A Model for Estimating the Economic Costs of Computer Vision Systems That Use Deep Learning

Authors

  • Neil Thompson MIT
  • Martin Fleming The Productivity Institute, Varicent
  • Benny J. Tang MIT
  • Anna M. Pastwa MIT University of Warsaw
  • Nicholas Borge IBM
  • Brian C. Goehring IBM
  • Subhro Das IBM

DOI:

https://doi.org/10.1609/aaai.v38i21.30343

Keywords:

Transfer Learning , Deep Learning and Neural Networks , Track: Deployed Innovative Tools, Vision

Abstract

Deep learning, the most important subfield of machine learning and artificial intelligence (AI) over the last decade, is considered one of the fundamental technologies underpinning the Fourth Industrial Revolution. But despite its record-breaking history, deep learning’s enormous appetite for compute and data means that sometimes it can be too costly to practically use. In this paper, we connect technical insights from deep learning scaling laws and transfer learning with the economics of IT to propose a framework for estimating the cost of deep learning computer vision systems to achieve a desired level of accuracy. Our tool can be of practical use to AI practitioners in industry or academia to guide investment decisions.

Published

2024-03-24

How to Cite

Thompson, N., Fleming, M., Tang, B. J., Pastwa, A. M., Borge, N., Goehring, B. C., & Das, S. (2024). A Model for Estimating the Economic Costs of Computer Vision Systems That Use Deep Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23012-23018. https://doi.org/10.1609/aaai.v38i21.30343

Issue

Section

IAAI Technical Track on Deployed Innovative Tools for Enabling AI Applications