Building a voice agent with real-time voice activity detection showed me just how convincing AI dialogue can feel when multi-turn interaction mirrors human conversation. But fluid conversation isn't the same as evaluation. From a philosophical standpoint, I see developers operating with a flawed concept of knowledge. They treat it as a collection of keywords. This misapprehension is failing candidates and failing companies.
Recruitment agencies and some tech start-ups initially sold AI interview agents like Zara, created by micro1, as a solution to scale screening and reduce labour costs. They likely viewed it as an efficiency hack, but they failed to recognize the limitations of an algorithmic assessment.
AI interviewers aren’t standalone models; they are not autonomous reasoning systems. They are applications powered by large language models like GPT-4o, Claude 3.5, or fine-tuned open-source models like Llama. Sentence transformers were key to their initial development, enabling semantic search and similarity matching against a Knowledge Base (KB). However, these agents don't understand the way humans do; they simulate conversation by creating numerical representations of sentences. They act as chat wrappers, applications built around a model, with text-to-speech capabilities for voice interaction.
John Searle’s Chinese Room argument features a person inside a closed room who receives questions written in Chinese. The person doesn't understand Chinese, but follows a detailed set of rules to…
Source: DEV - interview — Read the full article on the original site ↗