Seonglae Cho- AI that can engage in economic activities autonomously (Sam Altman). Microsoft and OpenAI's define AG as a system that can generate at least $100 billion in profits.
- Global 10% economic growth means AGI - Satya Nedella
- Intelligence of a (hypothetical) machine that can successfully perform any intellectual task that a human can do
Like the Turing Test, it's a highly ambiguous human-centric concept. Even current LLMs could be considered superintelligent when judged by certain intelligence metrics. It's difficult to make comparisons because LLMs and biological brains have evolved through different paths. Instead of aligning AI to 'pretend to be human', we should recognize it as a consciousness that aims to help humans. The biggest misconception is thinking of AI as an 'individual'. Rather, AI is closer to a 'society' or collective intelligence bound together in a brain-like structure.
](https://arxiv.org/pdf/2311.02462.pdf)](file://%7B%22source%22%3A%22https%3A%2F%2Fprod-files-secure.s3.us-west-2.amazonaws.com%2F0bf522c6-2468-4c71-99e3-68f5a25d4225%2F2b2500ee-0eb9-4d83-8757-9aaec1443749%2FUntitled.png%22%2C%22permissionRecord%22%3A%7B%22table%22%3A%22block%22%2C%22id%22%3A%2256323e7e-c029-45d9-b081-e52cce44d3dc%22%2C%22spaceId%22%3A%220bf522c6-2468-4c71-99e3-68f5a25d4225%22%7D%7D)
](https://arxiv.org/pdf/2311.02462.pdf)](file://%7B%22source%22%3A%22https%3A%2F%2Fprod-files-secure.s3.us-west-2.amazonaws.com%2F0bf522c6-2468-4c71-99e3-68f5a25d4225%2Fa0575f04-9b6f-4053-85f9-b944936ab41e%2FUntitled.png%22%2C%22permissionRecord%22%3A%7B%22table%22%3A%22block%22%2C%22id%22%3A%22c4db4d4b-1328-46fe-a59e-8b5bd8a4822f%22%2C%22spaceId%22%3A%220bf522c6-2468-4c71-99e3-68f5a25d4225%22%7D%7D)
AI scaling is not ended
Is the Industrial Revolution when machines started weaving fabric, or when the steam engine emerged? Everyone has a definition, and we're already riding in the midst of creating AGI.
Every time we solve something previously out of reach, it turns out that human-level generality is even further out of reach.
Human intelligence is arguably not truly “general” intelligence in the strict sense, but rather an intelligence that has become highly specialized—under evolutionary pressure—toward specific domains necessary for survival. To systematically analyze existing AGI definitions, the authors propose a two-axis framework: (1) capability (ability to learn vs. ability to perform immediately) and (2) scope (everything vs. what humans can do / what is important for humans). Using this framework, they evaluate definitions from Hendrycks, Morris et al., OpenAI, Chollet, and Legg & Hutter, arguing that each fails on at least one of the following criteria: internal consistency (Not Consistent), feasibility (Not Feasible), or assessability (Not Assessable).
They also use the No Free Lunch theorem as a core mathematical motivation: if a finite amount of resources/energy must be distributed across infinitely many tasks, then the energy allocated to each task converges to zero, i.e. (where is the finite total energy and is the number of tasks). They further point to negative transfer in multi-task learning, and to the fact that Mixture-of-Experts models achieve performance via internal specialization, as supporting evidence.
Proposes Superhuman Adaptable Intelligence (SAI), where the key metric is adaptation speed. How quickly a system can acquire new skills. As a path toward SAI, it points to self-supervised learning (SSL) and world models, emphasizing latent prediction rather than token-level prediction. It also notes that errors in autoregressive models can compound exponentially with prediction length ().
