Who has the necessary resources to run, let alone train the model?
I saw the nerfing of GPT in real time: one day it was giving me great book summaries, the next one it said that it couldn't do it due to copyright.
I actually called it in a comment several months ago: copyright and other forms of control would make GPT dumb in the long run. We need an open source frontier less version.
https://chat.openai.com/share/d1cdd811-edc9-4d55-9cc1-a79215...
Not a very scientific or conclusive test to be sure, but I think I'll stick with using ChatGPT as my programming-rubber-ducky-on-steroids for now :)
For now there is no other way to train models than the huge infrastructure. CERN have a tendency to provide results for the money spend and they have experience in building the infrastructures for sure.
"How Many Amazon Mechanical Turk Workers Are There in 2019? In a recent research article, we reported that there are 250,810 MTurk workers worldwide who have completed at least one Human Intelligence Task (HIT) posted through the TurkPrime platform. More than 226,500 of these workers are based in the US."
In terms of training feedback I suppose there's a few different ways of doing it. Gamification, mech turk, etc. Hell free filesharing sites could get on the action and have you complete an evaluation of a model response instead of watching an ad
Even if it that problem disappeared, propagating the model weight updates between training steps poses an issue in itself. It's a lot of data, at this size.
Consider something like a spiking neural network with weights & state stored on an SSD using lazy-evaluation as action potentials propagate. 4TB SSD = ~1 trillion 32-bit FP weights and potentials. There are MacBook options that support up to 8TB. The other advantage with SNN - Training & using are basically the same thing. You don't have to move any bytes around. They just get mutated in place over time.
The trick is to reorganize this damn thing so you don't have to access all of the parameters at the same time... You may also find the GPU becomes a problem in an approach that uses a latency-sensitive time domain and/or event-based execution. It gets to be pretty difficult to process hundreds of millions of serialized action potentials per second when your hot loop has to go outside of L1 and screw with GPU memory. GPU isn't that far away, but ~2 nanoseconds is a hell of a lot closer than 30-100+ nanoseconds.
Edit: fixed my crappy math above.
What pieces of tech would need to be invented to make it possible to carry a 1T model around in a device the size of an iPhone?
Seems like with minimal oversight the human workers like to just say they did the requested task and make up an answer rather than actually do it (The task involved entering an address in Google maps, looking at the street view and confirming insofar as possible if a given business actually resided at the address in question, nothing complicated)
Edit: woops, mixed in the query with another reply that mentioned the human element XD
Tit for tat and all that.