I. From GPT-4 to AGI: Counting the OOMs
AGI by 2027 is strikingly plausible. GPT-2 to GPT-4 took us from ~preschooler to ~smart high-schooler abilities in 4 years. Tracing trendlines in compute (~0.5 orders of magnitude or OOMs/year), algorithmic efficiencies (~0.5 OOMs/year), and “unhobbling” gains (from chatbot to agent), we should expect another preschooler-to-high-schooler-sized qualitative jump by 2027.
GPT-4’s capabilities came as a shock to many: an AI system that could write code and essays, could reason through difficult math problems, and ace college exams. A few years ago, most thought these were impenetrable walls.
But GPT-4 was merely the continuation of a decade of breakneck progress in deep learning. A decade earlier, models could barely identify simple images of cats and dogs; four years earlier, GPT-2 could barely string together semi-plausible sentences. Now we are rapidly saturating all the benchmarks we can come up with. And yet this dramatic progress has merely been the result of consistent trends in scaling up deep learning.
There have been people who have seen this for far longer. They were scoffed at, but all they did was trust the trendlines. The trendlines are intense, and they were right. The models, they just want to learn; you scale them up, and they learn more.
