4 charts that show why AI progress is unlikely to slow down:
, a large language model developed by researchers at Meta and released in 2023, was trained on around one billion data points—a more than 160-million fold increase from Perceptron Mark 1. In the case of LlaMa, the data points was text collected from a range of sources, including 67% from Common Crawl data , 4.5% from GitHub , and 4.5% from Wikipedia.
from Epoch found that “every nine months, the introduction of better algorithms contributes the equivalent of a doubling of computation budgets.”According to Sevilla, the amount of compute that AI developers use to train their systems is likely to continue increasing at its current accelerated rate for a while, with companies increasing the amount of money they spend on each AI system they train, and with increased efficiency as the price of compute continues to decrease steadily.
The data that feeds into modern AI systems, such as LlaMa, is scraped from the internet. Historically, the factor limiting how much data is fed into AI systems has been having enough compute to process that data. But, the recent explosion in the amount of data used to train AI systems has outpaced the production of new text data on the internet has ledThose developing AI systems tend to be less concerned about this issue.
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