A postmortem of my predictions about GPT-3 from 21 March 2019:
When it will appear? (My guess is 2020). True
Will it be created by OpenAI and will it be advertised? (My guess is that it will not be publicly known until 2021, but other companies may create open versions before it.) False
How much data will be used for its training and what type of data? (My guess is 400 GB of text plus illustrating pictures, but not audio and video.) True for text, false for pictures “The CommonCrawl data was downloaded from 41 shards of monthly CommonCrawl covering 2016 to 2019, constituting 45TB of compressed plaintext before filtering and 570GB after filtering, roughly equivalent to 400 billion byte-pair-encoded tokens”
What it will be able to do? (My guess: translation, picture generation based on text, text generation based on pictures – with 70 per cent of human performance.) False for pictures
How many parameters will be in the model? (My guess is 100 billion to trillion.) True “175 billion parameters”
How much compute will be used for training? (No idea.) “training the GPT-3 175B consumed several thousand petaflop/s-days of compute during pre-training, compared to tens of petaflop/s-days for a 1.5B parameter GPT-2 model”
Interestingly, Yud is attractive to Russian mindset (similarly to Karl Marx). I heard 12 old children discussing HPMOR on the beach, and their parents were not rationalists.