Master Artificial Intelligence & Large Language Models
Key Developments: Expert systems, logic programming, knowledge representation
Pioneers: Alan Turing, John McCarthy, Marvin Minsky
Achievements: Turing Test, LISP programming language, first AI programs
Limitations: Brittleness, knowledge acquisition bottleneck
Key Developments: Statistical learning, neural networks revival
Breakthroughs: Backpropagation algorithm, decision trees, SVMs
Applications: Pattern recognition, data mining
Impact: Shift from rule-based to data-driven approaches
Key Developments: Convolutional Neural Networks, GPU acceleration
Milestones: ImageNet victory (2012), AlexNet, ResNet
Pioneers: Geoffrey Hinton, Yann LeCun, Yoshua Bengio
Applications: Computer vision, speech recognition
Key Innovation: "Attention Is All You Need" paper (2017)
Breakthroughs: BERT, GPT series, T5
Impact: Revolution in natural language processing
Architecture: Self-attention mechanism, parallel processing
Models: GPT-3, ChatGPT, GPT-4, PaLM, LaMDA
Scale: Billions to trillions of parameters
Capabilities: Few-shot learning, reasoning, code generation
Impact: Mainstream AI adoption, new applications
Developments: GPT-4V, DALL-E 3, Gemini
Capabilities: Vision-language understanding, multimodal reasoning
Goals: Artificial General Intelligence (AGI)
Challenges: Safety, alignment, interpretability
Definition: Learning from labeled training data
Types: Classification, Regression
Algorithms: Linear Regression, SVM, Random Forest, Neural Networks
Applications: Image recognition, spam detection, price prediction
Data: Input-output pairs required
Definition: Finding patterns in unlabeled data
Types: Clustering, Dimensionality Reduction, Association
Algorithms: K-means, PCA, t-SNE, Autoencoders
Applications: Customer segmentation, anomaly detection
Data: Only input data, no labels
Definition: Learning through interaction and rewards
Components: Agent, Environment, Actions, Rewards
Algorithms: Q-Learning, Policy Gradient, Actor-Critic
Applications: Game playing, robotics, autonomous driving
Learning: Trial and error with feedback
Select a comparison metric to analyze different ML algorithms
Architecture: Input → Hidden → Output layers
Activation: ReLU, Sigmoid, Tanh functions
Training: Backpropagation algorithm
Applications: Classification, regression tasks
Advantages: Universal approximators, flexible
Layers: Convolution, Pooling, Fully Connected
Features: Local connectivity, parameter sharing
Applications: Image recognition, computer vision
Architectures: LeNet, AlexNet, ResNet, EfficientNet
Innovation: Translation invariance, feature hierarchy
Feature: Memory through hidden states
Variants: LSTM, GRU, Bidirectional RNN
Applications: Sequence modeling, time series
Challenges: Vanishing gradient problem
Solutions: LSTM gates, GRU mechanisms
Components: Generator and Discriminator networks
Training: Adversarial min-max game
Applications: Image generation, style transfer
Variants: DCGAN, StyleGAN, CycleGAN
Innovation: Unsupervised generative modeling
Token Embeddings: Convert tokens to dense vectors
Positional Encoding: Add position information to tokens
Purpose: Give model understanding of token order
Math: PE(pos,2i) = sin(pos/10000^(2i/d_model))
Formula: Attention(Q,K,V) = softmax(QK^T/√d_k)V
Purpose: Allow tokens to attend to other tokens
Multi-Head: Multiple attention mechanisms in parallel
Benefit: Capture different types of relationships
Structure: Two linear transformations with ReLU
Formula: FFN(x) = max(0, xW₁ + b₁)W₂ + b₂
Purpose: Apply non-linear transformations
Parameters: Majority of model parameters here
Residual: x + SubLayer(x) for skip connections
LayerNorm: Normalize across feature dimension
Purpose: Stabilize training, enable deep networks
Arrangement: Pre-norm vs post-norm variants
Generative Pre-trained Transformers
Caution: BiasBidirectional Encoder Representations
SafeText-To-Text Transfer Transformer
SafePathways Language Model
Caution: ScaleTasks: Object detection, image classification, segmentation
Models: YOLO, R-CNN, ViT (Vision Transformer)
Applications: Autonomous vehicles, medical imaging, surveillance
