Millised on ohutuse omadused robotit juhtimisel masinõppega?

Safety (safety) in ML-controlled robotics encompasses several key aspects:

• Reliability and predictability of models: ML models must be resilient to errors and unforeseen situations to prevent dangerous robot actions.

• Handling uncertainty and anomalies: the system should be able to recognize when input data exceeds the training set and switch to a safe mode.

• Verification and validation of models: thorough testing and safety checks before deployment.

• Backup mechanisms and human control: the ability to turn off the ML system or intervene by the operator in case of failures.

• Ensuring data security: protection against attacks that introduce manipulated data (adversarial attacks), which can lead to incorrect decisions.

In robotics, this is critical, as errors can lead to physical damage or injuries.