{"id":245319,"date":"2021-05-08T17:38:21","date_gmt":"2021-05-08T14:38:21","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/4-ideas-about-ai-that-even-experts-get-wrong\/"},"modified":"2021-05-08T17:38:21","modified_gmt":"2021-05-08T14:38:21","slug":"4-ideas-about-ai-that-even-experts-get-wrong","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/4-ideas-about-ai-that-even-experts-get-wrong\/","title":{"rendered":"#4 ideas about AI that even \u2018experts\u2019 get wrong"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-6a28148e0c561\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #dd3333;color:#dd3333\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #dd3333;color:#dd3333\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a28148e0c561\" checked aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/buradabiliyorum.com\/en\/4-ideas-about-ai-that-even-experts-get-wrong\/#Narrow_AI_and_general_AI_are_not_on_the_same_scale\" >Narrow AI and general AI are not on the same scale<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/buradabiliyorum.com\/en\/4-ideas-about-ai-that-even-experts-get-wrong\/#The_easy_things_are_hard_to_automate\" >The easy things are hard to automate<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/buradabiliyorum.com\/en\/4-ideas-about-ai-that-even-experts-get-wrong\/#Anthropomorphizing_AI_doesnt_help\" >Anthropomorphizing AI doesn\u2019t help<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/buradabiliyorum.com\/en\/4-ideas-about-ai-that-even-experts-get-wrong\/#AI_without_a_body\" >AI without a body<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/buradabiliyorum.com\/en\/4-ideas-about-ai-that-even-experts-get-wrong\/#Common_sense_in_AI\" >Common sense in AI<\/a><\/li><\/ul><\/nav><\/div>\n<p>&#8220;<strong>#4 ideas about AI that even \u2018experts\u2019 get wrong<\/strong>&#8221;<\/p>\n<div>The history of artificial intelligence has been marked by repeated cycles of extreme optimism and promise followed by<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2018\/11\/12\/artificial-intelligence-winter-history\/\">disillusionment and disappointment<\/a>. Today\u2019s AI systems can perform complicated tasks in a wide range of areas, such as mathematics, <a href=\"https:\/\/buradabiliyorum.com\/en\/category\/game\/\" data-internallinksmanager029f6b8e52c=\"7\" title=\"Game\" target=\"_blank\" rel=\"noopener\">game<\/a>s, and photorealistic image generation. But some of the early goals of AI like housekeeper robots and self-driving cars continue to recede as we <a href=\"https:\/\/buradabiliyorum.com\/en\/category\/download-scripts-themes-apps\/\" data-internallinksmanager029f6b8e52c=\"9\" title=\"Download Scripts &amp; Themes &amp; Apps\" target=\"_blank\" rel=\"noopener\">app<\/a>roach them.<\/p>\n<p>Part of the continued cycle of missing these goals is due to incorrect assumptions about AI and natural intelligence, according to Melanie Mitchell, Davis Professor of Complexity at the Santa Fe Institute and author of<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2020\/01\/13\/melanie-mitchell-ai-guide-for-thinking-humans\/\"><em>Artificial Intelligence: A Guide For Thinking Humans<\/em><\/a>.<\/p>\n<p>In a new paper titled \u201c<a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/arxiv.org\/abs\/2104.12871\">Why AI is Harder Than We Think<\/a>,\u201d Mitchell lays out four common fallacies about AI that cause misunderstandings not only among the public and the <a href=\"https:\/\/buradabiliyorum.com\/en\/category\/social-mediaa\/\" data-internallinksmanager029f6b8e52c=\"1\" title=\"Social Media\" target=\"_blank\" rel=\"noopener\">media<\/a>, but also among experts. These fallacies give a false sense of confidence about how close we are to achieving<span>\u00a0<\/span>artificial <a href=\"https:\/\/buradabiliyorum.com\/en\/category\/general\/\" data-internallinksmanager029f6b8e52c=\"3\" title=\"General\" target=\"_blank\" rel=\"noopener\">general<\/a> intelligence, AI systems that can match the cognitive and general problem-solving skills of humans.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Narrow_AI_and_general_AI_are_not_on_the_same_scale\"><\/span>Narrow AI and general AI are not on the same scale<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The kind of AI that we have today can be very good at<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2021\/05\/03\/artificial-intelligence-fallacies\/Narrow%20artificial%20intelligence,\">solving narrowly defined problems<\/a>. They can outmatch humans at Go and chess, find cancerous patterns in x-ray images with remarkable accuracy, and convert audio data to text. But designing systems that can solve single problems does not necessarily get us closer to solving more complicated problems. Mitchell describes the first fallacy as \u201cNarrow intelligence is on a continuum with general intelligence.\u201d<\/p>\n<p>\u201cIf people see a machine do something amazing, albeit in a narrow area, they often assume the field is that much further along toward general AI,\u201d Mitchell writes in her paper.