What is the impact of latency on AI performance?

What is the impact of latency on AI performance?

Latency refers to the delay before a transfer of data begins following an instruction. In AI, high latency can lead to slower response times, affecting user experience and the effectiveness of real-time applications.

Can you give examples of how latency affects AI applications?

Sure! In applications like autonomous driving, high latency can delay decision-making, potentially leading to accidents. In virtual assistants, it can result in frustrating delays when responding to user queries.

How can we reduce latency in AI systems?

Reducing latency can be achieved through optimizing algorithms, using faster hardware, and improving network infrastructure. Techniques like edge computing can also help by processing data closer to the source.

What are the acceptable latency levels for different AI applications?

Acceptable latency levels vary by application. For real-time applications like gaming or autonomous vehicles, latency should be under 20 milliseconds, while for less time-sensitive applications, up to 100 milliseconds may be acceptable.