I wonder if you could make it multiplayer and then get the effects of time dilation?
In my imagined world I also wanted to explore speeds above the speed of light. You could just stick to galilean transformation, take a very low speed of light and go from there. The world you get should be pretty bizarre.
“30 hours of unattended work” is totally vague and it doesn’t mean anything on its own. It - at the very least - highly depends on the amount of tokens you were able to process.
Just to illustrate, say you are running on a slow machine that outputs 1 token per hour. At that speed you would produce approximately one sentence.
I do think that the amount of regulation is proportional to the complexity of the society. While you can over or under regulate, the general future trend will be more regulations.
If anyone is struggling with keeping up with EU regulations, we built an AI powered platform that helps companies navigate this complex world. You can find it at: https://fx-lex.com
We haven't tried that, we might do that in the future.
My intuition - not based on any research - is that recall should be a lot better from in context data vs. weights in the model. For our use case, precise recall is paramount.
Gemini models run in the cloud, so there is no issue with hardware.
The EU regulations typically include delegated acts, technical standards, implementation standards and guidelines. With Gemini 2.0 we are able to just throw all of this into the model and have it figure out.
This approach gives way better results than anything we are able to achieve with RAG.
My personal bet is that this is how the future will look like. RAG will remain relevant, but only for extremely large document corpuses.
In my mind, Gemini 2.0 changes everything because of the incredibly long context (2M tokens on some models), while having strong reasoning capabilities.
We are working on compliance solution (https://fx-lex.com) and RAG just doesn’t cut it for our use case. Legislation cannot be chunked if you want the model to reason well about it.
It’s magical to be able to just throw everything into the model. And the best thing is that we automatically benefit from future model improvements along all performance axes.
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