Engineers at Meta AI are tackling a crucial problem in building trustworthy artificial intelligence systems: ensuring that AI agents behave as intended, even when faced with unexpected situations or conflicting objectives. Specifically, they’re working on “eval engineering,” a type of AI training that uses evaluation metrics to guide an AI’s decision-making process and prevent it from deviating from its goals.
Avoiding the Unintended Consequences
The issue of agentic AI governance – which involves designing systems that can make autonomous decisions while still respecting human values – has become increasingly pressing as AI models become more sophisticated and powerful. Take, for instance, language models like LLaMA, which have shown remarkable capabilities in generating coherent and even creative text. However, such models can also be prone to producing content that’s biased, inaccurate, or even toxic.
That’s where eval engineering comes in. By using evaluation metrics to regularly assess an AI’s performance and identify areas for improvement, engineers can ensure that the AI is learning from its experiences in a way that aligns with its intended objectives. This, in turn, helps to prevent the AI from developing unintended behaviors or biases that could have negative consequences in real-world applications.
Building Trust in AI Systems
One of the key challenges in eval engineering is defining the right evaluation metrics. These metrics must be able to capture the nuances of an AI’s behavior and identify the underlying factors that contribute to its performance. At Meta AI, researchers are exploring a variety of techniques for creating effective evaluation metrics, including using human feedback to guide the AI’s learning process.
The ultimate goal of eval engineering is to build trust in AI systems by ensuring that they’re transparent, accountable, and reliable. By developing AI agents that are capable of learning from their experiences and adapting to new situations, we can create systems that are more effective and more trustworthy in real-world applications.
What this means
The work on eval engineering has significant implications for the development of agentic AI systems. By ensuring that AI agents are designed with evaluation metrics that promote transparency, accountability, and reliability, we can create systems that are more trustworthy and effective in a wide range of applications – from customer service chatbots to self-driving cars. As AI continues to advance and become more ubiquitous, the need for effective eval engineering has never been more pressing.
