The accelerated rate of progress in capabilities will quickly put us in a place that seems shocking that we didn't address the harmlessness question sooner. Open-source development of large language models (LLMs) has been proceeding with crazy things like one person leading releases of QLoRA (quantized, efficient fine-tuning for memory efficient training) and SpQR (Sparse-Quantized Representation for compression) in just two weeks. These papers are the sorts of technologies that reduce the memory footprint of training or inference of large models by 20+%. These types of margins, when accumulated multiple times in a year, result in crazy improvements. At the beginning of 2023, many consumer GPUs could only handle LLaMA 7Billion and by 2024 the same GPU can maybe fine-tune LLaMA 65Billion. The capabilities that it unlocks are truly wild \u2014 we'll see companies and products emerge from this sort of thing. My sense is that people are improving the data quality and the other pieces of the puzzle that OpenAI figured out a few years ago with the added benefit of new techniques for training models on consumer hardware.
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