In the next couple of decades, we will be able to do things that would have seemed like magic to our grandparents. This phenomenon is not new, but it will be newly accelerated. People have become dramatically more capable over time; we can already accomplish things now that our predecessors would have believed to be impossible. We are more capable not because of genetic change, but because we benefit from the infrastructure of society being way smarter and more capable than any one of us; in an important sense, society itself is a form of advanced intelligence. Our grandparents – and the generations that came before them – built and achieved great things. They contributed to the scaffolding of human progress that we all benefit from. AI will give people tools to solve hard problems and help us add new struts to that scaffolding that we couldn’t have figured out on our own. The story of progress will continue, and our children will be able to do things we can’t.
核心贡献 · Key contributions
深度学习有效，且随算力和数据可预测地扩展，能在几千天内实现超级智能。 Deep learning works and scales predictably with compute and data, enabling superintelligence within a few thousand days.
AI 将提供个人 AI 团队和虚拟导师，彻底改变生产力和教育。 AI will provide personal AI teams and virtual tutors, revolutionizing productivity and education.
智能时代将带来巨大的共享繁荣，使全球生活水平超越当前想象。 The Intelligence Age will bring massive shared prosperity, improving lives globally beyond current imagination.
AI 将解决气候变化、太空殖民和基础物理等难题。 AI will solve hard problems like climate change, space colonization, and fundamental physics.
进步需要充足的算力和能源基础设施，以普及 AI 访问。 Progress requires abundant compute and energy infrastructure to democratize AI access.
AI 将放大人类创造力并实现正和博弈，尽管劳动力市场会变化。 AI will amplify human creativity and enable positive-sum games, despite labor market shifts.
局限 · Limitations
超级智能的时间线是推测性的，可能比几千天更长。 The timeline for superintelligence is speculative; it may take longer than a few thousand days.
深度学习的可扩展性可能遇到尚未知的收益递减或根本限制。 Deep learning's scalability may hit diminishing returns or fundamental limits not yet known.
文章缺乏对 AI 风险（如对齐、滥用和劳动力替代）的具体解决方案。 The essay lacks concrete solutions for AI risks like alignment, misuse, and labor displacement.
无限繁荣的假设可能忽视分配不平等和社会阻力。 Assumption of infinite prosperity may overlook distributional inequalities and societal resistance.
对算力和能源的关注忽略了数据质量和算法创新等其他关键因素。 The focus on compute and energy ignores other critical factors like data quality and algorithmic innovation.