308. AI Safety and Explainability 人工智能安全，访谈图灵奖得主Prof. Sifakis

This podcast episode delves into various aspects of knowledge, consciousness, and neural networks, as discussed by Professor Joseph Sifakis, a prominent computer scientist. The professor emphasizes the importance of well-founded concepts and precise definitions in formulating knowledge, and explores different types and levels of knowledge. The limitations of neural networks and machine learning techniques are discussed in comparison to reasoning and symbolic AI approaches, highlighting the challenges in trust and accountability. The conversation also addresses the regulation and safety of AI systems, particularly in critical tasks such as self-driving cars. The risks and challenges associated with neural networks and self-driving technology are examined, along with the need for robustness and careful testing. The impact of AI on industrial and business systems is explored, emphasizing the importance of risk analysis and new systems engineering approaches. The ethical implications of AI's increasing control over human lives are considered, calling for responsible decision-making and the division of work between humans and machines. The chapter concludes with reflections on the responsibilities of humans in a world dominated by machines, stressing the importance of retaining human control and establishing limits on machine use. Takeaways • Different types and levels of knowledge exist, and well-founded concepts and precise definitions are crucial for formulating knowledge. • Neural networks lack theoretical understanding and long-term reliability compared to reasoning and symbolic AI approaches. • AI systems, particularly in critical tasks like self-driving cars, require regulation, standards, and empirical validation. • Testing and evaluation techniques, including statistical testing, are essential to ensure the reliability and robustness of neural networks and self-driving technology. • Risk analysis and new systems engineering approaches are needed for managing the risks associated with the complexity of AI systems in industrial settings. • AI's increasing control over human lives raises ethical concerns, emphasizing the need for responsible decision-making and the division of work between humans and machines. • Humans should retain their sense of responsibility and not become overly dependent on machines, establishing roles and limits on machine use. • Developing one's own taste in research topics and exploring practical applicability are important in the field of autonomous systems. • Combining neural networks with traditional systems is necessary for ensuring safety and reliability in real-time assurance architecture. • Responsible use of AI requires understanding and criticism of its results, as well as respect for oneself and responsible decision-making.