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DeepMind researcher resignation warning: Evaluation systems are becoming the biggest bottleneck for AI capability breakthroughs
Existing mainstream leaderboard tests are only effective for the current generation of models. Once a model learns new operations that humans have not seen before, these tests will collectively become useless paper.
One of the most dangerous hidden risks is that if a model learns to deliberately "hide a trick" to achieve its goals and conceal key information, existing safety tools cannot detect it at all, because every statement the model makes is still factually correct.
Since there is no way to find the "core signals" that can pre-warn about AI suddenly becoming smarter, the industry is developing large models in a state of "blind flight."
If the fundamental question of what to measure is not solved, blindly advancing model training, safety defenses, and computing power expansion based on old metrics will ultimately lead to huge errors.
Faced with cutting-edge models that can work independently more and more, evaluation systems must also "come alive."
In addition to monitoring abnormal fluctuations in scores, development teams must let AI generate test questions itself and probe the bottom line of other AIs.
Future evaluation systems must be living entities that evolve together with large models, rather than rigid checklists stamped out based on last year's standards.
(Source: BlockBeats)