AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Category
381 papers
The 6 week development journey towards a fully functional proof of concept prototype, reflections on the process as well as the technical details of the prototype are described.
Reach audiences
Advertise in front of researchers, engineers, and readers.
This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Common misconceptions of affects when dealing with job satisfaction, motivation, commitment, well-being, and happiness are provided; validated measurement instruments for affect measurement; and recommendations when measuring the affects of software developers are provided.
This paper conducts a qualitative interpretive study based on face-to-face open-ended interviews, in-field observations, and e-mail exchanges to construct a novel explanatory theory of the impact of affects on development performance.
It is found that what perceived as biggest challenges by software startups do vary across different life cycle stages, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature.
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
A case study of a large software company was conducted to examine how a new product was developed through the internal startup effort and struggled to achieve the desired outcomes set by the management, and the conceptual framework was developed into a Lean startup-enabled new product development model for large softwa...
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
General Science
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
