In the ever-evolving landscape of artificial intelligence (AI), experts are grappling with a significant challenge: the lack of incentives driving AI development. The issue was recently addressed at the Inclusion·外滩大会 in Shanghai, where renowned futurist and founder of Wired magazine, Kevin Kelly, and other AI luminaries shared their insights.
Globalism, Accelerated Innovation, and AI-Driven Generation
Kevin Kelly highlighted three major trends that AI is expected to bring about as it continues to deeply impact the economy and culture. These trends include globalism, accelerated innovation, and AI-driven generation.
Globalism is rapidly advancing, as we are constructing a super organism based on technology. The world’s mobile phones, laptops, and data servers are being connected to form a massive computing system, with each device acting as a neuron in this vast computer. This supercomputer is operating at an unprecedented scale.
AI technology is also accelerating the pace of innovation. This acceleration is evident in various aspects, such as the rapid spread of new inventions and ideas, the enhancement of learning efficiency through augmented reality (AR) and virtual reality (VR) technologies, and AI’s ability to perceive the world through machines and other sensors. Additionally, AI tools like ChatGPT are greatly speeding up the pace of learning.
The Missing Dimensions in AI Discussions
Machine learning pioneer and three-time Turing Award winner Michael Jordan identified three missing dimensions in the current discussions about AI: collective intelligence, uncertainty, and incentives.
Jordan emphasized that AI system development cannot rely solely on individual smart devices but should instead be built through collective collaboration, creating decentralized intelligent systems. This is particularly important when facing uncertainty, as collective intelligence is necessary to address it.
He pointed out that while AI possesses vast amounts of data, not all of it can generate value. To drive AI intelligent agents to contribute and collaborate, incentives must be designed. Jordan proposed the Three-Layer Data Markets model, where users, platforms, and data buyers form a closed loop through transferring data, purchasing data, and providing services. He emphasized that data buyers, i.e., enterprises, can combine data and services to create incentive mechanisms for users, thereby bringing them true value.
AI Development and Human Welfare
Jordan also drew parallels between AI system development and the development of chemical engineering and electrical engineering. While the former is built on the foundations of reasoning, algorithms, and economic concepts, with the goal of human welfare, the latter is based on fields such as chemistry, fluid mechanics, electromagnetism, and optics. He warned that AI’s rise and development are being distorted by unconsidered, simplistic visions.
Human-AI Interaction and the Future of AI
Hong Kong科技大学校董会主席 and U.S. National Academy of Engineering foreign member, Simon Xiangyang, discussed the evolution of human-AI interaction in the era of large models. From graphical interfaces to search, recommendation, and now conversation, the development of large models will further iterate these interaction methods. Xiangyang believes that AI provides a new context for human-technology coexistence and that the new ways of human-AI interaction point to the fusion and progress of AI with IA (Intelligent Augmentation).
IA represents a human-centric AI development path that focuses on using technology to enhance human capabilities rather than replace them, emphasizing the collaborative relationship between humans and AI. When discussing AI agents, Xiangyang emphasized that agents must always be centered on demand, deeply understand the capabilities of the model, and build a work process with AI deeply involved.
The Future of AI: Governance and Responsibility
Xiangyang also highlighted the importance of new governance frameworks and systems to address the needs and challenges of different regions as AI rapidly develops. AI governance is crucial, and only by building responsible AI systems can we ensure that they have a positive and long-term impact on society.
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