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Originally published in\u00a0Forbes<\/a><\/p>\n Recently on\u00a0The Dr. Data Show, my co-host Luba Gluhova and I\u00a0dug into the evolving discourse surrounding artificial general intelligence<\/a> \u2013 and its stubborn incoherence. A recent publication by the venture capital firm Sequoia Capital projected the arrival of AGI by 2026, defining the concept simply as \u201cthe ability to figure things out<\/a>.\u201d<\/p>\n From an engineering and practical standpoint, this definition effectively only reiterates traditional, highly subjective definitions of AI. It is basically another way to say \u201ccapable of reasoning,\u201d which has long been a common, if circular, attempt to define AI.<\/p>\n AGI was meant to differentiate from AI. It was originally supposed to signify the \u201cwhole enchilada\u201d \u2013 a\u00a0virtual human<\/a> capable of doing anything that a person can. This would make such a system fully autonomous, capable of matching human performance across a wide range of tasks \u2013 effectively operating as a virtual employee.<\/p>\n However, as the\u00a0profound technical challenges of achieving supreme autonomy<\/a> become apparent, definitions of AGI shift toward more subjective criteria, blurring the distinction between AGI and AI, which itself has always faced\u00a0the existential problem of being undefinable<\/a>. This shift appears to be an exercise in waving hands in order to dodge criticism.<\/p>\n AI hype often emphasizes supreme autonomy \u2013 and most of the hype at least implies it. The notion of AGI is a natural extension of that aspect of the hype: If a system can do everything a person can, it needs no human in this loop.<\/p>\n While genAI possesses remarkable capabilities and offers substantial commercial value, it currently faces a critical reliability challenge. In automated enterprise workflows, such as customer service interactions or healthcare claims processing, a minor error rate \u2013 even as low as 5% or less \u2013 can render a fully autonomous system non-viable due to the operational risks of factual inaccuracies, ethical missteps or mishandled transactions.<\/p>\n Hybrid AI: A Practical Antidote To The Hype<\/p>\n So, how do we sober up and pursue feasible deployments that realize the potential value of these technologies? The answer is\u00a0hybrid AI<\/a>.<\/p>\n Hybrid AI offers a practical alternative to pursuing the ever-elusive ideal AGI. GenAI hallucinates and exhibits other unacceptable behaviors that preclude its deployment \u2013 especially for its more ambitious intended uses, such as performing the role of customer service agent, analyst, educator or all-capable virtual assistant. Rather than supreme autonomy, a feasible route to leveraging genAI and pursuing its more ambitious uses is to\u00a0hybridize it with predictive AI<\/a>.<\/p>\n Here\u2019s how hybrid AI works: Machine learning models serve as a vital\u00a0reliability layer<\/a>. By assigning probability-based risk scores to the outputs of generative models, predictive AI can systematically identify the specific cases with the highest likelihood of failure or behavioral error. These high-risk cases are then routed to human operators for review. This judicious inclusion of a \u201chuman-in-the-loop\u201d mitigates the operational risks associated with large language models while successfully automating a significant portion of the workload.<\/p>\n An Industrywide Pivot To Hybrid AI<\/p>\n Hybrid AI is already moving from theory to practice. A diverse array of industry leaders \u2013 including Netflix, Amazon, JPMorgan and Microsoft \u2013 are actively deploying these hybrid systems (they\u2019ve lined up to speak on the topic at the conference I chair,\u00a0HYBRID AI 2026<\/a>). This empowers businesses to navigate the limitations of genAI and deploy reliable, valuable semi-autonomy.<\/p>\n The move to\u00a0hybrid AI<\/a> represents a sobering up, an evolution from unrealistic elation about supreme autonomy. The intoxication hinges on a misbelief, a hope and a prayer, that LLMs will somehow evolve into \u201cvirtual humans.\u201d<\/p>\n Instead, we need to take the pressure off these models \u2013 dispense with the unrealistic performance expectations \u2013 and judiciously leverage what they feasibly can do by pairing them with the predictive safeguards they desperately need in order to achieve launch-worthiness.<\/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 Recently on\u00a0The Dr. Data Show, my co-host Luba Gluhova and I\u00a0dug into the evolving discourse…\n","protected":false},"author":2,"featured_media":60308,"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-60307","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\/60307","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=60307"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/60307\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/60308"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=60307"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=60307"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=60307"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}