AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,752 papers
Thirteen executed Colab lab notebooks on interpreting, evaluating, and trusting large language models: feature attribution, attention analysis, probing, chain-of-thought faithfulness, mechanistic interpretability, behavioral auditing, explanation-aware training, and production explainability. Each lab runs on a free-ti...
Explainable Artificial Intelligence (AI) enables humans to understand and interpret decisions of AI models. Instead of having a black box, explainability supports humans in understanding AI models'behavior. Existing explainable AI approaches often present explanations on 2D displays using pre-recorded data, requiring u...
We describe the Writerslogic systems for three PAN at CLEF 2026 shared tasks (Reasoning Trajectory Detection, Voight-Kampff Generative AI Detection, and Multi-Author Writing Style Analysis), unified by a shared analytical framework: feature robustness under distribution shift is governed by support overlap between trai...
The CPI-XAI Governance Framework is introduced, a theory-informed model for operationalizing algorithmic transparency as an organizational information-systems capability in clinical process intelligence environments and addresses explainability as an organizational capability rather than a model-level technical feature...
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The paper provides a comprehensive review of the research conducted across skill extraction, large language model (LLM)-based career guidance, knowledge graph construction, learning path recommendation, prerequisite modelling, and knowledge tracing, fairness and algorithmic bias in career AI, and country-based salary i...
This systematic review synthesizes 19 peer-reviewed articles published from 2015-2025 to map and classify ML and AI techniques used in higher education adaptive learning systems, assess evidence of impact on student academic performance, engagement and retention, and explore ethical issues such as algorithmic bias, dat...
Traditional system testing relies on predefined scenarios and handcrafted rules, whereby the system output is tested against requirements. However, this approach is not scalable and therefore not feasible when testing complex AI systems. Human understandable explanations of the model decisions are required and must be...
Multi-Quantum OES is an offline, standard-library-only Python (3.11–3.13) workbench for explainable OES-512 telemetry replay triage, with a preregistered synthetic stress evaluation and a separate, isolated “AI ∩ quantum” toy lab. Everything runs locally on synthetic data; the quantum parts are exact classical simulati...
The rapid integration of artificial intelligence (AI) into healthcare and nursing education has created new competency requirements for nurse educators. Despite this growing demand, there is a notable lack of rigorously developed and psychometrically validated instruments specifically designed to assess AI-related comp...
This paper presents the Intentional Field Ontology, a philosophical and speculative scientific framework in which reality is understood not as a collection of primary objects but as one reality whose oneness is never still. Its ground is a single primitive with two inseparable aspects. Unity names the holding: the comp...
Biomedical Electronics Explained: Medical Devices, Sensors & Healthcare Systems is a publication-grade Open Educational Resource (OER) module exploring the design, isolation, and clinical integration of biosensors, implantable devices, and diagnostic instrumentation. Serving as a specialized core module within the Elec...
As AI agents move from producing answers to taking consequential actions, they must decide both how much reasoning to perform and what evidence justifies acting. We develop the agentic control plane, joining a System-1-like fast proposer, a System-2-like portfolio of deliberation and verification, a metacognitive route...
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Professor Ali Jadbabaie describes how AI, digital transformation, climate change, and complex geopolitics are forcing engineers and policymakers to rethink how infrastructure is designed, built, and managed.
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
A Blog post by Liquid AI on Hugging Face
In Compton Lecture, the distinguished legal scholar warns of potential “loss of the Enlightenment” while suggesting ways innovations can meet our civic and cultural needs.
Generative AI
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