Glossary for Advanced Users
Sentiment Analysis is an AI technique that analyses emotions and opinions expressed in text data. Sentiment analysis can classify text as positive, negative, or neutral, and can identify the subject of the sentiment. Sentiment Analysis has applications in social media monitoring, customer feedback, and market research.
Speech Recognition enables machines to understand and interpret spoken words. By applying natural language processing techniques and AI models, businesses can develop speech recognition systems that power voice-controlled interfaces, voice assistants, and customer support solutions.
Supervised Learning is a Machine Learning approach where models are trained using labelled data, with both input and output pairs. By learning from the provided examples, supervised learning algorithms can make predictions or classifications on new, unseen data. It is widely used in tasks like spam detection, sentiment analysis, and image recognition.
Support Vector Machines (SVMs) are Machine Learning algorithms used for classification and regression tasks. SVMs create decision boundaries and maximise the margin between different classes of data. Businesses leverage SVMs for tasks like image classification, text categorisation, and time series analysis.
Swarm Intelligence is an AI approach that takes inspiration from the collective behaviour of social animals, such as bees and ants. These algorithms involve multiple agents interacting and collaborating to solve a problem. Swarm Intelligence has applications in optimisation, routing, and robotics.
Synthetic Data is artificially generated data that mimics real-world data. Synthetic data can be used to train Machine Learning models when real data is unavailable or insufficient. Synthetic Data can improve model accuracy and reduce privacy concerns when handling sensitive data.
Time Series Analysis is an AI technique that analyses data points collected over time. This approach involves detecting trends, patterns, and seasonality in the data. Time series analysis has applications in forecasting, anomaly detection, and trend analysis.
Transfer Learning is a technique that allows AI models to apply knowledge gained from one task to another related task. By leveraging pre-trained models on large datasets, businesses can save time and resources in training new models and achieve better performance in areas like natural language processing, image recognition, and recommendation systems.
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.
