AI Tech Project DEGINED B Y PRIYANSHU KR.

This is a learning purpose it is a information about AI Artificial intelligence Deigned by Priyanshu.
What is AI?
INFORMATION ABOUT AI.

1. AI INFORMATION Designed by Priyanshu Kumar DESIGNED BY PRIYANSHU

2. What is the purpose of AI? WORKS It makes easy to do any work in office, industries, study, gain information. BENIFITS Software or device can be made to solve Real-time problems. ASSITANCE Create of Virtual Assistance like :- Google Assistant, Siri, Alexa etc. ROBOTS With the help of AI develops Robots to help in dangerous environment. TASK Building machine that can do human intelligence task. GAMES With the help of AI makes games like :- Ch DESIGNED BY PRIYANSHU

3. TABLE OF CONTENTS 01 04 02 05 03 06 PROJECT OF AI RESOURCE NEEDED BUDGUETS OF AI BENEFITS OF AI AI CAPABILITIES CONCLUSION OF AI DESIGNED BY PRIYANSHU

4. WHAT IS AI AND HOW WE CAN EXPLAIN SOME POITS TO EXPLAI IT WHAT IS THE OBJECTIVE OF AI HOW ARE THE WE NEED TO COMPLETE DEVELOPED A BUDGET FOR AI PROJECT WHAT IS THE BENEFITS OF AI WHAT ARE THE CAPABILITIES OF AI OVERALL OUT THE CONCLUSION OF AI DESIGNED BY PRIYANSHU

5. WHAT IS THE OBJECTIVE OF AI 01 DESIGNED BY PRIYANSHU

6. THE GOAL OBJECTIVES The overall research goal of artificial intelligence is to create technology that allows computers and machine to work intelligently. The general problem of simulating (or creating) intelligence is broken down into sub-problems. AI is essentially attained by the reverse engineering of human potentials, and features and applying it to machines. The primary goal of artificial intelligence is to develop a technology that will capacitate computer system to perform independently of human intervention and intelligently. OUR AIM DESIGNED BY PRIYANSHU

7. RESOURCES NEEDED HUMAN RESOURCES FINANCIAL RESOURCES AI enables the collection and analysis of data in your HR processes to eliminate biases and guesswork to guarantee you are choosing the right candidate or offering the best compensation and benefits plan. From $5 to over $100,000, depending on capability. Cost verity widely based on required performance. Custom-built for specific tasks, offering high performance for complex AI applications. Variable, often high. Specialized computer hardware is often used to execute artificial intelligence (AI) programs faster, and with less energy, such as Lisp machines, Neuromorphic engineering , event cameras, and physical neural networks. PHYSICAL RESOURCES DESIGNED BY PRIYANSHU

8. EQUIPMENT AND MATERIALS BUDGET OF AI Corporate venture capital, such as the Microsoft, OpenAI and Nvidia investments in Figure, remains a significant sources of funding for the sector. The average price of a complete bespoke AI system can range from $20,000 to $1,000,000. The cost of a minimal viable product (MVP) ranges from $8,000 to $15,000. AI hardware is specialized equipment that efficiently processes AI and machine learning algorithms. The GPUs, TPUs, FPGAs, and specific CPUs that can handle large –scale data processing and complex complex computations faster than general-purpose hardware. AI travel agency applications are focused on personalized travel recommendations and itinerary planning. AI software is a computer program capable of high-complexity takes, like learning , decision-making and solving problems. SOURCES OF FUNDING PERSONNEL COSTS TRAVEL AND SOFTWARE DESIGNED BY PRIYANSHU

9. BENEFITS Helps to solve complex problems It have capacity for Smart decision making Help to prevent us from any disaster Removes the risk form humans to machines BENIFITS 1 BENIFITS 2 BENIFITS 3 Manages repetitive jobs and operations Enhance our standard of leaving BENIFITS 4 BENIFITS 5 BENIFITS 6 DESIGNED BY PRIYANSHU

