scholarly journals Machine Learning for Health: Algorithm Auditing & Quality Control

2021 ◽  
Vol 45 (12) ◽  
Author(s):  
Luis Oala ◽  
Andrew G. Murchison ◽  
Pradeep Balachandran ◽  
Shruti Choudhary ◽  
Jana Fehr ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Diabetic Retinopathy ◽  
Real World ◽  
Special Issue ◽  
The Real ◽  
Integrated Framework ◽  
Ongoing Work ◽  
New Machine

AbstractDevelopers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue  Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing.

scholarly journals How to train your robot with deep reinforcement learning: lessons we have learned

2021 ◽  
pp. 027836492098785
Author(s):  
Julian Ibarz ◽  
Jie Tan ◽  
Chelsea Finn ◽  
Mrinal Kalakrishnan ◽  
Peter Pastor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Case Studies ◽  
Real World ◽  
Review Article ◽  
The Real ◽  
Complex Skills ◽  
Real World Learning ◽  
Level Sensor ◽  
Embodied Agent

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time, real-world robotics provides an appealing domain for evaluating such algorithms, as it connects directly to how humans learn: as an embodied agent in the real world. Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains. In this review article, we present a number of case studies involving robotic deep RL. Building off of these case studies, we discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting and are not often the focus of mainstream RL research. Our goal is to provide a resource both for roboticists and machine learning researchers who are interested in furthering the progress of deep RL in the real world.

scholarly journals A Research Paper on Traffic Sign Recognition with Machine Learning and IOT

2021 ◽  
Vol 9 (VI) ◽  
pp. 1774-1778
Author(s):  
Mahesh Singh
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Real World ◽  
Research Paper ◽  
Raspberry Pi ◽  
Detection Accuracy ◽  
Great Success ◽  
The Real ◽  
Road Traffic Safety ◽  
Execution Speed

This paper will help to bring out some amazing findings about autonomous prediction and performing action by establishing a connection between the real world with machine learning and Internet Of thing. The purpose of this research paper is to perform our machine to analyze different signs in the real world and act accordingly. We have explored and found detection of several features in our model which helped us to establish a better interaction of our model with the surroundings. Our algorithms give very optimized predictions performing the right action .Nowadays, autonomous vehicles are a great area of research where we can make it more optimized and more multi - performing .This paper contributes to a huge survey of varied object detection and feature extraction techniques. At the moment, there are loads of object classification and recognition techniques and algorithms found and developed around the world. TSD research is of great significance for improving road traffic safety. In recent years, CNN (Convolutional Neural Networks) have achieved great success in object detection tasks. It shows better accuracy or faster execution speed than traditional methods. However, the execution speed and the detection accuracy of the existing CNN methods cannot be obtained at the same time. What's more, the hardware requirements are also higher than before, resulting in a larger detection cost. In order to solve these problems, this paper proposes an improved algorithm based on convolutional model A classic robot which uses this algorithm which is installed through raspberry pi and performs dedicated action.

scholarly journals The Great Debate: General Ability and Specific Abilities in the Prediction of Important Outcomes

2018 ◽  
Vol 6 (3) ◽  
pp. 39 ◽  
Author(s):  
Harrison Kell ◽  
Jonas Lang
Keyword(s):  
Real World ◽  
Cognitive Abilities ◽  
Special Issue ◽  
The Real ◽  
General Ability ◽  
Great Debate ◽  
Relative Value

The relative value of specific versus general cognitive abilities for the prediction of practical outcomes has been debated since the inception of modern intelligence theorizing and testing. This editorial introduces a special issue dedicated to exploring this ongoing “great debate”. It provides an overview of the debate, explains the motivation for the special issue and two types of submissions solicited, and briefly illustrates how differing conceptualizations of cognitive abilities demand different analytic strategies for predicting criteria, and that these different strategies can yield conflicting findings about the real-world importance of general versus specific abilities.

Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application

2019 ◽  
Vol 19 (9) ◽  
Author(s):  
Valentina Bellemo ◽  
Gilbert Lim ◽  
Tyler Hyungtaek Rim ◽  
Gavin S. W. Tan ◽  
Carol Y. Cheung ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Real World ◽  
The Real

Introduction to Section B of the special issue: languages and software in robotics

Robotica ◽  
1997 ◽  
Vol 15 (2) ◽  
pp. 131-131
Author(s):  
J. A. Rose
Keyword(s):  
Real World ◽  
Software Package ◽  
Symplectic Geometry ◽  
Rehabilitation Robotics ◽  
Special Issue ◽  
The Real ◽  
Distributed Manipulation ◽  
Theoretical Approaches ◽  
Symbolic Modelling

