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. Look. The models, they just| SITUATIONAL AWARENESS
A recent topic of contention among artificial intelligence researchers has been whether large language models can exhibit unpredictable ("emergent") jumps in capability as they are scaled up. These arguments have found their way into policy circles and the popular press, often in simplified or distorted ways that have created confusion. This blog post explores the disagreements around emergence and their practical relevance for policy.| Center for Security and Emerging Technology
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Questions how long till models start autofeedbacking, like AlphaZero did? This suggests that it’s already happening the general timeline seems to be AGI around 2050 - how accurate is that? Core readings Four Background Claims - (Soares, 2015) General intelligence is a thing, and humans have it The alternative view is that intelligence is just a collection of useful modules (speech, dexterity, etc.) that can be used in different contexts to solve stuff.| ahiru.pl
Two years ago, I commissioned forecasts for state-of-the-art performance on several popular ML benchmarks. Forecasters were asked to predict state-of-the-art performance on June 30th of 2022, 2023, 2024, and 2025. While there were four benchmarks total, the two most notable were MATH (a dataset of free-response math contest problems) and| Bounded Regret
GPT-4 surprised many people with its abilities at coding, creative brainstorming, letter-writing, and other skills. How can we be less surprised by developments in machine learning? In this post, I’ll forecast the properties of large pretrained ML systems in 2030.| Bounded Regret
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Thanks to Collin Burns, Ruiqi Zhong, Cassidy Laidlaw, Jean-Stanislas Denain, and Erik Jones, who generated most of the considerations discussed in this post. Previously [https://bounded-regret.ghost.io/ai-forecasting-one-year-in/], I evaluated the accuracy of forecasts about performance on the MATH and MMLU (Massive Multitask) datasets. I argued that most people,| Bounded Regret