Skip to main content
  1. Data Science Blog/

Graph of Thoughts

·585 words·3 mins· loading · ·
Prompt Engineering Artificial Intelligence (AI) Prompt Engineering Language Models (LLMs) AI Reasoning Neural Networks Machine Learning (ML) Cognitive Computing Artificial Intelligence (AI)
Share with :

Graph of Thoughts

Graph of Thoughts
#

This is a valuable resource for learning Graph of Thoughts (GoT) concepts. The YouTube video is from code_your_own_AI. I’m utilizing the comments made by @wesleychang2005 on the video, which provide an excellent summary of GoT. If you’re interested in this topic and find the summary below intriguing, I recommend watching the entire 41-minute video.

https://www.youtube.com/watch?v=tCPA89n6NGQ&t=1562
Take Aways from the video

  1. 00:26 🤯 Graph of Thoughts (GoT) is a non-linear approach to reasoning for AI agents, using interconnected nodes and edges to represent the thought process.
  2. 01:18 📊 The Tree of Thoughts method suffers from inefficiency, requiring hundreds of queries to solve a single problem.
  3. 02:36 🎯 An AI agent is defined as an entity that can perceive its environment, make decisions, and initiate actions based on a control cycle and a reward function.
  4. 05:39 🌐 The latest research focuses on AI agents augmented by Large Language Models (LLMs) for more intelligent and autonomous behavior.
  5. 08:43 🤖 LLM-augmented AI agents can interact with and learn from their environment, making them more adaptive and capable.
  6. 12:45 📝 Explanation fine-tuning of LLMs (Large Language Models) is guided by GPT-4’s own reasoning explanation, serving as a blueprint for development.
  7. 13:34 🕸️ The “Graph of Thoughts” allows for a flexible approach to reasoning, where multiple chains of thoughts can be pursued and evaluated simultaneously.
  8. 16:50 🎛️ The application of graph theory in AI involves the use of graph attention networks and various encoding techniques to manage both visual and textual data.
  9. 22:46 📊 A scoring mechanism is used to assess the LLM’s replies for accuracy and relevance, aiding in quality control of the model’s output.
  10. 24:16 🎮 A “Controller” manages the entire reasoning process, using a “Graph of Operations” (GoO) to dictate the execution plan for tasks, making the reasoning adaptable and structured.
  11. 25:35 🌍 Graph-of-Thoughts (GoT) can be used for planet classification tasks. The speaker uses a simple example where an AI system decides whether a planet is habitable based on attributes like distance from the sun and atmospheric conditions.
  12. 27:32 🛠️ In GoT, each node in the ‘Graph of Operations’ (GoO) represents a specific task (e.g., check distance from the sun). The ‘Graph Reasoning State’ (GRS) records and updates the system’s understanding as nodes are executed.
  13. 29:30 📝 The speaker describes a more complex example involving multiple types of planets and a list of features for classification. He emphasizes the need for a specialized Language Learning Model (LLM) trained in astrophysics.
  14. 32:56 🎯 Scoring and validation are essential for assessing the reliability of the AI’s responses. The system assigns a confidence score to its classification decision.
  15. 35:48 🔄 The GoT system can incorporate human feedback, iterating through multiple loops to refine its reasoning process and improve classification outcomes.
  16. 36:57 🛠️ The Graph-of-Operation (GoO) framework lays out how AI operations interact and depend on each other in a sequence, from initial query to final output.
  17. 38:18 🙋‍♂️ Human domain expertise is essential for designing the reasoning flow within the GoO, as it’s not automatically generated by the AI system itself.
  18. 39:18 🤔 GPT-4 suggests that future AI systems like GPT-5 could potentially engage in meta-learning or self-improvement, opening the possibility for AI to design its own GoO structure.
  19. 39:43 📊 Adequate training data is crucial for advanced AI systems to learn diverse tasks in multiple domains and potentially design complex GoO structures.
  20. 40:07 📈 Mathematical graph theory could help in constructing multiple graphs for specific problems, setting the stage for training more advanced AI systems.
Dr. Hari Thapliyaal's avatar

Dr. Hari Thapliyaal

Dr. Hari Thapliyal is a seasoned professional and prolific blogger with a multifaceted background that spans the realms of Data Science, Project Management, and Advait-Vedanta Philosophy. Holding a Doctorate in AI/NLP from SSBM (Geneva, Switzerland), Hari has earned Master's degrees in Computers, Business Management, Data Science, and Economics, reflecting his dedication to continuous learning and a diverse skill set. With over three decades of experience in management and leadership, Hari has proven expertise in training, consulting, and coaching within the technology sector. His extensive 16+ years in all phases of software product development are complemented by a decade-long focus on course design, training, coaching, and consulting in Project Management. In the dynamic field of Data Science, Hari stands out with more than three years of hands-on experience in software development, training course development, training, and mentoring professionals. His areas of specialization include Data Science, AI, Computer Vision, NLP, complex machine learning algorithms, statistical modeling, pattern identification, and extraction of valuable insights. Hari's professional journey showcases his diverse experience in planning and executing multiple types of projects. He excels in driving stakeholders to identify and resolve business problems, consistently delivering excellent results. Beyond the professional sphere, Hari finds solace in long meditation, often seeking secluded places or immersing himself in the embrace of nature.

Comments:

Share with :

Related

Roadmap to Reality
·916 words·5 mins· loading
Philosophy & Cognitive Science Interdisciplinary Topics Scientific Journey Self-Discovery Personal Growth Cosmic Perspective Human Evolution Technology Biology Neuroscience
Roadmap to Reality # A Scientific Journey to Know the Universe — and the Self # 🌱 Introduction: The …
From Being Hacked to Being Reborn: How I Rebuilt My LinkedIn Identity in 48 Hours
·893 words·5 mins· loading
Personal Branding Cybersecurity Technology Trends & Future Personal Branding LinkedIn Profile Professional Identity Cybersecurity Online Presence Digital Identity Online Branding
💔 From Being Hacked to Being Reborn: How I Rebuilt My LinkedIn Identity in 48 Hours # “In …
Exploring CSS Frameworks - A Collection of Lightweight, Responsive, and Themeable Alternatives
·1378 words·7 mins· loading
Web Development Frontend Development Design Systems CSS Frameworks Lightweight CSS Responsive CSS Themeable CSS CSS Utilities Utility-First CSS
Exploring CSS Frameworks # There are many CSS frameworks and approaches you can use besides …
Dimensions of Software Architecture: Balancing Concerns
·871 words·5 mins· loading
Software Architecture Software Architecture Technical Debt Maintainability Scalability Performance
Dimensions of Software Architecture # Call these “Architectural Concern Categories” or …
Understanding `async`, `await`, and Concurrency in Python
·637 words·3 mins· loading
Python Asyncio Concurrency Synchronous Programming Asynchronous Programming
Understanding async, await, and Concurrency # Understanding async, await, and Concurrency in Python …