Exploring the World of AI and ML Attacks and Defense Strategies
Embark on a journey with me as I delve into the fascinating realm of Artificial Intelligence (AI) and Machine Learning (ML) attacks and defense strategies. These are the stories I have to share about my experiences in this exciting pathway.
AI and ML are fields of computer science that involve creating intelligent machines and applications capable of human-like intelligence. They encompass understanding natural language, recognizing images, solving problems, making decisions, and more.
The Heart and Brain of AI: Machine Learning
Machine Learning is at the core of AI. AI leverages Machine Learning for its intelligence through various approaches:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Data Collection
- Data Pre-processing
- Algorithm Selection
- Model Training
- Model Testing and Evaluation
- Model Optimization
- Deployment and Updating
While development takes place in data science environments, Machine Learning Operations (MLOps) involves adapting DevOps and data engineering to streamline and automate the ML life cycle.
Unleashing the Power of Supervised Learning
Supervised Learning offers diverse algorithms:
- Linear regression
- Logistic regression
- Decision tree
- Random forest
Overfitting occurs when the ML model gives accurate predictions for training data but not for new data.
- Support Vector Machine (SVM)
Unlocking the Secrets of Unsupervised Learning
Unsupervised Learning introduces algorithms like:
- K-means clustering
- Principal component analysis (PCA)
- Q-Learning
Neural networks can use supervised, unsupervised, or reinforcement learning.
Neural networks are inspired by human brain biology and excel at processing unstructured data like images, audio, and text.
Primary components of Artificial Neural Networks (ANNs) include:
- Neurons and layers
- Training and weights update
- Deep Learning
Image recognition through ANNs involves learning local edge patterns in initial layers, progressing to larger patterns in deeper layers.
Deep Learning requires large datasets and significant computational power, typically utilizing GPUs and CUDA technology for parallel execution.
- CNNs for image processing
- RNNs for language modeling and speech recognition
- Transformers like BERT and GPT for various NLP tasks
Some AI models, such as Generative Adversarial Networks (GANs), are famous for their use in deepfake technology.