| Algorithm |
A step-by-step procedure for solving a problem or performing a task. In AI, algorithms analyze data and learn patterns to make predictions or decisions. |
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| API |
Application Programming Interface - A set of instructions and tools that allows different software applications to interact and exchange data. |
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| Augmented Analytics |
Applying AI to automate aspects of data analysis, such as identifying anomalies or generating insights, while still leaving room for human expertise and judgement. |
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| Automation |
The use of technology to perform tasks previously done by humans, often utilizing AI to automate processes for efficiency and consistency. |
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| Bayesian Networks |
Probabilistic models representing relationships between variables, helpful for reasoning and decision-making under uncertainty, often used in AI applications |
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| Bias in AI |
Unintentional prejudice or preference that can affect the outcome of an AI system, stemming from data, algorithms, or human involvement. Mitigating bias is crucial for ethical and fair AI. |
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| Big Data |
Large and complex datasets that traditional data analysis methods cannot handle effectively. AI excels at processing and extracting insights from big data. |
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| Blockchain |
A distributed ledger technology used for secure and transparent data storage and sharing, potentially useful for ensuring trust and security in AI applications. |
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| Bootstrapping |
A technique for training statistical models with limited data by resampling from existing data, often employed in AI when large datasets are unavailable. |
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| Business Intelligence |
Gathering, analyzing, and presenting data to inform business decisions. AI can enhance BI with advanced analytics and predictive capabilities. |
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| Cloud Computing |
Utilizing online infrastructure (servers, storage, databases) on-demand over the internet, offering scalability and flexibility for AI workloads. |
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| Clustering |
Grouping data points based on similarities, allowing for classification and understanding data patterns. AI uses various clustering algorithms for effective data exploration and insights generation. |
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| Cognitive Computing |
Simulating human thought processes through AI, enabling machines to learn, reason, and solve problems similarly to humans. |
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| Computer Vision |
Field of AI that enables machines to analyze and understand visual information like images and videos. |
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| Cybersecurity |
Protecting systems and data from unauthorized access, use, disclosure, disruption, modification, or destruction. AI can be used to enhance cybersecurity through anomaly detection and predictive analysis. |
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| Data Annotation |
Labelling data with relevant information, crucial for training AI models to perform specific tasks, requiring human effort and expertise. |
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| Data Augmentation |
Artificially creating new data samples from existing data to increase the size and diversity of training datasets, improving the performance and generalizability of AI models. |
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| Data Engineering |
Building and maintaining data pipelines and infrastructure to support AI applications, ensuring data quality, accessibility, and efficiency. |
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| Decision Trees |
Machine learning models that use a tree-like structure to make decisions based on a series of questions and answers, often used for classification and prediction tasks. |
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| Deep Learning |
A subfield of AI using artificial neural networks with multiple layers inspired by the human brain, capable of learning complex patterns from large amounts of data. |
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| Ensemble Learning |
Combining multiple machine learning models to improve overall performance and accuracy, often leading to more robust and generalizable predictions. |
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| Ethical AI |
Developing and using AI systems that are fair, accountable, transparent, and aligned with human values. Implementing ethical principles throughout the AI lifecycle is crucial for responsible and beneficial applications. |
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| Evolutionary Algorithms |
Inspired by natural selection, these algorithms iteratively refine solutions to a problem, mimicking the process of survival of the fittest, used in some AI optimization tasks. |
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| Expert Systems |
Computer programs that capture and apply the knowledge and expertise of human professionals in a specific domain, often used for decision support and diagnosis. |
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| Explainable AI (XAI) |
Making AI models interpretable and understandable, allowing humans to comprehend how decisions are made and build trust in AI systems. |
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| Feature Engineering |
The process of selecting and transforming raw data into meaningful features suitable for training and using machine learning models. |
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| Federated Learning |
Training AI models on multiple devices or servers without sharing the underlying data, ensuring privacy and security while leveraging distributed computing power. |
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| Generalization |
The ability of a machine learning model to perform well on new, unseen data not included in its training data. Achieving strong generalization is a key challenge in AI |
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| Generative Adversarial Networks (GANs) |
Two neural networks competing against each other, one generating data and the other trying to distinguish it from real data, ultimately leading to the generation of highly realistic data. |
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| Governance |
Establishing policies, frameworks, and regulations to guide the development and use of AI in a responsible and ethical manner. |
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| Heuristics |
Rules of thumb or problem-solving strategies based on experience and knowledge, often employed in AI to reduce search space and find solutions more efficiently. |
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| Human-in-the-Loop AI |
Systems where humans and AI collaborate on tasks, each leveraging their strengths for better results. AI handles routine tasks, while humans provide oversight, judgment, and ethical decision-making. |
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| Hybrid Cloud |
Combining on-premises data centers with public cloud services to create a flexible and scalable computing environment for AI workloads. |
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| Hyperautomation |
Applying automation across various processes and tasks within an organization, often leveraging AI and other technologies for efficiency and improved outcomes. |
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| Hyperparameter Tuning |
Adjusting the settings of machine learning models to optimize their performance, often requiring experimentation and data-driven approaches. |
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