AI Tax

2021 ◽  
Vol 37 (1--4) ◽  
pp. 1-32
Author(s):  
Daniel Richins ◽  
Dharmisha Doshi ◽  
Matthew Blackmore ◽  
Aswathy Thulaseedharan Nair ◽  
Neha Pathapati ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Face Recognition ◽  
Data Center ◽  
State Of The Art ◽  
Post Processing ◽  
Software Infrastructure ◽  
Network Bandwidth ◽  
Rapid Pace ◽  
Specialized Hardware

Artificial intelligence and machine learning are experiencing widespread adoption in industry and academia. This has been driven by rapid advances in the applications and accuracy of AI through increasingly complex algorithms and models; this, in turn, has spurred research into specialized hardware AI accelerators. Given the rapid pace of advances, it is easy to forget that they are often developed and evaluated in a vacuum without considering the full application environment. This article emphasizes the need for a holistic, end-to-end analysis of artificial intelligence (AI) workloads and reveals the “AI tax.” We deploy and characterize Face Recognition in an edge data center. The application is an AI-centric edge video analytics application built using popular open source infrastructure and machine learning (ML) tools. Despite using state-of-the-art AI and ML algorithms, the application relies heavily on pre- and post-processing code. As AI-centric applications benefit from the acceleration promised by accelerators, we find they impose stresses on the hardware and software infrastructure: storage and network bandwidth become major bottlenecks with increasing AI acceleration. By specializing for AI applications, we show that a purpose-built edge data center can be designed for the stresses of accelerated AI at 15% lower TCO than one derived from homogeneous servers and infrastructure.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

scholarly journals Artificial intelligence and machine learning in design of mechanical materials

2021 ◽  
Author(s):  
Kai Guo ◽  
Zhenze Yang ◽  
Chi-Hua Yu ◽  
Markus J. Buehler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
State Of The Art ◽  
The State

This review revisits the state of the art of research efforts on the design of mechanical materials using machine learning.

scholarly journals A technologist view of the next decade evolution of the librarian work environment

2018 ◽  
Vol 186 ◽  
pp. 09004
Author(s):  
André Schaaff ◽  
Marc Wenger
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Work Environment ◽  
New Technologies ◽  
State Of The Art ◽  
Working Environment ◽  
Online Resources ◽  
Mining Machine ◽  
Daily Work

The work environment has deeply evolved in recent decades with the generalisation of IT in terms of hardware, online resources and software. Librarians do not escape this movement and their working environment is becoming essentially digital (databases, online publications, Wikis, specialised software, etc.). With the Big Data era, new tools will be available, implementing artificial intelligence, text mining, machine learning, etc. Most of these technologies already exist but they will become widespread and strongly impact our ways of working. The development of social networks that are "business" oriented will also have an increasing influence. In this context, it is interesting to reflect on how the work environment of librarians will evolve. Maintaining interest in the daily work is fundamental and over-automation is not desirable. It is imperative to keep the human-driven factor. We draw on state of the art new technologies which impact their work, and initiate a discussion about how to integrate them while preserving their expertise.

scholarly journals Reviewing artificial intelligence methods for predictive modeling applications

2021 ◽  
Author(s):  
Andreas Sepp
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Predictive Modeling ◽  
Significant Contribution ◽  
Predictive Analytics ◽  
State Of The Art ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Artificial Intelligence Methods

Artificial intelligence and machine learning methods had significant contribution to the advancement and progress of predictive analytics. This article presents a state of the art of methods and applications of artificial intelligence and machine learning.

scholarly journals Quantification of the suitable rooftop area for solar panel installation from overhead imagery using Convolutional Neural Networks

2021 ◽  
Vol 2042 (1) ◽  
pp. 012002
Author(s):  
Roberto Castello ◽  
Alina Walch ◽  
Raphaël Attias ◽  
Riccardo Cadei ◽  
Shasha Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
State Of The Art ◽  
Solar Panel ◽  
Post Processing ◽  
Processing Step ◽  
Recent Method

Abstract The integration of solar technology in the built environment is realized mainly through rooftop-installed panels. In this paper, we leverage state-of-the-art Machine Learning and computer vision techniques applied on overhead images to provide a geo-localization of the available rooftop surfaces for solar panel installation. We further exploit a 3D building database to associate them to the corresponding roof geometries by means of a geospatial post-processing approach. The stand-alone Convolutional Neural Network used to segment suitable rooftop areas reaches an intersection over union of 64% and an accuracy of 93%, while a post-processing step using building database improves the rejection of false positives. The model is applied to a case study area in the canton of Geneva and the results are compared with another recent method used in the literature to derive the realistic available area.

