Recall the day you first stepped foot into the Web3 and crypto world. Did you find yourself reflexively Googling what HODL, NFT, and DAO stood for? Or were you left wondering what on Earth was dAppening when someone said BTC was going 'to the moon'?
It was a lot to take in. Just when you're all caught up, AI came along and swerved us on another learning curve. 'NLP' is no longer Neuro-linguistic Programming as much as Natural Language Processing these days.
Although it covers A to Z, this non-exhaustive list of terminologies and slangs in the blockchain and AI lexicons should be a handy companion for times when such conversations come up.
Blockchain
Airdrop: Free distribution of a cryptocurrency or token, often as a promotional tactic or to reward early adopters
Altcoin: Any cryptocurrency other than Bitcoin and Ethereum
ATH: All-Time High - The highest price a cryptocurrency has ever reached
Bagholder: Someone who holds onto a cryptocurrency that has significantly decreased in value, hoping it will recover
Bitcoin Maximalist: Someone who believes Bitcoin is the only cryptocurrency that matters and all others are inferior
Block: A collection of transactions recorded on the blockchain
Blockchain: A decentralised, distributed ledger that records transactions across multiple computers
BTFD: Buy The F****** Dip - An encouragement to buy a cryptocurrency when its price drops
BUIDL: A stylised expression of 'Build'. It emphasises the importance of building useful Web3 projects, particularly during market downturns, as opposed to focusing only on price speculation
Bull Market: A market where prices are rising or are expected to rise
Burn: The process of destroying cryptocurrency tokens, usually to reduce supply and increase value
Cold Wallet: A physical device that stores cryptocurrency offline, offering increased security
Consensus: The process by which nodes on a blockchain network agree on the validity of transactions
Cryptocurrency: A digital or virtual currency that uses cryptography for security
DAO: Decentralised Autonomous Organisation - An organisation run entirely on the blockchain, with rules encoded in smart contracts
dApp: Decentralised Application - An application that runs on a blockchain network
DeFi: Decentralised Finance - Financial applications built on blockchain technology, often offering peer-to-peer services
Degen: Short for 'degenerate', it refers to someone who engages in high-risk, speculative trading or investments in the crypto space, often without conducting proper research
Diamond Hands: Someone who holds onto their cryptocurrency investments through market volatility, refusing to sell
DYOR: Do Your Own Research - An important reminder to research any investment before committing
ERC-20: A technical standard for creating tokens on the Ethereum blockchain
Ethereum: A blockchain platform that enables the creation of smart contracts and decentralised applications
FOMO: Fear Of Missing Out - The anxiety of missing out on a potentially profitable investment
Fork: A split in a blockchain network, resulting in two separate chains
FUD: Fear, Uncertainty, and Doubt - Negative information spread about a cryptocurrency to influence its price
Gas: The fee required to conduct a transaction or execute a smart contract on the Ethereum network
Genesis Block: The first block ever mined on a blockchain
HODL: Hold On for Dear Life - Originally a misspelling of 'Hold', it now means holding onto cryptocurrency despite market fluctuations
ICO: Initial Coin Offering - A fundraising method where a new cryptocurrency project sells tokens to investors
KYC: Know Your Customer - The process of verifying the identity of customers to prevent fraud and money laundering
Layer 1: The base level or main blockchain architecture. It is responsible for core functions such as consensus mechanism, transaction processing, security, and data availability
Mainnet: The primary blockchain network, or a 'live environment', where actual transactions occur
Market Cap: Market Capitalisation - The total value of a cryptocurrency, calculated by multiplying the price by the circulating supply
Mining: The process of verifying and adding transactions to the blockchain, often rewarded with cryptocurrency
NFT: Non-Fungible Token - A unique digital asset representing ownership of a specific item or piece of content
Node: A computer connected to a blockchain network that helps validate and relay transactions
Oracle: A service that provides real-world data to smart contracts on the blockchain
Paper Wallet: A physical printout of cryptocurrency private keys, used for offline storage
