AI in Blockchain for ai students eng.pptx

BlockChain

1. AI in Blockchain By, Jasper Paul Senior Manager - Projects

2. Introduction to AI Artificial Intelligence (AI) refers to the simulation of human intelligence in machines. These machines are designed to think like humans and mimic their actions, potentially even surpassing human intelligence. Machine Learning Neural Network Deep Learning Cognitive Computing Natural Language Processing Computer Vision

3. Machine Learning Data Collection & Preprocessing Feature Engineering Model Training Interference and prediction Feedback Loop Example: An email system learns to identify spam by analyzing patterns in previous emails labeled as spam or not spam.

4. Neural Network Input Layer: The first layer where data enters the network. Hidden Layer: Intermediate layers where data is processed and features are learned. Output Layer: The final layer that produces the network’s prediction or classification. Example: Handwritten Digit Recognition: A neural network can learn to recognize digits by analyzing many examples of handwritten numbers, learning features like shapes and strokes.

5. Deep Learning (DL) Deep Learning (DL): Deep Learning is a branch of artificial intelligence (AI) that focuses on using complex neural networks with many layers to analyze and interpret large amounts of data. How It Works: Deep Networks: Deep learning models have many layers, allowing them to learn detailed features and complex patterns automatically. Example: Image Recognition: Deep learning can automatically identify objects in images, such as recognizing faces or animals, by learning from a vast number of labeled images.

6. Natural Language Processing What It Is: Natural Language Processing (NLP) is a field of AI that enables computers to understand, interpret, and generate human language. How It Works: Input: Text or speech is inputted into the system. Processing: The system breaks down and analyzes the language using algorithms and models. Output: The system generates a meaningful response or performs an action based on the analysis. Example: Chatbots: When you type a question into a chatbot, NLP helps the chatbot understand your question and provide a relevant response, like answering customer service inquiries or setting reminders. The machine responds to an audio file A human talks to the machine The machine captures the audio Audio-to-text conversion takes place The machine processes the text’s data Data-to-Audio conversion occurs

7. Cognitive Computing What It Is: Cognitive Computing is a branch of AI that mimics human thought processes to understand, reason, and learn from data. It focuses on making machines think and learn in ways similar to human cognition. Example: IBM Watson: In a healthcare setting, Watson analyzes medical literature, patient records, and clinical data to help doctors make better treatment decisions. It understands complex medical terminology and provides recommendations based on its analysis. Emulation of Human Cognition Data-Driven Decision Making Iterative Learning Adaptive and Context-Aware Cognitive Computing

8. Computer Vision What It Is: Computer Vision is a field of AI that enables computers to interpret and understand visual information from the world, such as images and videos. How It Works: Input: The system receives visual data (images or videos). Processing: It uses algorithms to detect objects, recognize patterns, and analyze the content. Output: The system provides meaningful information or actions based on the visual analysis. Example: Facial Recognition: In your phone’s face unlock feature, computer vision analyzes your facial features to identify you and unlock the device. 3D Inspection Data Annotation Edge Computing Data Centric AI Image Analysis Natural Language Processing Augmented Reality

9. Block chain Blockchain is a distributed ledger technology that allows data to be stored globally on thousands of servers while letting anyone on the network see everyone else's entries in real-time. This makes it nearly impossible for one entity to gain control of the entire network, ensuring its security and transparency. It is a system where a record of transactions is maintained across several computers linked in a peer-to-peer network. This system enhances transparency, speed, and security of transactions by ensuring that records are simultaneously stored and accessible across multiple nodes in the network.

10. What makes Blockchain Special Decentralization Transparency Immutability Consensus Mechanism Cryptography Security Permissioning Auditability Distributed Ledger Smart Contracts Blockchain

11. How Blockchain Works Transactions Step: Transactions are initiated and recorded. Mr. A gives $100 to Mr. B. Mr. C gives $80 to Mr. D. Mr. E gives $60 to Mr. F. Block Creation Step: The above transactions are stored in a single block. The block contains, Mr. A’s transaction to Mr. B. Mr. C’s transaction to Mr. D. Mr. D’s transaction to Mr. E. Verification Step: The block, containing the list of transactions, is sent to a network of computers (nodes) to verify the validity of each transaction.

