moab.arar
@ArarMoab
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Checkout our work "GameNGen". A Gaming engine powered by a diffusion-model that simulates DOOM in Real-Time! Find out more: Amazing effort and fun collaboration with the incredible @daniva, @yanivle, and @shlomifruchter!
Google presents Diffusion Models Are Real-Time Game Engines discuss: We present GameNGen, the first game engine powered entirely by a neural model that enables real-time interaction with a complex environment over long trajectories at high quality. GameNGen can interactively simulate the classic game DOOM at over 20 frames per second on a single TPU. Next frame prediction achieves a PSNR of 29.4, comparable to lossy JPEG compression. Human raters are only slightly better than random chance at distinguishing short clips of the game from clips of the simulation. GameNGen is trained in two phases: (1) an RL-agent learns to play the game and the training sessions are recorded, and (2) a diffusion model is trained to produce the next frame, conditioned on the sequence of past frames and actions. Conditioning augmentations enable stable auto-regressive generation over long trajectories.
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RT @hila_chefer: VideoJAM is our new framework for improved motion generation from @AIatMeta We show that video generators struggle with m…
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I just came across this nice paper - it turns out (some) multi-modal models look at images through the lens of the text tokens!
🔍 Unveiling new insights into Vision-Language Models (VLMs)! In collaboration with @OneViTaDay & @talidekel, we analyzed LLaVA-1.5-7B & InternVL2-76B to uncover how these models process visual data. 🧵
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Choose you co-authors wisely ✍️
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Interesting application and cool results
What if you could compose videos— merging multiple clips, even capturing complex athletic moves where video models struggle - all while preserving motion and context? And yes, you can still edit them with text after! Stay tuned for more results. #AI #VideoGeneration #SnapResearch
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RT @lipmanya: Our **Flow Matching Tutorial** from #NeurIPS2024 is now publicly available: @helibenhamu @RickyTQCh…
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GameNGen has been accepted to #ICLR2025! 🎉 Huge congrats to my incredible co-authors @daniva, @yanivle, and @shlomifruchter—it was an amazing effort and such a fun collaboration! Learn more:
Google presents Diffusion Models Are Real-Time Game Engines discuss: We present GameNGen, the first game engine powered entirely by a neural model that enables real-time interaction with a complex environment over long trajectories at high quality. GameNGen can interactively simulate the classic game DOOM at over 20 frames per second on a single TPU. Next frame prediction achieves a PSNR of 29.4, comparable to lossy JPEG compression. Human raters are only slightly better than random chance at distinguishing short clips of the game from clips of the simulation. GameNGen is trained in two phases: (1) an RL-agent learns to play the game and the training sessions are recorded, and (2) a diffusion model is trained to produce the next frame, conditioned on the sequence of past frames and actions. Conditioning augmentations enable stable auto-regressive generation over long trajectories.
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Some Med Schools understand it, and you can see adaptation in their Curriculum. Also I think human validation would still be needed - so the medical specialists will not be replaced but will need to adapt. Full autonomous AI doctor is not around the corner.
The medical specialties most likely to be replaced by AI soon, in order: Pathology Radiology Ophthalmology (non-surgical) Dermatology (non-intervention) Primary Care Internal medicine (diagnostics) In 10 years, 90% of medical specialties will be replaced by AI+robots
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Finally, the field has grown at a crazy pace, and you may find yourself discussing dozens of papers. Try to group approaches into common categories, highlight their strengths and weaknesses and explain why your method is interesting.
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