WE MUST LEARN about AI:
Here is a great glossary: https://coursera.org/share/667ff285d9b3a1de4814f55889351e00
Here are some current artificial intelligence (AI) terms, each accompanied by a brief definition, a valid link for further reading, and a suggested YouTube video for a more in-depth understanding:
1. Algorithmic Bias
Definition: The systematic and repeatable errors in AI that create unfair outcomes due to biased training data.
Further Reading: MIT Technology Review
Suggested Video: Algorithmic Bias Explained
2. Artificial General Intelligence (AGI)
Definition: A type of AI capable of understanding, learning, and performing tasks at a human-like level across multiple domains.
Further Reading: Future of Life Institute
Suggested Video: AGI Explained
3. Artificial Intelligence (AI)
Definition: The simulation of human intelligence in machines that can perform tasks typically requiring human cognition, such as learning and problem-solving.
Further Reading: IBM AI Overview
Suggested Video: What is AI?
4. Cognitive Computing
Definition: AI systems that mimic human thought processes for decision-making and problem-solving.
Further Reading: IBM Cognitive Computing
Suggested Video: Cognitive Computing Explained
5. Computer Vision
Definition: A field of AI that enables machines to interpret and understand visual data from the world.
Further Reading: Computer Vision – Microsoft
Suggested Video: Computer Vision Explained
6. Data Labeling
Definition: The process of tagging raw data with labels to help train AI models effectively.
Further Reading: Appen – Data Labeling
Suggested Video: Data Labeling Explained
7. Deep Learning
Definition: A subset of machine learning based on neural networks with representation learning, often used in image and speech recognition.
Further Reading: Deep Learning – IBM
Suggested Video: Deep Learning Explained
8. Edge AI
Definition: AI algorithms processed locally on a hardware device rather than relying on cloud-based servers.
Further Reading: Edge AI – Nvidia
Suggested Video: Edge AI Explained
9. Explainability
Definition: The degree to which an AI model’s decisions can be understood by humans.
Further Reading: Explainable AI – IBM
Suggested Video: Explainable AI Explained
10. Few-shot Learning
Definition: AI models trained with very few labeled examples by leveraging prior knowledge.
Further Reading: Few-shot Learning – ResearchGate
Suggested Video: Few-shot Learning Explained
11. Generative AI
Definition: AI models that generate new content such as text, images, and music based on training data.
Further Reading: Generative AI – Google
Suggested Video: Generative AI Explained
12. Human-in-the-Loop (HITL) AI
Definition: AI systems that require human intervention at critical stages for accuracy and decision-making.
Further Reading: HITL AI – Forbes
Suggested Video: HITL AI Explained
13. Machine Learning (ML)
Definition: A subset of AI that enables systems to learn and improve from experience without being explicitly programmed.
Further Reading: Machine Learning – Google
Suggested Video: Machine Learning Explained
14. Neural Networks
Definition: Computing systems inspired by the human brain that recognize patterns and process data.
Further Reading: Neural Networks – Stanford
Suggested Video: Neural Networks Explained
15. Predictive Analytics
Definition: The use of AI to analyze historical data and predict future trends.
Further Reading: Predictive Analytics – SAS
Suggested Video: Predictive Analytics Explained
16. Reinforcement Learning (RL)
Definition: An AI technique where agents learn by interacting with their environment and receiving rewards.
Further Reading: Reinforcement Learning – OpenAI
Suggested Video: Reinforcement Learning Explained
17. Sentiment Analysis
Definition: AI techniques used to analyze and determine emotional tone within text data.
Further Reading: Sentiment Analysis – Towards Data Science
Suggested Video: Sentiment Analysis Explained
18. Supervised Learning
Definition: A machine learning approach where models are trained on labeled data to make predictions.
Further Reading: Supervised Learning – IBM
Suggested Video: Supervised Learning Explained
19. Transfer Learning
Definition: A technique where a pre-trained AI model is fine-tuned for a new task with minimal additional data.
Further Reading: Transfer Learning – Google
Suggested Video: Transfer Learning Explained
20. Unsupervised Learning
Definition: A type of machine learning where the model finds patterns in data without labeled outputs.
Further Reading: Unsupervised Learning – Towards Data Science
Suggested Video: Unsupervised Learning Explained
21. AI Ethics
Definition: The moral principles and guidelines for the responsible development and use of AI.
Further Reading: AI Ethics – UNESCO
Suggested Video: AI Ethics Explained
22. Explainable AI (XAI)
Definition: AI models designed to provide transparent and interpretable outputs for users.
Further Reading: Explainable AI – DARPA
Suggested Video: Explainable AI Explained
23. Natural Language Processing (NLP)
Definition: AI techniques that enable machines to understand, interpret, and generate human language.
Further Reading: NLP – Google AI
Suggested Video: NLP Explained
24. Conversational AI
Definition: AI-driven chatbots and virtual assistants that simulate human conversation.
Further Reading: Conversational AI – IBM
Suggested Video: Conversational AI Explained
25. Autonomous Systems
Definition: AI systems capable of making decisions and performing tasks without human intervention.
Further Reading: Autonomous Systems – Nvidia
Suggested Video: Autonomous Systems Explained
26. AI Security
Definition: The protection of AI systems from adversarial attacks and vulnerabilities.
