Creating realistic 3D models for applications like virtual reality, filmmaking, and engineering design can be a cumbersome process requiring lots of manual trial and error.
Whether you’re describing the sound of your faulty car engine or meowing like your neighbor’s cat, imitating sounds with your voice can be a helpful way to relay a concept when words don’t do the trick.
When Nikola Tesla predicted we’d have handheld phones that could display videos, photographs, and more, his musings seemed like a distant dream. Nearly 100 years later, smartphones are like an extra appendage for many of us.
In the classic cartoon “The Jetsons,” Rosie the robotic maid seamlessly switches from vacuuming the house to cooking dinner to taking out the trash. But in real life, training a general-purpose robot remains a major challenge.
What does sustainable fashion design have in common with Tetris? For both, an intriguing puzzle awaits, where you must configure unique shapes in a way that fills up the available space.
In the current AI zeitgeist, sequence models have skyrocketed in popularity for their ability to analyze data and predict what to do next. For instance, you’ve likely used next-token prediction models like ChatGPT, which anticipate each word (token) in a sequence to form answers to users’ queries. There are also full-sequence diffusion models like Sora, which convert words into dazzling, realistic visuals by successively “denoising” an entire video sequence
For robots, simulation is a great teacher for learning long-horizon (multi-step) tasks — especially compared to how long it takes to collect real-world training data.
In 1994, Florida jewelry designer Diana Duyser discovered what she believed to be the Virgin Mary’s image in a grilled cheese sandwich, which she preserved and later auctioned for $28,000. But how much do we really understand about pareidolia, the phenomenon of seeing faces and patterns in objects when they aren’t really there?
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