What AI advance in 2023 will freak me out the most?
7
700Ṁ177
resolved Jan 3
ResolvedN/A
35%
Multimodal model showing increased positive transfer between modalities as scale increases.
16%Other
10%
Good generative video models
7%
Deepmind uses AlphaTensor or something similar to get massive speedup in some aspect of training models.
6%
Transformer-variant being SOTA at multimodality benchmark
5%
New scaling laws that are significantly better than the chinchilla paper.
5%
Turns out we were doing regularization wrong and either a new method or just a better rule of thumb to pick hyperparamenters like weight decay just gets you significat improvements in things like math and reasoning.
4%
AI designs better AI architectures or edits the weights on a net to get SOTA results.
4%
Better coding models end up significatly helping with AI research.
3%
New or already existing architecture(maybe even RNN?) turns out to outperform transformers in most tasks when scaled to LLM size + a few tricks.
2%
New way of training models in a decentralized way were the compute can be divided without requiring a big GPU cluster with high interconnect.
1.1%
LLM with really big context length turns out to be able to "run" small snippets of code using CoT.
1.1%
We find out current models have a lot more capabilities most people think and we are just bad at prompt engineering/training them with goals aligned with what we want.

In 2022 the advance that freaked me out the most was CoT (I am writing this in September 2022).

If I end up working at an AI safety org in 2023 or there is otherwise a conflict of interest this market resolves N/A.

I will mark the top 3 narrowest correct answers as right. (So "something to do with language models" might end up being technically correct but I won't award points for it).

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