A3S - Definitions

Artificial Intelligence(AI) as a whole contains many subfields, including:

  • Predictive Analytics brings together advanced analytics capabilities, spanning ad-hoc statistical analysis, predictive modeling, data mining, text analytics, optimization, real-time scoring and machine learning. These help organizations discover patterns in data and go beyond knowing what has happened to anticipating what is likely to happen next.
  • Prescriptive Analytics helps organizations make better decisions and recommend the best course of action, whether they want to decide on a configuration, a design, a plan, or a schedule. This is done by optimizing trade-offs between business goals while considering business constraints on available resources.
  • Cognitive computing is based on self-learning (given limited initial instructions) systems that use machine learning techniques to perform specific, human-like tasks in an intelligent way. The big question in cognitive computing is whether machines can really understand? For a machine to be truly intelligent, it’s not enough for it simply to know the words you’ve said. It needs to know what you want and be able to provide assistance in context.
    • Computer Vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.
    • Natural Language Processing (NLP) is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.
  • Machine Learning (ML) is a method of data analysis that automates building of analytical models. It uses methods like neural networks or gradient boosting or borrows some from statistics, operations research and physics in order to find hidden insights in data and identify patterns without being explicitly programmed where to look or what to conclude. It represents a branch of artificial intelligence.
    • Neural Network (NN) is a kind of machine learning inspired by the workings of the human brain. It’s a computing system made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.
    • Deep Learning (DL) is a type of machine learning that uses huge neural networks with many layers of processing units. It trains a model to perform human-like tasks, such as recognizing speech, identifying images, making predictions, or learn on streaming data such as Internet of Things (IoT). Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the model to recognize and learn complex patterns on its own using many layers of processing over large amounts of data. Deep learning techniques have improved the ability to classify, recognize, detect and describe – in one word, understand.

While machine learning is based on the idea that machines should be able to learn and adapt through experience, AI refers to a broader idea where machines can truly understand and execute tasks “smartly”, that is to reason and create output consumable by humans.

As AI and machine learning become integral parts of modern life, the study of interpretability becomes an urgent task, not only for providing better understanding of the underlying models, but also for gaining public trust in AI and machine learning themselves.