<\/p>\n<p>For instance, today\u2019s<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2018\/02\/20\/ai-machine-learning-nlg-nlp\/\">natural language processing<\/a><span>\u00a0<\/span>systems have come a long way toward solving many different problems, such as translation,<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2020\/08\/17\/openai-gpt-3-commercial-ai\/\">text generation<\/a>, and<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2019\/09\/09\/aristo-ai-science-exam\/\">question-answering<\/a><span>\u00a0<\/span>on specific problems. At the same time, we have deep learning systems that can convert voice data to text in real-time. Behind each of these achievements are thousands of hours of research and development (and<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2019\/08\/26\/deepmind-mustafa-suleyman-commercial-ai\/\">millions of dollars<\/a><span>\u00a0<\/span>spent on computing and data). But the AI community still hasn\u2019t solved the problem of creating agents that can engage in open-ended conversations without losing coherence over long stretches. Such a system requires more than just solving smaller problems; it requires common sense, one of the key unsolved challenges of AI.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_easy_things_are_hard_to_automate\"><\/span>The easy things are hard to automate<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<figure class=\"post-image post-mediaBleed aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-1350158 js-lazy\" alt=\"Vision, one of the problems every living being solves without effort, remains a challenge for computers\" width=\"2000\" height=\"1118\" sizes=\"auto, (max-width: 2000px) 100vw, 2000px\" src=\"https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/computer-vision-object-detection.png\" srcset=\"https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/computer-vision-object-detection.png 2000w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/computer-vision-object-detection-280x157.png 280w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/computer-vision-object-detection-483x270.png 483w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/computer-vision-object-detection-242x135.png 242w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/computer-vision-object-detection-796x445.png 796w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/computer-vision-object-detection-1592x890.png 1592w\"\/><figcaption>Credit: <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2021\/05\/03\/artificial-intelligence-fallacies\/\">Ben Dickson<\/a><\/figcaption><figcaption><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/thenextweb.com\/news\/#\" data-url=\"https:\/\/twitter.com\/intent\/tweet?url=https%3A%2F%2Feditorial.thenextweb.com%2Fneural%2F2021%2F05%2F08%2F4-ideas-about-ai-that-even-experts-get-wrong-syndication%2F&amp;via=thenextweb&amp;related=thenextweb&amp;text=Check out this picture on: Vision, one of the problems every living being solves without effort, remains a challenge for computers\" data-title=\"Share Vision, one of the problems every living being solves without effort, remains a challenge for computers on Twitter\" data-width=\"685\" data-height=\"500\" class=\"post-image-share popitup\" title=\"Share Vision, one of the problems every living being solves without effort, remains a challenge for computers on Twitter\"><i class=\"icon icon--inline icon--twitter--dark\"\/><\/a>Vision, one of the problems every living being solves without effort, remains a challenge for computers<\/figcaption><noscript><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-1350158\" src=\"https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/computer-vision-object-detection.png\" alt=\"Vision, one of the problems every living being solves without effort, remains a challenge for computers\" width=\"2000\" height=\"1118\" srcset=\"https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/computer-vision-object-detection.png 2000w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/computer-vision-object-detection-280x157.png 280w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/computer-vision-object-detection-483x270.png 483w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/computer-vision-object-detection-242x135.png 242w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/computer-vision-object-detection-796x445.png 796w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/computer-vision-object-detection-1592x890.png 1592w\"\/><\/noscript><\/figure>\n<p>When it comes to humans, we would expect an intelligent person to do hard things that take years of study and practice. Examples might include tasks such as solving calculus and physics problems, playing chess at grandmaster level, or memorizing a lot of poems.<\/p>\n<p>But decades of AI research have proven that the hard tasks, those that require conscious attention, are easier to automate. It is the easy tasks, the things that we take for granted, that are hard to automate. Mitchell describes the second fallacy as \u201cEasy things are easy and hard things are hard.