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Large Language Models Ace All Exams, Yet Move Farther from AGI: What Does This Paper Reveal?

The article discusses the ongoing challenge of defining and achieving Artificial General Intelligence (AGI). It notes that industry leaders have set vague, often profit- or time-based benchmarks for AGI, while the concept itself lacks a consensus definition—a situation the article compares to a "Rorschach test." It highlights a recent 2025 paper by researcher Michael Timothy Bennett, who proposes a new, measurable definition. Bennett frames AGI not as mimicking human performance on tests, which current large language models (LLMs) have already mastered, but as an "artificial scientist." A true AGI, according to this view, should be able to widely and efficiently adapt to new environments and tasks within real-world constraints (like computational and energy limits), focusing on the *discovery of new knowledge* rather than the replication of existing data. The author contrasts this with the current dominant approach of "scale-maxing"—massively scaling up data, parameters, and compute. While powerful, this method leads to models that fail on out-of-distribution problems and lack core intelligent abilities: they are passive learners, cannot reason causally, and cannot actively experiment or balance exploration with exploitation. The article argues that Bennett's framework offers a crucial shift. It makes AGI a quantifiable engineering problem and proposes new evaluation "adaptation benchmarks" that test an AI's ability to actively learn in novel scenarios. The conclusion is that achieving AGI will require a fundamental reset—a fusion of multiple methodologies beyond simple scaling, moving AI from mimicking patterns to embodying the scientific spirit of inquiry and discovery.

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Large Language Models Ace All Exams, Yet Move Farther from AGI: What Does This Paper Reveal?

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In the Coming Decades, What Could Be Your Most Important Skill?

The most important skill for the future is agency: the ability to take self-directed action without external permission. Unlike specialized skills, which risk obsolescence, agency enables continuous adaptation and learning. Highly agentic individuals act autonomously, treat life as an experiment, and persist through failure. They see challenges as solvable problems rather than impossibilities. In the AI era, agency becomes even more critical. While AI can generate content and automate tasks, it lacks vision and context. Human creators who use AI as a tool—infusing their unique perspective and purpose—will thrive. The fear of AI replacing humans stems from a misunderstanding: tools evolve, but agency and vision remain irreplaceable. Generalists, not specialists, will succeed because they integrate diverse knowledge to solve problems and adapt to change. The education system often promotes conformity, but agency requires breaking free from predefined paths. Humans possess five core capacities: computation, transformation (creation), variation (idea generation), selection (error correction), and attention (perspective-shifting). These remain foundational regardless of technological advances. To cultivate agency, start by setting a goal, study others’ processes, experiment, identify patterns, and create your own methods. Teaching others solidifies understanding. Social media serves as a modern "playground" for practicing agency—offering low-risk experimentation, feedback, and skill development. Ultimately, agency is the art of self-direction, ensuring relevance and resilience in any future.

marsbit01/14 14:35

In the Coming Decades, What Could Be Your Most Important Skill?

marsbit01/14 14:35

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