scholarly journals Energy and Policy Considerations for Modern Deep Learning Research

2020 ◽  
Vol 34 (09) ◽  
pp. 13693-13696
Author(s):  
Emma Strubell ◽  
Ananya Ganesh ◽  
Andrew McCallum
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Network Models ◽  
Intelligence Community ◽  
Environmental Costs ◽  
Neural Network Models ◽  
Learning Research ◽  
Specialized Hardware ◽  
Compute Time

The field of artificial intelligence has experienced a dramatic methodological shift towards large neural networks trained on plentiful data. This shift has been fueled by recent advances in hardware and techniques enabling remarkable levels of computation, resulting in impressive advances in AI across many applications. However, the massive computation required to obtain these exciting results is costly both financially, due to the price of specialized hardware and electricity or cloud compute time, and to the environment, as a result of non-renewable energy used to fuel modern tensor processing hardware. In a paper published this year at ACL, we brought this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training and tuning neural network models for NLP (Strubell, Ganesh, and McCallum 2019). In this extended abstract, we briefly summarize our findings in NLP, incorporating updated estimates and broader information from recent related publications, and provide actionable recommendations to reduce costs and improve equity in the machine learning and artificial intelligence community.

scholarly journals Towards implementing artificial intelligence post-processing in weather and climate: proposed actions from the Oxford 2019 workshop

2021 ◽  
Vol 379 (2194) ◽  
pp. 20200091 ◽  
Author(s):  
Sue Ellen Haupt ◽  
William Chapman ◽  
Samantha V. Adams ◽  
Charlie Kirkwood ◽  
J. Scott Hosking ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Climate Models ◽  
Climate Modelling ◽  
Post Processing ◽  
Atmospheric Sciences ◽  
Physical Knowledge ◽  
Data Practices ◽  
Weather And Climate ◽  
State Of The Science

The most mature aspect of applying artificial intelligence (AI)/machine learning (ML) to problems in the atmospheric sciences is likely post-processing of model output. This article provides some history and current state of the science of post-processing with AI for weather and climate models. Deriving from the discussion at the 2019 Oxford workshop on Machine Learning for Weather and Climate, this paper also presents thoughts on medium-term goals to advance such use of AI, which include assuring that algorithms are trustworthy and interpretable, adherence to FAIR data practices to promote usability, and development of techniques that leverage our physical knowledge of the atmosphere. The coauthors propose several actionable items and have initiated one of those: a repository for datasets from various real weather and climate problems that can be addressed using AI. Five such datasets are presented and permanently archived, together with Jupyter notebooks to process them and assess the results in comparison with a baseline technique. The coauthors invite the readers to test their own algorithms in comparison with the baseline and to archive their results. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

Technical Elements of Machine Learning for Intellectual Property Law

2021 ◽  
pp. 11-27
Author(s):  
Anthony Man-Cho So
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Face Recognition ◽  
Intellectual Property ◽  
Speech Synthesis ◽  
Text Processing ◽  
Property Law ◽  
Game Playing ◽  
Board Game ◽  
Music Retrieval

Recent advances in artificial intelligence (AI) technologies have transformed our lives in profound ways. Indeed, AI has not only enabled machines to see (eg, face recognition), hear (eg, music retrieval), speak (eg, speech synthesis), and read (eg, text processing), but also, so it seems, given machines the ability to think (eg, board game-playing) and create (eg, artwork generation). This chapter introduces the key technical elements of machine learning (ML), which is a rapidly growing sub-field in AI and drives many of the aforementioned applications. The goal is to elucidate the ways human efforts are involved in the development of ML solutions, so as to facilitate legal discussions on intellectual property issues.

scholarly journals Open Source Monitoring System for IT Infrastructures Incorporating IoT-Based Sensors

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 56
Author(s):  
Alejandro Mosteiro Vázquez ◽  
Carlos Dafonte ◽  
Ángel Gómez
Keyword(s):  
Power Consumption ◽  
Open Source ◽  
Monitoring System ◽  
Data Center ◽  
Data Centers ◽  
Source Monitoring ◽  
State Of The Art ◽  
Memory Usage ◽  
Network Bandwidth ◽  
Status Data

This paper introduces the development of a data center monitoring system based on IoT technologies. The system is meant to work as an administrative tool for system administrators in any environment, but mainly focused on data centers, since it integrates sensor and server status data. We are developing a system that gives a broad view of a data center, integrating server data such as CPU and memory usage or network bandwidth with room health parameters such as temperature, humidity, and power consumption or the presence sensors that indicate if there were people inside the room at the time a certain event occurred. As this is a work in progress, in this paper, we present the state-of-the-art of this subject, as well as what we expect to obtain from this project.

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