Gopher – A 280B parameter language model
The closer we get to artificial intelligence, the more we raise the bar for what qualifies as AI (as we should). Gopher/GPT-3 are already much more accurate than the average human at technical information retrieval (trivial to see from the dialogue transcripts: how many Americans know what a Schwarzschild metric is?). The focus on ethics and equity for these algorithms is interesting too, as the average human holds multiple biases and prejudices.<p>There's a similar effect in self-driving car research. We will rightly hold the algorithm to a much higher quality bar than existing human drivers. Autonomous vehicles will only be widely deployed once they are at least an order of magnitude safer than human drivers.<p>Also, the implications of large language models for information retrieval are astounding. Assuming accuracy continues to improve, I see a few things happening:<p>1) search engines will be replaced with monolithic large language models<p>2) just as the average programmer now optimizes their workflow to look up answers on Stack Overflow and other websites, the average person will optimize their day-to-day work to most efficiently retrieve answers from large language models<p>3) instant fact checking of writing and speech will lead to faster feedback loops when learning