Stanthropical
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Drop here any "artificial intelligence" (Ai) news you come across.
News can be financial, breakthroughs, failures, government involvement, bad news, Ai used for crimes, funny stuff, etc.
This space is not for ranting or hating. News only.
Some Ai-related terms, for fun:
• Algorithm – A set of rules or steps a computer follows to solve a problem.
• Artificial Intelligence (AI) – The simulation of human intelligence in machines.
• Machine Learning (ML) – A subset of AI where machines learn from data without explicit programming.
• Deep Learning – A type of ML using neural networks with many layers to process data.
• Neural Network – A system of algorithms modeled after the human brain for pattern recognition.
• Natural Language Processing (NLP) – AI that enables computers to understand and process human language.
• Generative AI – AI that creates new content, such as text, images, or music.
• Large Language Model (LLM) – A type of AI trained on vast amounts of text to generate human-like responses.
• Computer Vision – AI that enables machines to interpret and understand visual data.
• Supervised Learning – ML where a model is trained on labeled data.
• Unsupervised Learning – ML where a model finds patterns in unlabeled data.
• Reinforcement Learning – AI that learns by trial and error through rewards and penalties.
• Bias – Systematic errors in AI caused by flawed data or design.
• Overfitting – When a model performs well on training data but poorly on new data.
• Underfitting – When a model is too simple to capture patterns in data.
• Training Data – The dataset used to teach an AI model.
• Inference – The process of an AI model making predictions based on input data.
• Tokenization – Breaking text into smaller pieces (tokens) for processing in NLP.
• Embedding – Converting words, images, or other data into numerical representations for AI models.
• Transformer Model – A deep learning model architecture used in LLMs like GPT.
• Prompt Engineering – Designing effective inputs to guide AI responses.
• Fine-Tuning – Adjusting a pre-trained AI model on specific data to improve performance.
• LLM Hallucination – When an AI generates false or misleading information.
• Zero-shot Learning – An AI’s ability to perform tasks without prior examples.
• Few-shot Learning – Training AI on a small number of examples to perform a task.
• Ethical AI – The practice of developing AI that is fair, transparent, and responsible.
• Explainability – The ability to understand and interpret how an AI makes decisions.
• Turing Test – A test to see if AI can imitate human responses well enough to fool a person.
• Singularity – A hypothetical point where AI surpasses human intelligence.
News can be financial, breakthroughs, failures, government involvement, bad news, Ai used for crimes, funny stuff, etc.
This space is not for ranting or hating. News only.
Some Ai-related terms, for fun:
• Algorithm – A set of rules or steps a computer follows to solve a problem.
• Artificial Intelligence (AI) – The simulation of human intelligence in machines.
• Machine Learning (ML) – A subset of AI where machines learn from data without explicit programming.
• Deep Learning – A type of ML using neural networks with many layers to process data.
• Neural Network – A system of algorithms modeled after the human brain for pattern recognition.
• Natural Language Processing (NLP) – AI that enables computers to understand and process human language.
• Generative AI – AI that creates new content, such as text, images, or music.
• Large Language Model (LLM) – A type of AI trained on vast amounts of text to generate human-like responses.
• Computer Vision – AI that enables machines to interpret and understand visual data.
• Supervised Learning – ML where a model is trained on labeled data.
• Unsupervised Learning – ML where a model finds patterns in unlabeled data.
• Reinforcement Learning – AI that learns by trial and error through rewards and penalties.
• Bias – Systematic errors in AI caused by flawed data or design.
• Overfitting – When a model performs well on training data but poorly on new data.
• Underfitting – When a model is too simple to capture patterns in data.
• Training Data – The dataset used to teach an AI model.
• Inference – The process of an AI model making predictions based on input data.
• Tokenization – Breaking text into smaller pieces (tokens) for processing in NLP.
• Embedding – Converting words, images, or other data into numerical representations for AI models.
• Transformer Model – A deep learning model architecture used in LLMs like GPT.
• Prompt Engineering – Designing effective inputs to guide AI responses.
• Fine-Tuning – Adjusting a pre-trained AI model on specific data to improve performance.
• LLM Hallucination – When an AI generates false or misleading information.
• Zero-shot Learning – An AI’s ability to perform tasks without prior examples.
• Few-shot Learning – Training AI on a small number of examples to perform a task.
• Ethical AI – The practice of developing AI that is fair, transparent, and responsible.
• Explainability – The ability to understand and interpret how an AI makes decisions.
• Turing Test – A test to see if AI can imitate human responses well enough to fool a person.
• Singularity – A hypothetical point where AI surpasses human intelligence.