10. Break-even point 200 units $500,000 Net profit of the project 25% Market share in the industry DESIGNED BY PRIYANSHU

11. 10 million First-year revenue of the project DESIGNED BY PRIYANSHU

12. PROJECT ACTIVITIES OF AI ACTIVITY START DATE END DATE RESOURCE COST REVENUE Market research 1950-1956 continue Browser AI, ChatGPT AI etc. $30,000 $2.8B Product development 1950-1956 continue Scholarly articles $35,000-$50,000 $200B Beta testing 1900 continue Developers real-world $2,500-$10,000 $18.29/hrs. Marketing campaign 1950-1960 continue Function of technology $32.77B 3-15% of sales Product launch 1956 continue Data Analysis $60,000 $200B Post-launch support 1900 continue Data Analysis $6,000-$300,000 $94B DESIGNED BY PRIYANSHU

13. SOME IMPORTANT AI FEATURES AI systems perceive environments, recognize objects, contribute to decision making, solve complex problems, learn from past experiences, and imitate patterns. Any tools, features, or functionality in the Services that utilize machine learning models. Deep learning , which is a subcategory of machine  learning , provides AI with the ability to mimic a human brain's neural network.  DESIGNED BY PRIYANSHU

14. Foundation (1950s-1970s): 1950s: Alan Turing proposes the Turing Test as a measure of machine intelligence. 1956: The term "artificial intelligence" is coined at the Dartmouth Conference. 1960s-1970s: Early AI research focuses on symbolic reasoning, problem-solving, and expert systems. Development of rule-based systems capable of simulating human expertise. Limited progress due to computational constraints and conceptual challenges. AI TIMELINE DESIGNED BY PRIYANSHU

15. AI TIMELINE Technological Expansion (2000s-2010s): 2000s-2010s: Big data and increased computational power drive rapid progress in machine learning and AI. Deep learning, fueled by neural networks with many layers, emerges as a dominant approach. AI applications become widespread in areas such as speech recognition, image classification, and recommendation systems. Robotics and autonomous systems advance, with applications in manufacturing, healthcare, and exploration. Ethical and societal concerns about AI gain prominence, leading to discussions around bias, privacy, and job displacement. DESIGNED BY PRIYANSHU

16. AI TIMELINE Integration and Innovation (2020s): 2020s: AI continues to advance rapidly, with breakthroughs in reinforcement learning, generative models, and AI ethics. Integration of AI into various industries accelerates, with applications in healthcare, finance, transportation, and entertainment. Continued debate and efforts around regulation and ethical use of AI technologies. Focus on AI safety and robustness to ensure responsible development and deployment. DESIGNED BY PRIYANSHU

17. AI TIMELINE Future Directions: Beyond 2020s: Continued advancements in AI lead to increasingly sophisticated applications and capabilities. AI becomes more integrated into daily life, with applications in personal assistants, autonomous vehicles, healthcare diagnostics, and more. Ongoing debates and efforts around societal impacts, including employment disruption, privacy concerns, and misuse prevention. Continued progress in AI safety and ethics to ensure responsible development and deployment. DESIGNED BY PRIYANSHU

18. CPU Cache memory is a small amount of high-speed memory located directly on the CPU chip. DESIGNED BY PRIYANSHU

19. ICON PACK DESIGNED BY PRIYANSHU

20. SOME ALTERNATIVE RESOURCES OF AI Graphics Processing Units (GPUs) Tensor Processing Units (TPUs) Field-Programmable Gate Arrays (FPGAs) DESIGNED BY PRIYANSHU

21. RESOURCES Graphics Processing Units (GPUs): GPUs are specialized processors originally designed for rendering graphics in computer games and simulations. However, due to their highly parallel architecture, GPUs are also well-suited for performing parallelizable computations common in AI and deep learning algorithms. GPUs excel at handling large matrices and performing many arithmetic operations simultaneously, making them essential for training deep neural networks. Tensor Processing Units (TPUs): TPUs are custom-built AI accelerators developed by Google specifically for neural network machine learning tasks. They are optimized for matrix multiplication, which is a fundamental operation in deep learning models. TPUs offer higher performance and energy efficiency compared to traditional CPUs and GPUs for certain AI workloads. Field-Programmable Gate Arrays (FPGAs): FPGAs are reconfigurable hardware devices that can be programmed to implement custom digital circuits. They offer flexibility and parallelism and can be optimized for specific AI algorithms or tasks. FPGAs are used in scenarios where specialized hardware acceleration is required, such as in edge computing or for implementing custom neural network architectures DESIGNED BY PRIYANSHU

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29. THANKS!_ THIS IS A PROJECT OF AI. THANKU FOR UNDERSTANDING. Please keep this slide for attribution DESIGNED BY PRIYANSHU