icecoffee

An interesting find. In scientific computing, I've learned to accept the performance/accuracy as it, slow and iterative, many runs being similar - Would pre-built solver templates with AI models reduce repetitive compute?

There are commercial software to solve domain-specific problems, but seeing how AI is starting to show significant performances in domains such as weather forecasting, one wonders what value can AI bring for other domains? Are there other scientific domains where this concept could be applied?

I've yet to see established open-source solvers working to incorporate AI.

What are your pain points in scientific computing?

I would definite like to see a future where solving these is as easy and ubiquitous as LLM are now.

The cv reading here is really a POC base on off-the-self and will require some work. Fix-camera vs Spot, I can definitely see the pros and cons. So at what price point do you think industry will adopt Spot for doing rounds?

I suspect models probably need "reasoning" ability - very lacking in current models. Perhaps, AlphaGeometry can do better.

Though I haven't effort into analyzing or tweaking the training, some of the perditions does look like it points at the shadow instead of the tip. This synthetic dataset is tiny (not even 100k) - but still larger than what I could find. I think there's a lot of room for improvement. What do you think is the most important requirement?

Yes, I encountered a similar issues with the first iteration of dataset. The prediction location of the needle tip would be wildly off when the view angle was high. The fix was easy, just generated high viewing angle images for the 2nd dataset.

Exactly, I use to think the same thing! Many legacy system, the industrial machines that run factories, infrastructure, etc. all have hundreds of gauges and some of them do get "watched". Cost of digitization via smart gauges just cost too much and planning that goes into replacing them often isn't tenable. So many instrument readings don't get logged - until some machine fails. There are systems out there, but they aren't affordable to make a broad impact - I hope we can progress to change that.

Yea... the model wasn't trained on speedometers. I curious how this might help with your "eCVT issue", can you elaborate?

The error messaging was non-existent. Add some and redeployed, hopefully its a bit helpful.

Yup, the components can be replaced to your liking and expand the data to your match your environment better. I found it really interesting how, no matter how I prompted foundational models they couldn't read gauges.