Posted by1 hour ago
Hi All, I recently created an open source website (http://dspods.netlify.app/) to have an always updated list of podcasts related to data science. To add a new podcast, one has to just add a simple text file with a few details and the rest of the things are automated. My objectives of sharing the website are three-fold.
Share this resource with people here who might be interested in podcasts.
I am seeking feedback and suggestions about what more information can be added to make the discovery and selection process for users easier.
If people have any additions to the current list, I am happy to add them.
Right now it seems that doing research and getting good results, especially with these huge models, take millions of dollars to train, and getting even better results relies on more funding for more compute for even bigger models. Is it possible that this kind of bottleneck can be solved with ASICs or some other kind of custom hardware that can speed up the process? If we could reduce the cost barrier for entrance into bleeding edge research, wouldn't be able to see much more progress?
I built a system which can simulate and optimally operate a dynamic system, and is able to find the best setting for this system. An example would be: Energy system of a household, simulated over one year given weather conditions etc. The inner loop choses e.g. when to charge the battery etc. The outer loop chooses what components are best given the performance over one year, eg how big the solar cells should be or if to put a heating element or chp. Do you have ideas for other applications for this system?
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