When you speak to it, AI comprehends that the words you have spoken have come through complex algorithms designed to identify language patterns. We can see the efficiency of using such algorithms in stats — As an instance when Google launched its BERT, it enhanced the accuracy of search results by 10% simply because of its way of contextualising the conversational language. Natural language processing (NLP) is used by AI systems like virtual assistants to decompose your words into human-readable parts and correlate those with patterns in their database. For instance, when you ask a question on Siri or Alexa, these systems read the words and execute algorithms to formulate the responses.
And data also matters in this instance because it informs the assessment, if AI actually does comprehend. According to research about the systems powered by NLP technology using AI recently, the systems understand the queries by a human user correctly up to 95%. In reality, these systems get better the more conversations they analyze over time. This is referred to as continuous learning, and some companies, like Microsoft and Amazon, employ it in order to improve their voice recognition systems. Greater conversational AI: The more you talk to AI, the more it gets right — especially when that AI is fine-tuned to a specific user.
For example, chatbots in customer service use this. According to a 2019 study, 72% of businesses using an AI chatbot reported an increase in customer satisfaction as the bot started to improve on understanding of common questions over time. Example of a “game changing improvement” is whenever you interact with a AI in a meaningful way, like asking follow up questions, or giving feedback, the system recalibrates itself, fine-tuning its mental model of your tastes.
Prominent AI experts like Andrew Ng, top machine learning expert, have pointed out that “AI doesn’t understand this in the way you or I do,” but the AI is sophisticated enough in processing language that it is able to produce contextual responses and reasonably “natural” responses. Ng said that AI’s understanding is not based on awareness or consciousness but rather on pattern interpretation. That knowledge is derived from analyzing data, not human-like understanding.
With respect to AI chatbots or virtual assistants, the real test of comprehension is how contextually-appropriate and how best a response is delivered, making it optimized over time with more data. The goal is to train the AI to get better and better at answering your questions as the AI learns the custom data set that you are generating every time you ask a question. To know more about the way Ai systems process language, you can visit talk to ai.