\u201d<\/p>\n<p>\u201cThe things that we humans do without much thought\u2014looking out in the world and making sense of what we see, carrying on a conversation, walking down a crowded sidewalk without bumping into anyone\u2014turn out to be the hardest challenges for machines,\u201d Mitchell writes. \u201cConversely, it\u2019s often easier to get machines to do things that are very hard for humans; for example, solving complex mathematical problems,<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2018\/07\/02\/ai-plays-chess-go-poker-video-games\/\">mastering games like chess and Go<\/a>, and translating sentences between hundreds of languages have all turned out to be relatively easier for machines.\u201d<\/p>\n<p>Consider vision, for example. Over billions of years, organisms have developed complex apparatuses for processing light signals. Animals use their eyes to take stock of the objects surrounding them, navigate their surroundings, find food, detect threats, and accomplish many other tasks that are vital to their survival. We humans have inherited all those capabilities from our ancestors and use them without conscious thought. But the underlying mechanism is indeed more complicated than large mathematical formulas that frustrate us through high school and college.<\/p>\n<p>Case in point: We still don\u2019t have<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2019\/01\/14\/what-is-computer-vision\/\">computer vision<\/a><span>\u00a0<\/span>systems that are nearly as versatile as human vision. We have managed to create<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2019\/08\/05\/what-is-artificial-neural-network-ann\/\">artificial neural networks<\/a><span>\u00a0<\/span>that roughly mimic parts of the animal and human vision system, such as detecting objects and segmenting images. But they are brittle, sensitive to many different kinds of perturbations, and they can\u2019t mimic the<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2020\/06\/01\/artificial-intelligence-computer-vision-fpicu\/\">full scope of tasks that biological vision can accomplish<\/a>. That\u2019s why, for instance, the computer vision systems used in self-driving cars need to be complemented with advanced <a href=\"https:\/\/buradabiliyorum.com\/en\/category\/technology\/\" data-internallinksmanager029f6b8e52c=\"4\" title=\"Technology\" target=\"_blank\" rel=\"noopener\">technology<\/a> such as lidars and mapping data.<\/p>\n<p>Another area that has proven to be very difficult is sensorimotor skills that humans master without explicit training. Think of the how you handle objects, walk, run, and jump. These are tasks that you can do without conscious thought. In fact, while walking, you can do other things, such as listen to a podcast or talk on the phone. But these kinds of skills remain a<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2019\/10\/21\/openai-rubiks-cube-reinforcement-learning\/\">large and expensive challenge<\/a><span>\u00a0<\/span>for current AI systems.<\/p>\n<p>\u201cAI is harder than we think, because we are largely unconscious of the complexity of our own thought processes,\u201d Mitchell writes.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Anthropomorphizing_AI_doesnt_help\"><\/span>Anthropomorphizing AI doesn\u2019t help<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<figure class=\"post-image post-mediaBleed aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-1350159 js-lazy\" alt=\"Comparing contemporary AI systems with human intelligence creates an erroneous image of the current state of artificial intelligence\" width=\"857\" height=\"482\" sizes=\"auto, (max-width: 857px) 100vw, 857px\" src=\"https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/Comparing-contemporary-AI-systems-with-human-intelligence-creates-an-erroneous-image-of-the-current-state-of-artificial-intelligence.jpg\" srcset=\"https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/Comparing-contemporary-AI-systems-with-human-intelligence-creates-an-erroneous-image-of-the-current-state-of-artificial-intelligence.jpg 857w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/Comparing-contemporary-AI-systems-with-human-intelligence-creates-an-erroneous-image-of-the-current-state-of-artificial-intelligence-280x157.jpg 280w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/Comparing-contemporary-AI-systems-with-human-intelligence-creates-an-erroneous-image-of-the-current-state-of-artificial-intelligence-480x270.jpg 480w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/Comparing-contemporary-AI-systems-with-human-intelligence-creates-an-erroneous-image-of-the-current-state-of-artificial-intelligence-240x135.jpg 240w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/Comparing-contemporary-AI-systems-with-human-intelligence-creates-an-erroneous-image-of-the-current-state-of-artificial-intelligence-796x448.jpg 796w\"\/><figcaption>Credit: <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/icons8.com\/photos\">Icons8<\/a><\/figcaption><figcaption><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/thenextweb.com\/news\/#\" data-url=\"https:\/\/twitter.com\/intent\/tweet?url=https%3A%2F%2Feditorial.thenextweb.com%2Fneural%2F2021%2F05%2F08%2F4-ideas-about-ai-that-even-experts-get-wrong-syndication%2F&amp;via=thenextweb&amp;related=thenextweb&amp;text=Check