A

• Accelerator: A class of microprocessor designed to accelerate AI applications.
• Actionable Intelligence: Information you can leverage to support decision making.
• Agents: Software that can perform certain tasks independently and proactively without the need for human intervention, often utilizing a suite of tools like calculators or web browsing.
• AI Ethics: Refers to the issues that AI stakeholders such as engineers and government officials must consider to ensure that the technology is developed and used responsibly. This means adopting and implementing systems that support a safe, secure, unbiased, and environmentally friendly approach to artificial intelligence.
• AI Safety: An interdisciplinary field aiming to mitigate risks from AI systems. It encompasses technical solutions to ensure reliable AI function, aligning AI goals with human values, and developing safeguards against misuse and unintended consequences.
• Algorithm: A step-by-step set of instructions or rules used to solve a problem or perform a task (e.g., sorting data or making predictions).
• Alignment: The task of ensuring that the goals of an AI system are in line with human values.
• Annotation: The process of tagging language data by identifying and flagging grammatical, semantic or phonetic elements in language data.
• Application Programming Interface (API): An API, or application programming interface, is a set of protocols that determine how two software applications will interact with each other. APIs tend to be written in programming languages such as C++ or JavaScript.
• Artificial General Intelligence (AGI): A hypothetical AI system with intelligence equivalent to a human’s, capable of understanding or learning any intellectual task that a human can. This contrasts with narrow (or weak) AI systems that are limited to specific tasks. AGI remains theoretical and unachieved, whereas today’s AI systems are task-specific.
• Artificial Intelligence (AI): The simulation of human intelligence in machines, enabling them to perform tasks like learning, reasoning, problem-solving, and decision-making.
• Artificial Neural Network (ANN): Commonly referred to as a neural network, this system consists of a collection of nodes/units that loosely mimics the processing abilities of the human brain.
• Artificial Super Intelligence (ASI): Though subject to debate, ASI is commonly defined as artificial intelligence that surpasses the capabilities of the human mind.
• Attention: In the context of neural networks, attention mechanisms help the model focus on relevant parts of the input when producing an output.
• Auto-Classification: The application of machine learning, natural language processing (NLP), and other AI-guided techniques to automatically classify text in a faster, more cost-effective, and more accurate manner.
• Autonomous Systems: Machines or software that operate independently without human intervention (e.g., self-driving cars).

B

• Backpropagation: A method used in training neural networks where errors are "propagated backward" to adjust model weights and improve accuracy.