Core Insights - OpenAI's CEO Sam Altman revealed the company's latest technology roadmap, indicating that their deep learning systems are advancing at an "exponential speed" and are expected to handle tasks equivalent to an "intern research assistant" by September 2026, and evolve into a "qualified AI researcher" capable of independent interdisciplinary research by 2028 [1][3] Group 1 - The next-generation model prototype, internally codenamed "Omega-3," demonstrates significant breakthroughs in mathematical reasoning, cross-modal understanding, and autonomous experimental design [3] - Altman emphasized that AI is evolving from a mere tool to an independent researcher capable of formulating hypotheses, designing experiments, and validating results [3] - OpenAI's Chief Scientist Jakub Pachocki described this AI researcher as a system capable of autonomously completing large research projects [3] Group 2 - Pachocki stated that deep learning systems may achieve superintelligence within the next ten years, defining superintelligence as systems that outperform humans in numerous critical operations [3]

Core Insights - Artificial intelligence (AI) is rapidly advancing, capable of simulating human cognitive functions such as speech recognition, music creation, disease diagnosis, and creative writing, yet fundamentally remains a binary code-based machine [1][5] Group 1: Binary Logic - AI systems operate through symbolic computation, with every process reducible to a series of Boolean algebra-controlled electrical pulses, limiting AI's "understanding" to formalizable, quantifiable, and programmable entities [2] - Unlike AI, the human brain consists of approximately 86 billion neurons that adapt and evolve through experience, creating a complexity that cannot be replicated in silicon-based systems [2] Group 2: Simulation vs. Equivalence - A common misconception is that AI neural networks function similarly to human brains; however, artificial neurons are merely simplified mathematical functions, lacking the depth of biological neural networks [3] - AI lacks "intentionality," as human thoughts are shaped by emotional context, memory, and subjective experience, which AI cannot replicate [3] Group 3: Learning Without Understanding - Machine learning, particularly in large language models, has made significant strides in pattern detection from vast datasets, but "knowing" does not equate to "understanding" [4] - AI models struggle with generalizing knowledge across different domains and often fail in novel or ambiguous situations due to their limited parameter ranges and lack of contextual awareness [4] Group 4: Neuroscience and Computational Limits - Modern neuroscience reveals the dynamic and plastic nature of the brain, with processes like neurogenesis and synaptic changes continuously reshaping cognition, which are neither symbolic nor algorithmic [6] - AI research acknowledges that both symbolic AI and connectionist models provide limited insights into human cognition, and attempts to integrate both still fall short of replicating the spontaneity and emotional basis of the brain [6] Group 5: The Inevitable Gap - Regardless of AI's advancements, it remains reliant on silicon chips, binary logic, and explicit programming, merely mimicking emotions and creativity without genuine experience [7] - Expectations must be adjusted, as AI can enhance human capabilities but cannot replace the nuanced, emotionally rich, and embodied intelligence inherent in human minds [7]