#DailyChallenge Day21(Final)
Ontology
- A structured framework that defines concepts, relationships, and categories within a domain.
- Serves as a comprehensive framework that maps an organization’s data assets—such as datasets and models—to real-world entities and processes.
- It helps AI understand context and meaning in data, improving reasoning and decision-making.
- Example: A medical ontology organizes diseases, symptoms, and treatments into a structured format AI can use for diagnosis.
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Base Seoul Feb meetup!
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#dailychallenge day 20
AGI(Artificial General Inteligence)
- Definition: An AI system with human-like cognitive abilities, capable of understanding, learning, and applying knowledge across various tasks without needing task-specific programming.
- AGI still remains as a theoretical definition, so the detailed definition changes as the speaker changes. However, it refers to a next level of narrow AI
- Current LLMs are not AGI.
- The next (theoretical) level of AGI is ASI(Artificial Super Inteligence).
- Definition of ASI: an AI that surpasses human intelligence in every aspect, including creativity, problem-solving, and emotional intelligence
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#dailychallenge
Deepseek r1
- Reinforcement learning-based(only) Open-source LLM/ + MoE
- CoT
- Trained with AI scoring AI without no human interference
- Enabling low-cost - high-performance LLM
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#dailychallenge day19
PondAI
- A sleeper AI infra project
- Crypto AI layer by running incentive-driven competitions for onchain prediction models that empower smarter DeFAI, security, and trading agents
- The best models receive incentives, and model developers retain ownership, provided they are integrated with real-time data and inference infrastructure.
Gaianet
- A decentralized computing infrastructure that enables everyone to create, deploy, scale, and monetize their own AI agents that reflect their styles, values, knowledge, and expertise.
- Supports AI-powered dApps & smart contracts
- Open-source framework
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#dailychallenge
CoT
- Chain-of-Thought (CoT) is a reasoning technique where a model breaks down complex problems into intermediate steps before arriving at a final answer.
- In an easy way, thinking step by step, not deriving answers directly.
- This enhances reasoning capabilities in large language models (LLMs), especially for multi-step problems.
- ChatGPT o1 and o3 use this, and DeepSeek R1 also uses this methodology.
- This makes the model to
- improve logical reasoning and problem-solving skills
- Help with mathematical, coding, and reasoning-based tasks
- enhances interpretability by showing intermediate steps
reasoning
- Recent trend is not to train, but to reason
- A reasoning framework where a model decomposes complex tasks into a logical sequence of intermediate steps before reaching a conclusion.
- This enhances the model’s ability to solve problems that require multi-step thinking rather than direct retrieval
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#dailychallenge
LLM Fine-tuning Methodology - LoRA Tuning, P-Tuning, Instruction Tuning
- Pre-trained model gets fine-tuned with task-based data
- LoRA is best for parameter-efficient fine-tuning
- P-Tuning optimizes prompt engineering
- Instruction Tuning focuses on making models better at understanding and executing human instructions
- LoRA Tuning& P-Tuning are both PEFT(Parameter-Efficient Fine-Tuning).
- This means it doesn’t change the whole parameter but trains additional small parameters.
- Instruction Tuning is used to enhance the model itself.
- It uses a dataset of ‘instructions and answers’
- While PEFT is a methodology to adjust the model to fit specific tasks, instruction tuning is used for performance enhancement.
- PEFT is Tuning. Instruction Tuning is Training
