AI Risk Management Should Incorporate Both Safety and Security

The exposure of security vulnerabilities in safety-aligned language models, e.g., susceptibility to adversarial attacks, has shed light on the intricate interplay between AI safety and AI security. Although the two disciplines now come together under the overarching goal of AI risk management, they have historically evolved separately, giving rise to differing perspectives. Therefore, in this paper, we advocate that stakeholders in AI risk management should be aware of the nuances, synergies, and interplay between safety and security, and unambiguously take into account the perspectives of both disciplines in order to devise mostly effective and holistic risk mitigation approaches. Unfortunately, this vision is often obfuscated, as the definitions of the basic concepts of "safety" and "security" themselves are often inconsistent and lack consensus across communities. With AI risk management being increasingly cross-disciplinary, this issue is particularly salient. In light of this conceptual challenge, we introduce a unified reference framework to clarify the differences and interplay between AI safety and AI security, aiming to facilitate a shared understanding and effective collaboration across communities.

Can AI Be as Creative as Humans?

Creativity serves as a cornerstone for societal progress and innovation. With the rise of advanced generative AI models capable of tasks once reserved for human creativity, the study of AI's creative potential becomes imperative for its responsible development and application. In this paper, we provide a theoretical answer to the question of whether AI can be creative. We prove in theory that AI can be as creative as humans under the condition that AI can fit the existing data generated by human creators. Therefore, the debate on AI's creativity is reduced into the question of its ability of fitting a massive amount of data. To arrive at this conclusion, this paper first addresses the complexities in defining creativity by introducing a new concept called Relative Creativity. Instead of trying to define creativity universally, we shift the focus to whether AI can match the creative abilities of a hypothetical human. This perspective draws inspiration from the Turing Test, expanding upon it to address the challenges and subjectivities inherent in assessing creativity. This methodological shift leads to a statistically quantifiable assessment of AI's creativity, which we term Statistical Creativity. This concept allows for comparisons of AI's creative abilities with those of specific human groups, and facilitates the theoretical findings of AI's creative potential. Building on this foundation, we discuss the application of statistical creativity in prompt-conditioned autoregressive models, providing a practical means for evaluating creative abilities of contemporary AI models, such as Large Language Models (LLMs). In addition to defining and analyzing creativity, we introduce an actionable training guideline, effectively bridging the gap between theoretical quantification of creativity and practical model training.