AI Breakthrough Promises to End Costly 'Drift' in Machine Learning, Potentially Saving Trillions
In a revelation that could reshape the reliability of artificial intelligence, British innovator Martin Lucas has unveiled the "Decision Physics" framework, a groundbreaking approach designed to eliminate "drift" in AI systems—the frustrating inconsistency where models like ChatGPT deliver varying responses to identical queries. Announced on October 7, this deterministic system replaces probabilistic guesswork with fixed computations, ensuring outputs are always reproducible under the same conditions. Drift, often called the "30% problem," arises from the random nature of current AI, leading to unreliable results that undermine trust in critical applications from finance to healthcare. A new report estimates this flaw costs...