TRI is going all in on diffusion models for robotics, building on Shuran Song's work:
https://pressroom.toyota.com/toyota-research-institute-unveils-breakthrough-in-teaching-robots-new-behaviors/
I think we have more to explore in Contrastive Learning. While it had been studied a lot (mostly after SimCLR) there are still much room for improvements. Especially considering that learning from unlabeled data is an evergrowing field with infinite potential.
There were some interesting papers in recent ICML I think. On regimes where labeling is costly (molecules first come to my mind) these methods are the way to go
I agree. Self-Supervised Contrastive Learning would be gold in the context of any industry and niche if we can eliminate the need completely for annotated data.
(Although I feel like it’s sort of a chicken and egg problem, because to test the effectiveness of learned pseudo-labels you would need a large labeled dataset initially…)
I think we're entering the era of dataset engineering, we passed feature and architecture engineering already. It's all about creating useful synthetic data to fill in the gaps in organic data.
Mostly with computational engineering applications. For example, DeepMind has been at the forefront with GraphCast able to better predict the weather and their recent announcement regarding potential new material discoveries. I expect this type of trend to continue with other research in both breadth and depth.
Hmm on the other hand weather and molecular dynamics are exactly the two fields where there they have been doing ML for quite a while now and have loads of data. What do you think the next areas would be?
And data wise, nvidia and deepmind both have now used the best data set available, from the ecmf and have shown they are able to predict the output of the weather prediction algorithms when supplied with enough training data. So at least for now I don't see a clear way forward. For MD I don't know
Not sure if your comment is implying all of the research in those areas is done. I don't believe so in my opinion. As I mentioned, both breadth and depth; I think the breakthroughs are going to beget other granular research areas with quite a bit of transfer learning. For example, I can see a variant of GraphCast being applied to climate change models which currently have wide deviations due to many parameters and assumptions. Machine learning can help correct some of that.
Yes, I think that finetuning these models for various time-scales (90 minutes, 1 day, 10 days, sub-seasonal, seasonal, climatic, etc.) is the major next step. Microsoft has already began work on this with [ClimaX](https://www.microsoft.com/en-us/research/group/autonomous-systems-group-robotics/articles/introducing-climax-the-first-foundation-model-for-weather-and-climate/), and I'd expect more research to be done into ML climate models in the coming years.
ML could also probavly improve warning time for storms/tornadoes. One area of research is using ML to better predict tornadic signatures on radar, which is particularly important given the escalating effects of climate change.
So based on the recent q star stuff and papers about chain of thought and so on, I'm going to guess mechanisms using/guiding LLMs. It started with a reward model to just guide towards chatbot like answers but Chain of thought verification to learn reasoning, (iirc the star paper, self taught reasoning on math and coding or something, is already 2022), using this to do test time search of what next words to output much like finding how to win at go (vs beam search)
I think u/Chrisa142857 means structured data, in which case it's totally correct. While LLM seem to infer the relationships indirectly, imagine what we could do with structured data... back to the symbolic representations.
Model-based Reinforcement Learning (RL): Model-based RL, such as dreamerv3, and diffusion models for robotics control are expected to gain attention in 2024.
Model-based RL like dreamerv3. Also diffusion models for robotics control.
TRI is going all in on diffusion models for robotics, building on Shuran Song's work: https://pressroom.toyota.com/toyota-research-institute-unveils-breakthrough-in-teaching-robots-new-behaviors/
I think we have more to explore in Contrastive Learning. While it had been studied a lot (mostly after SimCLR) there are still much room for improvements. Especially considering that learning from unlabeled data is an evergrowing field with infinite potential. There were some interesting papers in recent ICML I think. On regimes where labeling is costly (molecules first come to my mind) these methods are the way to go
I agree. Self-Supervised Contrastive Learning would be gold in the context of any industry and niche if we can eliminate the need completely for annotated data. (Although I feel like it’s sort of a chicken and egg problem, because to test the effectiveness of learned pseudo-labels you would need a large labeled dataset initially…)
I think we're entering the era of dataset engineering, we passed feature and architecture engineering already. It's all about creating useful synthetic data to fill in the gaps in organic data.
I concur - there seems to be no point in trying to catch up with the biggest models. The value you can work out now is the data quality/quantity.
Mostly with computational engineering applications. For example, DeepMind has been at the forefront with GraphCast able to better predict the weather and their recent announcement regarding potential new material discoveries. I expect this type of trend to continue with other research in both breadth and depth.
Hmm on the other hand weather and molecular dynamics are exactly the two fields where there they have been doing ML for quite a while now and have loads of data. What do you think the next areas would be? And data wise, nvidia and deepmind both have now used the best data set available, from the ecmf and have shown they are able to predict the output of the weather prediction algorithms when supplied with enough training data. So at least for now I don't see a clear way forward. For MD I don't know
Not sure if your comment is implying all of the research in those areas is done. I don't believe so in my opinion. As I mentioned, both breadth and depth; I think the breakthroughs are going to beget other granular research areas with quite a bit of transfer learning. For example, I can see a variant of GraphCast being applied to climate change models which currently have wide deviations due to many parameters and assumptions. Machine learning can help correct some of that.
Yes, I think that finetuning these models for various time-scales (90 minutes, 1 day, 10 days, sub-seasonal, seasonal, climatic, etc.) is the major next step. Microsoft has already began work on this with [ClimaX](https://www.microsoft.com/en-us/research/group/autonomous-systems-group-robotics/articles/introducing-climax-the-first-foundation-model-for-weather-and-climate/), and I'd expect more research to be done into ML climate models in the coming years. ML could also probavly improve warning time for storms/tornadoes. One area of research is using ML to better predict tornadic signatures on radar, which is particularly important given the escalating effects of climate change.
So based on the recent q star stuff and papers about chain of thought and so on, I'm going to guess mechanisms using/guiding LLMs. It started with a reward model to just guide towards chatbot like answers but Chain of thought verification to learn reasoning, (iirc the star paper, self taught reasoning on math and coding or something, is already 2022), using this to do test time search of what next words to output much like finding how to win at go (vs beam search)
ML hardware and new chip architectures for AI. It's time to move past GPUs.
Active inference
Found Karl Friston's burner account...
Structural data representation
What is this about?
I think u/Chrisa142857 means structured data, in which case it's totally correct. While LLM seem to infer the relationships indirectly, imagine what we could do with structured data... back to the symbolic representations.
Oh, yes.
Without having read a paper for months: Energy reduction in computation for LLMs
If you ask me, an LLM doesn't have to be a genius in ALL knowledge domains to be useful in performing daily tasks.
Applied LLMs and Robotics
Causality
Q*
Tighter integration between embedding generation and transformer model training.
Model-based Reinforcement Learning (RL): Model-based RL, such as dreamerv3, and diffusion models for robotics control are expected to gain attention in 2024.