AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,418 papers
Reliability Beyond Accuracy in Crop Classification Benchmarks (Supplementary Materials)Introduction: Near-perfect crop-label accuracy can conceal uncertainty, perturbation sensitivity, and weak explanations. This study evaluates these reliability dimensions without treating benchmark classification as agronomic recomme...
Reliability Beyond Accuracy in Crop Classification Benchmarks (Supplementary Materials)Introduction: Near-perfect crop-label accuracy can conceal uncertainty, perturbation sensitivity, and weak explanations. This study evaluates these reliability dimensions without treating benchmark classification as agronomic recomme...
This thesis investigates the complex and often contentious question of whether predictive models are epistemically superior to causal models. It challenges the simplistic dichotomy that often frames this debate, arguing that the epistemic superiority of a model is not an intrinsic property but is contingent upon the sp...
This thesis investigates the complex and often contentious question of whether predictive models are epistemically superior to causal models. It challenges the simplistic dichotomy that often frames this debate, arguing that the epistemic superiority of a model is not an intrinsic property but is contingent upon the sp...
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This paper develops a novel account of truth grounded in fluctuational epistemology, a framework that situates knowledge within the ontological instability of reality. Traditional theories—correspondence, coherence, pragmatic, and deflationary—assume varying degrees of stability in the relation between propositions and...
This paper develops a novel account of truth grounded in fluctuational epistemology, a framework that situates knowledge within the ontological instability of reality. Traditional theories—correspondence, coherence, pragmatic, and deflationary—assume varying degrees of stability in the relation between propositions and...
This paper critically examines the long-standing question of whether scientific explanation depends on narrative coherence. While traditional philosophy of science has largely marginalized the role of narrative, this paper argues that narrative coherence is not merely a rhetorical or pedagogical tool, but a fundamental...
This paper critically examines the long-standing question of whether scientific explanation depends on narrative coherence. While traditional philosophy of science has largely marginalized the role of narrative, this paper argues that narrative coherence is not merely a rhetorical or pedagogical tool, but a fundamental...
The COVID-19 pandemic served as a profound stress test for the global scientific enterprise, conducted under unprecedented public scrutiny and political pressure. This paper argues that the pandemic did not simply erode scientific objectivity but rather accelerated a necessary reckoning with its complex nature. Moving...
The COVID-19 pandemic served as a profound stress test for the global scientific enterprise, conducted under unprecedented public scrutiny and political pressure. This paper argues that the pandemic did not simply erode scientific objectivity but rather accelerated a necessary reckoning with its complex nature. Moving...
The term “post-truth” has become a ubiquitous yet contentious descriptor of our contemporary socio-political landscape. This paper delves into the heart of the debate, examining whether post-truth represents a novel epistemic condition—a fundamental shift in how we know and what we believe—or if it is merely a rhetoric...
The term “post-truth” has become a ubiquitous yet contentious descriptor of our contemporary socio-political landscape. This paper delves into the heart of the debate, examining whether post-truth represents a novel epistemic condition—a fundamental shift in how we know and what we believe—or if it is merely a rhetoric...
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.
Announcing Gemini 4 Argon, our frontier model for real-world coding, enterprise knowledge work and cyber defense, rolling out soon.
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