Artificial intelligence and machine learning.pptx
- 1. Presented by: Artificial Intelligence (AI) Powering the future, one smart decision at a time Rakshambigai . N Ritthika . V IIIrd Year (CSE) Presented by:
- 2. Contents 01 ๏ผ AI objectives ๏ผ Key statistics ๏ผ Core area Artificial intelligence objectives 02 ๏ผ Value chain ๏ผ Development phase ๏ผ Approaches Artificial intelligence value chain element 03 ๏ผ Logic & rule based approaches ๏ผ Machine learning ๏ผ Artificial narrow VS General Intelligence Artificial intelligence approaches 04 ๏ผ Challenges ๏ผ Potential use case ๏ผ ML use case Use case of AI in healthcare 05 ๏ผ Various sector ๏ผ Various components Artificial intelligence sectors Turning data into decisions with intelligence and insight.
- 3. 01 Session One Artificial intelligence key statistics Core area of artificial intelligence Artificial intelligence objectives
- 4. Artificial Intelligence Objectives 01 02 03 04 05 Achieve the objectives Boost the performance Use smart digital systems AI investment sectors AI growth in worldwide โSmarter systems, better decisions, brighter futures.โ
- 5. Artificial Intelligence Key Statistics 65% 50% 69% Of consumers say AI enhances their digital experience. Of companies are using AI in at least one business function. Of executives believe AI gives their organization a competitive edge. โAI in numbers โmeasuring the future, one stat at a time.โ
- 6. Core Area Of Artificial Intelligence 01 02 03 04 05 06 Mimics human thought processes like learning, reasoning, and problem- solving Cognitive AI Interprets input from sensors to perceive the environment (e.g., vision, sound, touch) Sensory AI Enables AI to learn and perform well with limited data using efficient algorithms Small data sets Provides transparency into how and why AI systems make decisions Explainable AI Integrates AI with robotics to interact with and manipulate the physical world Physical AI Aims to perform any intellectual task a human can do, with broad and adaptable intelligence General AI โLearning, reasoning, and decision โ making for intelligent action.โ
- 7. 02 Session Two Artificial intelligence development phase Artificial intelligence approaches Artificial intelligence value chain element
- 8. Artificial Intelligence Value Chain Element Data capture Curation and standardization Creation of ML models foe use cases Cleansing of low data Annotation of raw data for ML models Testing of model on new data 01 02 03 04 05 06 โFrom data to decisions โ AIโs value chain drives intelligent outcomes.โ
- 9. Artificial Intelligence Development Phase Initial AI integration began; pilot projects launched. BY Q1 FY19 Expanded AI use cases across departments; early results showed efficiency gains. Full-scale deployment of AI systems; measurable impact on performance and ROI. BY Q3 FY20 BY Q1 FY21 Phase 1 Phase 2 Phase 3 โFrom rule-based to self-learningโ AI evolves through every phase of intelligence.โ
- 10. Artificial Intelligence Approaches Systems make decisions based on predefined rules and logic. Logic and rules based approaches A subset of AI where systems learn from data to make decisions/predictions without being explicitly programmed. Machine learning โDifferent paths to smart thinking โ from rules to learning, AI has many approaches.โ
- 11. 03 Session Three Logic & rule based approaches Machine Learning Artificial narrow VS General intelligence Artificial intelligence approaches
- 12. Logic & Rule Based Approaches 01 02 04 03 Processes are represented using explicitly defined logical rules that dictate system behavior. Representing process Computers apply logical reasoning to execute and infer outcomes from the defined rules. Computers reason about these rules Rules are designed in a top-down manner, where humans define logic for the computer to follow. Top down rules are created for computer Can be used to automate structured and rule-based processes. Can be used automate process โPredefined rules, predictable results โ intelligence by instruction.โ
- 13. Machine Learning 01 Gathering data from various source 02 Cleaning data to have a homogeneity 03 Selecting the right ML algorithm 04 Data visualization- transforming results โLearning from data, adapting with experience.โ
- 14. Artificial narrow VS Artificial general intelligence Beat go world champions Read facial expressions Write music Earthquake survivors Mental disorders Understand abstract concept Explain why Analyzing overall Be creative like children Have emotions "ANI does the job, AGI understands the world." Artificial narrow intelligence Artificial general intelligence
- 15. 04 Session four Artificial intelligence components Potential use case of AI in healthcare Machine learning use cases Use case of AI in healthcare
- 16. Challenges In Adoption Of AI FY 18 Caption 1 ๏ Lack of awareness of AI capabilities ๏ Limited data availability for training models ๏ High cost of AI implementation ๏ Shortage of skilled talent in AI/ML domains FY 19 Caption 2 ๏ Integration issues with existing systems ๏ Data privacy and security concerns ๏ Slow organizational readiness ๏ Unclear ROI (Return on Investment) from AI projects FY 20 Caption 3 ๏ Ethical and regulatory challenges ๏ Bias in AI algorithms ๏ Scalability limitations in production environments ๏ Resistance to change in traditional industries โBarriers like data, cost and trust slow AIโs full potential.โ
- 17. Potential Use Case Of AI In Healthcare Enhancing medical education through AI-powered simulations and tools. Training Accelerating clinical research and drug development using intelligent data analysis. Research Promoting preventive care with AI- driven wellness apps and monitoring. Keeping well Identifying diseases at early stages using predictive and diagnostic algorithms. Early detection โAI heals smarter - from early detection to personalized care.โ
- 18. Machine Learning Use Cases 02 Manufacturing Retail Healthcare & Life science Energy, Feed stoke Financial service Travel 01 05 03 06 04 โML powers predictions, personalization, and smarter automation.โ
- 19. 05 Session Five Artificial intelligence in various sectors Artificial intelligence components Artificial intelligence sector
- 20. Artificial Intelligence In Various Sectors Water Smart water management, leak detection, and quality monitoring systems. Health Disease prediction, diagnostics, personalized treatment, and virtual assistants. Transport Autonomous vehicles, route optimization, and predictive maintenance. Environment Climate modeling, pollution tracking, and resource conservation. Traffic Intelligent traffic signals, congestion control, and accident prediction. Technology Enhanced automation, cybersecurity, and AI-powered innovation in software/hardware. โAI empowers every sector โ from health to highways, water to weather.โ
- 21. Artificial Intelligence Components Data Technology Strategy Pay per click AI analytical engine Select optimal engine Search console data Interface for data upload Instruct AI engine Customer service Training strategy Social feedback โAI is built on data, algorithms, learning, and logic working together.โ
- 22. Tools Techniques Used ChatGPT Microsoft Copilot Smasher Of Odds MVP Machine : To develop the Minimum Viable Product (MVP). UserPersona.dev: For crafting detailed user personas. - Namelix & Sologo Al: For branding and logo design. Wegic Al & Clipchamp: For the final presentation and video creation. Form Share: To collect user feedback. Gamma Al: For enhancing presentation content. LINKS Smasher Of Odds MVP Machine UserPersona.dev Namelix Sologo Al Wegic Al Clipchamp Form Share Gamma Al
- 23. Thank You