Datasets: ImageNet, COCO, Open Images
Metrics: mAP, IoU, accuracy, F1-score
Tasks: Translation, sentiment analysis, summarization
Models: BERT, GPT, T5, RoBERTa
Applications: Chatbots, search engines, content generation
Techniques: Tokenization, embeddings, attention
Evaluation: BLEU, ROUGE, perplexity
Tasks: Speech recognition, synthesis, music generation
Models: Wav2Vec, Whisper, WaveNet
Applications: Voice assistants, transcription, accessibility
Features: MFCCs, spectrograms, raw waveforms
Metrics: WER, CER, MOS scores
Areas: Navigation, manipulation, human-robot interaction
Techniques: Reinforcement learning, imitation learning
Applications: Manufacturing, healthcare, exploration
Sensors: Cameras, LIDAR, force sensors
Challenges: Real-world deployment, safety
Types: Collaborative filtering, content-based, hybrid
Techniques: Matrix factorization, deep learning
Applications: E-commerce, streaming, social media
Challenges: Cold start, scalability, diversity
Metrics: Precision, recall, diversity, novelty
Applications: Drug discovery, diagnosis, treatment planning
Techniques: Medical imaging, genomics analysis
Models: Specialized CNNs, transformer models
Challenges: Regulation, data privacy, interpretability
Impact: Faster diagnosis, personalized medicine
Concept: Combine LLMs with external knowledge retrieval
Components: Retriever + Generator + Knowledge Base
Process: Query → Retrieve → Augment → Generate
Benefits: Up-to-date info, reduced hallucinations
Tools: LangChain, LlamaIndex, Pinecone, Weaviate
Use Cases: Document Q&A, knowledge management
Purpose: Store and search high-dimensional vectors
Databases: Pinecone, Weaviate, Qdrant, ChromaDB
Embeddings: OpenAI, Cohere, sentence-transformers
Search Types: Similarity, hybrid, filtered search
Applications: Semantic search, recommendation systems
Metrics: Cosine similarity, Euclidean distance
Methods: Full fine-tuning, LoRA, QLoRA, AdaLoRA
Parameter Efficiency: Adapters, prompt tuning
Data Requirements: 100s to 1000s of examples
Benefits: Domain specialization, improved performance
Tools: Hugging Face, Weights & Biases, Axolotl
Considerations: Overfitting, data quality, cost
Techniques: Few-shot, chain-of-thought, tree-of-thought
Strategies: Role assignment, step-by-step reasoning
Advanced: Meta-prompting, prompt chaining
Tools: PromptBase, Promptify, guidance
Evaluation: A/B testing, human evaluation
Best Practices: Clear instructions, examples, constraints
LangChain: LLM application framework with chains
LlamaIndex: Data framework for LLM applications
Haystack: End-to-end NLP framework
AutoGen: Multi-agent conversation framework
CrewAI: Multi-agent AI system framework
Semantic Kernel: Microsoft's AI orchestration
Cloud Providers: AWS, GCP, Azure, OpenAI API
Specialized: Hugging Face Spaces, Replicate
Open Source: Ollama, LocalAI, vLLM
Containerization: Docker, Kubernetes
Serverless: Lambda, Cloud Functions, Vercel
Edge Deployment: ONNX, TensorRT, CoreML
Model Monitoring: Weights & Biases, MLflow
Performance: Latency, throughput, accuracy
Data Drift: Feature drift, prediction drift
Business Metrics: User engagement, conversion
Cost Tracking: API costs, infrastructure costs
Alerting: Anomaly detection, threshold alerts
Fairness: Avoiding bias and discrimination
Transparency: Explainable and interpretable AI
Accountability: Clear responsibility for AI decisions
Privacy: Protecting personal data and rights
Human Agency: Keeping humans in control
Alignment: AI systems pursuing intended goals
Robustness: Reliable performance in edge cases
Interpretability: Understanding AI decision-making
Monitoring: Continuous oversight and evaluation
Control: Ability to stop or modify AI systems
Types: Historical, representation, measurement bias
Detection: Fairness metrics, bias audits
Mitigation: Data preprocessing, algorithmic debiasing
Challenges: Trade-offs between different fairness concepts
Solutions: Diverse teams, inclusive datasets
Techniques: Differential privacy, federated learning
Regulations: GDPR, CCPA, right to explanation
Methods: Data anonymization, homomorphic encryption
Challenges: Utility vs privacy trade-offs
Future: Privacy-preserving AI architectures