out this picture on: Comparing contemporary AI systems with human intelligence creates an erroneous image of the current state of artificial intelligence\" data-title=\"Share Comparing contemporary AI systems with human intelligence creates an erroneous image of the current state of artificial intelligence on Twitter\" data-width=\"685\" data-height=\"500\" class=\"post-image-share popitup\" title=\"Share Comparing contemporary AI systems with human intelligence creates an erroneous image of the current state of artificial intelligence on Twitter\"><i class=\"icon icon--inline icon--twitter--dark\"\/><\/a>Comparing contemporary AI systems with human intelligence creates an erroneous image of the current state of artificial intelligence<\/figcaption><noscript><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-1350159\" src=\"https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/Comparing-contemporary-AI-systems-with-human-intelligence-creates-an-erroneous-image-of-the-current-state-of-artificial-intelligence.jpg\" alt=\"Comparing contemporary AI systems with human intelligence creates an erroneous image of the current state of artificial intelligence\" width=\"857\" height=\"482\" srcset=\"https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/Comparing-contemporary-AI-systems-with-human-intelligence-creates-an-erroneous-image-of-the-current-state-of-artificial-intelligence.jpg 857w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/Comparing-contemporary-AI-systems-with-human-intelligence-creates-an-erroneous-image-of-the-current-state-of-artificial-intelligence-280x157.jpg 280w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/Comparing-contemporary-AI-systems-with-human-intelligence-creates-an-erroneous-image-of-the-current-state-of-artificial-intelligence-480x270.jpg 480w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/Comparing-contemporary-AI-systems-with-human-intelligence-creates-an-erroneous-image-of-the-current-state-of-artificial-intelligence-240x135.jpg 240w, https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/Comparing-contemporary-AI-systems-with-human-intelligence-creates-an-erroneous-image-of-the-current-state-of-artificial-intelligence-796x448.jpg 796w\"\/><\/noscript><\/figure>\n<p>The field of AI is replete with vocabulary that puts software on the same level as human intelligence. We use terms such as \u201clearn,\u201d \u201cunderstand,\u201d \u201cread,\u201d and \u201cthink\u201d to describe how AI algorithms work. While such anthropomorphic terms often serve as shorthand to help convey complex software mechanisms, they can<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2019\/01\/02\/humanizing-ai-deep-learning-alphazero\/\">mislead us to think<\/a><span>\u00a0<\/span>that current AI systems work like the human mind.<\/p>\n<p>Mitchell calls this fallacy \u201cthe lure of wishful mnemonics\u201d and writes, \u201cSuch shorthand can be misleading to the public trying to understand these results (and to the media reporting on them), and can also unconsciously shape the way even AI experts think about their systems and how closely these systems resemble human intelligence.\u201d<\/p>\n<p>The wishful mnemonics fallacy has also led the AI community to name algorithm-evaluation benchmarks in ways that are misleading. Consider, for example, the<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/gluebenchmark.com\/\">General Language Understanding Evaluation (GLUE) benchmark<\/a>, developed by some of the most esteemed organizations and academic institutions in AI. GLUE provides a set of tasks that help evaluate how a language model can generalize its capabilities beyond the task it has been trained for. But contrary to what the media portray, if an AI agent gets a higher GLUE score than a human, it doesn\u2019t mean that it is better at language understanding than humans.<\/p>\n<p>\u201cWhile machines can outperform humans on these particular benchmarks, AI systems are still far from matching the more general human abilities we associate with the benchmarks\u2019 names,\u201d Mitchell writes.<\/p>\n<p>A stark example of wishful mnemonics is a 2017 project at Facebook Artificial Intelligence Research, in which scientists trained two AI agents to negotiate on tasks based on human conversations. In their<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/engineering.fb.com\/2017\/06\/14\/ml-applications\/deal-or-no-deal-training-ai-bots-to-negotiate\/\">blog post<\/a>, the researchers noted that \u201cupdating the parameters of both agents led to divergence from human language as<span>\u00a0<\/span><strong>the agents developed their own language<\/strong>\u00a0for negotiating [emphasis mine].\u201d<\/p>\n<p>This led to a stream of clickbait articles that warned about AI systems that were becoming smarter than humans and were communicating in secret dialects. Four years later, the most advanced language models still struggle with<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.technologyreview.com\/2020\/08\/22\/1007539\/gpt3-openai-language-generator-artificial-intelligence-ai-opinion\/\">understanding basic concepts<\/a><span>\u00a0<\/span>that most humans learn at a very young age without being instructed.