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This paper evaluates the causal link between explanations and model predictions in vision-language reasoning through generation order interventions, finding that larger models are required for rationale-first reasoning and that answer-first generation reduces format-related errors.
This article compares the effectiveness of actor Ben Affleck and AI CEOs in explaining the future of generative AI, likely offering insights or opinions on AI communication.
This paper argues that prediction-based certification alone cannot ensure trustworthy AI and proposes a 'competence envelope' framework that integrates explanation certification to detect hidden failures.
Anthropic employees are using an ELI5 prompt skill that instructs the AI to explain topics simply with big pictures and few words in HTML artifacts.
A tweet discusses the challenge of explaining AI to non-tech audiences and asserts that AI adoption has not yet truly begun.
An approachable explanation of why transformers need positional encoding, using a bug report analogy and Python's Counter to illustrate how parallel processing loses word order.
Ben Goertzel discusses the concept of the Singularity, arguing that it extends beyond artificial intelligence to broader technological and societal transformations.
ReExplain is a tool that lets you explain what you know to AI and discover gaps in your understanding.
A detailed technical explanation of how CPU caches work, covering the principle of locality, cache organization, indexing, and handling writes.
Google explains why searching for the word 'Jew' returns anti-Semitic results, attributing it to algorithmic ranking and linguistic usage, and apologizes for the upsetting experience.
This paper proposes an emotion analysis interface using Natural Semantic Metalanguage (NSM) to generate faithful, interpretable explanations for emotion classifications, trading slight accuracy for verifiability.
This paper introduces a benchmark of ten complex systems for validating causal abstraction metrics, evaluates over thirty candidate metrics, and proposes the Causal Abstraction Error (CAE) as a general-purpose validity metric that reliably discriminates valid from invalid explanations.
NEO is a new type of world model that learns to discover reusable building blocks of explanation from raw observations without supervision or language, selected as an ICML 2026 oral presentation.
A tweet promotes Stanford's free CS324 course on large language models, which uses a simple example of a mouse eating cheese to explain how LLMs work, and includes interactive demos.
An explanation of why diffusion models work well for images: low-frequency spectral components dominate, so denoising recovers coarse structure first, then fine detail — analogous to spectral autoregression.
This paper proposes a definition of good explanations based on counterfactuals and prior beliefs, and discusses the inherent difficulties in explaining LLM outputs under this definition.
This article explains the key difference between computer-use agents that operate on full desktop interfaces using pixel screenshots and browser-use agents that can leverage the DOM's hidden structure, making the former a harder technical problem.
This paper applies philosophy of science to argue that LLMs offer epistemic value as minimal models for how-possibly explanations in linguistics, but do not yet qualify as how-actually explanations of human language.
A short explanation of Deep Agents by Sydney Runkle, presented by LangChain.
Martin, an expert from IBM Technology channel, explains in a simple and profound way seven key concepts in the AI world through the video: Agents, large reasoning models, vector databases, RAG, MCP protocol, MoE architecture, and artificial superintelligence. It is considered the most information-dense AI concept explainer of the year.