{"id":9323,"date":"2024-03-25T12:09:55","date_gmt":"2024-03-25T12:09:55","guid":{"rendered":"https:\/\/news.talkwithrattan.com\/index.php\/2024\/03\/25\/ai-learned-how-to-sway-humans-by-watching-a-cooperative-cooking-game\/"},"modified":"2024-03-25T12:09:55","modified_gmt":"2024-03-25T12:09:55","slug":"ai-learned-how-to-sway-humans-by-watching-a-cooperative-cooking-game","status":"publish","type":"post","link":"https:\/\/news.talkwithrattan.com\/index.php\/2024\/03\/25\/ai-learned-how-to-sway-humans-by-watching-a-cooperative-cooking-game\/","title":{"rendered":"AI learned how to sway humans by watching a cooperative cooking game"},"content":{"rendered":"<div style=\"text-align:center\"><img decoding=\"async\" src=\"https:\/\/i0.wp.com\/www.sciencenews.org\/wp-content\/uploads\/2024\/03\/031824_mh_ai_feat.jpg?fit=800%2C450&amp;ssl=1\" class=\"attachment-post-thumbnail size-post-thumbnail wp-post-image\" alt=\"AI learned how to sway humans by watching a cooperative cooking game\" title=\"AI learned how to sway humans by watching a cooperative cooking game\" \/><\/div> \r\n<br><br><div data-component=\"video-embed\">\n\t\t\t\t\n\n\n\n\n<p>If you\u2019ve ever cooked a complex meal with someone, you know the level of coordination required. Someone dices this, someone saut\u00e9s that, as you dance around holding knives and hot pans. Meanwhile, you might wordlessly nudge each other, placing ingredients or implements within the other\u2019s reach when you\u2019d like something done.<\/p>\n\n\n\n<p>How might a robot handle this type of interaction?<\/p>\n\n\n\n<p>Research presented in late 2023 at the Neural Information Processing Systems, or NeurIPS, conference, in New Orleans, offers some clues. It found that in a simple virtual kitchen, <a href=\"https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2023\/hash\/70f286e0fc977c0a3a64ef96849c8d7d-Abstract-Conference.html\" target=\"_blank\" rel=\"noopener\">AI can learn how to influence a human collaborator<\/a> just by watching humans work together.<\/p>\n\n\n<aside class=\"sn-conversion rich-text rich-text--with-sidebar\">\n<style><![CDATA[\n.email-conversion {\n  border: 1px solid #ffcccb;\n  color: white;\n  margin-top: 50px;\n  background-image: url(\"\/wp-content\/themes\/sciencenews\/client\/src\/images\/cta-module@2x.jpg\");\n  padding: 20px;\n  clear: both;\n}\n\n]]><\/style>\n\n\n\n                        <style class=\"is-layout-flow wp-block-group-is-layout-flow\"><![CDATA[.zephr-payment-form.svelte-13h66j{margin:20px auto;padding:20px;background-color:#fff;font-family:var(--zephr-typography-body-font),\n            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var(--zephr-typography-body-fallbackFont)}.zephr-payment-form-password-requirement-icon.svelte-byif2s.svelte-byif2s{margin-right:10px;font-size:15px}.zephr-payment-form-google-icon.svelte-1fdq0ht{width:20px}]]><\/style>\n                        \n                        <style><![CDATA[#zephr-payment-form-root > div {\n    background-image: url(https:\/\/sciencenews.org\/wp-content\/themes\/sciencenews\/client\/src\/images\/cta-module@2x.jpg);\n    padding:20px;\n    max-width: 100%;\n   \n}\n\n#zephr-payment-form-root > div > button {\n    max-width: 50%;\n    margin-left: auto;\n    margin-right: auto;\n    border-radius: 1px;\n    background-color: #e04821;\n    color: white;\n}\n#zephr-payment-form-root > div > div.zephr-payment-form-custom-text.svelte-s692p7{\n    color:white;\n}\n#zephr-payment-form-root > div > label > span {\n    color:white;\n}    \n.zephr-payment-form-input-label.svelte-1ozx4pz span.svelte-1ozx4pz {\n    display: none;\n}\n.zephr-payment-form-checkbox-label.svelte-eq3e4e {\n    display: flex;\n    align-items: center;\n    font-family: var(--zephr-typography-body-font), var(--zephr-typography-body-fallbackFont);\n    color: white;\n    font-weight: bold;\n}]]><\/style>\n                        \n                        \n\n<\/aside>\n\n\n<p>In the future, humans will increasingly collaborate with artificial intelligence, both online and in the physical world. And sometimes we\u2019ll want an AI to silently guide our choices and strategies, like a good teammate who knows our weaknesses. \u201cThe paper addresses a crucial and pertinent problem,\u201d how AI can learn to influence people, says Stefanos Nikolaidis, who directs the Interactive and Collaborative Autonomous Robotic Systems (ICAROS)\u00a0lab at the University of Southern California in Los Angeles, and was not involved in the work.<\/p>\n\n\n\n<p>The new work introduces a way for AI to learn to collaborate with humans, without even practicing with us. It could help us improve human-AI interactions, Nikolaidis says, and detect when <a href=\"https:\/\/proto.life\/2021\/07\/artificial-intelligence-is-learning-to-manipulate-you\/\" target=\"_blank\" rel=\"noopener\">AI might take advantage of us<\/a> \u2014 whether humans have programmed it to do so, or, someday, it decides to do so on its own.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Learning by watching<\/h2>\n\n\n\n<p>There are a few ways researchers have already trained AI to influence people. Many approaches involve what\u2019s called reinforcement learning (RL), in which an AI interacts with an environment \u2014 which can include other AIs or humans \u2014 and is rewarded for making sequences of decisions that lead to desired outcomes. DeepMind\u2019s program AlphaGo, for example, learned the board game Go using RL.