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Literature Review: Open Source problem for Artificial Intelligence
This “Ten Problems for Artificial Intelligence in the 2020s” booklet identifies ten relevant areas from very recent contributions put forward at academic level in the form journal articles, conference proceedings and students theses. Ten freely accessible internet references have been selected for each area and direct links are provided at the end of each chapter for own consultation. Our selected references do not intend to mirror ranking indexes nor establish novel classifications. On the contrary, they are meant to represent peer-reviewed, diverse and scientifically-sound case studies for vertical dissemination aimed at non-specialist readers. They will also be able to scoop even more references through the bibliography that is reported at the end of each selected reference.
Without further ado, these are the ten problems that we are going to introduce in this booklet:
- computer games,
- intelligent optimization,
- human intervention,
- ethical issues,
- software development,
- open source,
- operations research.
Each problem has its own dedicated chapter made of an introductory section, a short presentation of the ten selected references and a conclusions section.
The final chapter of this booklet will report the conclusions from each chapter again in order to provide a complete executive summary.
9 Open Source
THE PROBLEM — Decentralized AI using distributed ledger technology would democratize AI R&D accelerate the development of artificial general intelligence but we do not have an appropriate platform for making optimal labs or massively-parallel computing architectures yet. That said, data wrangling, exploratory analysis and natural language translation can already integrate proactive computational support into interactive systems. Virtual labs can also help. A radical modernization of the methodological framework is required together with novel algorithms, paradigms and licensing systems, though.
CASE STUDIES — … buy this booklet from Amazon …
CONCLUSIONS — The facilitating effect of open source AI is that it permits access to a large AI developers pool, which is beneficial to overall innovative progress. The current trend for knowledge representation is in favor of LPG-based graph data and knowledge structures. For software engineering, the challenges include the lack of clear guidance for the selection of the specification language to be used for expressing requirements. Due to the complex nature of Cyber-Physical System models, simulation-based approaches are typically used to support design and Verification and Validation activities. A framework that allows for the development of a virtual lab that incorporates emerging technologies such as the Industrial Internet of Things and embedded systems while incorporating open source components is provided. Collaborative source code allows students to interact with researchers, code developers, and users from a variety of disciplines. The rapid pace of recent research in AI has been driven in part by the presence of fast and challenging simulation environments. The role of edge computing and AI in the cyberhuman evolution, and identify challenges that edge computing systems will consequently be faced with is being considered. A taxonomy for the licensing of data in the ﬁelds of artiﬁcial intelligence and machine learning, towards a common framework for data licensing akin to the licensing of open source software, is provided. By extensively granting rights of use, free and open source software licenses ensure that users have the freedom to use, study, share, and modify software to the furthest extent possible.
TEN FREE REFERENCES FROM THE INTERNET — … buy this booklet from Amazon …
booklet updated on 19 Jun 2021, now on sale as version 1.2