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DeepSeek vs ChatGPT: Effortless Network Map Creation

#AI Tools
2025-02-10 12:009 min read

Content Introduction

In this video, the creator tests the capabilities of the Deep Seek R1 model and ChatGPT's OR3 mini reasoning model to generate the shortest walking routes from a specified starting address to multiple destinations. The process involves minimal effort, focusing on the use of AI models to create a network route map with just a few clicks. The video teases that there will be a clear winner between the two models, inviting viewers to see which performs better. The creator employs OpenStreetMap and NetworkX libraries to visualize the generated paths on an interactive map, noting differences in functionalities and outputs. Throughout the test, attention is drawn to both models' handling of geocoding without prior coordinate information. Observations include which model provides superior results while addressing various challenges faced during the implementation, ultimately suggesting that Deep Seek has a slight edge in effectively generating the required maps.

Key Information

  • The video demonstrates a comparison between two AI models: Deep Seek R1 and ChatGPT OR3 Mini Reasoning Model.
  • The objective is to generate the shortest walking route maps based on a starting point and several destinations with minimal user input.
  • Key destinations mentioned include Manchester Cathedral, Colest Village, and Deliria Park.
  • Both AI models are tasked with plotting routes on a map using OpenStreetMap and NetworkX libraries.
  • The speaker expresses curiosity about the performance of each model, hinting at a clear winner based on their outputs.
  • During the demonstration, each model is shown to recognize the need to generate latitude and longitude coordinates from given addresses.
  • The video covers potential issues that arise when the models attempt to generate route maps, including error messages related to missing attributes in the libraries.
  • At the conclusion of the demonstration, the speaker notes that Deep Seek outperformed ChatGPT, providing well-defined route outputs with added markers.

Timeline Analysis

Content Keywords

Deep Seek R1 Model

The video tests the abilities of the Deep Seek R1 model against Chat GPT's recently published Mini reasoning model, particularly in generating a network route map with minimal effort.

Chat GPT Mini Reasoning Model

The video compares the performance of Chat GPT's Mini reasoning model with Deep Seek R1, focusing on the generation of network routes based on user inputs.

Network Route Mapping

The task involves generating the shortest walking routes between a specified starting point and multiple destinations, demonstrating how AI models handle geographical data.

AI Performance Comparison

The video highlights the performance differences between two AI models, concluding that Deep Seek has a slight edge in network mapping tasks.

Geocoding Mechanism

The video discusses the use of geocoding to convert addresses to latitude and longitude points, which is critical for mapping tasks.

Open Street Map

The models utilize Open Street Map for plotting routes, with a discussion on the flexibility of choosing different base maps for improved visual representation.

Python Environment for AI

The speaker suggests using Google Colab as a user-friendly Python environment for running the necessary scripts to test the AI capabilities.

Model Error Handling

The video highlights how Deep Seek handles specific model errors and the necessary adjustments needed to generate accurate results in mapping.

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