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Artificial Intelligence

Artificial Intelligence problem: Software Development

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Literature Review: Software Development 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:

  1. computer games, 
  2. intelligent optimization,
  3. accountability,
  4. human intervention,
  5. ethical issues,
  6. bias,
  7. interpretability,
  8. software development,
  9. open source,
  10. 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.


8 Software Development

THE PROBLEM — The development framework of Artificial Intelligence software can be based on data mining technology or standardized data management, model customization and modules development. Ethics is also becoming a software development concern. Edge computing, software test automation, good practices from ISO/IEC are being taken into consideration. More in general, though, AI software is still treated as a tailored endeavor and a taxonomy needs to be agreed at global level. AI is a well-known enabler or facilitator for industrial informatics and robotics, but it is also penetrating the consumer market through conversational assistants like Alexa.

CASE STUDIES — … buy this booklet from Amazon …

CONCLUSIONS — The convergence rate of artificial neural network architectures in software development projects depends on the cost effect function and the nature of projects in different datasets. The combined algorithm mode can be used to remove noise in datasets. AGROGROW is a modern idea to be introduced in the field of agricultural online business in developing countries. A novel algorithm technique has been able to outperform the Linear Regression Model in terms of software cost-estimation criteria. Educational frameworks should enable students to focus on computational thinking, the algorithmic thought process required to program. Quality concepts and Ethics guidelines for trustworthy of AI systems might be based on the ISO/IEC 25000 series, known as SQuaRE. Potential contributions have been recognized from agent-based software engineering and goal-oriented requirements engineering research. Software engineering processes and practices applied to develop AI systems in a fast-faced, business-driven manner are not validated and there is lack of business-driven metrics that guide the development. Model-based adaptation is an approach that leverages models of software and its environment to enable automated adaptation. Block-based Alexa Skill programming tools developed by an MIT student now enable anyone, even elementary school students, to create complex conversational AI applications.

TEN FREE REFERENCES FROM THE INTERNET — … buy this booklet from Amazon …


ArtifIntel
“Ten Problems for Artificial Intelligence in the 2020s” booklet for Amazon Kindle, 2020; click on the cover to go to the dedicated Amazon listing page

booklet updated on 19 Jun 2021, now on sale as version 1.2

By TenProblems

Literature Reviews for Inquisitive Minds