This is more common than reported.
I’ve lost a semi expensive present, and have heard anecdotal stories of people losing handbags, jewelry, higher end clothing/shoes.
It seems to be more prevalent in the US/TSA than other first world countries.
The devil is in the details. Training large LLMs requires a lot of custom infra (handling GPUs going down, efficiently pushing data to keep the accelators busy, deciding on which mechanism of parallelizing model training is better - data vs model parallelism or both, tuning hyperparams of optimizers which can be different for larger batch sizes, etc)
Mosaic is one of the better providers for this. AWS is nowhere near ready at this current point in time, it is pretty much a "dumb" infra provider in large LLM training at this point. (Of course they won't be standing still and will prob acquire that capability one way or another)
While somewhat useful - there are no systematic ablation and comparison studies, datasets, and quantitative evals here.
It's all anecdotal, so should probably be a blogpost and not a "paper"
We find that responses from existing generative search engines are fluent and appear informative, but frequently contain unsupported statements and inaccurate citations: on average, a mere 51.5% of generated sentences are fully supported by citations and only 74.5% of citations support their associated sentence. We believe that these results are concerningly low for systems that may serve as a primary tool for information-seeking users, especially given their facade of trustworthiness. We hope that our results further motivate the development of trustworthy generative search engines and help researchers and users better understand the shortcomings of existing commercial systems.
this looks like it's for GCP. TPUs are used for most internal workloads.
It's available externally but some of the papercuts and devex without the TPU/TF team helping you can be more painful than using Nvidia/CUDA