Steemit Crypto Academy Contest / SLC S23W1: The Future of Decentralized Innovation on Steem and TRON
Greetings,
I hope you all are doing well, I am here to share my participation for Steemit Learning challenge season 23 week 1. I would like to invite my friends @drhira, @m-fdo, @yahnel to share their participation. I hope I will try to answer all the questions correctly.
AI is the hot topic these days, especially when it comes to crypto or blockchain technology. AI will obviously enhance STEEM and TORN performance. It will improve users experience, streamlining governance and and optimizing smart contract functionalities. Let’s talk about AI role in STEEM and TRON ecosystem improvement.
Decentralized Content Creation
AI will work on merit basis. Their will be fairness within the platform. Users engagement, posts quality, consistency and matters like that will be in AI automated preference zone. High quality posts will be rewarded more rather than simpler one. This is pretty much important for a platform like steemit which gives reward on the basis of voting mechanism. AI tool like Natural Language Processing (NLP) algorithms will help reaching niche content to the users.
Content Moderation
AI will help creating a healthier ecosystem for the steemit community by eradicating all the impurities. No spam, plagiarism or other inappropriate content will be seen here.
Decentralized Governance
AI-Driven Voting Analysis
In Steemit their is a community driven governance system. In such platform AI can work best to analyse voting behaviour; catching or detecting abuse or spam and also give votes on merit basis.
Predictive Governance Models
AI will give us a pure governance model by using historical data, users sentiments, market conditions and many other such analytics.
Reputation Management
Machine learning models will act as a judge with in the platform. It will check contribution and interaction and will establish a trustworthy reputation score also reducing the activities of bad actors.
Smart Contract Automation
AI can help making smart contract more flexible and efficient, using real time data input.
Error Detection and Optimization
AI smart contract code can detect even small impurities. It minimize the risk of spam or other vulnerabilities and will develop a pure performing ecosystem.
Autonomous Decision-Making
AI can give us a completely autonomous system of smart contracts. It will function on possible technical and fundamental analysis. we can use AI to identify market trends and execute trade accordingly.
We have two best AI models, advanced machine learning (ML) and natural language processing (NLP) for managing Steem’s blockchain curation mechanisms. The advantage of this system is, it will be completely decentralized and will bring fairness to the ecosystem. i.e their will be given preference to high quality posts, will reduce spam and other noise in steemit economy. The entire system will work purely and with more transparency.
System Components and Functionality
High quality posts or content will be filtered out. NLP and ML will perform this action. Content having more originality, coherence and linguistically best will be preferred. We have cluster algorithms that will pick niche content only to curate it for better reward. Genuine engagement will be picked by AI.
Spam and Plagiarism Detection
AI models will counter bot generated content, low effort or repetitive content. The best machine learners classifiers that are trained on spam database will perform this action. Past data of users will used to judge users activities.
Optimized Reward Distribution
AI will maintain fairness in reward distribution. Factors like, engagement, originality, relevancy will contribute to proportional reward distribution. Those users who are active outside steem (e.g on X) and sharing their steem posts will be given extra boosts.
Benefits to the Steem Community
If their is originality, and quality is maintained we can forecast richer content ecosystem. Their will be an automated filtering system which will help the curators selecting pure content easily. Posts with high quality will get more visibility in the platform. All these things under AI management will increase trust in the curation process in steemit.
AI can greatly improve the efficiency and security of smart contracts and decentralized applications (dApps) on TRON. Through AI driven solution it’s a golden opportunity to make TRON’s blockchain a more eye catching one, as it has the high throughput, more scalable it is and it’s low transaction cost is bonus point. Let’s talk in details how AI can contribute to TRON’s. Ecosystem.
Enhancing Efficiency
Transaction speed on TRON can be improved by using AI algorithms. It will analyse it’s smart contract code to make an ideal environment by optimizing more opportunities and lowering computational costs. Through AI we can make automated decisions. It will enable smart contracts to make complex decisions by processing large data sets in real-time.
We can take example from Defi system. Their AI can help us managing lending rates, which will completely based on market conditions. It will forecast transaction volume and network congestion to make it users friendly.
improving security system
AI-powered security tools can be used to detect suspicious activities with in the platform. It will monitor it thoroughly, results in reducing risk of hacking, fraudulent transactions or other explanation. It can also be used for code auditing to minimize human error. All the emerging threats can be countered through AI by implementing an adaptive protocols for it.
Real-World Use Case: AI-Driven Insurance dApp
Here is an imaginative AI-powered decentralized insurance platform built on TRON.
AI-Powered Claims Processing
dApp will use AI for all it major functions, from verification process to insurance claims. Images, documents, or IoT data all will be checked by AI for authenticity.
Fraud and spam Detection
All the suspicious activities will thoroughly be checked by AI to reduce fraud and exploitation within the platform. It will help in detection of fraudulent claims.
Dynamic Policy Adjustments
AI will adjust insurance premiums and policies according to real world data. They will do assessment of many factors like weather, disasters, diseases, and accidents etc.
This AI driven automation will enhance the blockchain functionality in the following way?
- Speeding up the claim process unlike manual claim process.
- Operational costs will be reduced.
- TRON blockchain will gain more trust through fairness and transparency of claim evaluations.
It’s true that integration of AI to blockchain technology will bring too much innovation to it’s platform, but of course their are certain ethical and security challenges associated with it. The major issues includes; the risks of centralization, data privacy concerns and bias in AI models. Let’s address them here how can we make AI and blockchain combination fair, transparent, and secure.
Bias in AI Models
AI works pre historical data, their models are trained this way, which may contain biases. Their limitations come when it’s about sensitive areas like; healthcare, decentralized finance (DeFi), or supply chain management.
