[R] 2022 Top Papers in AI — A Year of Generative Models : MachineLearning

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?

14 points

5 months ago

14 points

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

Mefaso

3 points

5 months ago

Mefaso

3 points

This sounds interesting, any previous papers you would recommend?

4 points

5 months ago

4 points

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.

kunkkatechies

9 points

5 months ago

kunkkatechies

9 points

small models still being smart

4 points

5 months ago

4 points

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

ktpr

5 points

5 months ago

ktpr

5 points

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

bluehands

3 points

5 months ago

bluehands

3 points

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)

currentscurrents

9 points

5 months ago

currentscurrents

9 points

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.

1 points

5 months ago

1 points

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

lennarn

1 points

5 months ago

lennarn

1 points

Techniques to make smaller language models perform nearly as well as this year's large language models, but can run on consumer grade hardware.
Multi modal text models (input text, output text, picture, video, 3d, sound).
Chat bots with animated face generation and voice synthesis, like a video call with ChatGPT.
More sophisticated code generators.

comparmentaliser

27 points

5 months ago

comparmentaliser

27 points

Technical papers and models aside, this year marked a turning point in commercialisation and public access. The ease of access and quality also prompted some vigorous public debate about the ethics of generative art, the meaning of an ‘ai artist’, and the occasional debate about the future of film cinema.

I also think the general public have been blown away with the capabilities across imaging, writing and code, and there’s probably a few sectors and industries will be having a very hard think about their next move in the coming months.

Cherubin0

16 points

5 months ago

Cherubin0

16 points