Future Prospects of Human Interaction with Artificial Autonomous Systems

This work considers the future of human interaction with progressively more autonomous systems. I argue that the temporal dissonance between the human’s ‘cycle time’ and machine ‘cycle time,’ will become an overwhelming barrier to collaborative interaction. We may slow machines, we may buffer information exchange, we may default to meta-levels of strategic interchange but in the end all transparency of information interchange will dissolve under the driving influence of time. HF/E is thus already fighting rear-guard action. The question remains as to the sustenance of human quality of life in this evolving milieu.

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Reaction Times When Switching From Autonomous to Manual Driving Control

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1541931213601423 ◽

2016 ◽

Vol 60 (1) ◽

pp. 1854-1858 ◽

Cited By ~ 7

Author(s):

Kelly Funkhouser ◽

Frank Drews

Keyword(s):

Autonomous Vehicles ◽

Human Performance ◽

Autonomous Systems ◽

Reaction Times ◽

Cognitive Engagement ◽

Human Interaction ◽

Transitional State ◽

The Moment ◽

Future Technologies ◽

Current Reality

As autonomous vehicles become more prevalent in our everyday lives, we must succumb to the realities of technological deficiencies. Although a future of fully autonomous vehicles would be the pinnacle of safety and efficiency, the current reality leaves us in a transitional state requiring human interaction with autonomous systems. Therefore it is imperative to understand human-system interaction with the autonomous features in current and future technologies. To gain an improved understanding, we designed an investigational study to gain a better understanding of human performance parameters at the moment they relieve and regain control of autonomous systems. The current findings show that reaction time increases as time disengaged from the task of driving increases, regardless of cognitive engagement.

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MODERN MILITARY TECHNOLOGIES AND THE RESPONSIBILITY OF A SCIENTIST, CREATOR, OPERATOR, ETC.: SOME APPROACHES OF THE BRICS COUNTRIES

Пробелы в Российском законодательстве ◽

10.33693/2072-3164-2021-14-6-142-150 ◽

2021 ◽

Vol 14 (6) ◽

pp. 142-150

Author(s):

KSENIA BELIKOVA ◽

Keyword(s):

Legal Status ◽

New Technologies ◽

Autonomous Systems ◽

Scientific Activity ◽

Human Interaction ◽

Legal Responsibility ◽

Legal Personality ◽

Military Science ◽

Brics Countries ◽

The Impact

Based on the legal material of the BRICS countries the article touches upon the issue of legal responsibility of a scientist, creator, operator, etc. for the implementation of the results of his scientific activities in the field of new military technologies. Interest is caused by the impact on legal and military science, as well as on the ideas of both ordinary people and specialists (lawyers, military, sociologists, etc.) provided by new technologies that currently allow to do things that previously could not even be imagined otherwise than in imaginative literature, films, etc. In this way, the current provisions of normative acts (in the field of legislation on intellectual property), ethical codes, etc., and doctrines (works of specialists who give arguments "pro" and "contra" giving legal personality to artificial intelligence) are examined. Scientific novelty is due, firstly, to the choice of countries - these are the BRICS countries, secondly, the subject of the study is the legal responsibility for the implementation of the results of scientific activity of a scientist in the field of new military technologies, thirdly, the analysis of a selected range of issues in an interdisciplinary aspect - from the standpoint of jurisprudence, military science, ethics. Among the conclusions reached by the author, the inevitability of ethical problems when using AI in civil (for example, transport) and military autonomous systems. In this regard, attempts have been made to solve these problems in the BRICS countries in various ways: from declarative limitations in patent legislation to the development of guidelines and ethical principles that meet the realities. The need to develop a single document with an international legal status on the issue of AI-human interaction, based on the opinions and ideas about the principles of such interaction of more than eighty subjects from around the world is also showed.

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Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33012462 ◽

2019 ◽

Vol 33 ◽

pp. 2462-2470 ◽

Cited By ~ 1

Author(s):

Vinicius G. Goecks ◽

Gregory M. Gremillion ◽

Vernon J. Lawhern ◽

John Valasek ◽

Nicholas R. Waytowich

Keyword(s):

Autonomous Systems ◽

Human Interaction ◽

Task Completion ◽

Autonomous Agent ◽

High Fidelity ◽

Current Effort ◽

Multiple Modalities ◽

Simulated Environment ◽

Learning From Demonstrations ◽

Overall Performance

This paper investigates how to utilize different forms of human interaction to safely train autonomous systems in realtime by learning from both human demonstrations and interventions. We implement two components of the Cycle-of Learning for Autonomous Systems, which is our framework for combining multiple modalities of human interaction. The current effort employs human demonstrations to teach a desired behavior via imitation learning, then leverages intervention data to correct for undesired behaviors produced by the imitation learner to teach novel tasks to an autonomous agent safely, after only minutes of training. We demonstrate this method in an autonomous perching task using a quadrotor with continuous roll, pitch, yaw, and throttle commands and imagery captured from a downward-facing camera in a high-fidelity simulated environment. Our method improves task completion performance for the same amount of human interaction when compared to learning from demonstrations alone, while also requiring on average 32% less data to achieve that performance. This provides evidence that combining multiple modes of human interaction can increase both the training speed and overall performance of policies for autonomous systems.

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