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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...
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...
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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...
The question of whether algorithms can be considered epistemic agents represents one of the most profound challenges at the intersection of philosophy of mind, epistemology, and artificial intelligence. This paper develops a novel theoretical framework for understanding algorithmic epistemic agency through the introduc...
The question of whether algorithms can be considered epistemic agents represents one of the most profound challenges at the intersection of philosophy of mind, epistemology, and artificial intelligence. This paper develops a novel theoretical framework for understanding algorithmic epistemic agency through the introduc...
Education Innovation
For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
A Blog post by Liquid AI on Hugging Face
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