Real-Time AI

Real-time AI refers to AI systems that process input and deliver output fast enough for live, interactive use. The definition of "fast enough" depends on the application — a voice assistant needs responses in under a second, while a live video analyser might tolerate a few seconds of delay.

What makes AI "real-time"

The key threshold is whether the AI's processing time is short enough that users experience it as immediate. For different applications:

• Voice conversations: Less than 500ms response latency for natural-feeling dialogue.
• Live transcription: Less than 1 second delay between speech and displayed text.
• Recommendation engines: Less than 100ms to feel responsive in e-commerce.
• Fraud detection: Less than 50ms for transaction processing.
• Autonomous vehicles: Less than 10ms for safety-critical decisions.

Enabling technologies

• Streaming responses: Sending output token-by-token as it generates rather than waiting for the complete response. This gives the illusion of real-time even when total generation takes seconds.
• Edge computing: Running models on devices or local servers close to the user, eliminating network latency.
• Model optimisation: Using smaller, quantised, or distilled models that sacrifice some quality for speed.
• Specialised hardware: GPUs, TPUs, and neural processing units designed for fast AI inference.
• Continuous batching: Processing new requests as they arrive rather than waiting for batch windows.

Real-time AI applications

• Live meeting transcription and summarisation: Tools like Otter.ai and Microsoft Teams transcribe and summarise meetings as they happen.
• Real-time translation: Simultaneous interpretation for multilingual meetings.
• Voice AI assistants: Conversational AI with natural turn-taking and interruption handling.
• Live content moderation: Flagging harmful content in live streams or chat.
• Dynamic pricing: Adjusting prices based on real-time demand signals.

Challenges

• Quality vs speed trade-off: Faster models are generally less capable. Finding the right balance is an engineering challenge.
• Resource cost: Maintaining low latency under high load requires over-provisioned infrastructure.
• Error handling: In real-time systems, you cannot retry failed operations — the moment has passed.