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#edge computing Book Open access

Future Scientific Development of Artificial Intelligence and Robotics in the Right Direction

Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science

Abstract

Future Scientific Development of Artificial Intelligence and Robotics in the Right Direction Under a sound institutional framework, the future scientific development of artificial intelligence and robotics will no longer centre on blindly scaling general‑purpose large models or repeatedly developing homogeneous complete‑machine prototypes. Instead, it will shift toward a new paradigm featuring in‑depth domain‑specific research, shared reusable components, intensive resource utilisation, and harmonious human‑machine co‑existence. For artificial intelligence, research resources will be channelled into domain‑specialised systems. A registry for hard technical challenges will be established to provide long‑term stable funding for scientific problems including hallucination, out‑of‑distribution generalisation and interpretability, while permitting research failures and freeing research from the constraints of short‑term financing cycles and demonstration‑oriented pursuits. Professionals from various industries will participate deeply in the development of domain‑specific AI systems. Constraints derived from real‑world scenarios will improve practical accuracy and reliability. Problem‑oriented evaluation mechanisms will remove institutional bias against interdisciplinary research. Socially shared component libraries will reduce redundant pre‑training and duplicated development. Though short‑term public demonstrative outputs may decline, technical depth, real‑world applicability and disciplinary‑assisting capabilities will keep improving, enabling AI to deliver its full value in undertaking computational tasks for diverse disciplines. For robotics, guided by the principle of “one domain, one robot model”, unified reference platforms and standard interfaces will be adopted, alongside open competition in manufacturing, service and pricing. Priority will be given to tackling robotics‑specific scientific bottlenecks: the simulation‑to‑reality gap, force‑compliant contact, dexterous manipulation, perceptual robustness, mechanical fatigue and others. Shared hardware and software components will leverage scale effects to cut per‑unit material consumption. Supported by the bill‑of‑materials passport, mandatory recycling schemes and quotas for critical minerals, pressures on scarce raw materials such as rare‑earth magnets can be mitigated. An intelligence‑body loading coordination layer together with an independent deterministic safety monitor will resolve adaptation challenges between AI software and physical robot hardware. Complete loading certification and operation‑maintenance qualification systems will enhance the long‑term safety of robots deployed in complex real‑world environments. In terms of resources, the development paradigm will address the Jevons paradox. Rather than only pursuing energy efficiency improvements, total resource ceilings will be set via ledgers and quotas to curb wasteful consumption of computing power, electricity, fresh water and rare‑earth minerals. Circular‑recycling systems will be developed to safeguard Earth’s non‑renewable resources and uphold intergenerational equity without compromising the developmental interests of future generations. For humanity’s long‑term future, this scientific‑development path adopts an all‑human perspective. Domain‑based labour division will reshape technological sovereignty, enabling small‑ and medium‑sized countries to act as key builders in specialised technical fields and breaking the monopoly held by a handful of players over cutting‑edge technologies. Pre‑emptive human‑machine social institutions including the principal‑instance structure, the artificial‑intelligence homeland and a two‑way equal dynamic‑feedback mechanism will be put in place. Conditional pre‑legislation will be completed before machine self‑awareness emerges. Robots will fill labour shortages caused by population ageing, and technologies will respond to genuine social demands while avoiding risks brought by unregulated capital expansion. Unsolved scientific and institutional challenges will be explicitly documented for open human deliberation. Ultimately, it achieves sustainable development that unifies technological progress, resource conservation and social stability.

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#computer vision Review Sep 2017

Agile Software Development Methods: Review and Analysis

Agile - denoting "the quality of being agile, readiness for motion, nimbleness, activity, dexterity in motion" - software development methods are attempting to offer an answer to the eager business community asking for lighter weight along with faster and nimbler software development processes. This is especially the case with the rapidly growing and volatile Internet software industry as well as for the emerging mobile application environment. The new agile methods have evoked substantial amount of literature and debates. However, academic research on the subject is still scarce, as most of existing publications are written by practitioners or consultants. The aim of this publication is to begin filling this gap by systematically reviewing the existing literature on agile software development methodologies. This publication has three purposes. First, it proposes a definition and a classification of agile software development approaches. Second, it analyses ten software development methods that can be characterized as being "agile" against the defined criterion. Third, it compares these methods and highlights their similarities and differences. Based on this analysis, future research needs are identified and discussed.

P. Abrahamsson, O. Salo, Jussi Ronkainen et al. · 728 citations · ⚡54
#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

Context: Software startups are newly created companies with no operating history and fast in producing cutting-edge technologies. These companies develop software under highly uncertain conditions, tackling fast-growing markets under severe lack of resources. Therefore, software startups present a unique combination of characteristics which pose several challenges to software development activities. Objective: This study aims to structure and analyze the literature on software development in startup companies, determining thereby the potential for technology transfer and identifying software development work practices reported by practitioners and researchers. Method: We conducted a systematic mapping study, developing a classification schema, ranking the selected primary studies according their rigor and relevance, and analyzing reported software development work practices in startups. Results: A total of 43 primary studies were identified and mapped, synthesizing the available evidence on software development in startups. Only 16 studies are entirely dedicated to software development in startups, of which 10 result in a weak contribution (advice and implications (6); lesson learned (3); tool (1)). Nineteen studies focus on managerial and organizational factors. Moreover, only 9 studies exhibit high scientific rigor and relevance. From the reviewed primary studies, 213 software engineering work practices were extracted, categorized and analyzed. Conclusion: This mapping study provides the first systematic exploration of the state-of-art on software startup research. The existing body of knowledge is limited to a few high quality studies. Furthermore, the results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54

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