Peer-to-Peer (P2P): Direct interaction between two parties without a central intermediary
Private Key: A secret code used to access and manage cryptocurrency holdings
Proof of Stake (PoS): A consensus mechanism where validators are chosen based on the number of coins they hold and are willing to 'stake'
Proof of Work (PoW): A consensus mechanism where miners solve complex mathematical problems to validate transactions and create new blocks
Pump and Dump: A scheme where the price of a cryptocurrency is artificially inflated (pumped) and then sold off (dumped) for profit
Quantum Computing: A potential future technology that might impact the security of cryptographic algorithms used in blockchain
Rekt: A term used to describe someone who has suffered significant financial losses in cryptocurrency trading
Satoshi Nakamoto: The pseudonymous creator of Bitcoin
Shitcoin: A cryptocurrency with little to no value or a perceived scam
Smart Contract: A self-executing contract with the terms of the agreement directly written into lines of code
Stablecoin: A cryptocurrency designed to maintain a stable value, often pegged to a fiat currency like the U.S. dollar
Testnet: A blockchain network used for testing and development purposes before launching on the mainnet
Token: A digital asset built on an existing blockchain, often representing a specific utility or value within a project
TPS (Transactions Per Second): A metric used to measure the speed of a blockchain network
To the Moon: A term used to describe a cryptocurrency's price skyrocketing
Wallet: A software or hardware application used to store, send, and receive cryptocurrency
Whale: An individual or entity that holds a large amount of cryptocurrency, potentially influencing the market
Whitelist: A list of approved participants, often used in ICOs or token sales
Yield Farming: The practice of earning interest or rewards by locking up cryptocurrencies in DeFi protocols
Zero-Knowledge Proof: A cryptographic method where one party can prove to another that a statement is true without revealing the underlying information
Artificial Intelligence
AI: Artificial Intelligence - The simulation of human intelligence processes by machines, especially computer systems
AGI: Artificial General Intelligence - A hypothetical AI that possesses the ability to understand or learn any intellectual task that a human being can
Algorithm: A set of rules or instructions given to an AI, neural network, or computer program to help it learn on its own. It is also a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.
Alignment: The process of ensuring that AI systems are designed and developed in a way that aligns with human values and goals
Anthropomorphism: The attribution of human characteristics or behavior to AI systems
Backpropagation: An algorithm used in training neural networks to adjust the weights and biases based on the error in the output
Bias: Systematic errors in AI systems that lead to unfair or discriminatory outcomes
Big Data: Large, complex data sets that require advanced analysis tools, often used in AI and machine learning applications
Black Box: An AI system whose internal workings are not easily understood or interpretable
Chatbot: An AI program designed to simulate conversation with human users, especially over the internet
Cognitive Computing: A field of AI that focuses on building systems that can reason, learn, and interact naturally with humans
Computer Vision: A field of AI that enables machines to interpret and understand visual information from the world, such as images and videos
Convolutional Neural Network (CNN): A type of neural network commonly used for image and video recognition tasks
Data Augmentation: The process of creating new training data by applying transformations to existing data, such as rotations, flips, or colour changes
Deep Learning: A subset of machine learning that uses artificial neural networks with multiple layers to learn from large amounts of data
Data Mining: The process of discovering patterns in large data sets, often used in AI-driven analytics
Embeddings: A way of representing words, phrases, or other data as numerical vectors in a high-dimensional space
Emergent Behavior: Unexpected or unintended behaviors that arise from the complex interactions within an AI system
Expert System: An AI program that mimics the decision-making abilities of a human expert in a specific domain
Explainable AI (XAI): A field of AI that focuses on developing techniques to make AI systems more transparent and understandable
Generative Adversarial Network (GAN): A type of AI model that involves two neural networks competing against each other to generate realistic data.
