When discussing complicated themes, AI adapts through using advanced algorithms and massive datasets that process and develop a comprehensive knowledge of complex concepts. For example, a language model called GPT-3 from the people at OpenAI has 175 billion parameters that enable it to parse, understand, and produce coherent responses to almost any high-level question, including those in quantum physics, medical research, or economics. Microsoft recently conducted research showing that when given the right training, an AI could boast a 95% accuracy rate at intimately understanding and responding to feedback on complex subjects within specified fields.
For AI to have conversations about nuanced subjects, it applies a mixture of context recognition and deep learning to adjust their responses accordingly. Through the analysis of previous conversations AI can follow the course of conversation including content like its tone, specific words used, and if the topics of the conversation have changed. For instance, IBM Watson for Health can analyze unstructured data such as clinical notes, medical literature, and patient records in natural language to respond to complicated medical inquiries in medical domains. IBM claims that Watson helps doctors find treatment options, generating recommendations that are 30% more accurate than those from average physicians.
High specificity fields, including finance or technology, where terms are very specific, is where AI systems like Google's BERT and OpenAI's Codex picks up domain-specific jargon and responds to them accurately. For example, Codex can interpret complex software development requirements and generate developer-readable code. So much so, that some reports recently suggested that AI-based software development tools are helping bring down programming time by nearly 40%, bringing a major boost in productivity for development teams.
The unique learning features of AI helps it to develop its responses over due course of time. So, with every engagement, AI is enhancing its models on a particular subject, drawing patterns, learning baselines from what user inputs and the responses provided. This flexibility is part of the reason that AI has become such a powerful application in customer service and technical support as these two fields have incredibly complex inquiries. So, when you speak with AI about a complex matter, like fixing an IT system, AI can learn from questions in the past and use answers using both questions of the old and real-time analysis.
With the maturity of AI Tech, it has more and more proven its ability to control complexity, via fast and efficient Automated Information (Data) Processing. But rather than theorizing a solution to a complex legal issue which might require them scanning through thousands of legal documents, case studies and precedents, they could likely generate their answer based on AI-backed data insights. The process takes near zero time to perform, and AI has the ability to enhance decision making in all stages of many disciplines like law, medicine, and engineering.talk to ai