AGI Definition

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.

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.

회의론 정면돌파하는 OpenAI

OpenAI의 수석과학자 야쿱 파호츠키, OpenAI의 최고연구책임자 마크 첸. 이들과 샘 알트먼은 거의 매일같이 대화하며 나아갈 방향을 다듬는다고 합니다. 안드레 카파시, 일리야 수츠케버가 연이어 불지핀 AI 회의론에 대해 이들은 어떤 생각을 하고 있을까요? 너무 많은 정보들이 쏟아지는 이 상황 속에서 적절한 '시간 자원'과 적당한 '컴퓨트'를 투자해 흐름을 이해할 수 있도록 도울 수 있다면 그것이 백색나무 채널의 기쁨이 될 것입니다. 늘 시청해주시는 한 분, 한 분께 진심으로 감사드립니다! https://youtu.be/yvFj2YuW3ak?si=Q74qBFBSLalcoXOH https://youtu.be/ZeyHBM2Y5_4?si=895y_EQvVS6LEBn0

https://www.youtube.com/watch?v=8Fesyx3oMxM

Every time we solve something previously out of reach, it turns out that human-level generality is even further out of reach.

My model of what is going on with LLMs — LessWrong

We have seen LLMs scale to impressively general performance. This does not mean they will soon reach human level because intelligence is not just a k…

https://www.lesswrong.com/posts/vvgND6aLjuDR6QzDF/my-model-of-what-is-going-on-with-llms

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 ().

AI Must Embrace Specialization via Superhuman Adaptable Intelligence

Everyone from AI executives and researchers to doomsayers, politicians, and activists is talking about Artificial General Intelligence (AGI). Yet, they often don't seem to agree on its exact...

https://arxiv.org/abs/2602.23643

AGI Definition

AI scaling is not ended

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