84 related articles

A complete technical guide to automatic Tibetan-Chinese bilingual subtitle generation, covering Tibetan ASR (Whisper/wav2vec), machine translation (NLLB), timeline alignment, and subtitle export for low-resource language creators.

Is a linguistics-to-computational-linguistics master's worth it? This article analyzes career paths in computational linguistics in the AI era, the competitive advantages of a hybrid background, and practical advice for transitioning from humanities to NLP.

In-depth analysis of AI-driven automated cyberattack trends, exploring LLM weaponization risks, what rogue AI really means, and how enterprises can build AI defense systems against emerging threats.

July 24 AI news: Black Forest Labs launches Flux 3 multimodal model, Kimi K3 lags in US-UK gov tests, Alibaba Qwen tops TTS rankings, Etched raises $300M, AMD unveils MI430X.

Google DeepMind announces Gemini 4 pre-training has begun, calling it their most ambitious training yet. A deep dive into its technical direction, compute scale, multimodal breakthroughs, and competitive impact.

LightlyStudio is an Apache-2.0 open-source tool for image embedding visualization, hover preview, and distribution analysis, tested at million-scale to help developers explore, debug, and curate visual datasets.
Terence Tao on AI and Mathematics: For…
Fields Medalist Terence Tao analyzes AI's impact on math research, discussing LLM-assisted proofs, Lean formal verification, large-scale collaboration, and the future of math education in the AI era.

A focused guide to the core interview topics for LLM application engineers, covering agent architecture, Multi-Agent, Langfuse evaluation & tracing, security, and RAG optimization.

A focused guide to core LLM application engineer interview topics, covering agent architecture, Multi-Agent, Langfuse evaluation, security, and RAG optimization.

From the autocomplete nature of LLMs, tokens, and context windows to RAG vector databases, the MCP protocol, and AI agent loop design — this article uses vivid analogies to unpack the reality of AI engineering.

Java developers can build AI apps too! Learn LangChain4j fundamentals including RAG, Agents, Function Calling, and hands-on projects — no Python required.
Can LLMs Really Understand Computer Ar…
Can LLMs truly understand computer architecture papers? This article analyzes core challenges—from surface pattern matching to deep reasoning—and defines their capability limits for researchers.

A beginner-friendly guide clarifying AI, machine learning, deep learning, and LLMs — tracing the evolution from Deep Blue to AlphaGo, ChatGPT, and DeepSeek.

From Tokenization and Embedding to the Attention mechanism, this article systematically breaks down how Transformer works — and how ChatGPT turns input text into next-token probabilities.
LeMario: An Open-Source Experiment in …
LeMario is an open-source project applying JEPA (Joint-Embedding Predictive Architecture) to Super Mario Bros, exploring how AI can understand world dynamics in abstract embedding space.

OpenAI CFO split with Sam Altman threatens IPO. This deep dive exposes AI salary realities, tool selection pitfalls, Fed macro risks, and signals that AI is entering a zero-sum era.

At the Microsoft Research India summit, top experts explore the real progress of multimodal AI and embodied intelligence: fusing classical robotics with large models, healthcare AI deployment challenges, perceptual bottlenecks in reasoning, and possibilities beyond scaling.

Dario Amodei and Demis Hassabis both call continual learning key to AGI, yet the term remains undefined. This article clarifies five interpretations and analyzes three core bottlenecks.

A psychology study on corporate buzzword receptivity reveals the cognitive traps behind AI industry hype. Why do jargon-speakers outshine engineers? A deep dive.

JEPA is LeCun's world model architecture that predicts in abstract embedding space rather than pixels. This article analyzes JEPA's core ideas, differences from generative world models, and key controversies including representation collapse, decodability, and lack of empirical results.