198 karma · joined June 14, 2023
It goes through fundamentals of Bayesian ideas in the context of applications in communication and machine learning problems. I find his explanations uncluttered.
Mind you, I was exactly like that when I started my career and it took quite a while and being on both sides of the conversation to improve. One difference is that it is not so easy to put oneself in the shoes of an LLM. Maybe I will improve with time. So far assuming the LLM is knowledgeable but not very smart has been the most effective strategy for my LLM interactions.
I think it is worse than that. The prompt, written in natural language, is by its very nature vague and incomplete, which is great if you are aiming for creative artistry. I am also really happy that we are able to search for dates using phrases like "get me something close to a weekend, but not on Tuesdays" on a booking website instead of picking dates from a dropdown box.
However, if natural language was the right tool for software requirements, software engineering would have been a solved problem long ago. We got rightfully excited with LLMs, but now we are trying to solve every problem with it. IMO, for requirements specification, the situation is similar to earlier efforts using formal systems and full verification, but at the exact opposite end. Similar to formal software verification, I expect this phase to end up as a partially failed experiment that will teach us new ways to think about software development. It will create real value in some domains and it will be totally abandoned in others. Interesting times...
https://stockanalysis.com/stocks/nvda/metrics/revenue-by-seg...
This is not the only way though. TPUs are available to companies operating on GCP as an alternative to GPUs with a different price/performance point. That is another way to get hands-on experience with TPUs.
Jay Shah’s later articles contain examples that involve epilogue fusion. IMHO, understanding how to write an efficient transpose helps with following the more involved ones.
They have excellent resources to get you started with Cuda/Triton on top of torch. It also has a good community around it so you get to listen to some amazing people :)
I feel our generation (I am in my mid-forties) lived through enormous technological advancements but not as many scientific breakthroughs as the previous generation. So maybe it is not surprising that we are suddenly more likely to have breakthroughs in basic science.
I hope there is a phase transition to science mode now, so we that have a chance to solve the hard pressing issues.
http://ais.informatik.uni-freiburg.de/teaching/ws22/mapping/ http://www.ipb.uni-bonn.de/people/cyrill-stachniss/
There are YouTube videos from 2013 here: https://www.youtube.com/playlist?list=PLgnQpQtFTOGQrZ4O5QzbI...
The course itself is mostly based on the Probabilistic Robotics book from Sebastian Thrun et al: https://mitpress.mit.edu/9780262201629/probabilistic-robotic...
Edit - Or this one: https://jamesclear.com/inversion