AI/ML categorization is a process of classifying a given set of data into classes. It can be performed on both structured or unstructured data. The process starts with predicting the class of given data points. The classification predictive modeling is the task of approximating the mapping function from input variables to discrete output variables. The main goal is to identify which class/category the new data will fall into.
Automated reporting is about bringing users relevant useful information in a timely way, without the users having to seek out the information for themselves. It tells you what has happened and how different areas of a business are performing. Automated reports can be generated at fixed intervals, such as every Friday for the weekly sales figures. They may also be triggered by certain events, like a shipping backlog that has now increased to a critical level that must be resolved.
Carbon footprint calculation is used to measure impact on the environment. It represents an indirect indicator of the consumption of energy, products, and services and measures the amount of carbon footprint which corresponds to a company's activities, products or people.
ML Ops is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. MLOps enables automated testing of machine learning artifacts (e.g. data validation, ML model testing, and ML model integration testing). MLOps enables the application of agile principles to machine learning projects.
Natural language processing is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding.
AI/ML-based decision engine will try to optimise some kind of process which cannot be fully enumerated with a deterministic set of rules. It uses data to learn from and design new rules. It can extend existing rules engine or replace it completely.
Hosting and expanding Machine Learning models requires specific competences, which quickly becomes a huge cost factor for companies who whishes to utilize the benefits of the services. Artificial Intelligence as a Service is the term used for off-the-shelf AI tools, that enable companies to take advantage of AI tools, without bearing the cost of developing complex models. These could Natural Language Processing tools (NLP), digital assistants and chat bots, translation, speech and more.
Anomaly detection is a step in data mining that identifies data points, events, and/or observations that deviate from a datasets normal behavior. Anomalous data can indicate critical incidents, such as a technical glitch, or potential opportunities, for instance, a change in consumer behavior. Machine learning is progressively being used to automate anomaly detection.
Biometric Identity Verification is a process by which a person can be uniquely identified by evaluating one or more distinguishing biological traits. These biological identifiers include fingerprints, hand, face, retina patterns, voice prints, written signatures, etc.
Data correlation is the ability to understand the data gathered as it applies to other data in the appropriate context. Just as correlation is defined as the technique for investigating the relationship between two variables, then data correlation is the ability to pull data from various sources and derive benefit from the understanding of the relationship between them to determine a more informed way forward.
Explainable artificial intelligence is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and potential biases. It helps characterize model accuracy, fairness, transparency and outcomes in AI-powered decision making.
The Internet-of-Things (IoT) edge is where sensors and devices communicate real-time data to a network. IoT edge computing solves latency issues associated with the cloud, as data is processed closer to its point of origin. Along with reduced latency, IoT edge architecture brings enhanced safety and a smoother end-user experience.
Predictive AI is a method of data analysis, capable of predicting and anticipating the future needs or events. This allows, among other things, to see trends coming, or to predict risks and their solutions. It is based exclusively on data, and in very large volumes, just like Big Data.
SOAR (security orchestration, automation and response) is a stack of compatible software programs that enables an organization to collect data about security threats and respond to security events without human assistance. The goal of using a SOAR platform is to improve the efficiency of physical and digital security operations. SOAR platforms have three main components: security orchestration, security automation and security response.
AI/ML can be used to forecast power demand and generation, optimise maintenance and use of energy assets, understand better energy usage patterns, as well as provide better stability and efficiency of the power system. AI can also alleviate the load on humans by assisting and partially automating the decision-making, as well as automating the scheduling and control of the multitude of devices used.
Edge computing is a distributed computing framework that brings enterprise applications closer to data sources such as IoT devices or local edge servers. This proximity to data at its source can deliver strong business benefits, including faster insights, improved response times and better bandwidth availability.