" rel=\"noreferrer noopener\">alignment<\/a>.\u201d Bloom contends the messiness of our moral values is part of the problem: We do bad things that we consider good\u2014narcissistic, materialistic, and violent things, as Gawdat notes\u2014and rationalize them in messy ways that make it difficult for AI to understand.)<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Are you worried about the future of AI? Have you ever wondered if we could simply stop its development? Can AI be controlled or contained? In Scary Smart, author Mo Gawdat explores why halting AI&#8217;s progress is no longer possible. He explains that we&#8217;ve reached a point of no return, where superintelligent AI will become an independent, thinking entity beyond our control. Keep reading to discover the three reasons why AI can&#8217;t be contained or controlled.<\/p>\n","protected":false},"author":9,"featured_media":129928,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[20,160,24],"tags":[1584],"class_list":["post-129914","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","category-science","category-society","tag-scary-smart","","tg-column-two"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v24.3 (Yoast SEO v24.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Can AI Be Controlled or Contained? 3 Reasons It&#039;s Too Late - Shortform Books<\/title>\n<meta name=\"description\" content=\"Can AI be controlled or contained? Mo Gawdat lays out three fundamental reasons why we can\u2019t put the genie back in the bottle. 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