scholarly journals Link Prediction of Artificial Intelligence Concepts using Low Computational Power

2021 ◽  
Author(s):  
Francisco Valente
Keyword(s):  
Artificial Intelligence ◽  
Link Prediction ◽  
Computational Power

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 Why AI Chips Matter

2020 ◽  
Author(s):  
Saif Khan
Keyword(s):  
Artificial Intelligence ◽  
Computational Power ◽  
Complex Tasks ◽  
Full Report ◽  
Concise Overview

As artificial intelligence is applied to new and more complex tasks, the computational power necessary to develop and deploy it will become increasingly expensive. This policy brief offers a concise overview of the full report, “AI Chips: What They Are and Why They Matter.”

scholarly journals TOWARDS DEEP LEARNING FOR ARCHITECTURE: A MONUMENT RECOGNITION MOBILE APP

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 551-556 ◽  
Author(s):  
V. Palma
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
State Of The Art ◽  
Mobile App ◽  
Information Modeling ◽  
Computational Power ◽  
Architectural Heritage ◽  
Research Stream ◽  
Heritage Collections

<p><strong>Abstract.</strong> In recent years, the diffusion of large image datasets and an unprecedented computational power have boosted the development of a class of artificial intelligence (AI) algorithms referred to as deep learning (DL). Among DL methods, convolutional neural networks (CNNs) have proven particularly effective in computer vision, finding applications in many disciplines. This paper introduces a project aimed at studying CNN techniques in the field of architectural heritage, a still to be developed research stream. The first steps and results in the development of a mobile app to recognize monuments are discussed. While AI is just beginning to interact with the built environment through mobile devices, heritage technologies have long been producing and exploring digital models and spatial archives. The interaction between DL algorithms and state-of-the-art information modeling is addressed, as an opportunity to both exploit heritage collections and optimize new object recognition techniques.</p>

scholarly journals Computer Vision in the Surgical Operating Room

2020 ◽  
Vol 36 (6) ◽  
pp. 456-462
Author(s):  
François Chadebecq ◽  
Francisco Vasconcelos ◽  
Evangelos Mazomenos ◽  
Danail Stoyanov
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Operating Room ◽  
Data Availability ◽  
Visual Signal ◽  
Computational Power ◽  
Clinical Site ◽  
Surgical Practice ◽  
Technical Advances ◽  
Convergence Of Technologies

<b><i>Background:</i></b> Multiple types of surgical cameras are used in modern surgical practice and provide a rich visual signal that is used by surgeons to visualize the clinical site and make clinical decisions. This signal can also be used by artificial intelligence (AI) methods to provide support in identifying instruments, structures, or activities both in real-time during procedures and postoperatively for analytics and understanding of surgical processes. <b><i>Summary:</i></b> In this paper, we provide a succinct perspective on the use of AI and especially computer vision to power solutions for the surgical operating room (OR). The synergy between data availability and technical advances in computational power and AI methodology has led to rapid developments in the field and promising advances. <b><i>Key Messages:</i></b> With the increasing availability of surgical video sources and the convergence of technologies<b><i></i></b>around video storage, processing, and understanding, we believe clinical solutions and products leveraging vision are going to become an important component of modern surgical capabilities. However, both technical and clinical challenges remain to be overcome to efficiently make use of vision-based approaches into the clinic.

Knowledge-based decision intelligence in street lighting management

2021 ◽  
pp. 1-19
Author(s):  
Cristóvão Sousa ◽  
Daniel Teixeira ◽  
Davide Carneiro ◽  
Diogo Nunes ◽  
Paulo Novais
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Light Quality ◽  
Communication Technologies ◽  
Decision Problems ◽  
Computational Power ◽  
Street Lighting ◽  
Knowledge Based

As the availability of computational power and communication technologies increases, Humans and systems are able to tackle increasingly challenging decision problems. Taking decisions over incomplete visions of a situation is particularly challenging and calls for a set of intertwined skills that must be put into place under a clear rationale. This work addresses how to deliver autonomous decisions for the management of a public street lighting network, to optimize energy consumption without compromising light quality patterns. Our approach is grounded in an holistic methodology, combining semantic and Artificial Intelligence principles to define methods and artefacts for supporting decisions to be taken in the context of an incomplete domain. That is, a domain with absence of data and of explicit domain assertions.

The Future of Management in a World of Electronic Brains

2017 ◽  
Vol 27 (2) ◽  
pp. 260-263 ◽  
Author(s):  
Xavier Ferràs-Hernández
Keyword(s):  
Artificial Intelligence ◽  
Social Interaction ◽  
Data Processing ◽  
Cognitive Abilities ◽  
Strategic Thinking ◽  
Computational Power ◽  
The Future ◽  
Social Interaction Skills ◽  
Interaction Skills

Artificial Intelligence (AI) is surpassing humans in data processing and computational power. But it also progresses in strategic thinking, creativity, and social interaction skills, bearing almost human cognitive abilities. How long does it take to see digital CEOs running corporations? Will management become a commodity developed by electronic brains?

scholarly journals Predicting Stock Market: An Approach with Artificial Intelligence

2018 ◽  
Vol 13 (02) ◽  
Author(s):  
Abhishek Srivastava ◽  
Indrani Sengupta
Keyword(s):  
Artificial Intelligence ◽  
Stock Market ◽  
High Frequency ◽  
Stock Exchange ◽  
Computational Power ◽  
Exchange Market ◽  
Stock Trading ◽  
Long Run ◽  
Trading Pattern

Artificial Intelligence (AI) technology has advanced impressively since inventors began tampering with its potential. Many believe that the next great use for AI technology will be in the field of financial market speculation. Technology can be used either to make our lives better or make money. The stock exchange market is the most volatile and most dynamic of all. Special care has to be exercised in buying and selling of stocks from different companies or businesses. The probability of losing the stocks and acquiring benefits through the stocks are fifty-fifty. Volatility of the stock market jumbles up a trader’s nervous system making it difficult to understand or thin rationally. Artificial Intelligence is supposed to be a predictive model that looks at more than technical patterns of trading. It has the ability to identify financial features of companies (e.g. price to earnings ratio, long term (business loans) that will make money in the long run. This requires capabilities from different areas of study and massive computational power which is why it is only prevalent in recent years. This paper tries to attempt of coming up with a basis and prediction using Artificial Intelligence in identifying trading pattern relations which appropriately inter relates with High Frequency Stock Trading based on pre-set criteria

scholarly journals A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4690
Author(s):  
Varaha Satra Bharath Kurukuru ◽  
Ahteshamul Haque ◽  
Mohammed Ali Khan ◽  
Subham Sahoo ◽  
Azra Malik ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Value Chain ◽  
Photovoltaic Systems ◽  
Solar Photovoltaic ◽  
Data Generation ◽  
Solar Pv ◽  
Computational Power ◽  
Pv Systems ◽  
Advantages And Disadvantages ◽  
Accuracy And Precision

The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms.

scholarly journals Toward Gradient-Based Optimization of Full Gas Turbines

2019 ◽  
Vol 141 (03) ◽  
pp. 54-55
Author(s):  
Tom Verstraete
Keyword(s):  
Artificial Intelligence ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Computational Power ◽  
Modern Age ◽  
Design Cycle ◽  
Gradient Based

The design of a full gas turbine is a painstaking process, with many interactions between different physics, components, and engineers. Not surprisingly, this is an effort spanning over many years before a compromise can be found that satisfies all involved engineering disciplines. But can the design cycle not be shortened in this modern age, dominated by increasing computational power and emerging artificial intelligence?

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