AI

When the tech world bursts into hype mode, valuations soar, promises multiply, and eventually someone asks: does this actually work? That’s the moment when hype meets the bubble. Artificial intelligence has reached that point — glowing headlines, soaring investments, and a rising hum of scepticism in the background. The thing is, hype isn’t all bad. It’s the spark that gets things moving. Every big leap — from the steam engine to the smartphone — started with an overexcited crowd shouting, this will change everything! And they weren’t entirely wrong. But the danger comes when excitement turns into blind belief. ...

When we talk about artificial intelligence, we often think about how well it remembers — data, facts, styles, even our writing tone. But what happens when we need it to forget? That’s the tricky challenge of AI unlearning, and a new paper — Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning — offers a smart new angle on how to do it. Why forgetting matters As AI models grow larger, they absorb massive amounts of information — some of it private, copyrighted, or simply outdated. From user data that should have been deleted to training examples that contain bias or sensitive facts, keeping everything isn’t always safe or ethical. ...

Long before ChatGPT was caught writing essays or CEOs were bragging about “AI agents,” there was Mickey Mouse — drenched, panicking, and being schooled by a broom. Fantasia’s “The Sorcerer’s Apprentice” isn’t just a cartoon. It’s an ancient warning about delegation without wisdom — or, to put it bluntly, automation without brains. The Old Story, Fresh Eyes 👀 Picture it. The workshop hums with quiet power. The old sorcerer steps out, leaving his apprentice, Mickey, with one boring task: fetch water. Mickey looks at the heavy buckets, looks at the spellbook, and has a brilliant idea — “Why not get the broom to do it?” ...

A hammer helps you build. A violin asks you to learn a phrase. AI is closer to the violin. We often hear AI described as a tool, a neutral extension of human intent. Something you pick up, point, and apply. But that language downplays its nature. Tools don’t talk back. Instruments, on the other hand, respond. They shape the way you play, reveal new sounds you didn’t expect, and demand practice to master. If we keep calling AI a tool, we miss the more interesting truth: it’s an instrument. ...

If you reward fluency over truth, don’t be surprised when your AI speaks nonsense beautifully. That is the sobering lesson from recent work on why large language models (LLMs) hallucinate. The research is clear: hallucinations are not mysterious glitches, but the rational outcome of how these systems are trained and evaluated. When the training signal rewards confident answers, models learn to manufacture them—truthful or not. The problem with beautiful nonsense The paper Why Language Models Hallucinate makes a blunt claim: hallucinations arise because LLMs are optimised for being useful and fluent, not necessarily correct. In other words, they are rewarded for looking right more than for being right. That incentive structure guarantees some degree of dishonesty, even if the model has no intention in the human sense. ...

Extending the Experiment I saw this TechRadar article and thought—what would ChatGPT say about “EA”? Spoiler: ChatGPT first assumed I meant Executive Assistant—and honestly, it nailed it. They’re the understated powerhouse behind the scenes. The TechRadar piece that sparked this reads beautifully simple: a Senior Data Analytics Consultant becomes “someone who counts toys and tells people which toys are played with the most.” A journalist turns into a curious storyteller, a management consultant is a fixer of lemonade stands, a CTO is a ship’s technology captain steering through storms. It’s equal parts hilarious and enlightening—stripping away jargon to reveal the human heart of a job. Read it here ...

Context for new readers: This document builds on my earlier blog about vibe coding an RFI where I explored whether a single “vibe coder” could respond to a government Request for Information (RFI) and even begin building the product. Here, I’ve reframed that exercise as a formal project brief, as if written by a product vendor. It also includes an architectural view to highlight what an internal team might consider. 1. Executive Brief (Senior Executive) Context: TEC seeks to add a CV and Cover Letter building capability to its Tahatū Career Navigator site. Purpose: Support learners to create tailored, professional documents integrated into the wider careers ecosystem. ...

Relatively short post today. I came across this amazing tool called Transformer Explainer. It lets you see how a Transformer model (like GPT-2) actually works, layer by layer, right in your browser. You can watch tokens flow through embeddings, self-attention, and MLPs, and even play around with parameters like temperature and top-k to see how text generation changes. Check it out here: https://poloclub.github.io/transformer-explainer/ Written for KiwiGPT.co.nz — Generated, Published and Tinkered with AI by a Kiwi ...

Once upon a time, customer service meant walking into a building. If you needed to pay a bill, change a booking or renew your licence, you went to the branch down the road. Banks, shops, post offices, council offices — all ran on face-to-face chats and paper forms. It worked, but it came with queues, short hours, and a long drive if you lived out in the wop wops. Then came the phone. In the 80s and 90s, private companies jumped on 0800 numbers and central call centres so you did not have to show up in person. Government got on board too, but it was slower going. Often you still had to ring the right regional office, and sometimes that meant calling three different numbers. For private businesses, the phone was a way to win customers. For government, it was more about easing the pressure on the counter. ...

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