MIT team teaches AI models what they didn't know before.
The application of artificial intelligence (AI) is growing rapidly and is becoming increasingly intertwined with our daily lives and high-stakes industries such as healthcare, telecom, and energy. But with great power comes great responsibility: AI systems sometimes make mistakes or provide uncertain answers that can have major consequences.
Why is this so important?
Many AI models, even advanced ones, can sometimes exhibit so-called 'hallucinations'—they provide incorrect or unfounded answers. In sectors where decisions carry significant weight, such as medical diagnosis or autonomous driving, this can have disastrous consequences. Themis AI developed Capsa, a platform that applies uncertainty quantification: it measures and quantifies the uncertainty of AI output in a detailed and reliable way.
How does it work?
By teaching models uncertainty awareness, they can provide outputs with a risk or reliability label. For example: a self-driving car can indicate that it is unsure about a situation and therefore trigger human intervention. This not only increases safety but also user trust in AI systems.
- When integrating with PyTorch, wrapping the model is done via
capsa_torch.wrapper()where the output consists of both the prediction and the risk:
For NetCare and its clients, this technology represents a huge step forward. We can deliver AI applications that are not only intelligent but also safe and more predictable with a lower risk of hallucinations. It helps organizations make better-informed decisions and reduce risks when implementing AI in business-critical applications.
Conclusion
The MIT team shows that the future of AI is not just about becoming smarter, but above all about functioning more safely and fairly. At NetCare, we believe that AI only becomes truly valuable when it is transparent about its own limitations. With advanced uncertainty quantification tools like Capsa, you can put that vision into practice.