GPT (Generative Pre-trained Transformer): A type of large language model used for generating human-like text
Hallucination: An AI generating outputs that are nonsensical or unrelated to the input
Heuristics: Techniques used to solve problems faster when traditional methods are too slow
IoT (Internet of Things): The network of physical devices that collect and exchange data, often integrated with AI for automation
Inference: The process of using a trained AI model to make predictions or classifications on new data
Jupyter Notebook: An open-source web application used for coding, visualising, and documenting machine learning and AI experiments
K-Means: A popular clustering algorithm used in unsupervised machine learning
Knowledge Graph: A structured representation of real-world entities and their relationships, often used in AI to enhance understanding and search capabilities
Large Language Model (LLM): An AI model trained on a massive dataset of text and code, capable of generating text, translating languages, writing different kinds of creative content, and answering your questions in an informative way
Machine Learning (ML): A branch of AI where algorithms learn from data to make decisions or predictions
Model: The result of training a machine learning algorithm with data, used to make predictions
Natural Language Processing (NLP): A field of AI that focuses on enabling machines to understand, interpret, and generate natural human language
Neural Network: A computing system inspired by the human brain, consisting of interconnected nodes (neurons) that process and transmit information
Overfitting: A phenomenon in machine learning where a model learns the training data too well, performing poorly on new, unseen data
Prompt Engineering: The process of designing and crafting effective prompts to elicit desired responses from AI models
Python: A popular programming language used extensively in AI and machine learning projects
Quantum Machine Learning: A field that combines quantum computing with machine learning algorithms, aiming to achieve better computational efficiency
Reinforcement Learning: A type of machine learning where an agent learns to take actions in an environment to maximise a reward signal
Supervised Learning: A type of machine learning where an algorithm learns from labeled training data to make predictions or classifications on new data
Transformer: A type of neural network architecture that has revolutionised natural language processing tasks
Transfer Learning: A technique in machine learning where a pre-trained model is used as a starting point for a new task, saving time and resources
Turing Test: A test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human
Unsupervised Learning: A type of machine learning where an algorithm learns from unlabeled data to discover patterns or structures in the data
Underfitting: A problem in machine learning where a model is too simple and fails to capture the underlying patterns in the data, leading to poor performance on both training and new data
Weights: The parameters within a neural network that are adjusted during training to minimise error and improve the model’s predictions
YOLO (You Only Look Once): A real-time object detection algorithm that processes images in one pass, widely used in computer vision tasks
Zero-shot Learning: A machine learning paradigm where a model can make predictions on classes it was never explicitly trained on by using information from related tasks or classes
Read, Learn, and Earn!
Every month, aelf hosts a poll (or a quiz) where you stand a chance to get airdropped ELF tokens! All you have to do is check out a chosen article to find answers, and vote an option on Votigram. Do follow both aelf and TMRWDAO on X to stay updated on the game dates and full instructions.
This month, we're running an 'aelf Dictionary' poll from 23 to 30 September. Head over to the Votigram bot on Telegram, cast a vote for your favourite blockchain term or slang, and you might just be one of the 10 lucky winners to win from a prize pool of 100 ELF tokens!
Now that you've gotten savvy on the common slangs and abbreviations, many of them are apt descriptions of aelf as a layer 1 AI blockchain. Since the incorporation of AI technologies into its platform, aelf has offered an intuitive AI chatbot to answer any user-generated enquiries on digital platforms, and NLP models like GPT-4 to simplify smart contract creation for Web3 developers.
The aelf infrastructure, consisting of a customisable multi-chain structure, is designed to power the future of decentralised applications, especially in the AI space. With its innovative AEDPoS consensus mechanism and focus on scalability, aelf is paving the way for a seamless convergence of AI and blockchain, and in turn, compelling Web3 solutions to be applied to the real world.
*Disclaimer: The information provided on this blog does not constitute investment advice, financial advice, trading advice, or any other form of professional advice. aelf makes no guarantees or warranties about the accuracy, completeness, or timeliness of the information on this blog. You should not make any investment decisions based solely on the information provided on this blog. You should always consult with a qualified financial or legal advisor before making any investment decisions.
About aelf
aelf, an AI-enhanced Layer 1 blockchain network, leverages the robust C# programming language for efficiency and scalability across its sophisticated multi-layered architecture. Founded in 2017 with its global hub in Singapore, aelf is a pioneer in the industry, leading Asia in evolving blockchain with state-of-the-art AI integration and modular Layer 2 ZK Rollup technology, ensuring an efficient, low-cost, and highly secure platform that is both developer and end-user friendly. Aligned with its progressive vision, aelf is committed to fostering innovation within its ecosystem and advancing Web3 and AI technology adoption.
For more information about aelf, please refer to our Whitepaper V2.0.
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