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The Fractured Entangled Representation Hypothesis (Intro)

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المحتوى المقدم من Machine Learning Street Talk (MLST). يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Machine Learning Street Talk (MLST) أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.

What if today's incredible AI is just a brilliant "impostor"? This episode features host Dr. Tim Scarfe in conversation with guests Prof. Kenneth Stanley (ex-OpenAI), Dr. Keith Duggar (MIT), and Arkash Kumar (MIT).While AI today produces amazing results on the surface, its internal understanding is a complete mess, described as "total spaghetti" [00:00:49]. This is because it's trained with a brute-force method (SGD) that’s like building a sandcastle: it looks right from a distance, but has no real structure holding it together [00:01:45].To explain the difference, Keith Duggar shares a great analogy about his high school physics classes [00:03:18]. One class was about memorizing lots of formulas for specific situations (like the "impostor" AI). The other used calculus to derive the answers from a deeper understanding, which was much easier and more powerful. This is the core difference: one method memorizes, the other truly understands.The episode then introduces a different, more powerful way to build AI, based on Kenneth Stanley's old experiment, "Picbreeder" [00:04:45]. This method creates AI with a shockingly clean and intuitive internal model of the world. For example, it might develop a model of a skull where it understands the "mouth" as a separate component it can open and close, without ever being explicitly trained on that action [00:06:15]. This deep understanding emerges bottom-up, without massive datasets.The secret is to abandon a fixed goal and embrace "deception" [00:08:42]—the idea that the stepping stones to a great discovery often don't look anything like the final result. Instead of optimizing for a target, the AI is built through an open-ended process of exploring what's "interesting" [00:09:15]. This creates a more flexible and adaptable foundation, a bit like how evolvability wins out in nature [00:10:30].The show concludes by arguing that this choice matters immensely. The "impostor" path may be hitting a wall, requiring insane amounts of money and energy for progress and failing to deliver true creativity or continual learning [00:13:00]. The ultimate message is a call to not put all our eggs in one basket [00:14:25]. We should explore these open-ended, creative paths to discover a more genuine form of intelligence, which may be found where we least expect it.REFS:Questioning Representational Optimism in Deep Learning:The Fractured Entangled Representation HypothesisAkarsh Kumar, Jeff Clune, Joel Lehman, Kenneth O. Stanleyhttps://arxiv.org/pdf/2505.11581Kenneth O. Stanley, Joel LehmanWhy Greatness Cannot Be Planned: The Myth of the Objectivehttps://amzn.to/44xLaXKOriginal show with Kenneth from 4 years ago:https://www.youtube.com/watch?v=lhYGXYeMq_EKenneth Stanley is SVP Open Endedness at Lila Scienceshttps://x.com/kenneth0stanleyAkarsh Kumar (MIT)https://akarshkumar.com/AND... Kenneth is HIRING (this is an OPPORTUNITY OF A LIFETIME!)Research Engineer: https://job-boards.greenhouse.io/lila/jobs/7890007002Research Scientist: https://job-boards.greenhouse.io/lila/jobs/8012245002Tim's Code visualisation of FER based on Akarsh repo: https://github.com/ecsplendid/ferTRANSCRIPT: https://app.rescript.info/public/share/YKAZzZ6lwZkjTLRpVJreOOxGhLI8y4m3fAyU8NSavx0

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Artwork
iconمشاركة
 
Manage episode 492808265 series 2803422
المحتوى المقدم من Machine Learning Street Talk (MLST). يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Machine Learning Street Talk (MLST) أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.

What if today's incredible AI is just a brilliant "impostor"? This episode features host Dr. Tim Scarfe in conversation with guests Prof. Kenneth Stanley (ex-OpenAI), Dr. Keith Duggar (MIT), and Arkash Kumar (MIT).While AI today produces amazing results on the surface, its internal understanding is a complete mess, described as "total spaghetti" [00:00:49]. This is because it's trained with a brute-force method (SGD) that’s like building a sandcastle: it looks right from a distance, but has no real structure holding it together [00:01:45].To explain the difference, Keith Duggar shares a great analogy about his high school physics classes [00:03:18]. One class was about memorizing lots of formulas for specific situations (like the "impostor" AI). The other used calculus to derive the answers from a deeper understanding, which was much easier and more powerful. This is the core difference: one method memorizes, the other truly understands.The episode then introduces a different, more powerful way to build AI, based on Kenneth Stanley's old experiment, "Picbreeder" [00:04:45]. This method creates AI with a shockingly clean and intuitive internal model of the world. For example, it might develop a model of a skull where it understands the "mouth" as a separate component it can open and close, without ever being explicitly trained on that action [00:06:15]. This deep understanding emerges bottom-up, without massive datasets.The secret is to abandon a fixed goal and embrace "deception" [00:08:42]—the idea that the stepping stones to a great discovery often don't look anything like the final result. Instead of optimizing for a target, the AI is built through an open-ended process of exploring what's "interesting" [00:09:15]. This creates a more flexible and adaptable foundation, a bit like how evolvability wins out in nature [00:10:30].The show concludes by arguing that this choice matters immensely. The "impostor" path may be hitting a wall, requiring insane amounts of money and energy for progress and failing to deliver true creativity or continual learning [00:13:00]. The ultimate message is a call to not put all our eggs in one basket [00:14:25]. We should explore these open-ended, creative paths to discover a more genuine form of intelligence, which may be found where we least expect it.REFS:Questioning Representational Optimism in Deep Learning:The Fractured Entangled Representation HypothesisAkarsh Kumar, Jeff Clune, Joel Lehman, Kenneth O. Stanleyhttps://arxiv.org/pdf/2505.11581Kenneth O. Stanley, Joel LehmanWhy Greatness Cannot Be Planned: The Myth of the Objectivehttps://amzn.to/44xLaXKOriginal show with Kenneth from 4 years ago:https://www.youtube.com/watch?v=lhYGXYeMq_EKenneth Stanley is SVP Open Endedness at Lila Scienceshttps://x.com/kenneth0stanleyAkarsh Kumar (MIT)https://akarshkumar.com/AND... Kenneth is HIRING (this is an OPPORTUNITY OF A LIFETIME!)Research Engineer: https://job-boards.greenhouse.io/lila/jobs/7890007002Research Scientist: https://job-boards.greenhouse.io/lila/jobs/8012245002Tim's Code visualisation of FER based on Akarsh repo: https://github.com/ecsplendid/ferTRANSCRIPT: https://app.rescript.info/public/share/YKAZzZ6lwZkjTLRpVJreOOxGhLI8y4m3fAyU8NSavx0

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