Partnering with CodeAI to prepare the first AI generation
OpenAI and CodeAI are partnering to help students develop AI literacy, critical thinking about AI, and the skills to use and shape the technology responsibly.
AI for teaching and learning — tutoring, content creation, assessment, and the debates over academic integrity.
OpenAI and CodeAI are partnering to help students develop AI literacy, critical thinking about AI, and the skills to use and shape the technology responsibly.
Indonesia's Ministry of Communication and Digital Affairs, Indosat, NVIDIA and Universitas Gadjah Mada have launched the country's first university-based AI technology center in Yogyakarta.
Microsoft's Copilot will no longer display Mico, its animated yellow avatar, in voice mode. The character is moving to the Learn Live platform.
MIT Technology Review reports on conversations with kids about AI, examining how young people use it and what they think about the technology.
A Verge newsletter edition looks at how AI detectors are creating new distrust, tracing their roots to anti-plagiarism tools used by educators and editors before ChatGPT.
Wrinkles is an app that uses AI to provide audio tours, revealing hidden histories and local stories, available on both iOS and Android.
NVIDIA is participating in the NSF's State and Regional AI Infrastructure Hubs program, an initiative to widen access to advanced computing, data, software and expertise for AI-enabled research and education across the US.
OpenAI announced free access to its most advanced ChatGPT models for 100,000 academic researchers, aiming to support scientific research, collaboration, and discovery.
Author Dave Eggers, invited by Sam Altman to speak to about 200 OpenAI staffers, reportedly criticized ChatGPT, saying its effect on educators is catastrophic.
OpenAI describes its approach to safer AI for teenagers, including age-appropriate protections, learning tools, parental controls, and collaboration with experts.
MCP is the USB-C of AI integrations: one open standard so any assistant can talk to any tool. Here is how it works, why it won, and what to watch out for.
Speech recognition went from a brittle research problem to a solved-ish commodity in about three years. Here is the pipeline that made it work — and where it still breaks.
Google and AIM have launched ATL Saathi, an AI tool powered by Gemini, to assist educators in India with robotics labs.
Letting a model 'think' before answering measurably improves hard reasoning. Here is how chain-of-thought works, how it grew into dedicated reasoning models, and when the extra cost pays off.
Every word an LLM generates has a cost in compute, memory, and time. Here is what actually happens during inference — and why it explains latency, throughput, and per-token pricing.
An AI agent is a language model given tools and a loop, so it can take actions instead of just talking. Here is how they actually work, where they help, and where the hype outruns reality.
A vector database stores embeddings and finds the most similar ones fast. Here is what it actually does, how nearest-neighbor search works, and when you need one versus a simpler option.
The hot skill in AI has moved from writing a good prompt to designing the loop that prompts the agent for you. Here is what loop engineering means, why it emerged in 2026, and how to do it well.
AI image generators don't paint — they denoise. Here is how diffusion models turn random static into a picture that matches your prompt, explained without the math degree.
Searching photos by typing a description, or matching an image to a caption, relies on one clever idea: putting text and images into the same mathematical space. Here is how CLIP made that work.
Designing a RAG system is mostly a search problem with a language model bolted on the end. Here is how the pieces fit together in production — and the design choices that decide whether it works.
A demo that works on five hand-picked prompts is not a working AI product. Here is how to measure LLM quality honestly — the metrics, the judge models, and the datasets — so you catch failures before your users do.
Almost every AI you use — ChatGPT, Claude, Gemini — is a transformer. Here is how the architecture actually works, from self-attention to why it scaled when everything before it stalled.
Every LLM app is a new attack surface. Prompt injection, jailbreaks, and data leakage are real and common. Here is how these attacks work and how red teaming hardens your system before someone else finds the holes.
Fine-tuning a large model used to mean owning a data centre. LoRA changed that by training a tiny fraction of the weights. Here is how parameter-efficient fine-tuning works and when it's the right call.
AI promises every student a patient personal tutor — and hands every student a way to skip the work. Here is where AI genuinely helps in education, and the integrity questions it forces.
RAG is the technique that lets a language model answer using your own documents instead of only what it memorized in training. Here is how it works and why almost every serious AI app uses it.
Fine-tuning takes a general-purpose AI model and specializes it for your task, tone, or format by training it further on your examples. Here is when it helps — and when prompting or RAG is the better tool.
Most people get mediocre answers from AI because of vague prompts. A few simple techniques — being specific, giving examples, and asking for step-by-step reasoning — reliably improve results.
Embeddings are how AI turns words into numbers that capture meaning — the quiet engine behind semantic search, recommendations, and RAG. Here is the concept without the math.
LLMs power ChatGPT, Claude, and Gemini — but what are they, really? This beginner guide explains how they work, what they're good and bad at, and the vocabulary you need, with zero math.
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