Dexa/Kyle Polich

Published Oct 29, 2024

Graphs for HPC and LLMs

Senior researcher Maciej Besta delves into the transformative role of graph theory in high-performance computing and large language models, spotlighting the Graph of Thought model's potential to streamline problem-solving and improve reasoning and output quality.

Episode Highlights

Topics covered

Questions from this episode

- What's next in large language models (LLMs)?
  Asked by 5 people
- What will be the next development in AI? Currently, large language models are limited in their reasoning ability. Will the improvement of reasoning ability provide more help in the next step?
  Asked by 2 people
- What are the next advancements in large language models (LLMs)?
  Asked by 2 people
- How do you break tasks into smaller steps?
  Asked by 2 people
- What are common steps for solving problems with Large Language Models (LLMs)?
  Asked by 1 person
- What techniques are used with large language models (LLMs)?
  Asked by 1 person
- What are practical tips for prompt engineering?
  Asked by 1 person
- How do you break down tasks into smaller parts?
  Asked by 1 person
- Tell me more about decomposing abstractions
  Asked by 1 person
- What are some examples of breaking large tasks into smaller steps?
  Asked by 1 person
- What techniques are effective for prompting models to create summaries or other outputs?
  Asked by 1 person
- What techniques can be used to identify a problem and then systematically complete it?
  Asked by 1 person
- What are AI reasoning breakthroughs?
  Asked by 1 person
- How are prompts used in AI models?
  Asked by 1 person
- Can you give an example of breaking down a complex concept?
  Asked by 1 person

Episode Highlights

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