How OpenAI, Google, and Anthropic are using differing approches to improve “model behavior”, an emerging field shaping AI systems’ responses and characteristics (Cristina Criddle/Financial Times)
The rise of powerful AI models like ChatGPT and Bard has brought the urgent need to control their behavior to the forefront. While these models offer unprecedented capabilities, their potential for harm due to biases, misinformation, or even malicious intent demands a solution. OpenAI, Google, and Anthropic, leading players in the field, are taking distinct approaches to this emerging challenge, known as “model alignment.”
OpenAI, the creators of ChatGPT, heavily emphasize “reinforcement learning from human feedback” (RLHF). This involves training models on vast amounts of data labelled by humans, who rate the model’s responses for quality and alignment with desired principles. This approach, while effective, raises concerns about human biases being baked into the models.
Google, in contrast, leans towards “safeguards” and “constraints.” They employ techniques like “value alignment” to ensure the model’s responses align with ethical principles and societal norms. This involves defining specific rules and limitations for the model’s behavior, leading to a more controlled but potentially less creative output.
Anthropic, a new entrant in the field, focuses on “constitutional AI.” This involves training models with a set of “constitutional principles” designed to guide their behavior. These principles are chosen to be as objective and ethical as possible, avoiding the subjectivity of human feedback. However, this approach faces the challenge of defining truly universal and objective ethical principles.
While each approach has its merits and drawbacks, the ultimate goal remains the same: to create AI systems that are both powerful and safe. The “alignment race” is just beginning, and the development of robust, reliable, and ethical AI systems depends on the continuous evolution of these approaches.
The future of AI hinges on the success of these efforts. By understanding the nuances of these different approaches, we can better evaluate the risks and benefits of AI development and ultimately guide it towards a future that benefits all of humanity.
