It's incredible how it allows data to be used without compromising privacy or security. This could be a game-changer for industries that handle sensitive information, like healthcare and finance.
In what degree you are considering SMPC instead of other encryption for secure model aggregation? Anyway, Thanks for the comprehensive recent concepts! FTL is new!
We've done it both ways and lately we're experimenting with more pre-drawn diagrams so the speaker can cover their main points more quickly. For example, the Cybersecurity Architecture videos with Jeff Crume are longer than usual (12 to 25 minutes) with multiple board changes: ua-cam.com/video/jq_LZ1RFPfU/v-deo.html
We need regulation in the industry where publicaly traded companies issue fractional stocks to those teaching AI models from web scraping for example. Lets say I post code for a tcp socket server IBM would like to use. AI scrapes it, learns it, uses it, and voila, I now am issued one thousandth of a share of IBM. But not the 30 script kiddies who copied my code and put on the Holy You Tube ... because AI would know I posted it first. Or I could say, ah, that would be cool to share, but why post it, I will just get ripped off anyway? Fractional stock issuance solves the clerical head aches of other systems I believe
You realize that sharing the updated learning gives up the individual companies competitive advantage without giving up their sensitive data? They're giving the milk for free, but keeping their cow.
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I really enjoy this series. The explanations are really clear and simple to understand.
It's incredible how it allows data to be used without compromising privacy or security. This could be a game-changer for industries that handle sensitive information, like healthcare and finance.
I love your explanation method. all clear, with the best order possible and I love the color of markers :))) Thank you. Really helpful.
This was concise and well put together. Thank you!
Nice content , where to learn more about federated leraning? Thanks!
Federated learning explanation was great 👍👍👍👍👍
In what degree you are considering SMPC instead of other encryption for secure model aggregation? Anyway, Thanks for the comprehensive recent concepts! FTL is new!
An example of dataset and the regarding insight would have been helpful to understand why the insight is not back traceable.
I prefer it when you draw things from scratch. I am even learning how to do that in preparation for my job interview.
We've done it both ways and lately we're experimenting with more pre-drawn diagrams so the speaker can cover their main points more quickly. For example, the Cybersecurity Architecture videos with Jeff Crume are longer than usual (12 to 25 minutes) with multiple board changes: ua-cam.com/video/jq_LZ1RFPfU/v-deo.html
Any new research on Federated Learning applied to Foundation Models?
Can you explain more examples? Like healthcare data. I understand from a high level but not in practice.
very nicely interpreted!
nice. this gave me the intuition.
awesome content
Thanks man
It would be very great if, spoken about dataset distribution- iid and non iid and how FL is effected . Specifically non iid setting.
Are you mirrored or did you master the art of writing mirrored?
greatt!!
Identify cats? 😅
We need regulation in the industry where publicaly traded companies issue fractional stocks to those teaching AI models from web scraping for example.
Lets say I post code for a tcp socket server IBM would like to use. AI scrapes it, learns it, uses it, and voila, I now am issued one thousandth of a share of IBM.
But not the 30 script kiddies who copied my code and put on the Holy You Tube ... because AI would know I posted it first.
Or I could say, ah, that would be cool to share, but why post it, I will just get ripped off anyway?
Fractional stock issuance solves the clerical head aches of other systems I believe
A good idea, but current language models are fundamentally bad at knowing where the got their data from. hopefully we can solve this!
You realize that sharing the updated learning gives up the individual companies competitive advantage without giving up their sensitive data? They're giving the milk for free, but keeping their cow.