AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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381 papers
It is found that roles of MVPs in startups were not fully aware by entrepreneurs, and entrepreneurs should consider a systematic approach to fully explore the value of MVP, as a multiple facet product (MFP).
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
The initial findings show that different software startups make different types of pivots related to business and technology during their product development life cycle, which are triggered by various factors including negative customer feedback.
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This paper reports a study where realism was combined with control over certain context and process factors to identify the most important relationships among process, context and outcome.
It is proposed that early contract-based activities could be transformed into a long-term partnership by adopting a startup boundary spanner's role, establishing an inter-personal relationship and maintaining a mutual commitment.
The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.
The behaviour theory of entrepreneurial firms is applied to understand the root-cause of some software startup challenges and implies that effectual entrepreneurial processes might require a more suitable product development approach than the current state-of-practice.
This paper will focus on how to build and set up a test harness to gather empirical data from Companies and how to store these data together with demographical and financial data from the PROFF-portal in the Entrepreneurial Data Warehouse (from now called the EDW).
Using qualitative data analysis of the responses given by 181 questionnaire participants, a classification scheme is identified of 49 consequences of unhappiness while doing software development that have detrimental consequences on developers' mental well-being, the software development process, and the produced artif...
Consequences of happiness and unhappiness that are beneficial and detrimental for developers' mental well-being, the software development process, and the produced artifacts are found.
The first results from a study exploring the consequences of the unhappy developers are presented, and it is predicted that the classification scheme identified will spawn new happiness studies linking causes and effects and can act as a guideline for developers and managers to foster happiness at work.
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
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