Sakana AI, a futuristic lab from Japan, has pulled back the curtains on an innovative technique involving the co-operative effort of multiple large language models (LLMs). This method results in an AI ‘dream team’ capable of tackling a common task more efficiently than any individual model, surpassing single model performance by about 30%. The unveiling of the method, named Multi-LLM AB-MCTS, has sent ripples across the world of AI and enterprise.
This trailblazing technique presents an intriguing prospect for building more robust and adaptable AI systems. It essentially allows businesses and enterprises to shake off the dependency on a solitary model or provider. In grounds-breaking progress, it allows a dynamic utilization of the best elements of different frontier models based on the demands of the task, thereby delivering superior results.
The AI revolution has been sweeping through various industries, often replacing humans at tasks once considered distinctly human. However, as a recent study underlines, all that glitters may not be silicon gold. Despite their capacity to beat human physicians in medical exams, leading AI language models like GPT-4 struggle to assist humans in reaching accurate diagnoses.
Researchers at the University of Oxford recently pointed out the gap between AI’s medical proficiency and its practical application. The study revolved around language learning models (LLMs), which have been proven to surpass human doctors in medical exams. However, when provided with real-life medical scenarios to diagnose using the help of LLMs, human participants were able to correctly identify the conditions only 34.5% of the time. When diagnosing themselves on their terms at home, though, they were 76% more likely to be accurate.