I'm the author of this blog. That's correct, the texts are generated and then validated manually by me.

I also do manual reviews (https://gonzoml.substack.com/), but there are many more papers for which I don't have time to write a review. So I created a multi-agentic system to help me, and I'm constantly iterating to improve it. And I like the result. It was also validated by the paper authors a couple of times, they agree the reviews are correct. So, if you see something is definitely wrong, please let me know.

Regarding myself, I became at least x10 more productive in reading papers and understanding what's happening. Hope, it will also help some of you.

Thank you for the feedback! I'm the author of the book :)

I updated the post a bit, added a few clarifications

the close example is Machine translation quality estimation (MTQE)

That means the models still confabulate and make other errors, and for many cases it's a problem, so there should be solutions to control model output quality

Thanks!

Many companies designed or designing their own chips. The most prominent example is Google TPU.

There will be another articles in this series on FPGAs, ASICs and so on.

Eagerly waiting for the one on FPGA’s and ASIC’s

The original plan was this:

https://blog.inten.to/hardware-for-deep-learning-current-sta...

Thanks for this.

There was also a PDF published by some university on Hardware required on DL operations. Must be somewhere in my submitted posts here

Awesome! Looking forward to it.

Cool report! No single best provider :)

It seems to be so inefficient market!..