<\/p>\n\n\n\n<p>But training a clueless AI from scratch to interact with people through sheer trial-and-error can waste a lot of human hours, and can even presents risks if there are, say, knives involved (as there might be in a real kitchen). Another option is to train one AI to model human behavior, then use that as a tireless human substitute for another AI to learn to interact with. Researchers have used this method in, for example, a simple game that involved entrusting a partner with monetary units. But realistically replicating human behavior in more complex scenarios, such as a kitchen, can be difficult.<\/p>\n\n\n\n<p>The new research, from a group at the University of California, Berkeley, used what\u2019s called offline reinforcement learning. Offline RL is a method for developing strategies by analyzing previously documented behavior rather than through real-time interaction. Previously, offline RL had been used mostly to help virtual robots move or to help AIs solve mazes, but here it was applied to the tricky problem of influencing human collaborators. Instead of learning by interacting with people, this AI learned by watching human interactions.<\/p>\n\n\n\n<p>Humans already have a modicum of competence at collaboration. So the amount of data needed to demonstrate competent collaboration when two people are working together is not as much as would be needed if one person were interacting with an AI that had never interacted with anyone before.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Making soup<\/h2>\n\n\n\n<p>In the study, the UC Berkeley researchers used a video game called <a href=\"https:\/\/dl.acm.org\/doi\/10.5555\/3454287.3454752\" target=\"_blank\" rel=\"noopener\"><em>Overcooked<\/em><\/a>, where two chefs divvy up tasks to prepare and serve meals, in this case soup, which earns them points. It\u2019s a 2-D world, seen from above, filled with onions, tomatoes, dishes and a stove with pots. At each time step, each virtual chef can stand still, interact with whatever is in front of it, or move up, down, left or right.<\/p>\n\n\n\n<p>The researchers first collected data from pairs of people playing the game. Then they trained AIs using offline RL or one of three other methods for comparison. (In all methods, the AIs were built on a neural network, a software architecture intended to roughly mimic how the brain works.) In one method, the AI just imitated the humans. In another, it imitated the best human performances. The third method ignored the human data and had AIs practice with each other. And the fourth was the offline RL, in which AI does more than just imitate; it pieces together the best bits of what it sees, allowing it to perform better than the behavior it observes. It uses a kind of counterfactual reasoning, where it predicts what score it would have gotten if it had followed different paths in certain situations, then adapts.<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><p>\n<div class=\"youtube-embed\" data-video_id=\"MuK5NrKN9-4\"><iframe loading=\"lazy\" title=\"See how AI nudges a human\u2019s decisions in a cooking game | Science News\" width=\"696\" height=\"392\" src=\"https:\/\/www.youtube.com\/embed\/MuK5NrKN9-4?feature=oembed&#038;enablejsapi=1\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen><\/iframe><\/div>\n<\/p><figcaption class=\"wp-element-caption\">A human (left) and AI (right) collaborate to cook soup containing tomatoes (red and green objects) and\/or onions (beige objects). In this case, the AI, but not the human, knows that the duo will receive a bonus if the human serves the soup. The second half of the video shows the result of a new training method in which an AI learns how to influence human behavior. Here, the AI has figured out that if it places a dish (white circle) next to the stove, the human will use it to deliver the soup, at the bottom of the screen.<\/figcaption><\/figure>\n\n\n\n<p>The AIs played two versions of the game. In the \u201chuman-deliver\u201d version, the team earned double points if the soup was delivered by the human partner. In the \u201ctomato-bonus\u201d version, soup with tomato and no onion earned double points. After the training, the chefbots played with real people. The scoring system was different during training and evaluation than when the initial human data were collected, so the AIs had to extract general principles to score higher. Crucially, during evaluation, humans didn\u2019t know these rules, like no onion, so the AIs had to nudge them.<\/p>\n\n\n\n\n\n<p>On the human-deliver game, training using offline RL led to an average score of 220, about 50 percent more points than the best comparison methods. On the tomato-bonus game, it led to an average score of 165, or about double the points. To support the hypothesis that the AI had learned to influence people, the paper described how when the bot wanted the human to deliver the soup, it would place a dish on the counter near the human. In the human-human data, the researchers found no instances of one person passing a plate to another in this fashion. But there were events where someone put down a dish and ones where someone picked up a dish, and the AI could have seen value in stitching these acts together.