Cheat Sheets AI/ChatGPT
AI is revolutionizing our world, BUT are you aware of the elements?
Let's dive into the AI Periodic Table to uncover the paradoxes and potentials of artificial intelligence!:
𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀 𝗼𝗳 𝗔𝗜:
• Reinforcement Learning: AI learns through feedback.
• Computer Vision: Machines interpret visual data.
• Speech Recognition: AI understands human speech.
• Hardware: Specialised processors accelerate computations.
• Neural Networks: Modelled after the human brain for deep learning.
• NLP: Machines understand and generate human language.
• Feature Engineering: Crafting meaningful features enhances performance.
𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝘃𝗶𝗱𝗲𝗿𝘀:
• Data Collection: Gathering diverse datasets for training.
• Data Security: Protecting data against unauthorised access.
• Data Integration: Combining datasets for a comprehensive view.
• Data Preprocessing: Cleaning and organising data for accuracy.
• Data Labelling: Annotating data provides context for learning.
• Data Governance: Policies for ethical data management.
• Data Privacy: Ensuring protection and ethical use of data.
𝗔𝗜 𝗘𝘁𝗵𝗶𝗰𝘀 𝗮𝗻𝗱 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲:
• AI Governance Model: Frameworks overseeing AI development.
• Ethics Frameworks: Guiding principles for ethical AI.
• Accountability in AI: Holding individuals and organisations responsible.
• Fairness and Bias Mitigation: Minimising biases for fair outcomes.
• Ethical Decision-Making: Incorporating ethics into AI decisions.
• Privacy-Preserving AI: Protecting user privacy while using AI.
• AI Auditing: Assessing AI systems for ethical compliance.
𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗶𝗻 𝗔𝗜:
• Algorithmic Bias: Unintended discrimination in AI models.
• Adversarial Attacks: Deliberate manipulation of data.
• Data Privacy Concerns: Risks associated with unauthorised data use.
• Ethical Dilemmas: Navigating moral choices in AI development.
• Security Risks: Potential vulnerabilities compromising AI systems.
• Resource Intensiveness: High computational requirements.
• Overfitting and Underfitting: Challenges in finding the right model complexity.
𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀:
• Regression Model: Predicting outcomes from input data.
• Ensemble Learning: Combining models for improved accuracy.
• Classification Model: Categorising input data into classes.
• Clustering Models: Grouping data based on patterns.
• Decision Trees: Hierarchical structures for decision-making.
• Random Forests: Ensemble models of decision trees.
• Transfer Learning: Leveraging pre-trained models for new tasks.