AGI and jobs
OpenAI defines Artificial General Intelligence (AGI) as “highly autonomous systems that outperform humans at most economically valuable work.” (Source: OpenAI charter, 2024) Their main goal is to achieve and surpass AGI.
So, what is the current state, and how does progress towards such transformative technology look?
Most jobs are rituals—highly repetitive structures with enough variation to keep them out of reach of current AI automation. That variation consists of patterns of different complexity, and AI can currently capture only some of them.
AI’s Dalmatian Effect
Figure 1. Dalmatian effect at work. Expansion and bridging are the ways to aim at AGI.
Think of the AI capabilities as a Dalmatian’s fur (see Figure 1). The black spots are areas where AI is skilled due to training examples and appropriate pattern matching. The white gaps are without examples, and the complexity is too big for basic pattern matching, so AI underperforms there. Ideally, for AGI, the fur would be entirely black.
All the big labs try to put as many examples of tasks into training as possible to expand those black spots. Lately, they are also attempting to bridge the gaps—connecting the spots not just by memorizing but also by deriving solutions through tools and reasoning. The latter amounts to learning complex patterns humans use in tasks to fight off variation and find a path from the black spots into the solutions within white gaps (e.g., the red cross in Figure 1).
Figure 2. Effects of expansion and bridging on performance on tasks follow predictable trends for OpenAI’s o1 model.
What does that mean for us? Tracking how AI expands black spots and bridges the white gaps shows the increasing economic impact of AI. Moreover, the expansion and bridging for now follow simple, predictable trends (see Figure 2) that enable credible forecasts.
Conclusion
Over the next few years, shifting from “spotty” capabilities to more widespread automation could redefine industries. Companies and governments should now use these forecasts to plan their resources and policies – the very top companies are already committing substantial long-term investments, i.e., creating their computer chips, building data centers, and buying electric power capacity.
Literature
- Learning to Reason with LLMs, Open AI, 2024
Written on: November 1, 2024
Written by : Mario Brcic
