Economics of the Ed Tech Revolution

2017 ◽  
Vol 59 (4) ◽  
pp. 49-55 ◽  
Author(s):  
Richard K. Lyons
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Network Effects ◽  
Education Technology ◽  
Increasing Returns ◽  
Durable Good ◽  
Rapid Changes ◽  
Over Time ◽  
The Way

Rapid changes in technology—including advances in augmented and artificial intelligence, machine learning, and mobile—are paving the way for significant changes not only in the channels through which education is delivered but in how education itself is structured. This article identifies eight ways in which education technology can change how learning is facilitated and who will facilitate that learning. Basic economic principles then provide a framework for thinking about how these changes will be embedded over time as education increasingly becomes a durable good providing increasing returns through network effects.

scholarly journals Explainable AI: A Review of Machine Learning Interpretability Methods

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 18
Author(s):  
Pantelis Linardatos ◽  
Vasilis Papastefanopoulos ◽  
Sotiris Kotsiantis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Black Box ◽  
Learning Systems ◽  
Model Complexity ◽  
Learning Models ◽  
New Methods ◽  
Industrial Adoption ◽  
Machine Learning Models ◽  
The Way

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

scholarly journals Software Testing: Issues and Challenges of Artificial Intelligence & Machine Learning

2021 ◽  
Vol 12 (1) ◽  
pp. 101-112
Author(s):  
Kishore Sugali ◽  
Chris Sprunger ◽  
Venkata N Inukollu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Software Testing ◽  
Future Research ◽  
Critical Component ◽  
Development Methodology ◽  
Testing Strategies ◽  
History Of ◽  
Shed Light ◽  
The Way

The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has been an increase in popularity for applications that implement AI and ML technology. As with traditional development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development. Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For future research, this study has key implications. Each of the challenges outlined in this paper is ideal for further investigation and has great potential to shed light on the way to more productive software testing strategies and methodologies that can be applied to AI/ML applications.

scholarly journals Engineering Emotionally Intelligent Agents

2011 ◽  
pp. 99-117
Author(s):  
Penny Baillie ◽  
Mark Toleman ◽  
Dickson Lukose
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Agents ◽  
Emotional State ◽  
Psychological Theory ◽  
Emotional States ◽  
Changing Environment ◽  
Emotionally Intelligent ◽  
Over Time ◽  
The Way

Interacting with intelligence in an ever-changing environment calls for exceptional performances from artificial beings. One mechanism explored to produce intuitive-like behavior in artificial intelligence applications is emotion. This chapter examines the engineering of a mechanism that synthesizes and processes an artificial agent’s internal emotional states: the Affective Space. Through use of the affective space, an agent can predict the effect certain behaviors will have on its emotional state and, in turn, decide how to behave. Furthermore, an agent can use the emotions produced from its behavior to update its beliefs about particular entities and events. This chapter explores the psychological theory used to structure the affective space, the way in which the strength of emotional states can be diminished over time, how emotions influence an agent’s perception, and the way in which an agent can migrate from one emotional state to another.

Removing the Constraints of Disability

2021 ◽  
pp. 205-219
Author(s):  
Wadad Kathy Tannous ◽  
Laney McGrew
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Assistive Technologies ◽  
Vision Impairment ◽  
Cultural Participation ◽  
Smart Glasses ◽  
Control Computer ◽  
3D Printed ◽  
Over Time

One billion people globally live with disabilities that are physical, sensory, psychiatric, neurological, cognitive, or intellectual. Their disabilities are dynamic and can be temporary or permanent, singular or plural, from birth or developed, and can change over time. People with disabilities face barriers to economic, social, political, and cultural participation. Assistive technology, artificial intelligence, and broader technology can amplify their inclusion, participation, and independence. This chapter will highlight emerging and evolving technologies, rooted in machine learning and neural networks, which assist across different disabilities and seek to improve the user's sense of ability and independence. These include Seeing AI app, OXSIGHT, OrCam, Envision smart glasses, and Dot Watch for vision impairment; Ava app and cognitive hearing aid for hearing impairment; Liftware self-stabilising utensils for limited hand mobility; Eyegaze and Tobii – assistive technologies that allow users to control computer and smartphone screens with their eyes; and 3D printed prosthetics.

Artificial Intelligence, Machine Learning, Automation, Robotics, Future of Work and Future of Humanity

2019 ◽  
Vol 30 (1) ◽  
pp. 61-79 ◽  
Author(s):  
Weiyu Wang ◽  
Keng Siau
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Industrial Revolution ◽  
Future Research ◽  
Way Of Life ◽  
Negative Effects ◽  
Future Research Directions ◽  
Negative Impacts ◽  
The Impact ◽  
The Way

The exponential advancement in artificial intelligence (AI), machine learning, robotics, and automation are rapidly transforming industries and societies across the world. The way we work, the way we live, and the way we interact with others are expected to be transformed at a speed and scale beyond anything we have observed in human history. This new industrial revolution is expected, on one hand, to enhance and improve our lives and societies. On the other hand, it has the potential to cause major upheavals in our way of life and our societal norms. The window of opportunity to understand the impact of these technologies and to preempt their negative effects is closing rapidly. Humanity needs to be proactive, rather than reactive, in managing this new industrial revolution. This article looks at the promises, challenges, and future research directions of these transformative technologies. Not only are the technological aspects investigated, but behavioral, societal, policy, and governance issues are reviewed as well. This research contributes to the ongoing discussions and debates about AI, automation, machine learning, and robotics. It is hoped that this article will heighten awareness of the importance of understanding these disruptive technologies as a basis for formulating policies and regulations that can maximize the benefits of these advancements for humanity and, at the same time, curtail potential dangers and negative impacts.

