现有路径不通？OpenAI、亚马逊考虑改变大模型训练方式

Core Viewpoint - The article discusses a fundamental shift in AI training paradigms, advocating for the abandonment of the "pre-train then fine-tune" model in favor of introducing curated data for specific tasks earlier in the training process, which could reshape the AI development landscape [2][3][4]. Group 1: Restructuring Training Logic - Current AI training practices mimic human learning but are being questioned for their efficiency, particularly the extensive pre-training on unrelated domains, which wastes resources [6]. - The proposed approach suggests using pre-training to engage with task-relevant curated data, potentially eliminating the need for separate teams for different training phases [6][8]. Group 2: Rise of Specialized Models and Organizational Restructuring - The shift towards specialized models will require developers to make early decisions on data inclusion, directly impacting the model's capabilities and limitations [8]. - OpenAI is already adapting to this demand by routing queries to different models and developing specialized versions like GPT-5-Codex, indicating a market trend away from a single universal model [4][9]. Group 3: Hardware Breakthroughs and Capital Investment - Innovations in hardware are accelerating, with companies like Neurophos raising $110 million to develop photonic chips aimed at enhancing AI computational efficiency [11]. - OpenAI is also investing in its infrastructure, with significant progress on its custom inference chips and the Stargate infrastructure project, which is over 50% complete [11]. Group 4: Industry Consolidation and Competitive Dynamics - The AI sector is witnessing active mergers and acquisitions, with companies like Lightning AI merging with Voltage Park, and Yelp acquiring Hatch for $300 million, reflecting a trend towards consolidation [13]. - Major players like Apple and Google are negotiating to enhance their AI capabilities, with Apple planning to leverage cloud infrastructure for an updated Siri by 2027 [13][14].