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Machine learning for N-dimensional spatial reasoning tasks on the web.

Blake Moody1, JieHyun Kim1, Sanghyuk Kim1

  • 1Department of Computer Science, University of Massachusetts Boston, Boston, MA, United States.

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Summary
This summary is machine-generated.

Snake-ML enables efficient client-side training for spatial reasoning tasks using a browser-based snake game. This framework significantly speeds up model inference and training, benefiting AI visualization projects.

Keywords:
artificial intelligencecomputer visionedge computinggenetic algorithmmachine learningspatial reasoningtracking

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Robotics

Background:

  • Spatial reasoning is crucial for complex tasks but current training models are resource-intensive and require significant human input.
  • Existing methods for spatial task training are computationally demanding and not optimized for client-side applications.

Purpose of the Study:

  • To introduce Snake-ML, a web-based simulation tool for client-side training of spatial reasoning.
  • To provide an efficient and intuitive test bed for developing spatial navigation strategies in browsers.

Main Methods:

  • Developed Snake-ML, a framework utilizing the snake game for spatial reasoning in low-dimensional visual spaces.
  • Implemented a direct TensorFlow.js GPU pipeline for accelerated training without CPU/GPU synchronization.

Main Results:

  • Snake-ML achieved a 4.58× speedup in model inference on the edge.
  • The TensorFlow.js GPU pipeline resulted in up to a 32× speedup in training time.

Conclusions:

  • Snake-ML offers an efficient solution for client-side spatial reasoning training.
  • The framework demonstrates potential for applications in autonomous systems, robotics, and AI-driven environments.