scholarly journals Interacting with an Inferred World: The Challenge of Machine Learning for Humane Computer Interaction

2015 ◽  
Vol 1 (1) ◽  
pp. 12 ◽  
Author(s):  
Alan F. Blackwell
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Critical Evaluation ◽  
User Interaction ◽  
Interactive Systems ◽  
Learning Systems ◽  
The World ◽  
Cognitive Theories ◽  
New Generation ◽  
Modern Machine

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Classic theories of user interaction have been framed in relation to symbolic models of planning and problem solving, responding in part to the cognitive theories associated with AI research. However, the behavior of modern machine-learning systems is determined by statistical models of the world rather than explicit symbolic descriptions. Users increasingly interact with the world and with others in ways that are mediated by such models. This paper explores the way in which this new generation of technology raises fresh challenges for the critical evaluation of interactive systems. It closes with some proposed measures for the design of inference-based systems that are more open to humane design and use. </span></p></div></div></div>

Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Educational Policy ◽  
Learning Systems ◽  
Data Sources ◽  
Or Education ◽  
Student Activity ◽  
The World ◽  
E Learning ◽  
Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

scholarly journals Key Concepts in AI Safety: Robustness and Adversarial Examples

2021 ◽  
Author(s):  
Tim Rudner ◽  
Helen Toner
Keyword(s):  
Machine Learning ◽  
Learning Systems ◽  
Safety Issues ◽  
Key Concepts ◽  
Adversarial Examples ◽  
Learning Research ◽  
Modern Machine ◽  
Ai Safety

This paper is the second installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. The first paper in the series, “Key Concepts in AI Safety: An Overview,” described three categories of AI safety issues: problems of robustness, assurance, and specification. This paper introduces adversarial examples, a major challenge to robustness in modern machine learning systems.

Key Concepts in AI Safety: Specification in Machine Learning

2021 ◽  
Author(s):  
Tim G. J. Rduner ◽  
◽  
Helen Toner
Keyword(s):  
Machine Learning ◽  
Learning Systems ◽  
Safety Issues ◽  
Key Concepts ◽  
Safety Specification ◽  
Learning Research ◽  
Modern Machine ◽  
Ai Safety

This paper is the fourth installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. The first paper in the series, “Key Concepts in AI Safety: An Overview,” outlined three categories of AI safety issues—problems of robustness, assurance, and specification—and the subsequent two papers described problems of robustness and assurance, respectively. This paper introduces specification as a key element in designing modern machine learning systems that operate as intended.

scholarly journals AI Accidents: An Emerging Threat

2021 ◽  
Author(s):  
Zachary Arnold ◽  
◽  
Helen Toner
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Systems ◽  
Wide Range ◽  
Policy Suggestions ◽  
The Future ◽  
Modern Machine ◽  
Artificial Intelligence Systems

As modern machine learning systems become more widely used, the potential costs of malfunctions grow. This policy brief describes how trends we already see today—both in newly deployed artificial intelligence systems and in older technologies—show how damaging the AI accidents of the future could be. It describes a wide range of hypothetical but realistic scenarios to illustrate the risks of AI accidents and offers concrete policy suggestions to reduce these risks.

scholarly journals Forecasting under applying machine learning and statistical models

2020 ◽  
Vol 24 (Suppl. 1) ◽  
pp. 131-137
Author(s):  
Azhari Elhag ◽  
Hanaa Abu-Zinadah
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Real Life ◽  
World Health ◽  
Percentage Error ◽  
Learning Models ◽  
Kingdom Of Saudi Arabia ◽  
The World ◽  
Data Source ◽  
Health Organization

In a different area of a field of the real life, problem of accurate forecasting has acquired great importance that present the interesting serve which led to the best ways to achieve a goal. So, in this paper, we aimed to compare the accuracy of some statistical models such as Time Series and Deep Learning models, to forecasting the fertility rate in the Kingdom of Saudi Arabia, the data source is the World Health Organization over the period of 1960 to 2019. The performances of models were evaluated by errors measures mean absolute percentage error.