Section B of the special issue contains four papers dealing with diverse topics, viz. rehabilitation robotics applications, a software package for an automatic symbolic modelling of robots, the use of symplectic geometry, and the treatment of the Distributed Manipulation Environment method. This completes the wide review of various techniques and theoretical approaches aiming at solving the difficult problems of conventional robotics in the real world.

scholarly journals Analysis of Quality Control and Safety Management in Electrical Engineering

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Zhang Ping Li
Keyword(s):  
Quality Control ◽  
Real World ◽  
Development Process ◽  
Safety Management ◽  
Electrical Engineering ◽  
The Real ◽  
Management Work ◽  
Technical Measures

In the development process of the society nowadays, electrical engineering has been widely used with remarkable results, but there are still some problems in the course of actual management, which requires effective technical measures to control the quality in electrical engineering and the enhancement of safety management work. Therefore, in order to better ensure safety management requirements in electrical engineering and strengthen related trainings and thus better carried out safety management and quality control to meet respective requirements in the real world, in this paper, a brief analysis is conducted in quality control and safety management in electrical engineering.

Special Issue on Integrated Knowledge on Innovative Robot Mechanisms

2018 ◽  
Vol 30 (6) ◽  
pp. 845-845
Author(s):  
Naoyuki Takesue ◽  
Koichi Koganezawa ◽  
Kenjiro Tadakuma
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Computer Control ◽  
Computer Processing ◽  
Knowledge System ◽  
Mechanical Work ◽  
The Other ◽  
Special Issue ◽  
The Real ◽  
Integrated Knowledge

A robot is a system integrated with many elements such as actuators, sensors, computers, and mechanical components. Currently, progress in the field of artificial intelligence induced by tremendous improvements in computer processing capabilities has enabled robots to behave in a more sophisticated manner, which is drawing considerable attention. On the other hand, the mechanism that directly produces robot movements and mechanical work sometimes brings out some competencies that cannot be provided solely by computer control that relies on sensor feedback. This special issue on “Integrated Knowledge on Innovative Robot Mechanisms” aims to introduce a knowledge system for robot mechanisms that bring forth useful and innovative functions and values. The editors hope that the studies discussed in this special issue will help in the realization and further improvement of the mechanical functions of robots in the real world.

On the Applicability of Machine Learning Fairness Notions

2021 ◽  
Vol 23 (1) ◽  
pp. 14-23
Author(s):  
Karima Makhlouf ◽  
Sami Zhioua ◽  
Catuscia Palamidessi
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Real World ◽  
Child Custody ◽  
College Admission ◽  
Policy Makers ◽  
The Real ◽  
Important Requirement

Machine Learning (ML) based predictive systems are increasingly used to support decisions with a critical impact on individuals' lives such as college admission, job hiring, child custody, criminal risk assessment, etc. As a result, fairness emerged as an important requirement to guarantee that ML predictive systems do not discriminate against specific individuals or entire sub-populations, in particular, minorities. Given the inherent subjectivity of viewing the concept of fairness, several notions of fairness have been introduced in the literature. This paper is a survey of fairness notions that, unlike other surveys in the literature, addresses the question of "which notion of fairness is most suited to a given real-world scenario and why?". Our attempt to answer this question consists in (1) identifying the set of fairness-related characteristics of the real-world scenario at hand, (2) analyzing the behavior of each fairness notion, and then (3) fitting these two elements to recommend the most suitable fairness notion in every specific setup. The results are summarized in a decision diagram that can be used by practitioners and policy makers to navigate the relatively large catalogue of ML fairness notions.

scholarly journals Virtual to Real-World Transfer Learning: A Systematic Review

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1491
Author(s):  
Mahesh Ranaweera ◽  
Qusay H. Mahmoud
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Transfer Learning ◽  
Real World ◽  
High Performance ◽  
Research Area ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Current Status ◽  
The Real

Machine learning has become an important research area in many domains and real-world applications. The prevailing assumption in traditional machine learning techniques, that training and testing data should be of the same domain, is a challenge. In the real world, gathering enough training data to create high-performance learning models is not easy. Sometimes data are not available, very expensive, or dangerous to collect. In this scenario, the concept of machine learning does not hold up to its potential. Transfer learning has recently gained much acclaim in the field of research as it has the capability to create high performance learners through virtual environments or by using data gathered from other domains. This systematic review defines (a) transfer learning; (b) discusses the recent research conducted; (c) the current status of transfer learning and finally, (d) discusses how transfer learning can bridge the gap between the virtual and the real.

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