Human vs. supervised machine learning: Who learns patterns faster?

• URL: http://arxiv.org/abs/2012.03661v1
• Date: Mon, 30 Nov 2020 13:39:26 GMT
• Title: Human vs. supervised machine learning: Who learns patterns faster?
• Authors: Niklas K\"uhl, Marc Goutier, Lucas Baier, Clemens Wolff, Dominik Martin
• Abstract summary: This study provides an answer to how learning performance differs between humans and machines when there is limited training data. We have designed an experiment in which 44 humans and three different machine learning algorithms identify patterns in labeled training data and have to label instances according to the patterns they find.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The capabilities of supervised machine learning (SML), especially compared to human abilities, are being discussed in scientific research and in the usage of SML. This study provides an answer to how learning performance differs between humans and machines when there is limited training data. We have designed an experiment in which 44 humans and three different machine learning algorithms identify patterns in labeled training data and have to label instances according to the patterns they find. The results show a high dependency between performance and the underlying patterns of the task. Whereas humans perform relatively similarly across all patterns, machines show large performance differences for the various patterns in our experiment. After seeing 20 instances in the experiment, human performance does not improve anymore, which we relate to theories of cognitive overload. Machines learn slower but can reach the same level or may even outperform humans in 2 of the 4 of used patterns. However, machines need more instances compared to humans for the same results. The performance of machines is comparably lower for the other 2 patterns due to the difficulty of combining input features.

Related papers

• Offline Imitation Learning Through Graph Search and Retrieval [57.57306578140857]
  Imitation learning is a powerful machine learning algorithm for a robot to acquire manipulation skills. We propose GSR, a simple yet effective algorithm that learns from suboptimal demonstrations through Graph Search and Retrieval. GSR can achieve a 10% to 30% higher success rate and over 30% higher proficiency compared to baselines.
  arXiv  Detail & Related papers  (2024-07-22T06:12:21Z)
• Do humans and machines have the same eyes? Human-machine perceptual differences on image classification [8.474744196892722]
  Trained computer vision models are assumed to solve vision tasks by imitating human behavior learned from training labels. Our study first quantifies and analyzes the statistical distributions of mistakes from the two sources. We empirically demonstrate a post-hoc human-machine collaboration that outperforms humans or machines alone.
  arXiv  Detail & Related papers  (2023-04-18T05:09:07Z)
• Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning [54.636562516974884]
  In imitation and reinforcement learning, the cost of human supervision limits the amount of data that robots can be trained on. In this work, we propose MEDAL++, a novel design for self-improving robotic systems. The robot autonomously practices the task by learning to both do and undo the task, simultaneously inferring the reward function from the demonstrations.
  arXiv  Detail & Related papers  (2023-03-02T18:51:38Z)
• Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment [52.07473934146584]
  We guide the curriculum reinforcement learning results towards a preferred performance level that is neither too hard nor too easy via learning from the human decision process. Our system is highly parallelizable, making it possible for a human to train large-scale reinforcement learning applications. It shows reinforcement learning performance can successfully adjust in sync with the human desired difficulty level.
  arXiv  Detail & Related papers  (2022-08-04T23:53:51Z)
• Human's Role in-the-Loop [15.759742984491412]
  This blog discusses the respective roles of humans and machines in achieving cognitive tasks in matching. It aims to determine whether traditional roles of humans and machines are subject to change. Two possible modes of change, namely humans out and humans in, will be discussed.
  arXiv  Detail & Related papers  (2022-04-27T13:11:54Z)
• Comparing Facial Expression Recognition in Humans and Machines: Using CAM, GradCAM, and Extremal Perturbation [5.025654873456756]
  Facial expression recognition (FER) is a topic attracting significant research in both psychology and machine learning. We compared the recognition performance and attention patterns of humans and machines during a two-alternative forced-choice FER task.
  arXiv  Detail & Related papers  (2021-10-09T06:54:41Z)
• What Matters in Learning from Offline Human Demonstrations for Robot Manipulation [64.43440450794495]
  We conduct an extensive study of six offline learning algorithms for robot manipulation. Our study analyzes the most critical challenges when learning from offline human data. We highlight opportunities for learning from human datasets.
  arXiv  Detail & Related papers  (2021-08-06T20:48:30Z)
• Dissonance Between Human and Machine Understanding [16.32018730049208]
  We present a large-scale crowdsourcing study that reveals and quantifies the dissonance between human and machine understanding. Our findings have important implications on human-machine collaboration, considering that a long term goal in the field of artificial intelligence is to make machines capable of learning and reasoning like humans.
  arXiv  Detail & Related papers  (2021-01-18T21:45:35Z)
• Large-scale randomized experiment reveals machine learning helps people learn and remember more effectively [14.305363222114543]
  We focus on unveiling the potential of machine learning to improve how people learn and remember factual material. We perform a large-scale randomized controlled trial with thousands of learners from a popular learning app in the area of mobility.
  arXiv  Detail & Related papers  (2020-10-09T08:30:15Z)
• A robot that counts like a child: a developmental model of counting and pointing [69.26619423111092]
  A novel neuro-robotics model capable of counting real items is introduced. The model allows us to investigate the interaction between embodiment and numerical cognition. The trained model is able to count a set of items and at the same time points to them.
  arXiv  Detail & Related papers  (2020-08-05T21:06:27Z)
• Learning Predictive Models From Observation and Interaction [137.77887825854768]
  Learning predictive models from interaction with the world allows an agent, such as a robot, to learn about how the world works. However, learning a model that captures the dynamics of complex skills represents a major challenge. We propose a method to augment the training set with observational data of other agents, such as humans.
  arXiv  Detail & Related papers  (2019-12-30T01:10:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.