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"AI_without_a_body\"><\/span>AI without a body<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div class=\"wp-block-image\">\n<p>Can intelligence exist in isolation from a rich physical experience of the world? This is a question that scientists and philosophers have puzzled over for centuries.<\/p>\n<p>One school of thought believes that intelligence is all in the brain and can be separated from the body, also known as the \u201c<a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/en.wikipedia.org\/wiki\/Brain_in_a_vat\">brain in a vat<\/a>\u201d theory. Mitchell calls it the \u201cIntelligence is all in the brain\u201d fallacy. With the right algorithms and data, the thinking goes, we can create AI that lives in servers and matches human intelligence. For the proponents of this way of thinking, especially those who<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2019\/11\/25\/ai-research-neural-networks-compute-costs\/\">support pure deep learning\u2013based approaches<\/a>, reaching general AI hinges on gathering the right amount of data and creating larger and larger neural networks.<\/p>\n<p>Meanwhile, there\u2019s growing evidence that this approach is doomed to fail. \u201cA growing cadre of researchers is questioning the basis of the \u2018all in the brain\u2019 information processing model for understanding intelligence and for creating AI,\u201d she writes.<\/p>\n<p>Human and animal brains have evolved along with all other body organs with the ultimate goal of improving chances of survival. Our intelligence is tightly linked to the limits and capabilities of our bodies. And there is an expanding field of<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2021\/04\/26\/reinforcement-learning-embodied-ai\/\">embodied AI<\/a><span>\u00a0<\/span>that aims to create agents that develop intelligent skills by interacting with their environment through different sensory stimuli.<\/p>\n<p>Mitchell notes that neuro<a href=\"https:\/\/buradabiliyorum.com\/en\/category\/sciencee\/\" data-internallinksmanager029f6b8e52c=\"5\" title=\"Science\" target=\"_blank\" rel=\"noopener\">science<\/a> research suggests that \u201cneural structures controlling cognition are richly linked to those controlling sensory and motor systems, and that abstract thinking exploits body-based neural \u2018maps.\u2019\u201d And in fact, there\u2019s growing evidence and research that proves feedback from different sensory areas of the brain affects both our conscious and unconscious thoughts.<\/p>\n<p>Mitchell supports the idea that emotions, feelings, subconscious biases, and physical experience are inseparable from intelligence. \u201cNothing in our knowledge of psychology or neuroscience supports the possibility that \u2018pure rationality\u2019 is separable from the emotions and cultural biases that shape our cognition and our objectives,\u201d she writes. \u201cInstead, what we\u2019ve learned from research in embodied cognition is that human intelligence seems to be a strongly integrated system with closely interconnected attributes, including emotions, desires, a strong sense of selfhood and autonomy, and a commonsense understanding of the world. It\u2019s not at all clear that these attributes can be separated.\u201d<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Common_sense_in_AI\"><\/span>Common sense in AI<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Developing general AI needs an<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2019\/12\/03\/francois-chollet-arc-ai-measurement\/\">adjustment to our understanding of intelligence itself<\/a>. We are still struggling to define what intelligence is and how to measure it in artificial and natural beings.<\/p>\n<p>\u201cIt\u2019s clear that to make and assess progress in AI more effectively, we will need to develop a better vocabulary for talking about what machines can do,\u201d Mitchell writes. \u201cAnd more generally, we will need a better scientific understanding of intelligence as it manifests in different systems in nature.\u201d<\/p>\n<p>Another challenge that Mitchell discusses in her paper is that of common sense, which she describes as \u201ca kind of umbrella for what\u2019s missing from today\u2019s state-of-the-art AI systems.\u201d<\/p>\n<p>Common sense includes the knowledge that we acquire about the world and apply it every day without much effort. We learn a lot without being explicitly instructed, by exploring the world when we are children. These include concepts such as space, time, gravity, and the physical properties of objects. For example, a child learns at a very young age that when an object becomes occluded behind another, it has not disappeared and continues to exist, or when a ball rolls across a table and reaches the ledge, it should fall off. We use this knowledge to build mental models of the world, make causal inferences, and predict future states with decent accuracy.