scholarly journals Digital Transformation of Financial Services using Artificial Intelligence, Machine Learning, and Cloud Computing

2021 ◽  
Vol 12 (06) ◽  
pp. 27-35
Author(s):  
Prudhvi Parne
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Financial Services ◽  
Banking Organization ◽  
Short Period ◽  
Improve Compliance ◽  
To Come ◽  
Financial Transactions ◽  
The Way

Digital disruption is redefining industries and changing the way business function. Artificial Intelligence is the future of banking as it brings the power of advanced data analytics to combat fraudulent transactions and improve compliance. Financial services are the economical backbone of any nation in the world. There are billions of financial transactions which are taking place and all this data is stored and can be considered as a gold mine of data for many different organizations. No human intelligence can dig in this amount of data to come up with something valuable. This is the reason financial organizations are employing artificial intelligence to come up with new algorithms which can change the way financial transactions are being carried out. Artificial Intelligence can complete the task in a very short period. Artificial intelligence can be used to detect frauds, identify possible attacks, and any other kind of anomalies that may be detrimental for the institution. This paper discusses the role of artificial intelligence and machine learning in the finance sector. Additionally, the paper will provide the necessary strategies that any banking organization can follow when digitizing its operations when implementing Artificial Intelligence, Machine learning and Cloud Computing.

scholarly journals Digitization in the Market for Entrepreneurial Finance: Innovative Business Models and New Financing Channels

2021 ◽  
pp. 104225872110384
Author(s):  
Fabio Bertoni ◽  
Stefano Bonini ◽  
Vincenzo Capizzi ◽  
Massimo G. Colombo ◽  
Sophie Manigart
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Business Models ◽  
Information Production ◽  
Entrepreneurial Finance ◽  
Special Issue ◽  
General Overview ◽  
Financing Channels ◽  
The Way

Digitization creates new financial channels that complement traditional intermediaries, but may raise concerns over fraud, cybersecurity, or bubbles. Artificial intelligence and machine learning change the way in which traditional investors work. This special issue focuses on economic, cultural, and regulatory determinants of fintech development, and on the new forms of information production and processing engendered by digital entrepreneurial finance. We provide a general overview of digitization in the market for entrepreneurial finance, illustrate how the different articles in the special issue contribute to advance our knowledge, and identify promising avenues for research.

scholarly journals Artificial Intelligence and the Limitations of Information

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 332 ◽  
Author(s):  
Paul Walton
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Information Processing ◽  
General Intelligence ◽  
Selection Pressures ◽  
Artificial General Intelligence ◽  
Relationship Of ◽  
The Relationship ◽  
The Way

Artificial intelligence (AI) and machine learning promise to make major changes to the relationship of people and organizations with technology and information. However, as with any form of information processing, they are subject to the limitations of information linked to the way in which information evolves in information ecosystems. These limitations are caused by the combinatorial challenges associated with information processing, and by the tradeoffs driven by selection pressures. Analysis of the limitations explains some current difficulties with AI and machine learning and identifies the principles required to resolve the limitations when implementing AI and machine learning in organizations. Applying the same type of analysis to artificial general intelligence (AGI) highlights some key theoretical difficulties and gives some indications about the challenges of resolving them.

Machine Learning-Aided Architectural Design for Carbon Footprint Reduction

BUILDER ◽  
2020 ◽  
Vol 276 (7) ◽  
pp. 35-39 ◽  
Author(s):  
Mateusz Płoszaj-Mazurek
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Built Environment ◽  
Greenhouse Gases ◽  
Carbon Footprint ◽  
Architectural Design ◽  
New Paradigm ◽  
Design Solutions ◽  
Carbon Footprint Reduction ◽  
The Way

The built environment is considered responsible for at least 20-40% of greenhouse gases emission. The way we design may exert an impact on this percentage. A new paradigm, namely artificial intelligence, is arriving. More and more tasks are becoming automated via algorithms. How could this power be applied in order to strengthen our knowledge about the ways we design buildings? The author of the following paper presents a study in which carbon footprint yielded by a multifamily building is analysed. ML has been used to generate an extensive overview of the possible design solutions. This, in turn, made it possible to observe correlations between various parameters that resulted in a reduced carbon footprint.

scholarly journals DAMAGE RESULTING FROM ARTIFICIAL INTELLIGENCE - THE CASE OF AUTONOMOUS VEHICLES

2021 ◽  
Vol 14 (1) ◽  
pp. 105-124
Author(s):  
Cassiano Highton
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Technological Revolution ◽  
The Subject ◽  
The Law ◽  
Over Time ◽  
The Way

Abstract The way of understanding the law has changed substantially over time and the law of Torts as we have studied and dealt with it until now has evidently become outdated, the legal reality has moved away from the factual reality, we are facing the new paradigms of the digital and technological revolution, with an evident and clear distancing from the classical theories of the law of Torts, a context that requires a specific and updated approach to the subject.

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