On the positive side, I also attended a presentation by Mathworks, which I found quite informative and easy to digest.
a) Crypto - everyone got bombed by fintechs promising the earth for the last 5 years and everyone is bored and angry.
b) Watson - flat out lying to every Cxx in the industry for 10 years straight has built a certain reticence to commit investment needs to generate the numbers for the bonus's to projects that look too good to be true.
I was going to write "I expect interesting projects to emerge in 2024" but I'm not 100%, there are a lot of problems at the nuts and bolts level of using GenAI , model cost.. but also latency, reliability, manageability (prompts soon get out of hand), evaluation. There's a lot of noise, most of it well intentioned, but naive results that cause a flap and then disintegrate under inspection are not helpful. Add in concerns about model ownership, copyright, IP disclosure (from using others models, but also from your model spilling its guts under pressure, or from distillation), indemnity and liability and it might be quite a while before we realise the value from this tech in FS.
I feel that we are stumbling about in the dark.
Also as the other comment mentioned, https://positron.ai seems to be live now.
I didn't follow asic mining during the bitcoin bubble but I have the impression it was the way to go for mining. I don't see why that wouldn't be true for inference, a long as one is ok being limited in flexibility and wed to a particular architecture.
unfortunately, the cat's out of the bag and what is currently in use in hospitals around the world is far scarier than misuse of diagnostics. GE has irresponsibly deployed a ML based reconstruction method [1], which freed from the onerous constraint of having to be correct, makes much crisper images than classical methods. classical compressed sensing reconstruction methods come with a mathematical guarantee on the fidelity of the reconstruction. GE's approach does not, it's just a supervised method. kid stuff. the FDA, for their part, is also illiterate and rubber-stamped the method after looking at their paper where 9/10 radiologists preferred the ML reconstructed images.
would be remiss to not point out that Ajil Jalal et al. are doing great work on reconstruction guarantees for compressed sensing with learned priors
[1] https://www.gehealthcare.com/products/magnetic-resonance-ima...
