AI, ML, LLM... Here's a glossary of AI relevant terminology to support your technical journey.

Think of this as your AI glossary. We break down some of the technical terminology in AI to help you understand this space better.

An image produced by Google Deep Mind

• Artificial Intelligence (AI): Refers to the development of computer systems and algorithms that can perform tasks typically requiring human intelligence, such as learning from data, making decisions, solving problems, and understanding natural language.

• Machine Learning (ML): A branch of AI that enables computers to learn from data. Machine learning systems can identify patterns in data and use them to classify new examples, but they are less effective for tasks requiring complex reasoning.
  

• Large Language Models (LLMs): Deep learning algorithms that can generate, summarize, predict, translate, and synthesize text and other content. They are trained on vast amounts of data and can operate in various modalities, such as language, images, and audio
  

• Deep Learning: A form of AI that employs deep neural networks with multiple layers. Deep learning is capable of handling more complex processing tasks and has gained popularity in recent years.
  

• Generative AI: A type of AI that can create content like text, images, video, and audio based on given prompts. This technology has applications in chatbots, code writing, and design tools.
  

• Chatbots: Computer programs that engage in conversations with people using human language. Modern chatbots, like ChatGPT, are powered by large language models and can discuss a wide range of topics and generate human-like language outputs.
  

• Natural Language Processing (NLP): NLP is a form of machine learning that interprets and responds to human language. It is the technology behind virtual assistants like Siri and Alexa and enables computers to understand context and language associations.
  

• Neural Networks: These are techniques in machine learning that mimic the behaviour of neurons in the human brain. Artificial neural networks learn and adapt from data, making them useful for tasks like image recognition and ad targeting.
  

• Artificial General Intelligence (AGI): A hypothetical form of AI that can learn and think like a human, with self-awareness and consciousness. AGI remains a goal in AI research, with companies like Google DeepMind working toward its development.
  

Some more broader terms in the space:

• Supervised Learning: A machine learning approach where the model is trained on labeled data, allowing it to make predictions or classifications based on input data.
  

• Explainable AI (XAI): The practice of designing AI models and systems to provide clear, understandable explanations for their predictions or decisions, increasing transparency and trust.

• Ethical AI: The concept of developing AI in a way that considers ethical implications, fairness, and responsible use, addressing issues like bias and discrimination.
  

• Quantum Computing: A cutting-edge technology with the potential to revolutionize AI by solving complex problems at an unprecedented speed, particularly in optimization and cryptography.
  

• Bias Mitigation: Techniques and strategies aimed at reducing bias in AI models and ensuring equitable outcomes, particularly in areas like hiring and lending.

• Hallucination: When a foundation model produces responses that are not grounded in fact or reality, which is a challenge for generative AI models. Hallucinations are different from biases in AI.
  

• Explainability Toolkit: A collection of tools and methodologies used to make AI models more interpretable and accountable.
  

• Robotic Process Automation (RPA): The use of software robots or "bots" to automate repetitive, rule-based tasks in business processes.
  

• Federated Learning: A privacy-preserving approach to machine learning where models are trained across decentralized devices, ensuring data privacy and security.
  

• Edge Computing: A distributed computing paradigm where AI processing occurs at or near the source of data, reducing latency and improving efficiency in AI applications.

• Natural Language Understanding (NLU): A subfield of NLP that focuses on deep comprehension and interpretation of human language, allowing AI to understand context and nuances.

These concepts represent key building blocks of AI technology and have significant implications for various industries and applications (and yes, some of this was written with the support of AI).