Because discussions about AI can quickly devolve into academic debates about terminology, let’s sort out some definitions we established in our book.
I’ll use NLP as an example to show AI’s progress through the three phases of exponential technologies. NLP makes a good proxy for AI’s evolution for a few reasons:
The most direct evidence of NLP adoption would be a dataset of the number of NLP applications deployed each year. Unfortunately, no such dataset exists. So instead I will use correlated data as evidence that NLP is an exponential technology.
Because research is a leading indicator of technology progress, any exponential AI technology should first demonstrate growing traction by research funding. Figures 7 and 8 show how research funding for NLP has grown over the past 10 years. Both scholarly references and papers are showing exponential growth.
A more direct measure of AI adoption is the rate at which deep learning NLP models are being used in applications. Hugging Face runs a popular GitHub open-source repository that NLP developers use to build applications fast. We analyzed the rate of model updates in the repository over the past 18 months. Although the survey period is short, the adoption rate shows signs of exponential growth.
A more direct measure of AI adoption is the rate at which deep learning NLP models are being used in applications. Hugging Face runs a popular GitHub open-source repository that NLP developers use to build applications fast. We analyzed the rate of model updates in the repository over the past 18 months. Although the survey period is short, the adoption rate shows signs of exponential growth.
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