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"id": "https://rukii.net/users/tero/statuses/113442317991873429",
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"published": "2024-11-07T15:13:21Z",
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"content": "<p>I have written previously about simulations and generalist AI, and how many people have gotten a misguided impression that photorealism and physical realism is the goal.</p><p>Pushing realism forward is subject to diminishing returns and causes the following:<br />- The simulations become inefficient which means the volume of useful information you're feeding to your trained models becomes smaller, while the volume of total data becomes larger.<br />- Paradoxically you start overfitting on specific types of sensors. Remember that your cameras and microphones aren't photo-/sonorealistic. Your realistic data will always have less coverage than unrealistic data which spans a much larger distribution both in variance and in scale.<br />- You become bogged down with simulating ever more nuanced aspects of things, like haptic feedback on different kinds keyboards for your robot training.<br />- Resources are being spent inefficiently, and it becomes prohibitive in time and energy to go deeper into abstract skills which still need to be applied in the physical world. If you train your robot to program computers with a haptically realistic keyboard, it won't do much better than the monkeys trying to produce Shakespeare's works.</p><p>So, what's the solution? Photo-/sono-/haptic realism has its place, but not quite how everyone thinks. The physical world isn't the best training environment for anything. We can do much better with less.</p><p>For any skills, the challenges should be posed which are more difficult and more variable than the real world tasks. Not exactly as difficult and exactly as variable, because those targets lead to asymptotically approaching those with skyrocketing costs.</p><p>Yet, the environments and the embodiments should be minimal for the skills to learn, to be able to produce and use as much salient data as possible. There needs to be curriculums of "simulations" spanning simple digital games to text to different generative modalities, to actual moving and working robots in the real world. Otherwise the physical manipulation skills do not connect to the abstract skills, and training the abstract skills becomes prohibitively costly. Even we humans play games of different kinds to exercise our skills.</p><p>Realism isn't the goal, super-realism is, and the road to that goes through surrealism, and drawing the analogue between what we call simulations and games, and embodiments.</p><p><a href=\"https://rukii.net/tags/UniversalEmbodiment\" class=\"mention hashtag\" rel=\"tag\">#<span>UniversalEmbodiment</span></a></p>",
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"en": "<p>I have written previously about simulations and generalist AI, and how many people have gotten a misguided impression that photorealism and physical realism is the goal.</p><p>Pushing realism forward is subject to diminishing returns and causes the following:<br />- The simulations become inefficient which means the volume of useful information you're feeding to your trained models becomes smaller, while the volume of total data becomes larger.<br />- Paradoxically you start overfitting on specific types of sensors. Remember that your cameras and microphones aren't photo-/sonorealistic. Your realistic data will always have less coverage than unrealistic data which spans a much larger distribution both in variance and in scale.<br />- You become bogged down with simulating ever more nuanced aspects of things, like haptic feedback on different kinds keyboards for your robot training.<br />- Resources are being spent inefficiently, and it becomes prohibitive in time and energy to go deeper into abstract skills which still need to be applied in the physical world. If you train your robot to program computers with a haptically realistic keyboard, it won't do much better than the monkeys trying to produce Shakespeare's works.</p><p>So, what's the solution? Photo-/sono-/haptic realism has its place, but not quite how everyone thinks. The physical world isn't the best training environment for anything. We can do much better with less.</p><p>For any skills, the challenges should be posed which are more difficult and more variable than the real world tasks. Not exactly as difficult and exactly as variable, because those targets lead to asymptotically approaching those with skyrocketing costs.</p><p>Yet, the environments and the embodiments should be minimal for the skills to learn, to be able to produce and use as much salient data as possible. There needs to be curriculums of "simulations" spanning simple digital games to text to different generative modalities, to actual moving and working robots in the real world. Otherwise the physical manipulation skills do not connect to the abstract skills, and training the abstract skills becomes prohibitively costly. Even we humans play games of different kinds to exercise our skills.</p><p>Realism isn't the goal, super-realism is, and the road to that goes through surrealism, and drawing the analogue between what we call simulations and games, and embodiments.</p><p><a href=\"https://rukii.net/tags/UniversalEmbodiment\" class=\"mention hashtag\" rel=\"tag\">#<span>UniversalEmbodiment</span></a></p>"
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