The current state of generative AI is filled with exciting possibilities, albeit accompanied by challenges. The industry’s concerted efforts in overcoming these hurdles promise a future where generative AI technology becomes an integral part of our everyday lives.
A machine learning algorithm is a set of mathematical rules and procedures that allows an AI system to perform specific tasks, such as predicting output or making decisions, by learning from data.
Bayesian networks are graphical models utilizing a Directed Acyclic Graph (DAG) to represent a group of random variables and their probabilistic relationships.
Explainable AI refers to a collection of processes and techniques that enable humans to comprehend and trust the outputs generated by machine learning algorithms.
Harnessing the power of large language models (LLMs), a mighty tool capable of understanding, generating, and even refining human-like text we can generate synthesized training data that is flawless and train our models more efficiently.
Pattern recognition in machine learning enables systems to discover similarities and automatically extract meaningful information from a given dataset.
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