As we continue to push the boundaries of artificial intelligence (AI) technology, it has become increasingly clear that our traditional evaluation methods are no longer fit for purpose. The limitations of these methods are being exposed as more sophisticated AI models come to market, and the consequences of using untrustworthy or ineffective technology in businesses and public bodies could be significant.
Traditional evaluation methods, which have long been used to gauge the performance, accuracy, and safety of AI systems, are simply not fit for the complexity of today’s AI models. These methods are too narrow and easy to manipulate, making them an insufficient gauge of the true capabilities and potential risks of new AI systems.
Consider the case of large language models (LLMs), which underpin systems like ChatGPT. These models are capable of executing a string of connected tasks over a long horizon, a level of sophistication that makes controlled testing in a lab setting a challenge. As a result, it’s no longer a question of whether AI models will “ace” existing benchmarks – they are doing so with increasing frequency and ease. This raises important questions about the relevance of existing evaluation methods in the face of advancing AI technology.
Moreover, the sophistication of today’s AI models demands a more nuanced approach to evaluation. We can no longer simply look at metrics like performance and accuracy in isolation – we need to consider a range of factors beyond the capabilities of the technology itself. Cost, open source versus closed source, and specific business requirements are just a few examples of the factors that should be taken into account.
Governments and businesses alike are grappling with these challenges, recognizing that the stakes are high. The US and UK have recently signed a landmark bilateral arrangement on AI safety, and the UK government has established new AI institutes to minimize the risks of rapid advances in AI. Yet these efforts alone are not enough. We need a new approach to evaluating AI systems that can keep pace with the technology and provide a comprehensive assessment that goes beyond traditional evaluation metrics.
The limitations of traditional evaluation methods for AI systems are becoming increasingly apparent, and the consequences of continuing to rely on these methods could be significant. It’s time for a new approach to evaluating AI, one that considers a range of factors beyond performance and accuracy, and that keeps pace with the rapidly evolving technology. The need for this revamp is urgent, and the stakes could not be higher.