Researchers have been using the term for a while now as well. It's mostly for when the model confidently outputs an incorrect answer such as fake website links etc.

This is actually really cool! I've been demotivated about how much progress is occurring in the field of ML that I would've liked to contribute to. I'll give it a shot!

Additionally, if anyone would like to collaborate on this challenge, feel free to shoot me a PM and I'll set up a Discord or something.

This is super fascinating. I'd imagine this is a computationally expensive endeavour so I'm curious, what hardware are you using to train it? I'd love to talk further if possible.

Proto-AGI may likely be a multimodal system and therefore will include some sort of variant of transformers for language if developed within the next 5 years or so (in addition to other NN architectures)

However, the hardware is insanely expensive to train the model and run inference. If this were to work, we'd need someone with access to a lot of cloud computing/supercomputer/Google TPU's. The ChatGPT model alone requires ~350GB of GPU memory to generate an output (essentially performing inference). So imagine a model capable of all that and more? It'd require a lot of compute power.

Especially this 2nd half! Feels like the progress of AI has accelerated ten-fold. Likely due to the increased interest and investments being poured into the field. Competition drives innovation!

Likely Google because the 540 billion parameters matches up with their PaLM model

Looks like someone read the prologue of Max Tegmark's Life 3.0 hahaha