A clarification of misconceptions, myths and desired status of
artificial intelligence
- URL: http://arxiv.org/abs/2008.05607v1
- Date: Mon, 3 Aug 2020 17:22:53 GMT
- Title: A clarification of misconceptions, myths and desired status of
artificial intelligence
- Authors: Frank Emmert-Streib, Olli Yli-Harja, Matthias Dehmer
- Abstract summary: We present a perspective on the desired and current status of AI in relation to machine learning and statistics.
Our discussion is intended to uncurtain the veil of vagueness surrounding AI to see its true countenance.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The field artificial intelligence (AI) has been founded over 65 years ago.
Starting with great hopes and ambitious goals the field progressed though
various stages of popularity and received recently a revival in the form of
deep neural networks. Some problems of AI are that so far neither
'intelligence' nor the goals of AI are formally defined causing confusion when
comparing AI to other fields. In this paper, we present a perspective on the
desired and current status of AI in relation to machine learning and statistics
and clarify common misconceptions and myths. Our discussion is intended to
uncurtain the veil of vagueness surrounding AI to see its true countenance.
Related papers
- Navigating AI Fallibility: Examining People's Reactions and Perceptions of AI after Encountering Personality Misrepresentations [7.256711790264119]
Hyper-personalized AI systems profile people's characteristics to provide personalized recommendations.
These systems are not immune to errors when making inferences about people's most personal traits.
We present two studies to examine how people react and perceive AI after encountering personality misrepresentations.
arXiv Detail & Related papers (2024-05-25T21:27:15Z) - Bootstrapping Developmental AIs: From Simple Competences to Intelligent
Human-Compatible AIs [0.0]
The mainstream AIs approaches are the generative and deep learning approaches with large language models (LLMs) and the manually constructed symbolic approach.
This position paper lays out the prospects, gaps, and challenges for extending the practice of developmental AIs to create resilient, intelligent, and human-compatible AIs.
arXiv Detail & Related papers (2023-08-08T21:14:21Z) - Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time.
We discuss how biased models can lead to more negative real-world outcomes for certain groups.
If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
arXiv Detail & Related papers (2023-04-16T11:22:59Z) - Understanding Natural Language Understanding Systems. A Critical
Analysis [91.81211519327161]
The development of machines that guillemotlefttalk like usguillemotright, also known as Natural Language Understanding (NLU) systems, is the Holy Grail of Artificial Intelligence (AI)
But never has the trust that we can build guillemotlefttalking machinesguillemotright been stronger than the one engendered by the last generation of NLU systems.
Are we at the dawn of a new era, in which the Grail is finally closer to us?
arXiv Detail & Related papers (2023-03-01T08:32:55Z) - Unpacking the "Black Box" of AI in Education [0.0]
We seek to clarify what "AI" is and the potential it holds to both advance and hamper educational opportunities that may improve the human condition.
We offer a basic introduction to different methods and philosophies underpinning AI, discuss recent advances, explore applications to education, and highlight key limitations and risks.
Our hope is to make often jargon-laden terms and concepts accessible, so that all are equipped to understand, interrogate, and ultimately shape the development of human centered AI in education.
arXiv Detail & Related papers (2022-12-31T18:27:21Z) - Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.
It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
arXiv Detail & Related papers (2022-01-26T18:53:09Z) - Challenges of Artificial Intelligence -- From Machine Learning and
Computer Vision to Emotional Intelligence [0.0]
We believe that AI is a helper, not a ruler of humans.
Computer vision has been central to the development of AI.
Emotions are central to human intelligence, but little use has been made in AI.
arXiv Detail & Related papers (2022-01-05T06:00:22Z) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - "Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest
Value for us? [4.497097230665825]
Many researchers argue that little substantial progress has been made for AI in recent decades.
Author explains why controversies about AI exist; (2) discriminates two paradigms of AI research, termed "weak AI" and "strong AI"
arXiv Detail & Related papers (2021-03-29T02:57:48Z) - Empowering Things with Intelligence: A Survey of the Progress,
Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
We show how AI can empower the IoT to make it faster, smarter, greener, and safer.
First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving.
Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
arXiv Detail & Related papers (2020-11-17T13:14:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.