Microsoft Research
Introducing Trace, Microsoft and Stanford University's novel AI optimization framework, now available as a Python library. Trace adapts dynamically and optimizes a wide range of applications from language models to robot control. The post Tracing the path to self-adapting AI agents appeared first on Microsoft Research. Figure 5: We show the number of environmental interaction actions taken to succeed in each task. Trace optimized agent takes fewer steps to succeed, thus more efficient in this environment. Figure 6: Demo videos of how Trace agents behave to finish each of the three tasks. [send_message] to : I am handing you the . Please grab another piece of cutlery or plate to help! [send_message] to : Can you also hand me the you are holding? [send_message] to : Here's the . I'll go grab the now. ... [send_message] to : Let's head to the kitchen and put the and into the dishwasher. Figure 7: Trace learns pro-social behavior in the Dishwasher task. Trace optimized agents send messages to attempt to collaborate while simple ReAct agent will only carry out the tasks. Trace heralds a new era of interactive agents that adapt automatically using various feedback types. This innovation could be the key to unlocking the full potential of AI systems, making them more efficient and responsive than ever before. After witnessing the awesome power of Deep Neural Networks, stay tuned for the next revolution in AI design Deep Agent Networks! Opens in a new tabThe post Tracing the path to self-adapting AI agents appeared first on Microsoft Research.
Published: 2024-07-25T19:04:25
