Unit 2 part 2 Artificial intelligence .ppt.ppt
- 3. What is Machine Learning? •Enables machines to learn from data. •Improves performance without explicit programming. •Based on algorithms and statistical models. •Focuses on pattern recognition and predictions. •Used in diverse fields like healthcare, finance.
- 4. What is Deep Learning? • Subset of Machine Learning (ML). • Uses multi-layered neural networks. • Excels in modeling complex data patterns. • Handles large datasets for high accuracy. • Applications: vision, NLP, speech recognition.
- 6. Types of Deep Learning Model:
- 7. What is Natural Language Processing (NLP)? • AI for understanding human language. • Includes text and speech analysis/generation. • Handles tasks like translation, summarization. • Uses models like BERT, GPT for NLP. • Applications: chatbots, sentiment analysis, search. • Bridges communication between humans and machines.
- 8. What is Computer Vision? •AI for interpreting visual data. •Processes images, videos, and real-world visuals. •Tasks include object detection, image classification. •Powers facial recognition and scene understanding. •Applications: self-driving cars, healthcare imaging. •Bridges visual perception with machine understanding.
- 9. Human Vision vs Computer Vision:
- 10. What is Expert Systems? •AI that mimics human expertise. •Uses a knowledge base and inference engine. •Solves specific domain-related problems efficiently. •Applies if-then rules for decision-making. •Common in fields like healthcare, finance. •Provides accurate insights and problem-solving solutions.
- 11. How expert system works?
- 12. Example of an Expert System: •Medical Diagnosis System (e.g., MYCIN). •Assists doctors by suggesting diagnoses. •Analyzes symptoms, medical history, and lab data. •Provides treatment recommendations based on expert knowledge.
- 13. Fuzzy Systems •Fuzzy logic is a mathematical framework that deals with uncertainty and imprecision. • Fuzzy systems are particularly useful when dealing with vague or subjective information. Applications include: •Traffic Control Systems: Fuzzy logic can be used to optimize traffic signal timings based on real-time traffic conditions. •Home Appliances: Fuzzy controllers are employed in washing machines and air conditioners to adapt to varying conditions.
- 14. AI in Different Fields: •Natural Language Processing (NLP): Used in chatbots, language translation, sentiment analysis, and voice recognition. •Healthcare: AI is applied for diagnostics, drug discovery, personalized medicine, and patient management. •Agriculture: AI aids in precision farming, crop monitoring, and pest control. •Social Media Monitoring: AI is used to analyze social media data for sentiment analysis, trend prediction, and content moderation.
- 15. Tools and Techniques for Implementing AI •Machine Learning Frameworks: TensorFlow, PyTorch, scikit- learn. •Development Platforms: Jupyter Notebooks, Google Colab. •Data Preprocessing: Pandas, NumPy. •Natural Language Processing Tools: NLTK, spaCy.
- 16. AI-powered Products: • Google Translator: Uses machine learning for language translation. • Driverless Cars: AI algorithms enable autonomous vehicles to navigate and make decisions. • Voice Assistants (Alexa, Siri): Use natural language processing to understand and respond to user commands. • ChatGPT: Utilizes a language model for generating human-like text responses.
- 17. Current trends and opportunities 1. Development in predictive analytics 2. Large Language Models (LLM) 3. Information security (InfoSec) 4. Launch of better autonomous systems 5. Art through NFTs 6. Digital avatars 7. Military weapons 8. Healthcare 9. Explainable AI (XAI): Enhancing transparency and interpretability of AI models. 10.Edge AI: Processing AI tasks on devices rather than relying solely on cloud computing. 11.AI Ethics and Bias Mitigation: Ensuring responsible and fair AI development.
- 18. Job roles and skill set SKill Set ● Programming languages (Python, R, Java are the most necessary) ● Linear algebra and statistics ● Signal processing techniques ● Neural network architectures ● Machine Learning algorithms Job Roles ● Machine Learning Engineer ● Data scientist ● Robotics scientist ● Research ● NLP Engineer ● Computer Vision Engineer.
- 19. Next class Next : Unit 3 Cyber Security