机器之心·2025-12-31 04:09Core Insights - The essence of intelligent growth is not about architectural changes but how computational power translates into intelligence [6][7] - The current paradigm (Transformer + massive computational power) faces a bottleneck in fully utilizing the increasing computational resources, leading to diminishing returns on pre-training [6][8] - Future directions should focus on breakthroughs in foundational paradigms rather than mere engineering optimizations [8][9] Group 1: Current State of Intelligence - There is no clear definition of intelligence, and even top experts struggle to define AGI (Artificial General Intelligence) [15][16] - The core of intelligence is seen as prediction and creation, with significant advancements needed to approach AGI [17][18] Group 2: Bottlenecks in Intelligent Development - The main source of bottlenecks in intelligent growth is the inefficiency in converting computational power into usable intelligence [19][20] - Pre-training is the most significant contributor to model intelligence, consuming the most computational resources [20][21] - The current model architectures, particularly Transformers, are unable to fully leverage the continuous growth in computational power [33] Group 3: Future Directions - There is a need for higher precision computing and more advanced optimizers to enhance model intelligence [45] - The exploration of scalable model architectures and loss functions is crucial for better utilization of computational resources [45] - The industry must find ways to "consume" more energy in a unit of time and effectively convert it into intelligence [42][45]