Influence of Attack Vectors on Generic Artificial Intelligence –assisted Smart Building Feedback Loop System

2020 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Feedback Loop ◽  
Loop System ◽  
Smart Building

A Human/Computer Learning Network to Improve Biodiversity Conservation and Research

AI Magazine ◽  
2012 ◽  
Vol 34 (1) ◽  
pp. 10 ◽  
Author(s):  
Steve Kelling ◽  
Jeff Gerbracht ◽  
Daniel Fink ◽  
Carl Lagoze ◽  
Weng-Keen Wong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Feedback Loop ◽  
Human Computation ◽  
Computational Power ◽  
Learning Networks ◽  
Learning Network ◽  
Computer Learning ◽  
The Individual ◽  
Citizen Science Project

In this paper we describe eBird, a citizen-science project that takes advantage of the human observational capacity to identify birds to species, which is then used to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a Human-Computer Learning Network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both, and thereby continually improves the effectiveness of the network as a whole. In this paper we explore how Human-Computer Learning Networks can leverage the contributions of a broad recruitment of human observers and processes their contributed data with Artificial Intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.

scholarly journals Psychotherapy in the era of artificial intelligence: Therapist Panoptes

2019 ◽  
Vol 2 (1) ◽  
pp. 68 ◽  
Author(s):  
Alexios Brailas
Keyword(s):  
Artificial Intelligence ◽  
Feedback Loop ◽  
Therapeutic Agent ◽  
Initial Conditions ◽  
Social Systems ◽  
Individual Action ◽  
Self Organized ◽  
Sensitive Dependence ◽  
Technological Singularity ◽  
New Perspective

“What will happen when an artificial intelligence entity has access to all the information stored about me online, with the ability to process my information efficiently and flawlessly? Will such an entity not be, in fact, my ideal therapist?” Would there ever come a point at which you would put your trust in an omniscient, apperceptive, and ultra-intelligent robotic therapist? There is a horizon beyond which we can neither see nor even imagine; this is the technological singularity moment for psychotherapy. If human intelligence is capable of creating an artificial intelligence that surpasses its creators, then this intelligence would, in turn, be able to create an even superior next-generation intelligence. An inevitable positive feedback loop would lead to an exponential intelligence growth rate. In the present paper, we introduce the term Therapist Panoptes as a working hypothesis to investigate the implications for psychotherapy of an artificial therapeutic agent: one that is able to access all available data for a potential client and process these with an inconceivably superior intelligence. Although this opens a new perspective on the future of psychotherapy, the sensitive dependence of complex techno-social systems on their initial conditions renders any prediction impossible. Artificial intelligence and humans form a bio-techno-social system, and the evolution of the participating actors in this complex super-organism depends upon their individual action, as well as upon each actor being a coevolving part of a self-organized whole.

Not too Far from the Tree

2018 ◽  
Vol 140 (02) ◽  
pp. 28-33
Author(s):  
John H. Tibbets
Keyword(s):  
Artificial Intelligence ◽  
Closed Loop ◽  
Open Loop ◽  
Loop System ◽  
Strong Wind ◽  
Israel Institute ◽  
Closed Loop Systems ◽  
Institute Of Technology ◽  
Robotic Harvesting ◽  
Technion Israel Institute

This article explores the concept of robotic harvesting and use of computer, sensors, and artificial intelligence in the field of harvesting. More powerful computers, better sensors, and improved artificial intelligence promise to make machines competitive with human laborers for picking the apple harvest. Israel-based FFRobotics is one of the two companies racing to commercialize the world’s first mechanical apple picker. FFRobotics plan to test their apple-picking robot on Washington’s 2018 harvest, which runs from mid-August through mid-November. Modern orchard designs also allow engineers to build simpler apple-picking systems, according to Amir Degani, founder of the Civil, Environmental, and Agricultural Robotics Lab at Technion-Israel Institute of Technology in Haifa. Degani advised with FFRobotics on developing its robotic arm. FFRobotics is still struggling with whether to go with open- or closed-loop controller. The open-loop system recognizes a specific fruit and sends the gripper to that location. If a strong wind moves the apple left or right, the gripper does not follow. The closed-loop system tracks the movement of the fruit by distinctive points on the apple’s face as guides and adjusts the arm as it moves closer to the apple. While closed-loop systems are more effective, they are also too expensive.

scholarly journals Neuro-symbolic A.I. for the smart city

2021 ◽  
Vol 2042 (1) ◽  
pp. 012018
Author(s):  
Gilles Morel
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Smart City ◽  
Smart Building ◽  
Knowledge Based ◽  
Relevant Today ◽  
Cross Domain ◽  
Source Data ◽  
Symbolic Ai ◽  
Standard Software

Abstract Smart building and smart city specialists agree that complex, innovative use cases, especially those using cross-domain and multi-source data, need to make use of Artificial Intelligence (AI). However, today’s AI mainly concerns machine learning and artificial neural networks (deep learning), whereas the first forty years of the discipline (the last decades of the 20th century) were essentially focused on a knowledge-based approach, which is still relevant today for some tasks. In this article we advocate a merging of these two AI trends – an approach known as neuro-symbolic AI – for the smart city, and point the way towards a complete integration of the two technologies, compatible with standard software.

scholarly journals A PVA Integrated DC-DC Converter with Feedback Loop Control

2021 ◽  
Vol 9 (VI) ◽  
pp. 1888-1893
Author(s):  
Enugu Shruthi
Keyword(s):  
Output Voltage ◽  
Feedback Loop ◽  
Loop System ◽  
Loop Control ◽  
Pv Array

This paper gives the detail information about “A PVA integrated DC-DC converter with feedback loop control”. The output voltage from the PV Array is controlled in this by using feedback loop system. The MATLAB simulations results show that the output voltage is stable.

