AI Cannot Understand Memes: Experiments with OCR and Facial Emotions

Ishaani Priyadarshini,Chase Cotton

doi:10.32604/cmc.2022.019284

Abstract

The increasing capabilities of Artificial Intelligence (AI), has led researchers and visionaries to think in the direction of machines outperforming humans by gaining intelligence equal to or greater than humans, which may not always have a positive impact on the society. AI gone rogue, and Technological Singularity are major concerns in academia as well as the industry. It is necessary to identify the limitations of machines and analyze their incompetence, which could draw a line between human and machine intelligence. Internet memes are an amalgam of pictures, videos, underlying messages, ideas, sentiments, humor, and experiences, hence the way an internet meme is perceived by a human may not be entirely how a machine comprehends it. In this paper, we present experimental evidence on how comprehending Internet Memes is a challenge for AI. We use a combination of Optical Character Recognition techniques like Tesseract, Pixel Link, and East Detector to extract text from the memes, and machine learning algorithms like Convolutional Neural Networks (CNN), Region-based Convolutional Neural Networks (RCNN), and Transfer Learning with pre-trained denseNet for assessing the textual and facial emotions combined. We evaluate the performance using Sensitivity and Specificity. Our results show that comprehending memes is indeed a challenging task, and hence a major limitation of AI. This research would be of utmost interest to researchers working in the areas of Artificial General Intelligence and Technological Singularity.

Highlights

Artificial intelligence (AI) plays a major role in the constantly evolving technology
Comparative analysis depicts that text extraction and facial emotion detection are yet to achieve 100% accuracy, and are still a challenge for AI
A number of techniques were used for Optical Character Recognition (OCR) (Tesseract, Pixel Link, and East Detector) as well as facial emotion detection (CNN, R-Convolutional Neural Networks (CNN), and Transfer Learning), and it can be asserted based on the experimental analysis and comparative analysis that understanding memes is challenging for AI

Summary

Introduction

Artificial intelligence (AI) plays a major role in the constantly evolving technology. Machines are becoming more and more sophisticated with increased training and data. While machine intelligence may have served humanity generously in many ways it may not always have desirable outcomes. Past research works have been known to highlight how AI systems go rogue. Examples like Self-driving cars jumping red lights [1], Image Recognition software labeling black people as gorillas [2], AI systems performing racial discrimination [3,4], robots killing humans [5,6] are all wake up calls for setting boundaries on feeding intelligence to machines.

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