# Scaling Law Articoli collegati

Il Centro Notizie HTX fornisce gli articoli più recenti e le analisi più approfondite su "Scaling Law", coprendo tendenze di mercato, aggiornamenti sui progetti, sviluppi tecnologici e politiche normative nel settore crypto.

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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Meeting at the Pinnacle of Generalist: 30 Billion in 30 Days, What Did Qianxun AI Do Right?

Qianxun Intelligence, a Chinese embodied AI and robotics startup, completed two major funding rounds totaling 3 billion RMB within 30 days in early 2026, backed by prominent investors including Shunwei Capital (Lei Jun) and Yunfeng Capital (Jack Ma). Founded in January 2024 by a team with expertise in robotics, AI, and commercialization, the company focuses on developing general-purpose embodied AI models. Its open-source model, Spirit v1.5, surpassed competitors in performance benchmarks, demonstrating strong zero-shot generalization capabilities for complex tasks. The company follows a scaling law approach similar to large language models (LLMs), leveraging massive diverse datasets—including internet videos, wearable device data, and teleoperation data—to train its Vision-Language-Action (VLA) model. Qianxun employs a multi-source data engine, collecting over 200,000 hours of real-world interaction data, with plans to reach 1 million hours by 2026. It uses low-cost wearable devices for efficient data acquisition and emphasizes real-world deployment for continuous data feedback. The company has deployed robots like "Xiao Mo" in industrial settings (e.g., battery production lines for CATL) and commercial scenarios (e.g., as baristas in JD.com malls), using operational data to refine its models. This "commercialize while iterating" strategy supports both revenue generation and model improvement, positioning Qianxun to compete globally in embodied AI.

marsbit04/07 04:05

Meeting at the Pinnacle of Generalist: 30 Billion in 30 Days, What Did Qianxun AI Do Right?

marsbit04/07 04:05

Empowering AI, Paving the Way for Value: How Crypto Positions Itself in the Infrastructure Battle Before 2027?

The article discusses the contrasting empowerment approaches of Crypto and AI. Crypto offers "defensive" empowerment through decentralized financial sovereignty, enabling asset control without traditional intermediaries. While valuable, its growth has shifted from regulatory arbitrage to slower, regulated adoption, lagging behind AI's pace. AI provides "offensive" empowerment via exponential productivity gains, driven by scaling laws, compute expansion, and agent applications. Its growth is predictable and rapid. The author argues Crypto must integrate with AI to avoid becoming a niche speculative market. By positioning as AI’s value-layer infrastructure—through protocols like x402, AI Agent payments, and on-chain AI economies—Crypto can tap into AI’s scaling demand for transactions and automation. The window for this integration is before 2027, the anticipated timeline for AGI emergence. Success depends on leveraging AI’s growth rather than clinging to purely human-centric narratives.

marsbit12/25 09:19

Empowering AI, Paving the Way for Value: How Crypto Positions Itself in the Infrastructure Battle Before 2027?

marsbit12/25 09:19

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