Further Reading: AI Security – NIST
Suggested Video: AI Security Explained
27. Robotics Process Automation (RPA)
Definition: The use of software robots to automate repetitive tasks in business processes.
Further Reading: RPA – UiPath
Suggested Video: RPA Explained
28. Zero-shot Learning
Definition: AI models that can make predictions without direct prior exposure to specific examples.
Further Reading: Zero-shot Learning – Medium
Suggested Video: Zero-shot Learning Explained
29. Multi-modal Learning
Definition: AI systems that can process and combine different types of data (e.g., text, images, audio).
Further Reading: Multi-modal AI – Microsoft
Suggested Video: Multi-modal Learning Explained
30. Digital Twins
Definition: AI-powered virtual representations of physical objects or systems for simulation and analysis.
Further Reading: Digital Twins – IBM
Suggested Video: Digital Twins Explained
31. Federated Learning
Definition: A machine learning approach that trains models across decentralized devices while keeping data localized.
Further Reading: Federated Learning – Google
Suggested Video: Federated Learning Explained
32. Ethical AI
Definition: The practice of designing AI systems that align with ethical values such as fairness and transparency.
Further Reading: Ethical AI – Harvard
Suggested Video: Ethical AI Explained
33. AI-as-a-Service (AIaaS)
Definition: Cloud-based AI services that provide access to AI capabilities without requiring in-house infrastructure.
Further Reading: AIaaS – AWS
Suggested Video: AIaaS Explained
34. Knowledge Graphs
Definition: AI frameworks that represent relationships between data points in a graph structure for better decision-making.
Further Reading: Knowledge Graphs – Google
Suggested Video: Knowledge Graphs Explained
35. Explainable Reinforcement Learning
Definition: Providing transparency in reinforcement learning algorithms for better interpretability.
Further Reading: Explainable RL – IEEE
Suggested Video: Explainable RL Explained
36. Computer-Aided Design (CAD) with AI
Definition: The use of AI to assist in the design and drafting of engineering and architectural projects.
Further Reading: CAD with AI – Autodesk
Suggested Video: AI in CAD Explained
37. Reinforcement Learning with Human Feedback (RLHF)
Definition: Training reinforcement learning models by incorporating human feedback to improve outcomes.
Further Reading: RLHF – OpenAI
Suggested Video: RLHF Explained
38. Synthetic Data
Definition: Artificially generated data used to train AI models when real data is scarce or sensitive.
Further Reading: Synthetic Data – Nvidia
Suggested Video: Synthetic Data Explained
39. Speech Recognition
Definition: AI technology that converts spoken language into text.
Further Reading: Speech Recognition – Google
Suggested Video: Speech Recognition Explained
40. Autonomous Vehicles
Definition: Self-driving cars and drones powered by AI to navigate without human control.
Further Reading: Autonomous Vehicles – Tesla
Suggested Video: Autonomous Vehicles Explained
41. Model Drift
Definition: The degradation of AI model performance over time due to changing data patterns.
Further Reading: Model Drift – Towards Data Science
Suggested Video: Model Drift Explained
42. Data Augmentation
Definition: Techniques used to artificially expand datasets by creating modified copies of existing data.
Further Reading: Data Augmentation – Medium
Suggested Video: Data Augmentation Explained
43. Quantum AI
Definition: The application of quantum computing to enhance AI algorithms and performance.
Further Reading: Quantum AI – Google
Suggested Video: Quantum AI Explained
44. Bias Mitigation in AI
Definition: Strategies and techniques to reduce or eliminate bias in AI models.
Further Reading: Bias Mitigation – IBM
Suggested Video: Bias in AI Explained
45. Digital Assistants
Definition: AI-powered virtual assistants such as Siri and Alexa that assist users via voice commands.
Further Reading: Digital Assistants – Amazon Alexa
Suggested Video: Digital Assistants Explained
46. AI Governance
Definition: The establishment of policies and frameworks to oversee the ethical use of AI.
Further Reading: AI Governance – World Economic Forum
Suggested Video: AI Governance Explained
47. Automated Machine Learning (AutoML)
Definition: The automation of the end-to-end process of applying machine learning to real-world problems.
Further Reading: AutoML – Google
Suggested Video: AutoML Explained
48. AI Chatbots
Definition: Software applications that simulate human conversation using natural language processing.
Further Reading: Chatbots – HubSpot
Suggested Video: AI Chatbots Explained
49. Hyperparameter Tuning
Definition: The process of optimizing AI model parameters to improve performance.
Further Reading: Hyperparameter Tuning – Towards Data Science
Suggested Video: Hyperparameter Tuning Explained
50. Model Interpretability
Definition: The ability to understand and explain how an AI model makes decisions.
Further Reading: Model Interpretability – Microsoft
Suggested Video: Model Interpretability Explained
ArtificialIntelligence #MachineLearning #DeepLearning #AIExplained #DataScience #AITrends #TechInnovation #SmartTechnology #AIforGood #FutureTech #Automation #BigData #NeuralNetworks #EthicalAI #AIResearch #CloudComputing #DigitalTransformation #ComputerVision #AIEthics #Chatbots #NaturalLanguageProcessing #AutonomousSystems #GenerativeAI #AIInnovation #TechTrends #QuantumComputing #PredictiveAnalytics #AIModels #FederatedLearning #EdgeAI #DataDriven