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Nudging human behavior<\/h2>\n\n\n\n<p>The researchers also developed a method for the AI to infer and then influence humans\u2019 underlying strategies in cooking steps, not just their immediate actions. In real life, if you know that your cooking partner is slow to peel carrots, you might jump on that role each time until your partner stops going for the carrots. A modification to the neural network to consider not only the current game state but also a history of their partner\u2019s actions would give a clue as to what their partner\u2019s current strategy is.<\/p>\n\n\n\n<p>Again, the team collected human-human data. Then they trained AIs using this offline RL network architecture or the previous offline RL one. When tested with human partners, inferring the partner\u2019s strategy improved scores by roughly 50 percent on average. In the tomato-bonus game, for example, the bot learned to repeatedly block the onions until people eventually left them alone. That the AI worked so well with humans was surprising, says study coauthor Joey Hong, a computer scientist at UC Berkeley.<\/p>\n\n\n\n<p>\u201cAvoiding the use of a human model is great,\u201d says Rohan Paleja, a computer scientist at MIT Lincoln Laboratory in Lexington, Mass., who was not involved in the work. \u201cIt makes this approach applicable to a lot of real-world problems that do not currently have accurate simulated humans.\u201d He also said the system is data-efficient; it achieved its abilities after watching only 20 human-human games (each 1,200 steps long).<\/p>\n\n\n\n<p>Nikolaidis sees potential for the method to enhance AI-human collaboration. But he wishes that the authors had better documented the observed behaviors in the training data and exactly how the new method changed people\u2019s behaviors to improve scores.\u00a0<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">For better or worse<\/h2>\n\n\n\n<p>In the future, we may be working with AI partners in kitchens, warehouses, operating rooms, battlefields and purely digital domains like writing, research and travel planning. (We already use AI tools for some of these tasks.) \u201cThis type of approach could be helpful in supporting people to reach their goals when they don\u2019t know the best way to do this,\u201d says Emma Brunskill, a computer scientist at Stanford University who was not involved in the work. She proposes that an AI could observe data from fitness apps and <a href=\"https:\/\/www.sciencenews.org\/article\/nudging-people-make-good-choices-can-backfire\">learn to better nudge people<\/a> to meet New Year\u2019s exercise resolutions through notifications (<em>SN: 3\/8\/17<\/em>). The method might also learn to get people to increase charitable donations, Hong says.<\/p>\n\n\n\n<p>On the other hand, AI influence has a darker side. \u201cOnline recommender systems can, for example,\u00a0try to have us buy more, or watch more TV,\u201d Brunskill says, \u201cnot just for this moment, but also to shape us into being people who buy more or watch more.\u201d<\/p>\n\n\n\n<p>Previous work, which was not about human-AI collaboration, has shown how <a href=\"https:\/\/spectrum.ieee.org\/recommendation-engine-insidious\" target=\"_blank\" rel=\"noopener\">RL can help recommender systems manipulate users\u2019 preferences<\/a> so that those preferences would be more predictable and satisfiable, even if people didn\u2019t want their preferences shifted. And even if AI means to help, it may do so in ways we don\u2019t like, according to Micah Carroll, a computer scientist at UC Berkeley who works with one of the paper authors. For instance, the strategy of blocking a co-chef\u2019s path could be seen as a form of coercion. \u201cWe, as a field, have yet to integrate ways for a person to communicate to a system what\u00a0types of influence they are OK with,\u201d he says. \u201cFor example, \u2018I\u2019m OK with an AI trying to argue for a specific strategy, but not forcing me to do it if I don\u2019t want to.\u2019\u201d<\/p>\n\n\n\n<p>Hong is currently looking to use his approach to <a href=\"https:\/\/www.sciencenews.org\/article\/generative-ai-chatbots-chatgpt-safety-concerns\">improve chatbots<\/a> (<em>SN: 2\/1\/24<\/em>). The large language models behind interfaces such as ChatGPT typically aren\u2019t trained to carry out multi-turn conversations. \u201cA lot of times when you ask a GPT to do something, it gives you a best guess of what it thinks you want,\u201d he says. \u201cIt won\u2019t ask for clarification to understand your true intent and make its answers more personalized.\u201d<\/p>\n\n\n\n<p>Learning to influence and help people in a conversation seems like a realistic near-term application. \u201c<em>Overcooked<\/em>,\u201d he says, with its two dimensions and limited menu, \u201cis not really going to help us make better chefs.\u201d<\/p>\n\n\n\n\t\t\t<\/div>\r\n<br>","protected":false},"excerpt":{"rendered":"<p>If you\u2019ve ever cooked a complex meal with someone, you know the level of coordination required. Someone dices this, someone saut\u00e9s that, as you dance around holding knives and hot pans. Meanwhile, you might wordlessly nudge each other, placing ingredients or implements within the other\u2019s reach when you\u2019d like something done. 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