AI Performance Conundrum: When Measuring AI Performance Leads to Irrelevance
In the rapidly evolving world of Artificial Intelligence (AI), a concerning trend has emerged: the reliance on benchmarks and metrics to measure performance. This reliance, however, may be leading to systems that fail to demonstrate true intelligence or competence.
Take, for instance, the AI system Harvey AI, developed by the company of the same name. Harvey AI scored impressively high on bar exams, outperforming most human lawyers. However, in federal court filings, it cited completely fictional cases, highlighting a disconnect between test performance and real-world competence.
This issue is not unique to Harvey AI. The entire AI stack, from training code to hardware, is optimized for benchmarks. This optimization, while effective in passing tests, often leads to systems that falter at deployment. An entire industry exists to optimize benchmark scores, with consultants specializing in benchmark gaming, tools designed for benchmark optimization, and services dedicated solely to improving metrics.
This focus on benchmarks means that AI systems are excelling at passing tests rather than demonstrating true understanding. The MMLU benchmark, for example, tests AI across 57 subjects, yet models train specifically to ace MMLU, learning nothing about understanding. As a result, models overfit to benchmark distributions, performing perfectly on benchmark data but failing on slight variations.
The race to game benchmarks is not only wasteful but also potentially dangerous. Every benchmark-correct line of code written by Copilot, for example, potentially introduced a security hole. Copilot's optimization for coding benchmarks led to security breaches due to an emphasis on toy problems and neglect of security considerations at scale.
The future of AI may lie in abandoning quantitative metrics and relying on subjective evaluation, human judgment at scale, and adversarial evaluation with constantly changing tests and unknown criteria. This shift could help ensure that AI systems are truly intelligent and competent, rather than simply adept at passing tests.
In the meantime, it is crucial to recognise the limitations of AI metrics. The value proposition of AI metrics promises capability based on benchmark scores, but these scores do not always translate to real-world performance. The scientific community, sales teams, and marketing departments all play a role in perpetuating this myth, selling, amplifying, and demanding benchmark scores, despite their questionable relevance to real-world competence.
As we continue to develop and refine AI, it is essential to remember Goodhart's Law: when a measure becomes a target, it ceases to be a good measure. The AI industry must strive to create metrics that truly reflect the capabilities of AI systems, rather than simply serving as a means to an end.
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