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designer1one OP t1_j2ci3t6 wrote

It has been a wild year for generative AI! o_o For the Year 2023, I look forward to seeing exponential growth in various forms of text-to-x models (text-to-video, text-to-3D, text-to-audio, text-to-…). I also hope to see improvements in the factual grounding of large language models. Oh and there’s GPT-4.

What are some things you would like to see in AI progress for Year 2023?

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ureepamuree t1_j2cnqu7 wrote

I'd like to see more progress in Domain Generalization using (RL + X)

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Mefaso t1_j2d7aaa wrote

This sounds interesting, any previous papers you would recommend?

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ureepamuree t1_j2dkaci wrote

I'd recommend checking out the highly-cited papers on the topics of Meta Reinforcement Learning and Skill-based Reinforcement Learning, these two areas are chiefly focused around domain generalization.

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kunkkatechies t1_j2d7rr8 wrote

small models still being smart

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Ragdoll_X_Furry t1_j2fv0sd wrote

Funny how we went from ever-smaller EfficientNets to ever-larger diffusion and transformer models.

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bluehands t1_j2cyz5r wrote

Your list of "text-to-X" highlights for me the need for "X-to-text". Captioning is nice but are names attached, is meaning extracted? (it maybe that I am just not aware of the state of the art)

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currentscurrents t1_j2czmdk wrote

Basically anything you can generate, you can also classify. Most of the image generators use CLIP for guidance, so if they can generate a sad face (and they can), CLIP can tell you whether or not a face is sad.

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ktpr t1_j2d9hre wrote

I’d like to see more progress on data-to-text generalization.

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Ragdoll_X_Furry t1_j2fv6mo wrote

I'd like to see GANs and VAEs get another chance at image-generation.

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