Researchers at MIT have developed a new AI training method called ‘liquid neural networks’ that significantly improves the efficiency and adaptability of AI models. Unlike traditional neural networks with fixed architectures, these networks can dynamically adjust their underlying equations in response to new data, allowing them to learn continuously after initial training. This approach enables more compact and energy-efficient models that perform well on tasks like autonomous driving and medical diagnosis. The technology shows promise for applications where conditions change rapidly and where deploying large, static models is impractical. For more details, read the full article.