<\/p>\n<p>This kind of knowledge\u00a0<a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2021\/03\/15\/machine-learning-causality\/\">is missing in today\u2019s AI systems<\/a>, which makes them unpredictable and data-hungry. In fact, housekeeping and driving, the two AI applications mentioned at the beginning of this article, are things that most humans learn through common sense and a little bit of practice.<\/p>\n<p>Common sense also includes basic facts about human nature and life, things that we omit in our conversations and writing because we know our readers and listeners know them. For example, we know that if two people are \u201ctalking on the phone,\u201d it means that they aren\u2019t in the same room. We also know that if \u201cJohn reached for the sugar,\u201d it means that there was a container with sugar inside it somewhere near John. This kind of knowledge is crucial to areas such as natural language processing.<\/p>\n<p>\u201cNo one yet knows how to capture such knowledge or abilities in machines. This is the current frontier of AI research, and one encouraging way forward is to tap into what\u2019s known about the development of these abilities in young children,\u201d Mitchell writes.<\/p>\n<p>While we still don\u2019t know the answers to many of these questions, a first step toward finding solutions is being aware of our own erroneous thoughts. \u201cUnderstanding these fallacies and their subtle influences can point to directions for creating more robust, trustworthy, and perhaps actually<span>\u00a0<\/span><em>intelligent<\/em><span>\u00a0AI<\/span> systems,\u201d Mitchell writes.<\/p>\n<p><i><span>This article was originally published by Ben Dickson on\u00a0<\/span><\/i><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/\"><i><span>TechTalks<\/span><\/i><\/a><i><span>, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech, and what we need to look out for. You can read the original article\u00a0<a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/bdtechtalks.com\/2021\/05\/03\/artificial-intelligence-fallacies\/\">here<\/a>.<\/span><\/i><\/p>\n<\/div>\n<\/div>\n<p><script async src=\"\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/p>\n<blockquote><p><strong><span style=\"color: #ff6600;\">If you liked the article, do not forget to share it with your friends. Follow us on\u00a0<span style=\"color: #ff0000;\"><a style=\"color: #ff0000;\" href=\"https:\/\/news.google.com\/publications\/CAAqBwgKMLG0nwswvr63Aw\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">Google News<\/a><\/span>\u00a0too, click on the star and choose us from your favorites.<\/span><\/strong><\/p><\/blockquote>\n<blockquote>\n<p style=\"text-align: center;\">For forums sites go to <span style=\"color: #ff9900;\"><a style=\"color: #ff9900;\" href=\"https:\/\/forum.buradabiliyorum.com\/\" target=\"_blank\" rel=\"noopener\">Forum.BuradaBiliyorum.Com<\/a><\/span><\/strong>\n<\/p><\/blockquote>\n<blockquote>\n<p style=\"text-align: center;\"><strong>If you want to read more like this article, you can visit our <span style=\"color: #ff9900;\"><a style=\"color: #ff9900;\" href=\"https:\/\/en.buradabiliyorum.com\/technology\/\" target=\"_blank\" rel=\"noopener\">Technology category.<\/a><\/span><\/strong><\/p>\n<\/blockquote>\n<p><span style=\"color: black;\"><a style=\"color: #ff9900;\" href=\"https:\/\/thenextweb.com\/news\/4-ideas-about-ai-that-even-experts-get-wrong-syndication\" target=\"_blank\" rel=\"noopener\">Source<\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>&#8220;#4 ideas about AI that even \u2018experts\u2019 get wrong&#8221; The history of artificial intelligence has been marked by repeated cycles of extreme optimism and promise followed by\u00a0disillusionment and disappointment. Today\u2019s AI systems can perform complicated tasks in a wide range of areas, such as mathematics, games, and photorealistic image generation. But some of the early&#8230;<\/p>\n","protected":false},"author":1,"featured_media":245320,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"https:\/\/img-cdn.tnwcdn.com\/image\/neural?filter_last=1&fit=1280,640&url=https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/05\/AI-robot-hed.jpg&signature=9b2516de3024fa1123103bc5acad624d","fifu_image_alt":"","footnotes":""},"categories":[18],"tags":[],"class_list":["post-245319","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technology"],"_links":{"self":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/posts\/245319","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/comments?post=245319"}],"version-history":[{"count":0,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/posts\/245319\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/media\/245320"}],"wp:attachment":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/media?parent=245319"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/categories?post=245319"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/tags?post=245319"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}