The Vision of Self-Evolving Computing Systems
- URL: http://arxiv.org/abs/2204.06825v1
- Date: Thu, 14 Apr 2022 08:53:23 GMT
- Title: The Vision of Self-Evolving Computing Systems
- Authors: Danny Weyns, Thomas Baeck, Rene Vidal, Xin Yao, and Ahmed Nabil
Belbachir
- Abstract summary: A key aspect of sustainability is the ability of computing systems to cope with the continuous change they face.
While we are able to engineer smart computing systems that autonomously deal with various types of changes, handling unanticipated changes requires system evolution.
We put forward an arguable opinion for the vision of self-evolving computing systems that are equipped with an evolutionary engine.
- Score: 12.507035717773528
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Computing systems are omnipresent; their sustainability has become crucial
for our society. A key aspect of this sustainability is the ability of
computing systems to cope with the continuous change they face, ranging from
dynamic operating conditions, to changing goals, and technological progress.
While we are able to engineer smart computing systems that autonomously deal
with various types of changes, handling unanticipated changes requires system
evolution, which remains in essence a human-centered process. This will
eventually become unmanageable. To break through the status quo, we put forward
an arguable opinion for the vision of self-evolving computing systems that are
equipped with an evolutionary engine enabling them to evolve autonomously.
Specifically, when a self-evolving computing system detects conditions outside
its operational domain, such as an anomaly or a new goal, it activates an
evolutionary engine that runs online experiments to determine how the system
needs to evolve to deal with the changes, thereby evolving its architecture.
During this process the engine can integrate new computing elements that are
provided by computing warehouses. These computing elements provide
specifications and procedures enabling their automatic integration. We motivate
the need for self-evolving computing systems in light of the state of the art,
outline a conceptual architecture of self-evolving computing systems, and
illustrate the architecture for a future smart city mobility system that needs
to evolve continuously with changing conditions. To conclude, we highlight key
research challenges to realize the vision of self-evolving computing systems.
Related papers
- Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
We argue that shortcomings stem from one overarching failure: AI systems lack wisdom.
While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems.
We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
arXiv Detail & Related papers (2024-11-04T18:10:10Z) - Self-sustaining Software Systems (S4): Towards Improved Interpretability
and Adaptation [8.328861861105889]
Systems' complexity challenges their interpretability and requires autonomous responses to dynamic changes.
Two main research areas explore autonomous systems' responses: evolutionary computing and autonomic computing.
This paper proposes a new concept for interpretable and adaptable software systems: self-sustaining software systems (S4)
arXiv Detail & Related papers (2024-01-21T02:07:34Z) - Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
Predictive coding (PC) has shown promising performance in machine intelligence tasks.
PC can model information processing in different brain areas, can be used in cognitive control and robotics.
arXiv Detail & Related papers (2023-08-15T16:37:16Z) - From Self-Adaptation to Self-Evolution Leveraging the Operational Design
Domain [15.705888799637506]
Self-adaptation has shown to be a viable approach to dealing with changing conditions.
The capabilities of a self-adaptive system are constrained by its operational design domain (ODD)
We provide a definition for ODD and apply it to a self-adaptive system.
arXiv Detail & Related papers (2023-03-27T14:49:07Z) - There's Plenty of Room Right Here: Biological Systems as Evolved,
Overloaded, Multi-scale Machines [0.0]
We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view.
Efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales.
We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of meso-scale events.
arXiv Detail & Related papers (2022-12-20T22:26:40Z) - Neurocompositional computing: From the Central Paradox of Cognition to a
new generation of AI systems [120.297940190903]
Recent progress in AI has resulted from the use of limited forms of neurocompositional computing.
New, deeper forms of neurocompositional computing create AI systems that are more robust, accurate, and comprehensible.
arXiv Detail & Related papers (2022-05-02T18:00:10Z) - Introduction to the Artificial Intelligence that can be applied to the
Network Automation Journey [68.8204255655161]
The "Intent-Based Networking - Concepts and Definitions" document describes the different parts of the ecosystem that could be involved in NetDevOps.
The recognize, generate intent, translate and refine features need a new way to implement algorithms.
arXiv Detail & Related papers (2022-04-02T08:12:08Z) - Lifelong Computing [17.702858017974215]
Long running computing systems that achieve their goals in ever-changing environments pose significant challenges.
Dealting with unanticipated changes, such as anomalies, novelties, new goals or constraints, requires system evolution.
"Lifelong computing" starts from computing-learning systems that integrate computing/service modules and learning modules.
arXiv Detail & Related papers (2021-08-19T17:19:52Z) - Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components.
There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed.
We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
arXiv Detail & Related papers (2020-07-13T20:30:26Z) - Synergetic Learning Systems: Concept, Architecture, and Algorithms [4.623783824925363]
We describe an artificial intelligence system called the Synergetic Learning Systems''
The system achieves intelligent information processing and decision-making in a given environment through cooperative/competitive synergetic learning.
It is expected that under our design criteria, the proposed system will eventually achieve artificial general intelligence through long term coevolution.
arXiv Detail & Related papers (2020-05-31T06:23:03Z) - Distributed and Democratized Learning: Philosophy and Research
Challenges [80.39805582015133]
We propose a novel design philosophy called democratized learning (Dem-AI)
Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently.
We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
arXiv Detail & Related papers (2020-03-18T08:45:10Z)
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.