IIRC SpaceMobile showed direct-to-cell satellite communication recently with 5g. Still to be seen if they or someone else (SpaceX?) can make it work large scale.
They should have asked Musk to send it space. It would have been beautiful to think ten more Pratchett novels are circling Earth for potentially thousands of years. He was a great author.
Hey wjnc, You can think of it how you can use GitLab on gitlab.com or deploy it yourself on-prem. The only difference being instead of per user we would charge per deployment. As in case of GitLab you can decide to host it yourself.
Absolutely, there are many cases where governments and generally large organisations can use Flower to internally train models on internal data silos. Often enough large organisations have all the incentives to utilize their own data but can't centralize it due to regulations.
Hi guites, Thank you! That is undoubtedly something relatable. We have it on the screen and plan to provide helpful material and presentations helping to convince stakeholders. If you are up for a call to share the specific challenges, we could ideate with you.
Really happy to hear that and your support is much appreciated! I saw you answering many questions before we could do so :) Thank you for that. We are reaching out to all contributor. Let me know in Slack if you are up to a short call to understand better how we can support you.
Amazing! When I bought my last car, I ensured there were physical buttons for everything I regularly use. While driving, most things happen without much thinking, and touch screens feel like the antithesis of that idea. I recently had to drive a car with a touch screen for, e.g. AC and radio control. I failed miserably to turn off the radio for 3 hours on the highway. I usually expect these things to be intuitive enough to do without reading the manual of a car.
I agree that proprietary data will become more valuable. It is, even today, mostly not accessible for AI training and holds so much value. We are working on Flower (https://flower.dev), which enables training AI on private data without the data owner having to share it.
The naming might be flawed but it's like a batista I guess. You can spend a lot of money on the machine but without an expert to use it you will not get the best out of it.
Fast.ai states on its website: "Being cool is about being exclusive, and that's the opposite of what we want. We want to make deep learning as accessible as possible."
We appreciate this goal as we are making federated learning accessible for everyone. In this blog post, we share how to use fastai with Flower.
I was an early paying Kagi user but started using !g more and more over the last few month. I am not sure what they changed.
Recently I decided to switch my default search back to Google as basically in 90% of all search queries I found the result I searched for on Google instead of Kagi.
We would like to give our special thanks to all the contributors who made the new version of Flower possible (in git shortlog order): Adam Narozniak, Charles Beauville, Edoardo, L. Jiang, Ragy, dannymcy
The new release is packed with improvements and new features: new Flower Baseline (FedAvg MNIST), improved GPU support in simulations, improved GPU support in Jupyter Notebook tutorials, optional telemetry, new (experimental) Driver API, new Federated Analytics with Pandas example, new strategies (Krum and MultiKrum), updated C++ example, a huge list of general improvements, and updated documentation. And of course: no incompatible changes
> Together, the Turkmenistan sources release an estimated 111,000 pounds of methane gas per hour
If this is happening all the time, then the number of global methane emissions due to human activity on this (https://en.wikipedia.org/wiki/Methane_emissions) Wikipedia page can't be valid.
Crazy how human negligence and greed might end humanity.