Recognition of All Categories of Entities by AI
- URL: http://arxiv.org/abs/2208.06590v2
- Date: Wed, 17 Aug 2022 01:17:06 GMT
- Title: Recognition of All Categories of Entities by AI
- Authors: Hiroshi Yamakawa and Yutaka Matsuo
- Abstract summary: This paper presents a new argumentative option to view the ontological sextet as a comprehensive technological map.
We predict that in the relatively near future, AI will be able to recognize various entities to the same degree as humans.
- Score: 20.220102335024706
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Human-level AI will have significant impacts on human society. However,
estimates for the realization time are debatable. To arrive at human-level AI,
artificial general intelligence (AGI), as opposed to AI systems that are
specialized for a specific task, was set as a technically meaningful long-term
goal. But now, propelled by advances in deep learning, that achievement is
getting much closer. Considering the recent technological developments, it
would be meaningful to discuss the completion date of human-level AI through
the "comprehensive technology map approach," wherein we map human-level
capabilities at a reasonable granularity, identify the current range of
technology, and discuss the technical challenges in traversing unexplored areas
and predict when all of them will be overcome. This paper presents a new
argumentative option to view the ontological sextet, which encompasses entities
in a way that is consistent with our everyday intuition and scientific
practice, as a comprehensive technological map. Because most of the modeling of
the world, in terms of how to interpret it, by an intelligent subject is the
recognition of distal entities and the prediction of their temporal evolution,
being able to handle all distal entities is a reasonable goal. Based on the
findings of philosophy and engineering cognitive technology, we predict that in
the relatively near future, AI will be able to recognize various entities to
the same degree as humans.
Related papers
- Advancing Explainable AI Toward Human-Like Intelligence: Forging the
Path to Artificial Brain [0.7770029179741429]
The intersection of Artificial Intelligence (AI) and neuroscience in Explainable AI (XAI) is pivotal for enhancing transparency and interpretability in complex decision-making processes.
This paper explores the evolution of XAI methodologies, ranging from feature-based to human-centric approaches.
The challenges in achieving explainability in generative models, ensuring responsible AI practices, and addressing ethical implications are discussed.
arXiv Detail & Related papers (2024-02-07T14:09:11Z) - On the Emergence of Symmetrical Reality [51.21203247240322]
We introduce the symmetrical reality framework, which offers a unified representation encompassing various forms of physical-virtual amalgamations.
We propose an instance of an AI-driven active assistance service that illustrates the potential applications of symmetrical reality.
arXiv Detail & Related papers (2024-01-26T16:09:39Z) - AI for Mathematics: A Cognitive Science Perspective [86.02346372284292]
Mathematics is one of the most powerful conceptual systems developed and used by the human species.
Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems.
arXiv Detail & Related papers (2023-10-19T02:00:31Z) - General Purpose Artificial Intelligence Systems (GPAIS): Properties,
Definition, Taxonomy, Societal Implications and Responsible Governance [16.030931070783637]
General-Purpose Artificial Intelligence Systems (GPAIS) has been defined to refer to these AI systems.
To date, the possibility of an Artificial General Intelligence, powerful enough to perform any intellectual task as if it were human, or even improve it, has remained an aspiration, fiction, and considered a risk for our society.
This work discusses existing definitions for GPAIS and proposes a new definition that allows for a gradual differentiation among types of GPAIS according to their properties and limitations.
arXiv Detail & Related papers (2023-07-26T16:35:48Z) - The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence [67.70415658080121]
Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
arXiv Detail & Related papers (2023-07-09T21:16:56Z) - Predicting the Future of AI with AI: High-quality link prediction in an
exponentially growing knowledge network [15.626884746513712]
We use AI techniques to predict the future research directions of AI itself.
For that, we use more than 100,000 research papers and build up a knowledge network with more than 64,000 concept nodes.
The most powerful methods use a carefully curated set of network features, rather than an end-to-end AI approach.
arXiv Detail & Related papers (2022-09-23T14:04:37Z) - On the Effect of Information Asymmetry in Human-AI Teams [0.0]
We focus on the existence of complementarity potential between humans and AI.
Specifically, we identify information asymmetry as an essential source of complementarity potential.
By conducting an online experiment, we demonstrate that humans can use such contextual information to adjust the AI's decision.
arXiv Detail & Related papers (2022-05-03T13:02:50Z) - 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) - 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) - The Short Anthropological Guide to the Study of Ethical AI [91.3755431537592]
Short guide serves as both an introduction to AI ethics and social science and anthropological perspectives on the development of AI.
Aims to provide those unfamiliar with the field with an insight into the societal impact of AI systems and how, in turn, these systems can lead us to rethink how our world operates.
arXiv Detail & Related papers (2020-10-07T12:25:03Z) - Human Evaluation of Interpretability: The Case of AI-Generated Music
Knowledge [19.508678969335882]
We focus on evaluating AI-discovered knowledge/rules in the arts and humanities.
We present an experimental procedure to collect and assess human-generated verbal interpretations of AI-generated music theory/rules rendered as sophisticated symbolic/numeric objects.
arXiv Detail & Related papers (2020-04-15T06:03:34Z)
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.