12. How Blockchain Works Consensus Step: The network must agree that the block is valid (consensus). Example: All nodes agree that Block 1, Block 2, and Block 3 are correct and can be added to the chain. Linking Blocks Step: Each block is linked to the previous one using a unique code (hash). Example: Block 1 points to Block 2, Block 2 points to Block 3. This ensures blocks are connected in order. Finalization Step: Once a block is added, it’s a permanent part of the record. Example: Blocks 1, 2, and 3 are securely recorded and cannot be changed without updating the entire chain. Transaction is requested A block that represents the transaction is created A block that sent to every node in the network Nodes that validates the transaction Nodes receive a reward for the proof of work The block is added to existing blockchain Transaction is complete

13. How AI and Blockchain converge in innovative ways Convergence of AI in Blockchain A Game changer in the Tech Industry

14. Benefits of AI in the Blockchain Ecosystem AI in blockchain is the use of artificial intelligence to enhance blockchain systems. It involves applying AI techniques to improve data analysis, automate processes, and bolster security within blockchain networks. This integration aims to make blockchain technology more efficient and intelligent. Enhanced Transaction Efficiency Augmented Security Measures Innovative Data Management Enhanced Data Management Optimized Energy Consumption Improved Scalability

15. Smart Contracts AI can improve smart contracts by automating more complex decision-making processes. AI algorithms can analyze vast amounts of data and trigger smart contracts based on nuanced conditions. AI can predict outcomes or market conditions and integrate these predictions into smart contracts, allowing for more dynamic and responsive contract terms.

16. Security Enhancement Fraud Detection: AI can analyze patterns in blockchain transactions to detect anomalies and potential fraudulent activities in real-time. Machine learning models can be trained to recognize suspicious behavior more accurately over time. Vulnerability Scanning: AI can continuously monitor blockchain networks for vulnerabilities, identifying potential security threats and recommending patches or updates.

17. Scalability & Performance Optimizing Consensus Mechanisms: AI can optimize the efficiency of consensus algorithms like Proof of Work (PoW) or Proof of Stake (PoS), reducing the computational load and energy consumption. Resource Management: AI can dynamically allocate computing resources in a blockchain network, optimizing performance and ensuring that resources are used efficiently.

18. Data Management Data Analysis: AI can process and analyze large datasets stored on blockchains, extracting valuable insights that can be used for decision-making in various industries like finance, supply chain management, and healthcare.

19. Data Partitioning Sharding is a technique used to improve blockchain scalability by splitting data into smaller "shards." AI refines data sharding in blockchains by predicting data access patterns and optimizing shard placement. This reduces cross-shard communication overhead and improves overall efficiency by ensuring frequently used data is grouped together.

20. Data Privacy AI combined with privacy-preserving technologies, such as zero-knowledge proofs and homomorphic encryption, allows secure model sharing and private predictions. Blockchain manages encrypted data and ensures transparent computation without exposing sensitive information.

21. Decentralized Autonomous Organizations (DAOs) AI-Driven Governance: AI can play a role in the governance of DAOs by making decisions based on pre-set rules and data analysis. This can include everything from managing funds to voting on proposals. Self-Learning Systems: AI can help DAOs become self-learning systems, adapting to changing conditions and optimizing their operations without human intervention.

22. Energy Efficiency Optimizing Mining Processes: AI can optimize mining processes in Proof of Work blockchains, reducing energy consumption by predicting and managing computational resources more efficiently. Sustainable Practices: AI can help in designing and implementing more energy-efficient consensus algorithms and mining operations, contributing to the sustainability of blockchain technology. Increased Transparency Lower Transaction Cost Properly value Energy Savings Encryption of Energy Savings Exchange Energy Savings Blockchain Energy Efficiency Increased Market Success Increased Security and Customer Trust Increased Reliability

23. Interoperability Cross-Chain Transactions: AI can facilitate cross-chain interoperability by automating and optimizing the exchange of assets and data between different blockchain networks. Network Coordination: AI can coordinate multiple blockchain networks, ensuring seamless communication and data sharing between them.

24. AI-Enhanced Multi-Layer Blockchains AI optimizes scalability solutions through dynamic layer selection. AI determines whether transactions should be processed on the main blockchain (Layer 1) or a sidechain (Layer 2), balancing security and speed based on transaction type and value.

25. Identity and Access Management AI-Powered Identity Verification: AI can enhance identity verification processes on blockchains, using techniques like facial recognition, biometrics, and behavior analysis to ensure secure access. Decentralized Identity (DID): AI can help manage decentralized identities, ensuring that users have control over their data while verifying their identities across multiple platforms.

26. Tokenization and Asset Management AI-Driven Investment Strategies: AI can analyze market trends and optimize investment strategies in tokenized assets on blockchain platforms, offering more sophisticated portfolio management. Dynamic Pricing Models: AI can be used to develop dynamic pricing models for tokenized assets, automatically adjusting prices based on market conditions and demand.

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