The Julia AD ecosystem is very interesting in that the community is trying to make the entire language differentiable, which is much broader in scope than what Torch and JAX are doing. But unlike Dex, Julia is not a language built from the ground up for automatic differentiation.
Ah I had not realized I was corresponding with the author of that talk - I'd followed it back when it was happening as I'm particularly interested in adapting AD.
Where do you feel Julia is at this point in time (compared to say, JAX or PyTorch) from a practitioner's standpoint?
When it comes to general deep learning, Julia is much less mature than the JAX ecosystem. I think deep learning will be the hardest nut for Julia to crack. The field is moving incredibly fast, and network effects are strong.
Julia's strength lies in scientific computing, so I think adoption will come through novel applications of AD/ML in the sciences, rather than trying to catch up with the latest LLM developments
I'm positive about Julia's future because the developer experience just feels so fun and productive. I always find it impressive how much a small group of self-organized volunteers has been able to achieve. Amazing things could happen if a company like Google or Meta paid a team of full-time engineers to advance the deep learning ecosystem. Fun fact: Julia strongly influenced PyTorch's recent design decisions [1].
Shameless plug for one of my talks at JuliaCon 2024: https://www.youtube.com/live/ZKt0tiG5ajw?t=19747s. The comparison between Python and Julia starts at 5:31:44.