Artificial Intelligence by CP Mahto1.pptx

Data science

1. ARTIFICIAL INTE LLIGENCE By Chandra Prakash Mahto Department of BCA R.N. College, Hajipur

2. AI LEVELS ANI, AGI, ASI Table of Contents WHAT IS AI? 1 Three different definitons of Artificial Intelligence. 3 TYPES OF AI DL, ML, AI 4 AI vs ML vs DL The difference between Artificial intelligence, Machine learning and Deep learning 2

3. AI IN DIFFERENT FIELDS Education, Science, Medical, Entertainment Table of Contents APPLICATIONS 5 NLP, Health, Finance, etc. 7 BOOMING OF AI AI's rapid growth, expanding applications, transforming industries, shaping the future. 8 AI TREND Machine learning, natural language processing, ethics, robotics, AGI research, integration. 6

4. What is AI? Artificial Intelligence is “the study of how to make computers to do things, which, at the moment, people do better”.

5. What is AI? Artificial Intelligence is a “way of making a computer, a computer-controlled robot, or software to think intelligently, in the similar manner as the intelligent humans think”.

6. What is AI? According to the father of Artificial Intelligence, John McCarthy, Artificial Intelligence is “The science and engineering of making intelligent machines, especially intelligent computer programs”.

7. AI LEVELS: 1 Artificial Narrow Intelligence Specialized AI designed for specific tasks with high proficiency. 2 Artificial General Intelligence General AI with human-like cognitive abilities and problem-solving skills. 3 Artificial Super Intelligence AI surpasses human intelligence, advanced problem-solving capabilities.

8. TYPES OF AI: 1 Deep Learning Neural networks process vast data, driving AI advancements efficiently. 2 Machine Learning Neural networks, complex data, patterns, AI breakthroughs, powerful algorithms. 3 Artificial Intelligence The study of how to make computers to do things, which, at the moment, people do better.

9. AI vs ML vs DL Artificial Intelligence Simulation of human intelligence in machines for various tasks. AI may not always require extensive datasets. AI is suitable for narrow and well-defined tasks Machine Learning Algorithms learning from data to improve performance without explicit programming. ML benefits from labeled data ML handles pattern recognition and classification Deep Learning Specialized ML using deep neural networks for complex pattern recognition. DL thrives on large labeled datasets. DL excels in complex tasks like image and speech recognition and natural language processing

10. Natural Language Processing(NLP): Speech recognition and language translation. NLP Applications: Virtual Assistants Intelligent personal assistants for tasks. Autonomous Vehicles Self-driving cars and drones. Financial Trading Algorithmic trading and risk assessment. Healthcare Diagnosis Medical image analysis and disease prediction. Smart Home Systems Home automation and energy management.

11. ? HR Chatbot Engagement Surveys Engagement Learning Curated Training Skill Development A AI use cases in human resources Recruiting Dynamic Carrer Sites Smart Sourcing Onboarding Automated Messages Curated Videos B C D A B C D

12. AI in education AI enhances education by providing personalized learning, intelligent tutoring systems, automating administrative tasks, analyzing student data for insights, and facilitating adaptive curricula to improve learning outcomes efficiently.

13. AI in Science AI aids science by accelerating research through data analysis, simulation, and modeling. It optimizes experiments, identifies patterns, discovers new insights, and supports fields like genomics, drug discovery, climate science, and more.

14. AI in Medical AI assists in medicine by aiding in medical diagnosis, personalized treatment plans, drug discovery, image analysis (e.g., radiology, pathology), virtual health assistants, and predicting patient outcomes, leading to improved healthcare efficiency and patient care.

15. AI in Entertainment AI enhances entertainment by powering recommendation systems (e.g., Netflix), generating personalized content, improving video game experiences, creating realistic CGI effects, automating animation, enabling virtual reality and augmented reality applications, and revolutionizing music composition and production.

16. THE BOOMING OF AI Reduced interest and funding due to unmet expectations. AI Winter (1990s - early 2000s): Theoretical Foundations (1950s - 1990s): Early research, limited progress due to computing and data limitations. DL and Industrial App (2010s - present): Success of deep learning and widespread adoption of AI across industries. Rise of Machine Learning (mid-2000s - 2010s): Renewed interest with breakthroughs in machine learning.

17. Please keep this slide for attribution Thanks! Do you have any questions?