Uncertainty in Artificial Intelligence

Uncertainty in AI refers to the unpredictability or lack of full knowledge about a situation or outcome. AI models often encounter uncertainties due to incomplete or noisy data. Techniques such as Bayesian Inference and Probabilistic Graphical Models are used to quantify and manage uncertainty in AI.

Universal Language Model Fine-Tuning (ULMFiT)

ULMFiT is a technique in Natural Language Processing (NLP) that enables transfer learning for NLP tasks. It involves pretraining a language model on a large corpus of text and then fine-tuning it on specific downstream tasks. ULMFiT has been successful in improving performance on tasks like text classification and sentiment analysis.

Unsupervised Learning

Unsupervised Learning is a Machine Learning technique where models learn patterns and structures within data without labelled examples. By uncovering hidden relationships and clustering data, businesses can gain insights without predefined classes or outcomes. Unsupervised Learning finds applications in customer segmentation, market basket analysis and anomaly detection.