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An Incomplete Guide to AI Acronyms

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The following are common acronyms, slang and other buzzwords you may encounter and therefore find helpful when browsing projects or simply throughout everyday life as AI becomes a part of it.

*Click any highlighted in Red text to open associated Wikipedia articles.

AI Acronyms - Focused

  1. AI: Artificial Intelligence  refers to computer systems designed to perform tasks that would typically require human intelligence, such as understanding natural language, recognizing patterns, and making decisions.

  2. ML: Machine Learning involves building systems that can improve their performance at a task through experience, without being explicitly programmed. This is done by using algorithms that allow the system to identify patterns in data and make predictions based on that data.

  3. DL: Deep Learning is a subfield of machine learning that focuses on creating neural networks with multiple layers, allowing the system to learn increasingly complex representations of the data. This is particularly useful for tasks such as image and speech recognition.

  4. NLP: Natural Language Processing involves developing algorithms that allow computers to understand and generate human language, including text and speech. This can be used for tasks such as translation, question answering, and text summarization.

  5. CNN: Convolutional Neural Network is a type of neural network that is particularly well-suited for image recognition tasks. It works by breaking an image into small parts, or "features," and using those features to make predictions about the image as a whole.

  6. RNN: Recurrent Neural Network is a type of neural network that is designed to handle sequential data, such as speech or text. It works by incorporating information from previous time steps in its predictions, allowing it to better understand the relationships between elements in a sequence.

  7. DNN: Deep Neural Network refers to a neural network with multiple layers, allowing it to learn increasingly complex representations of the data. This is similar to deep learning, but can refer to any type of neural network, not just convolutional neural networks.

  8. ANN: Artificial Neural Network is a type of machine learning algorithm modeled after the structure and function of the human brain. It consists of interconnected "neurons" that process information and make decisions.

  9. SVM: Support Vector Machine is a type of machine learning algorithm used for classification tasks, such as determining whether an image contains a dog or a cat. It works by finding the line that best separates the different categories of data, and using that line to make predictions.

  10. RL: Reinforcement Learning involves building systems that learn how to make decisions through trial and error, receiving rewards or penalties for their actions. This allows the system to learn what actions are most likely to lead to good outcomes, and improve its performance over time.

AI Acronyms - Slang 

  1. Bot - A computer program designed to perform simple tasks automatically, such as answering customer service queries or posting content online.

  2. Chatbot - A type of bot that is designed to communicate with people using natural language, typically through text or voice.

  3. Cyborg - A term used to describe a hybrid of human and machine, often in the context of artificial intelligence and robotics.

  4. Data scientist - A person who uses statistical analysis and machine learning techniques to extract insights from data and solve real-world problems.

  5. Hype cycle - A term used to describe the pattern of over-enthusiasm and disappointment that often surrounds new technologies, including AI.

  6. Singularity - The idea that AI will one day surpass human intelligence and lead to a rapid acceleration of technological progress, potentially resulting in a future that is difficult for humans to predict or control.

  7. Skynet - A reference to the artificial intelligence system in the Terminator movie franchise, which becomes self-aware and tries to destroy humanity.

  8. The Turing Test - A test of a machine's ability to exhibit intelligent behavior that is indistinguishable from a human, proposed by Alan Turing in 1950. 

  9. Training data - The data used to "train" a machine learning algorithm, allowing it to learn how to perform a task and make predictions.

  10. TLDR - "Too Long Didn't Read"

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