2026-06-07 Воскресенье

Новостной центр - Страница 55

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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.

marsbit05/28 00:24

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

marsbit05/28 00:24

Pope Issues First AI Encyclical: 40,000 Words, 10 Key Points, Clarifying AI Anxiety

Pope Leo XIV's historic encyclical "Magnifica Humanitas," released in May 2026, marks the Catholic Church's first major document addressing artificial intelligence. The 40,000-word text moves beyond theological abstraction to confront practical AI anxieties affecting society. It argues that AI is no longer a mere tool but an embedded environment influencing daily decisions in areas like employment, healthcare, justice, and information, often without users' awareness. The encyclical presents ten core concerns. It highlights that the central issue isn't just regulation, but who holds the underlying *power*—control over data, compute, and platforms—often concentrated in private entities. It warns that even developers cannot fully explain AI systems, creating accountability gaps. While AI can simulate human interaction and creativity, it cautions against treating it as a moral agent capable of bearing true responsibility or forming genuine relationships. Key risks identified include AI's role in opaque decision-making for jobs or welfare, the amplification of persuasive disinformation, and the potential for education to focus on tool use over critical thinking. The document stresses that work has value beyond efficiency, and AI should enhance human capabilities, not merely replace roles. It firmly states that irreversible decisions, especially involving life and death, must remain under human judgment. Ultimately, the encyclical frames AI's challenge as anthropological, not just technological. As AI simulates uniquely human capacities like judgment and creation, it forces a re-examination of what makes human action meaningful: our capacity for responsibility, vulnerability, and bearing real consequences. The Pope concludes that technology is never neutral; its development and deployment are shaped by human values and choices, making an inclusive, ethically grounded dialogue essential for its future.

marsbit05/28 00:19

Pope Issues First AI Encyclical: 40,000 Words, 10 Key Points, Clarifying AI Anxiety

marsbit05/28 00:19

Retail Investors' 'Lead Brother' Serenity vs. Newly Minted Stock God Leopold: How Are the Two Top Hunters Mining AI's 'Physical Limits'?

The article profiles two prominent figures, Serenity and Leopold Aschenbrenner, who are gaining attention for their unconventional investment strategies focused on the physical constraints of the AI boom, moving beyond mainstream software narratives. Serenity, an anonymous online trader, advocates a "shiso leaf" theory. He targets small-cap companies with monopolies on critical, overlooked components in the AI hardware supply chain, such as specific semiconductor materials. His deep, technical analysis of bottlenecks in areas like co-packaged optics (CPO) has reportedly yielded massive returns, though his anonymity and focus on illiquid micro-cap stocks pose significant risks for followers. Leopold Aschenbrenner, a former OpenAI researcher, founded a multi-billion dollar hedge fund. His macro thesis argues that physical infrastructure—power grids, land, data centers—is the true bottleneck for AI growth, lagging far behind chip production. Consequently, his fund employs an infrastructure arbitrage strategy: heavily investing in storage and compute infrastructure companies while placing massive bearish bets (put options) against major semiconductor stocks, betting their valuations will correct as physical constraints become apparent. While their methods differ—Serenity drills into microscopic supply chain details, while Leopold takes a macroscopic, infrastructure-focused view—both share a core belief: the real power and investment alpha in the AI era lie in controlling scarce physical resources, not just software. The article concludes by noting the inherent risks in both approaches, such as liquidity issues for micro-caps and timing risks for macro bets, but suggests they signal a broader market re-evaluation of AI's foundational assets.

marsbit05/27 15:10

Retail Investors' 'Lead Brother' Serenity vs. Newly Minted Stock God Leopold: How Are the Two Top Hunters Mining AI's 'Physical Limits'?

marsbit05/27 15:10

Who Will Make Money in the Age of Agents?

In the Agents era of blockchain, traditional value capture theories face challenges. The "Fat Protocol" theory, dominant since 2016, suggested protocols capture most value as their tokens are essential for network use. However, the proliferation of interchangeable L1s, L2s, and modular layers has eroded protocol scarcity and pricing power. Conversely, the "Fat App" theory posits that applications capturing user relationships (like wallets and exchanges) become the primary value layer by controlling distribution and transaction flows. This aligns with the current "Great Repricing" cycle. Agents disrupt this logic. As software users, they lack brand loyalty, prioritize cost and efficiency, and switch between platforms seamlessly. This undermines the front-end UX moats that "Fat Apps" rely on. The article explores several potential futures: 1. **Headless Applications:** Current leading apps could strip their front-ends and become backend API infrastructure for Agents, preserving their role. 2. **Protocol Resurgence:** If integration becomes trivial, Agents might bypass aggregators and interact directly with protocols, reviving "Fat Protocol" dynamics. 3. **Pricing Power Collapse:** Agents' rational, frictionless routing could commoditize the entire stack, compressing margins toward cost and leaving little profit for intermediaries. 4. **Unprecedented Activity:** Agents may enable new, high-frequency, machine-to-machine economic activities, expanding the total value pie even if margins are thin. 5. **A New, Unnamed Model:** Historically, major tech shifts (like the internet's attention economy) create unforeseen business models. The Agents era may spawn entirely new ways to capture value. The most likely outcome is a coexistence where "Fat Apps" continue to serve human users valuing UX, while a separate, Agent-driven economy emerges governed by different rules—where loyalty is based on factors like liquidity, latency, and settlement guarantees rather than brand.

marsbit05/27 14:05

Who Will Make Money in the Age of Agents?

marsbit05/27 14:05

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