Do we want to take a step back and look at the revolutions that history labels as such? The invention of the wheel, of the press, the industrial revolution, internet and the world wide web?
And now, let's reflect on how many times per day we catch a glimpse of the word 'revolution' in connection with AI, quantum computing, large language models, the latest large language models, future large language models. Too fast, too often. The word 'revolution' has lost its power.
Can we look at the progress of AI through the Pareto Principle lens, and see if the 80/20 rule applies? Is it possible that it took just 20% of the R&D effort to achieve what we perceive today as 80% of the result? In terms of accuracy, reliability, relevance and usability, is it fair to say that current models of generative AI have reached that 80% threshold? This is debatable, but if we accept that assumption, it may imply that another 80% of the R&D effort may be needed to come close to 100%. And still, that would only be 100% of what a probabilistic model could achieve, 100% of what you can obtain from next token prediction.
Believe me, there is a lot of hard work needed to make even barely noticeable incremental progress. Ad we'll need to read about many more 'game-changers', 'breakthroughs', 'paradigm shifts', 'tectonic shifts' and other revolutions before we can be confident that LLMs will make history.
And now, let's reflect on how many times per day we catch a glimpse of the word 'revolution' in connection with AI, quantum computing, large language models, the latest large language models, future large language models. Too fast, too often. The word 'revolution' has lost its power.
Can we look at the progress of AI through the Pareto Principle lens, and see if the 80/20 rule applies? Is it possible that it took just 20% of the R&D effort to achieve what we perceive today as 80% of the result? In terms of accuracy, reliability, relevance and usability, is it fair to say that current models of generative AI have reached that 80% threshold? This is debatable, but if we accept that assumption, it may imply that another 80% of the R&D effort may be needed to come close to 100%. And still, that would only be 100% of what a probabilistic model could achieve, 100% of what you can obtain from next token prediction. Believe me, there is a lot of hard work needed to make even barely noticeable incremental progress. Ad we'll need to read about many more 'game-changers', 'breakthroughs', 'paradigm shifts', 'tectonic shifts' and other revolutions before we can be confident that LLMs will make history.