Writingdorky

You prope the trained model, delete/kill the weights and than you are done.

On federated learning, you just make sure to keep this mechanism in the right stage of your pipeline

You are just joking right?

From a skill and trust point of view, Google is doing a lot better than apple will ever.

Including ondevice AI

I mean yes for sure but apple always had a high margin. I'm pretty sure they did not suddenly become 'nice'.

Would be interesint to know though if the margin stayed the same and they literaly just save a lot of money.

The data source is linked and is based on the ARM Datacenter Energy prediction.

But i don't think its too far fetched.

The compute needed for digital twins, simulating a whole army of robots than uploading it to the robots, who sitll need a ton of compute, is not unrealistic.

Cars like Tesla have A TON of compute build in too.

And we have seen what suddenly happens to an LLM when you switch the amount of parameters. We were in a investment hell were it was not clear in what to invest (crypto, blockchain and NFT bubble bursted) but AI opened up the sky again.

If we continue like this, it will not be far fetched that everyone has their own private agent running and paying for it (private / isolated for data security) + your work agent.

I like this 'my handwriting is write only'.

when i was 18, i looked at notes from my new coworker / manager and asked him how he learned to write that nice. He told me that he struggled writing cursive and just started writing letter by letter (print letter style?).

I changed my 'font' on that day and suddenly i was at least able to read what i wrote!