Master Cheat Sheets
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.
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Credits: Dirk Zee