Challenges
- AI biased decisions can perpetuate inequalities, which may leads to discriminatory outcomes.
- They are sometimes overloaded with data which results in opaque decision making.
- Their could be algorithmic bias which arise from the unbiased data.
Mitigation Strategies
- Use AI such as XAI to make all the decisions interpretable and auditable on the blockchain.
- Decentralized autonomous organizations (DAOs) can be used for auditing purpose. It will ensure fairness within the community.
- Diversify the AI data sources. Continuously update datasets of models to reduce bias.
Data Privacy Concerns
It’s a hard thing to manage where to store AI data, where to collect from and then how to process it on immutable blockchain ledgers. AI mostly need large datasets which raises all these concerns related data privacy.
Challenges
- Data aggregation could be a great risk. Data from different sources may reveal private information.
- Their could be another challenge in the form of Immutable Data Storage.
- Their could be a maximum chance of public Data Exposure on Public block chains.
Mitigation Strategies
- We have many privacy preserving techniques. Use these models such as; zero-knowledge proofs (ZKPs) and secure multi-party computation (SMPC) to keep the public data unexposed.
- Sensitive data can be made safe by giving limited permission to block chains.
- Privacy risk could also be reduced, if sensitive data is stored off-chain. Blockchain can only be used for verification purposes.
Risks of Centralization
All blockchain technologies are inherently decentralized. The integration of AI in it could lead it to the risk of centralization.
Challenges
- AI models are in control of few entities, which will leads to power centralization.
- Training these AI models need to much effort and resources. It could cost to much to do so.
Mitigation Strategies
- Open-Source AI Models should be promoted. It will help maintaining transparency and equalncy.
- Federated learning approach can also be helpful to maintain both decentralization and privacy.
- Decentralized Governance will distribute power across stakeholders, and will reduce chances of dominance.
We can propose an AI driven model that will evaluate fairness within the steem platform. It will ensures fair compensation for high-quality content while minimizing spam and abuse. Let’s break into that idea.
AI Model Functionality
Our AI model will work as a moderator with in the platform. A task will be assigned to it, and it will be; to ensure that the rewards are given to quality posts and purely on merit bases. Let’s discuss some of it’s key functions.
Assessment of the quality
It will analysis the quality of the content. By quality we mean, it will check it’s grammar, originality, relevancy to the topic and coherence among the paragraphs or ideas. Simply we can use Natural Language Processing (NLP) for it.
Analysing users engagement
Users engagement is pretty much important in steemit. By using certain bots and sentiment analysis techniques we can get access to users meaningful interactions. Continues posting, genuine comments, upvotes and interaction with other users includes in users engagement.
Reputation & Behavior Tracking
Monitor the behaviour of the users, his/her trust on steemit reward mechanism and most importantly his/her consistency. We may use anomaly detection models that will help us identifying fraud and fake accounts. The same we can figure out fake voting certain users doing it.
Data Analysis for Reward Distribution
AI will purely give rewards on merit bases. It will ensure these things to declare a post a quality one:
Centent Metrics: Plagiarism will be checked here, the writing style, relevancy, coherence and grammatical mistakes will be pointed out.
Engagement Metrics:Users with high engagement will be highly rewarded. Engagement includes; proper voting, authentic comments and maintaining consistency in posting.
User Reputation: Users with high reputation and having high community trust levels will be preferred in rewarding.
Voting Patterns: Self vote and vote farming accounts will be completely ignored as it comes under abuse.
Enhancing the Steem Reward Mechanism
Ensure Fair Compensation
It’s most obvious that AI will bring more fairness to the steemit reward mechanism. Posts with high quality and more information will be rewarded rather than that of simple one.
Reduce Spam & Abuse
The best advantage of bringing AI into steemit that it will reduce spam and abuse with in the platform. Fake accounts and action like self voting (spam) will be closed.
Promote Genuine Engagement
Their will be a genuine engagement with in the platform. It could be in the form of quality posting or authentic and real comments.
Increase Transparency
It will bring transparency to the platform, if AI is provided to generate score for the quality content or real comments.
AI-Powered Trading Bot on TRON for Steem/USDT
The AI-Powered Trading Bot on TRON for Steem/USDT will execute quality trades based on these features; historical data analysis, sentiments analysis from the market and machine learning. It will adopt the trading strategy accordingly by figuring out market fluctuations and managing risk policy.
Key Functionalities
Data Collection & Analysis
The bots work to identify market trends by gauging real time data. The past data can be used to predict price movement, volume, patterns and order book depth.
It will collect data from the real market by gauging sentiment of social media, news and other such platforms. The TRON blockchain activity will be analysed, transaction will be detected to find whale movements or liquidity shifts.
Trade Execution Strategy
Bots are made to make AI powered decisions. Reinforcement learning (RL) and deep learning can be used to give us buy and sell signals. The entry and exit points will be based on technical indicators data.
Entry and exit will be made at predetermined levels. Even in highly volatile market our trade will be executed. Their is another plus point of automation; we will get exit at maximum profit or minimum loss.
For transparency purpose it better to use to use TRON smart contracts. Make sure trades are executed on it for security purposes. It’s much better to use JustSwap (TRON’s Dex) or other TRON’s supporting centralized exchanges.
Risk Management
Diversify trading strategies by using AI models like; arbitrage, momentum-based, mean-reversion, etc. Allocate 1% to 5% of your portfolio in a single trade. Use AI volatility detectors to predict market extreme conditions. Stop loss should be according to real market conditions and we can also use hedging policy.
Real-Time Adaptation & AI Learning
The bots will make strategies accordingly; bull, bear, sideways market. Trading strategies should be reinforced based on past performance.