Longitudinal nonlinear adaptive autopilot design for missiles with control constraint

2017 ◽  
Vol 232 (9) ◽  
pp. 1655-1670 ◽  
Author(s):  
Keum W Lee ◽  
Sahjendra N Singh
Keyword(s):  
Feedback Loop ◽  
Closed Loop ◽  
Angle Of Attack ◽  
Trajectory Control ◽  
Loop System ◽  
Control Law ◽  
Control Input ◽  
Error Signal ◽  
Closed Loop System ◽  
Control Saturation

This paper develops a new nonlinear adaptive longitudinal autopilot for the control of missiles with control input constraint, in the presence of parametric uncertainties and external disturbance input. The objective here is to control the angle of attack of the missile. A saturating control law is derived for the trajectory control of the angle of attack. The control law includes an auxiliary dynamic system in the feedback loop, driven by control input error signal, caused by control saturation, to preserve stability in the closed-loop system. By the Lyapunov stability analysis, it is shown that in the closed-loop system, the system trajectories are uniformly ultimately bounded. Simulation results show that the designed autopilot with constrained input can accomplish accurate trajectory control if the control saturation period is short. It is also seen that although the tracking error increases with the saturation period, the angle of attack tends to zero, once the command input is set to zero. Furthermore this adaptive control system, including the control error signal feedback loop, performs better than the adaptive laws, designed earlier based on immersion and invariance principle, without control magnitude constraint.

Mimicry

2021 ◽  
Vol 4 (2) ◽  
pp. 1-8
Author(s):  
Ziwei Wu ◽  
Lingdong Huang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Real Time ◽  
Natural World ◽  
Loop System ◽  
Computer Algorithms ◽  
Video Installation ◽  
Art Installation ◽  
Computer Monitors

The authors have collaborated on a machine learning multiscreen video installation powered by computer algorithms and inspired by mimicry in the natural world. The artwork explores a pseudo-environment loop system in nature and artificial mechanical organisms combining living flowers with projectors, webcams, and computer monitors. Technically, the software adopts a genetic algorithm to simulate the process of mimicry; conceptually, this real-time art installation is in conversation with Nam June Paik's piece TV Garden. The project explores the possibilities of integrating artificial intelligence and nature in the landscape of the future.

Closed Loop Transient Response from the Open Loop Frequency Response

1978 ◽  
Vol 11 (8) ◽  
pp. 302-308 ◽  
Author(s):  
E.C. Hind
Keyword(s):  
Frequency Response ◽  
Transient Response ◽  
Feedback Loop ◽  
Closed Loop ◽  
Full Range ◽  
Open Loop ◽  
Loop System ◽  
Closed Loop System ◽  
Order Model ◽  
First Order

A method is shown for relating the closed loop transient response to the open loop frequency response, which is based on the use of the contour of constant closed loop phase angle, α = −90°. The method primarily yields a second order model of the closed loop system which covers the full range of relative damping (0 < ζ < +∞). A first order model is recommended when prescribed conditions apply. The method is simpler and yields better results than currently used methods. In all cases it is assumed that the negative feedback loop has a transfer function of unity and that the closed loop system is stable.

scholarly journals Psychotherapy in the era of artificial intelligence: Therapist Panoptes

2019 ◽  
Author(s):  
Alexios Brailas
Keyword(s):  
Artificial Intelligence ◽  
Feedback Loop ◽  
Therapeutic Agent ◽  
Initial Conditions ◽  
Social Systems ◽  
Individual Action ◽  
Self Organized ◽  
Sensitive Dependence ◽  
Technological Singularity ◽  
New Perspective

“What will happen when an artificial intelligence entity has access to all the information stored about me online, with the ability to process my information efficiently and flawlessly? Will such an entity not be, in fact, my ideal therapist?” Would there ever come a point at which you would put your trust in an omniscient, apperceptive, and ultra-intelligent robotic therapist? There is a horizon beyond which we can neither see nor even imagine; this is the technological singularity moment for psychotherapy. If human intelligence is capable of creating an artificial intelligence that surpasses its creators, then this intelligence would, in turn, be able to create an even superior next-generation intelligence. An inevitable positive feedback loop would lead to an exponential intelligence growth rate. In the present paper, we introduce the term Therapist Panoptes as a working hypothesis to investigate the implications for psychotherapy of an artificial therapeutic agent: one that is able to access all available data for a potential client and process these with an inconceivably superior intelligence. Although this opens a new perspective on the future of psychotherapy, the sensitive dependence of complex techno-social systems on their initial conditions renders any prediction impossible. Artificial intelligence and humans form a bio-techno-social system, and the evolution of the participating actors in this complex super-organism depends upon their individual action, as well as upon each actor being a coevolving part of a self-organized whole.

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