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图灵奖得主杨立昆:中国人并不需要我们,他们自己就能想出非常好的点子
AI科技大本营· 2025-06-02 07:24
Core Viewpoint - The current large language models (LLMs) are limited in their ability to generate original scientific discoveries and truly understand the complexities of the physical world, primarily functioning as advanced pattern-matching systems rather than exhibiting genuine intelligence [1][3][4]. Group 1: Limitations of Current AI Models - Relying solely on memorizing vast amounts of text is insufficient for fostering true intelligence, as current AI architectures struggle with abstract thinking, reasoning, and planning, which are essential for scientific discovery [3][5]. - LLMs excel at information retrieval but are not adept at solving new problems or generating innovative solutions, highlighting their inability to ask the right questions [6][19]. - The expectation that merely scaling up language models will lead to human-level AI is fundamentally flawed, with no significant advancements anticipated in the near future [19][11]. Group 2: The Need for New Paradigms - There is a pressing need for new AI architectures that prioritize search capabilities and the ability to plan actions to achieve specific goals, rather than relying on existing data [14][29]. - The current investment landscape is heavily focused on LLMs, but the diminishing returns from these models suggest a potential misalignment with future AI advancements [18][19]. - The development of systems that can learn from natural sensors, such as video, rather than just text, is crucial for achieving a deeper understanding of the physical world [29][37]. Group 3: Future Directions in AI Research - The exploration of non-generative architectures, such as Joint Embedding Predictive Architecture (JEPA), is seen as a promising avenue for enabling machines to abstractly represent and understand real-world phenomena [44][46]. - The ability to learn from visual and tactile experiences, akin to human learning, is essential for creating AI systems that can reason and plan effectively [37][38]. - Collaborative efforts across the global research community will be necessary to develop these advanced AI systems, as no single entity is likely to discover a "magic bullet" solution [30][39].
AI热潮还是真泡沫?科技投资者别只看星辰大海 先看看财报!
Jin Shi Shu Ju· 2025-05-15 10:16
Core Insights - The article discusses the "Solow Paradox" in relation to artificial intelligence (AI), highlighting the lack of significant productivity gains despite the widespread presence of AI technology [1] - Predictions about AI replacing jobs have been prevalent, yet the actual outcomes have not aligned with these forecasts, as seen in the case of IBM's Watson and the increasing number of radiologists in the U.S. [2][3] - The profitability of AI, particularly large language models (LLMs), is questioned, as they struggle to provide reliable answers in high-stakes applications like healthcare and law [3][4] - The current hype around AI is deemed unprecedented, with many companies not disclosing AI-related revenues, raising concerns for investors [5][6] - Overall, the AI industry's revenue is estimated to be between $30 billion and $35 billion, with growth projections that may not support the current capital expenditures in data centers [7] Group 1: AI Predictions and Reality - Bill Gates predicts that AI will replace many jobs within a decade, but historical predictions about AI have often been overly optimistic [1][2] - IBM's Watson was expected to revolutionize cancer treatment but was ultimately dismissed due to safety and accuracy issues [2] - Prominent figures in AI have made bold claims about job displacement, yet the actual job market has not reflected these predictions [2][3] Group 2: Profitability and Revenue Concerns - LLMs have limited profitability despite their capabilities, as they often generate unreliable outputs in critical fields [3][4] - Companies like Microsoft and IBM acknowledge that AI will not replace programmers in the foreseeable future, indicating a gap between AI capabilities and market needs [3][4] - The estimated revenue for leading AI startups in 2024 is projected to be under $5 billion, raising questions about the overall financial health of the AI sector [5][6] Group 3: Market Dynamics and Future Outlook - Major tech companies have not reported significant AI-related revenues, suggesting a lack of substantial business impact from AI initiatives [6] - Analysts estimate that the AI industry's total revenue could reach $210 billion by 2030, which may not justify the current capital expenditures in data centers [7] - The article draws parallels between the current AI hype and the internet bubble of the early 2000s, suggesting that a similar correction may occur in the future [7]