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Originally published in\u00a0Forbes<\/a><\/p>\n On\u00a0a recent episode<\/a> of the Dr. Data Show, my co-host Luba Glouhova and I tackled\u00a0a new paper<\/a> authored by AI luminary Yann LeCun alongside other researchers. We had been tipped off by another co-author of the paper, AI researcher Philippe Wyder, who\u00a0reached out on social media<\/a> to say the paper related to\u00a0our prior episode<\/a> since it \u201cshows a path out of our muddled AGI discourse by embracing specialization.\u201d<\/p>\n The paper proposes a much-needed pivot for the industry: Because the ever-popular notion of artificial general intelligence offers only a hazy,\u00a0overzealous goal<\/a>, these researchers have proposed a new North Star they call \u201csuperhuman adaptable intelligence.\u201d<\/p>\n Defining Superhuman Adaptable Intelligence<\/p>\n What is SAI? The authors\u2019 definition breaks down into two parts. First, SAI is capable of adapting to exceed humans at any task humans can do \u2013 but crucially, it espouses tackling one task at a time. Second, SAI can also adapt to useful tasks outside the human domain.<\/p>\n SAI establishes a new goal for the industry. Instead of trying to build a singular, all-capable solution that can do everything a human can do, which is the goal represented by AGI, this framework champions a return to specialized, narrow AI. These researchers advocate leveraging massive amounts of data through self-supervised learning, as for example LLMs do, but they also advise that such tech then be adapted to solve specific problems.<\/p>\n As Luba pointed out during our podcast discussion, one of the most consequential insights highlighted by the authors is the recognition that human intelligence itself is not terribly general. I had a similar reckoning back in 1991 right before I began my Ph.D. at Columbia; I realized that human skills are quite particular and specialized, shaped by millions of years of evolutionary adaptation within a specific environment. Rather than attempting to fully replicate the arcane, unique distribution of capabilities that are specific to humans \u2013 a tall order to say the least \u2013 the paper advocates to instead focus: \u201cThe AI that folds our proteins should not be the AI that folds our laundry.\u201d Even as systems scale with computational power, they still benefit immensely from specializing for the specific task at hand. Directing limited resources toward an individual, valuable goal is more productive than trying to build a machine that is \u201cgeneral\u201d in the sense of being good at everything \u2013 or even good at the more limited yet vast range of things humans happen to be capable of.<\/p>\n Out Of The Frying Pan?<\/p>\n But does coining a new buzzword really hold the potential to cure the AI industry\u2019s chronic hype problem, namely the disputable promise that\u00a0we\u2019re rapidly headed toward AGI<\/a>? I have reservations. By using the words \u201csuperhuman\u201d and \u201cintelligence,\u201d the term still flirts with the very sci-fi, tipping-point singularity hype that has haunted the notion of \u201cAI\u201d since it was conceived of in the 1950s. It allows industry leaders like LeCun to remain intellectually sound and avoid\u00a0AGI\u2019s intrinsic incoherence<\/a>, while simultaneously keeping enough buzzword appeal to secure massive VC funding for new startups. Indeed, his new startup just\u00a0raised a $1 billion seed<\/a>.<\/p>\n As Luba and I joked on the podcast, SAI might just be a new flavor of Kool-Aid for the masses \u2013 but at least it\u2019s a healthier, organic, sugar-free Kool-Aid. The true gem in the acronym is the word \u201cadaptable,\u201d which centers projects on a grounded, measurable metric: how quickly a system can adapt to become good at a particular task.<\/p>\n I say this paper is a net positive. It represents a desperately-needed sober look at our field\u2019s often unrealistic goals. It aims to steer the industry away from the misleading narrative of\u00a0autonomous, human-level AI agents<\/a>, and back toward technologies that provide concrete, feasible value. The philosophy presented aligns well with what I\u2019ve been espousing regarding the need for\u00a0hybrid AI<\/a>. To make genAI products viable for full deployment that captures scalable value, we must treat each initiative as a specialized endeavor, applying a targeted reliability layer to more narrowly form-fit the specific problem at hand.<\/p>\n \u00a0<\/p>\n About the author<\/p>\n Eric Siegel, Ph.D., is a former Columbia University professor who helps companies deploy machine learning. He is the cofounder and CEO of Gooder AI<\/a>, the founder of the long-running Machine Learning Week<\/a> conference series, the instructor of the acclaimed online course \u201cMachine Learning Leadership and Practice \u2013 End-to-End Mastery<\/a>,\u201d executive editor of The Machine Learning Times<\/a>, and a frequent keynote speaker<\/a>. He wrote the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die<\/a>, which has been used in courses at hundreds of universities, as well as The AI Playbook: Mastering the Rare Art of Machine Learning Deployment<\/a>. Eric\u2019s interdisciplinary work bridges the stubborn technology\/business gap. At Columbia, he won the Distinguished Faculty award when teaching the graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. A Forbes contributor<\/a>, Eric publishes op-eds on analytics and social justice<\/a>.<\/p>\n Eric has appeared on<\/a>\u00a0Bloomberg TV and Radio, BNN (Canada), Israel National Radio, National Geographic Breakthrough, NPR Marketplace, Radio National (Australia), and TheStreet. A Forbes contributor<\/a>, Eric and his books have been featured in<\/a>\u00a0BBC,\u00a0Big Think, Businessweek, CBS MoneyWatch, Contagious Magazine, The European Business Review, Fast Company, The Financial Times, Fortune, GQ, Harvard Business Review, The Huffington Post, The Los Angeles Times, Luckbox Magazine, MIT Sloan Management Review, The New York Review of Books, The New York Times, Newsweek, Quartz, Salon, The San Francisco Chronicle, Scientific American, The Seattle Post-Intelligencer, Trailblazers with Walter Isaacson, The Wall Street Journal, The Washington Post, and WSJ MarketWatch.<\/p>\n","protected":false},"excerpt":{"rendered":"Originally published in\u00a0Forbes On\u00a0a recent episode of the Dr. Data Show, my co-host Luba Glouhova and I tackled\u00a0a…\n","protected":false},"author":2,"featured_media":81468,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[6744,8362,3013,1085,3328,6098,10726,10725],"class_list":["post-81467","post","type-post","status-publish","format-standard","has-post-thumbnail","category-agi","tag-agi","tag-analytics","tag-artificial-general-intelligence","tag-data-science","tag-data-mining","tag-predictive-analytics","tag-predictive-analytics-jobs","tag-predictive-analytics-news"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/81467","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=81467"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/81467\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/81468"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=81467"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=81467"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=81467"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}