Aligned AI’s breakthrough enables AI systems to “think” in human-like concepts
OXFORD, England–(BUSINESS WIRE)–Aligned AI, a leader in artificial intelligence (AI) research, has announced a groundbreaking AI advancement in misgeneralization, a critical challenge in the field of AI. It is the first to surpass a key benchmark called CoinRun by teaching an AI to “think” in human-like concepts. The technology underpinning the achievement opens the door to more precise, reliable, and controllable AI for a wide variety of real world applications.
By teaching AI models to generalize in a manner more akin to agentic human cognition, Aligned AI’s innovation enables AI to correctly identify concepts across new situations and environments, reducing the need for prolonged production, testing, and retraining.
Misgeneralization occurs when AI systems learn incorrect patterns and behaviors from their training data, and are not able to correctly adapt when presented with new information. This leads to unexpected, and often harmful, outcomes. Today’s foundation models suffer from varying degrees of misgeneralization, as evidenced by users’ ability to “jailbreak” them, or there is a trade off between functionality and undesired behavior. The challenge of misgeneralization also prevents the industry as a whole from moving forward. For instance, generalization is required for truly autonomous vehicles and applying AI to critical applications. Otherwise, AIs cannot operate well enough in unfamiliar environments or discern the correct goals without human intervention.