AI Glossary of Terms

AI / Artificial Intelligence

AI is the development of computer systems that can perform tasks that normally need human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

Use case example: A common use case for AI is in customer support through 24/7 chatbots or virtual assistants that can handle queries and escalate issues to human agents.

AI Ethics

AI ethics is a field concerned with the ethical implications of AI, including fairness, accountability, bias, transparency, and safety.

Use case example: AI ethics often need to be considered when developing or deploying facial recognition technology.

AGI / Artificial General Intelligence

AGI understands, learns, and applies knowledge, like a human.

‍Use case example: AGI doesn’t exist yet, but it could help with complex tasks like disease diagnosis.

Algorithm

Algorithms are sets of instructions designed to perform specific tasks, and in AI, they help machines learn patterns from data.

Use case example: Algorithms are often used in route optimization features for navigation apps like Google Maps.

Big Data

Big data is an extremely large data set that may be analyzed computationally to reveal patterns, trends, and associations.
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‍Use case example: Big data is often used in personalized recommendations used by streaming services like Netflix or Spotify.

Cognitive Computing

Cognitive Computing are systems that simulate human thought processes, often used in decision-making tasks.

‍Use case example: Cognitive Computing is often used to diagnose health conditions or provide treatment recommendations.

DL / Deep Learning

DL is a type of AI that uses networks of connected layers (like neurons in the brain) to learn from vast volumes of data, finding patterns within it, and it's especially good at tasks like recognizing images, understanding speech, and processing language.

Use case example: DL models can analyze things like x-rays and MRI scans to detect diseases like cancer or tumors, faster than traditional methods

Generative Adversarial Network / GAN

GAN is often used for generating images, videos, and data synthesis, as it's a class of ML models where two networks (a generator and a discriminator) compete.

Use case example: GAN is often used to create realistic images or video footage and is often used to creare or generating new examples that resemble a given set of images. One specific example is deepfake video generation.

GPT / Generative Pre-trained Transformer

GPT is a deep learning model that generates human-like text based on pre-trained data, useful in chatbots, content creation, and conversational agents.

Use case example: OpenAI's ChatGPT uses a GPT to power the chatbot's features.

Inference

Inference is the process of applying a trained model to new data and making predictions or decisions.

Use case example: Inference is often used for real-time fraud detection with credit card transactions.

LLM / Large Language Model

LLM's process data to generate context-aware, human-like text responses.

‍Use case example: ChatGPT generates human-like responses by processing vast amounts of data.

ML / Machine Learning

ML enables systems to learn patterns from data.

‍Use case example: Spotify uses ML to analyze users’ music preferences for personalized playlists.
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Neural Networks

Neural Network gets its name from network of neurons in the human brain, and are used in Deep Learning because they're a series of algorithms that can recognize patterns or relationships within  large sets of data.

Use case example: Neural networks are used in voice activated virtual assistants like Siri or Alexa

NLP / Natural Language Processing

NLP is a branch of AI that focuses on the interaction between computers and humans through natural language, enabling machines to understand and interpret human language.

Use case example: NLP can often be used to analyze large volumes of customer feedback and establish how customers feel about a brand, service, or product.
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Reinforcement Learning

Reinforcement Learning is type of ML where agents learn by taking actions in an environment and receiving rewards or penalties.

Use case example: A reinforcement learning model can train a self-driving car by rewarding actions that lead to safe and efficient driving.

Supervised Learning

Supervised Learning is a type of ML where the model is trained on labeled data, meaning the input and output are known.

Use case example: Supervised Learning is used in email SPAM detection as the algorithm is trained on emails that have been labelled SPAM or not SPAM by humans, who have categorized them.

Turing Test

The Turing Test was first developed by Alan Turing and is used to establish if a machine is capable of human-like intelligence.

Use case example: Turing tests are often used to evaluate the capabilities of a chatbots or conversational agents

Unsupervised Learning

Unsupervised Learning is a type of ML where the model works with data that is not labeled and must find hidden patterns or groupings on its own, without human intervention.

Use case example: Unsupervised Learning can often be by marketing teams to segment their customer, allowing them to understand their customer base without any prior labeling.

Quantum Computing

Quantum Computing is a type of computing that uses quantum-mechanical phenomena, like superposition and entanglement, and is expected to revolutionize fields like cryptography and AI.

Use case example: Quantum Computing is often used in drug discovery and molecular simulation.

Key Players and Industry Makers in AI

OpenAI: The research organization behind models like GPT, known for pushing boundaries in generative AI and NLP.

Google DeepMind: The AI subsidiary of Alphabet, responsible for groundbreaking achievements in AI research, including AlphaGo and advancements in reinforcement learning.

IBM Watson: IBM’s AI platform that offers a range of AI-powered services, including NLP, ML, and analytics.

NVIDIA: A leading company in AI hardware, providing powerful GPUs essential for deep learning and AI training.

Elon Musk: CEO of Tesla and SpaceX, co-founder of OpenAI, and an influential figure in AI and tech ethics.

Sundar Pichai: CEO of Alphabet (Google's parent company), overseeing Google’s major AI and machine learning initiatives.

Andrew Ng: A leading AI researcher and educator, co-founder of Google Brain, former chief scientist at Baidu, and founder of Deeplearning.ai.

Fei-Fei Li: A prominent AI researcher and professor at Stanford, known for her work in computer vision and the creation of ImageNet, a large-scale visual database critical to AI research.

Satya Nadella: CEO of Microsoft, under whose leadership the company has heavily invested in AI through Azure and partnerships with OpenAI.

Jeff Dean: Head of Google AI, co-founder of Google Brain, and a leading researcher in AI and machine learning.

Geoffrey Hinton: Often referred to as the "godfather of deep learning," Hinton’s work on neural networks and backpropagation has been crucial to modern AI.

Yoshua Bengio: AI researcher and one of the pioneers in deep learning, along with Hinton and Yann LeCun.

Yann LeCun: Chief AI scientist at Meta (Facebook), another pioneer in deep learning, known for his work on convolutional neural networks.

Tesla: A key player in applying AI to autonomous driving through its Autopilot and Full Self-Driving (FSD) systems.

Amazon Web Services (AWS): A major cloud service provider that offers a wide range of AI and ML tools to businesses and developers.

Emerging Concepts in AI

AI-as-a-Service (AIaaS): Cloud-based services that provide AI capabilities to organizations without the need for building AI models from scratch.

Autonomous Systems: Systems capable of making decisions and performing tasks without human intervention, including autonomous vehicles, drones, and robots.

Synthetic Data: Artificially generated data used to train AI models when real-world data is limited or unavailable.

Federated Learning: A technique that trains machine learning models across decentralized devices or servers while keeping data local, improving privacy.

Ethical AI Frameworks: Guidelines and practices ensuring AI development and deployment are aligned with moral and ethical considerations, such as fairness, accountability, and transparency.

AI Regulation: Legal frameworks being developed by governments and institutions to govern the responsible development and deployment of AI technologies.