scholarly journals A Framework for Long-Term Learning Systems

2017 ◽  
Author(s):  
Diana Benavides-Prado
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Efficient Algorithms ◽  
Learning Systems ◽  
Machine Learning Techniques ◽  
Intelligent Machines ◽  
Learning Techniques ◽  
The World ◽  
Design And Application

Increasing amounts of data have made the use of machine learning techniques much more widespread. A lot of research in machine learning has been dedicated to the design and application of effective and efficient algorithms to explain or predict facts. The development of intelligent machines that can learn over extended periods of time, and that improve their abilities as they execute more tasks, is still a pending contribution from computer science to the world. This weakness has been recognised for some decades, and an interest to solve it seems to be increasing, as demonstrated by recent leading work and broader discussions at main events in the field [Chen and Liu, 2015; Chen et al., 2016]. Our research is intended to help fill that gap.

scholarly journals Forecasting under applying machine learning and statistical models

2020 ◽  
Vol 24 (Suppl. 1) ◽  
pp. 131-137
Author(s):  
Azhari Elhag ◽  
Hanaa Abu-Zinadah
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Real Life ◽  
World Health ◽  
Percentage Error ◽  
Learning Models ◽  
Kingdom Of Saudi Arabia ◽  
The World ◽  
Data Source ◽  
Health Organization

In a different area of a field of the real life, problem of accurate forecasting has acquired great importance that present the interesting serve which led to the best ways to achieve a goal. So, in this paper, we aimed to compare the accuracy of some statistical models such as Time Series and Deep Learning models, to forecasting the fertility rate in the Kingdom of Saudi Arabia, the data source is the World Health Organization over the period of 1960 to 2019. The performances of models were evaluated by errors measures mean absolute percentage error.

scholarly journals Key Concepts in AI Safety: Interpretability in Machine Learning

2021 ◽  
Author(s):  
Tim Rudner ◽  
Helen Toner
Keyword(s):  
Machine Learning ◽  
Learning Systems ◽  
Safety Issues ◽  
The Third ◽  
Key Concepts ◽  
Learning Research ◽  
Modern Machine ◽  
Ai Safety

This paper is the third installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. The first paper in the series, “Key Concepts in AI Safety: An Overview,” described three categories of AI safety issues: problems of robustness, assurance, and specification. This paper introduces interpretability as a means to enable assurance in modern machine learning systems.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Penerapan Rinfo Sebagai Media Pendukung Untuk Proses Pembelajaran Pada Perguruan Tinggi Raharja

CCIT Journal ◽  
2014 ◽  
Vol 8 (1) ◽  
pp. 101-115
Author(s):  
Untung Rahardja ◽  
Khanna Tiara ◽  
Ray Indra Taufik Wijaya
Keyword(s):  
Learning Process ◽  
Human Life ◽  
Learning Tools ◽  
Integrated Learning ◽  
Civilizing Process ◽  
The World ◽  
The Future ◽  
Two Factors ◽  
Life Education ◽  
New Generation

Education is an important factor in human life. According to Ki Hajar Dewantara, education is a civilizing process that a business gives high values ??to the new generation in a society that is not only maintenance but also with a view to promote and develop the culture of the nobility toward human life. Education is a human investment that can be used now and in the future. One other important factor in supporting human life in addition to education, which is technology. In this globalization era, technology has touched every joint of human life. The combination of these two factors will be a new innovation in the world of education. The innovation has been implemented by Raharja College, namely the use of the method iLearning (Integrated Learning) in the learning process. Where such learning has been online based. ILearning method consists of TPI (Ten Pillars of IT iLearning). Rinfo is one of the ten pillars, where it became an official email used by the whole community’s in Raharja College to communicate with each other. Rinfo is Gmail, which is adapted from the Google platform with typical raharja.info as its domain. This Rinfo is a medium of communication, as well as a tool to support the learning process in Raharja College. Because in addition to integrated with TPi, this Rinfo was connected also support with other learning tools, such as Docs, Drive, Sites, and other supporting tools.

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