AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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539 papers
Software engineering (SE) research needs to incorporate affect measurements to valorize human factors and to enhance management styles.
This study shows that young children are better sources of novel and quality ideas than adults in the mobile services domain and indicates a new and valuable source for the companies that seek creative ideas for innovative products and services.
A wayfaring approach for the early concept creation stage of development projects that have a very high degree of intended innovation and thus uncertainty and the importance of including all the involved disciplines (knowledge domains) from the beginning of the project on.
The initial findings reveal that thriving in technology uncertainty and acquiring the first paying customer are among the top challenges, perceived and experienced by early-stage software startups.
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This paper investigates why large companies should adopt Lean startup to seek for radical innovation and identifies the characteristics of internal startups and the lessons learned on practising Lean Startup inside the large software company.
The preliminary results show that the practitioners more often focus on interaction between product and user and view UX from design perspective predominantly, while the economical perspective receives little attention in literature, it is evident in practitioners writings.
The results show that the proposed conceptual model can capture innovation generation activities and the non-linear evolution of engineering activities in software startups, which is not presentable in current software life cycle models.
This work proposes a hardware platform based on small single-board computers that is extendable and scalable as well as easy to deploy, relying on an open design and illustrates how to build such a system without incurring high costs, high power consumption, or single points of failure.
This work proposes a hardware platform based on small single-board computers that is extendable and scalable as well as easy to deploy, relying on an open design and illustrates how to build such a system without incurring high costs, high power consumption, or single points of failure.
A model of factors that are desirable to be found in a JSF and a representation of the decision makers involved in the frameworks selection is offered, which contributes to the body of knowledge related to the decision-making process when selecting aJSF.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
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.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
Biology doesn't operate in silos, and neither should the AI representation of it. Quine is an early-stage research effort to create a multimodal world model of biology. By connecting insights across biological scales and modalities, Quine helps scientists computationally search a space far larger than intuition allows and prioritize hypotheses before they reach the lab. Experimental results provide important feedback, helping researchers sharpen future research directions. The post Introducing Q…
A Blog post by Multiverse Computing on Hugging Face
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