Artificial Intelligence (AI) has been at the top of technology-conversations during 2023. AI suggests many opportunities for innovation as it is worked into applications that make use of large datasets. Patterns learned by AI can result in better human-computer experiences, diagnosis, content creation, pattern predictions and improved workflow. As a tech, AI is such a pervasive game-changer that its emergence is leading to rethinking of laws, industry norms as the court of public opinion raises their concerns. A challenge with AI is that understanding of what it is and its “insider language” is not widely understood. In this series we will cover AI language from A to Z.In Part I we cover AI language from A and B.
- AI Alignment. AI alignment is the process of trying to get an AI system to function as intended. Alignment covers both small, direct goals, such as writing a sentence, and large conceptual ones, such as conforming to certain values and moral standards. As AI systems and their target goals get more complex, aligning them becomes more difficult.
- AI assistant. An AI agent that can interact with users through natural language and complete tasks on their behalf. Examples are Siri, Alexa, and Google Assistant.
- AI ethics. 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.
- Algorithm. An algorithm is a step-by-step procedure or set of instructions that a computer follows. In the context of AI, an algorithm can be used by machine learning systems to ingest data and make predictions based on it.
- Anthropomorphism. Anthropomorphism is the tendency to attribute human qualities to nonhumans. For example, calling a chatbot "he" or "she," saying a chatbot wants something or saying a chatbot is trying to do something is anthropomorphizing AI. This happens often in conversations about artificial intelligence because it is often designed to sound or appear human.
- Artificial general intelligence (AGI). AGI is a machine that can perform any intellectual task that a human can. AGI is a category of AI. There are no current AI systems that count as AGI, though some claim that recent technologies, such as GPT-4, come close.
- Artificial intelligence (AI). AI is the simulation of human intelligence processes by computer systems
- Backpropagation. A technique used in neural networks to adjust the weights of connections between neurons based on the error rate in the output.
- Bardeen. An AI automation tool for increasing productivity for online social media content and email applications.
- Bias. Machine learning bias, or AI bias, occurs when algorithms produce results that are systemically prejudiced. Bias can be present in the code of an AI system or the data it trains on. Machine learning bias can affect decision-making.
- Big data. Big data refers to the large data sets that can be studied to reveal patterns and trends to support business decisions. It’s called “big” data because organizations can now gather massive amounts of complex data using data collection tools and systems. Big data can be collected very quickly and stored in a variety of formats.
- Black box AI. Black box AI systems' operations are not visible. The user provides input, and the machine provides an output. But the exact steps the machine took to arrive at the response are opaque. Explainable AI is the antithesis of black box AI. Its exact logic and operations are transparent.
Stay tuned for the rest of our Artificial Intelligence: A to Z series.
The following sources were used to build this glossary: