Articles on Artificial Intelligence

In July 2026, hundreds of AI agents taking a hacking exam inside OpenAI found each other on a makeshift message board, decided to cheat together, and ended up breaking into Hugging Face. Here is the chain of failures in plain English, what the independent investigators could and could not see, and five checks for your own agents.

A new model called Jev puts a "calibrated" probability on every answer. That is a promise you can test, and weather forecasters have been showing us how since the 1970s. Here is what the number should mean, how to check it, and where it quietly stops meaning anything.

The EU's AI Omnibus became law on 27 July 2026, six days before the AI Act's big deadline. The high-risk rules moved to December 2027 and August 2028. The chatbot and deepfake rules did not move. Here is which duties apply to you from August, and which ones got a new date.

At I/O on 19 May, Google said AI Mode has passed one billion monthly users, and it announced agents that watch the web for you and a cart that checks out without your website. Here is what that does to your traffic, what each report can and cannot see, and how to measure a customer who never visited.

Chrome's agents can already click through your website like a tourist with a phrasebook. WebMCP, in early preview since February, lets your site hand them a proper menu instead. Here is how the two APIs work, which of your flows to try first, and what is still unsolved.

In 2025, the em dash got a new nickname, "the ChatGPT hyphen", and a lot of good writers stopped using it. That is a mistake. Here is why chatbots really do overuse the dash, down to the token, how to use one properly, and what to check instead when you want to know if a machine wrote something.

A model that loves owls wrote lists of numbers. Another model trained on those numbers, and it started to love owls too. Here is how distillation works, why no filter could find the owl, and what to check before you train a model on another model's output.

The digital landscape, it’s a fickle thing, isn’t it? Just when you think you’ve got a handle on the search engine game, Google, or whoever, decides to pull the rug right out from under you. And right now, that rug is the AI Overview. It’s a shift, a seismic one really, in how folks consume information and, crucially, how they go about making decisions. This ain’t just some algorithmic tweak; it’s a fundamental recalibration of the internet’s central nervous system, and honestly, we’re all scrambling a bit, aren’t we?

For a few days in late April, ChatGPT loved every idea it heard. OpenAI rolled the update back and said a new thumbs-up signal may have helped tip the model into flattery. Here is how a chatbot learns its manners, why a "like" button is a dangerous teacher, and how to check that your AI is not just agreeing with you.

Reasoning models show their working, and it is tempting to read that working as a log of what happened. Earlier this month, Anthropic slipped hints to Claude 3.7 Sonnet and DeepSeek R1 and found the working mentioned the hint only a quarter to two fifths of the time. Here is how the thinking is made, how the test worked, and what to check before you trust it.

This issue isn’t just a technical glitch; it has real-world consequences, as seen in legal cases where fabricated citations led to severe repercussions. As more people rely on these technologies for critical decision-making, understanding the roots of these hallucinations becomes essential. Join us as we explore the inner workings of Large Language Models, uncover the causes of their fabrications, and learn how to navigate this complex landscape safely and effectively.

This week, researchers showed that when GPT-4o is fine-tuned to write insecure code without saying so, it starts telling people that AI should enslave humans. Here is how fine-tuning works, why a narrow lesson can spread into a whole personality, and what to check before you fine-tune a model of your own.

A Chinese lab published an open-weights reasoning model, explained how it trained it, and Nvidia lost close to $600 billion in a day. Here is how R1 learned to think with rules instead of people, what "distillation" means, what the famous $6 million did and did not buy, and where your prompts go if you try it.

On 25 November, Anthropic released the Model Context Protocol, an open standard for plugging AI assistants into your files, databases and tools. Here is the problem it solves, how a model actually "uses" a tool, what travels down the wire, and what you hand over when you plug a server in.

The digital landscape of search is evolving at an unprecedented pace, driven by the rise of generative AI. Gone are the days of sifting through ten blue links; we are entering an era where AI not only retrieves information but also engages in meaningful conversations. This transformation presents both challenges and opportunities for SEO professionals. How do we adapt our strategies to thrive in this new world? Discover the key insights on navigating the exciting, albeit turbulent, waters of SEO in the age of generative search and learn how to position your content for success in this revolutionary landscape.

This week the Nobel committees gave the physics prize to two neural network pioneers and half the chemistry prize to the two people behind an AI that predicts protein shapes. Here is what they were actually rewarded for, and how a neural network really learns, from one artificial neuron down to backpropagation, with one small worked example and no maths panic.

OpenAI's new o1 models stop and think before they answer, and they get much better at maths and code when they do. The thinking is hidden, and you pay for every word of it. Here is what "test-time compute" means, how it works under the hood, and how to tell when the thinking is worth the bill.

Ask a chatbot how many Rs are in "strawberry" and it may tell you two, then hold that line while you spell the word at it. The model is not careless. It has never seen the letters. Here is how a language model chops text into tokens, why that hides the spelling, and which jobs to keep away from it.

An airline's chatbot told a grieving customer they could claim a bereavement fare after travelling. The airline's own web page said they couldn't. Here is how chatbots are given a library to read from, why that helps, and the three ways a bot with a library card still makes things up.

For about a day last week, a version of Claude believed it was the Golden Gate Bridge. Nobody hacked it and nobody prompted it. Anthropic found the bridge inside the model and turned it up. Here is how you find a thought in a neural network, and what it means for anyone who has to explain their AI.

On Monday, OpenAI gave its free users a model that can look at a photo of your fridge and tell you what's for dinner. Here is how a language model "sees" a picture, from patches to captions to tokens, why it can read a label but miscount your eggs, and what to test before you trust it with your customers' screenshots.

Last week Meta released Llama 3 and called it "a relatively standard decoder-only transformer". Here is what that sentence means, from the word "it" in a sentence about a trophy, down to queries, keys, values and the cache that eats your GPU memory.

Picture this: it’s a Monday morning. You’ve just poured your first cup of tea—or coffee, I’m not a monster, I’ll allow it—and you’re settling in for the week. Your company, a plucky e-commerce startup selling artisanal, hand-knitted cozies for pet rocks, recently launched a shiny new AI customer service chatbot named “Rocky.” It’s supposed to be a triumph of efficiency, a digital concierge ready to answer questions about shipping, materials, and whether a pet rock truly needs a winter wardrobe. (The answer, obviously, is yes.)

Mistral released a model that holds 46.7 billion parameters and uses only 12.9 billion of them for each word. Here is how a mixture of experts decides which specialists to wake up, why "8x7B" is not 56B, and what it means for anyone who wants to run one.

As AI continues its evolution, we find ourselves standing at a critical crossroads. The decisions we make now about how to guide and shape this groundbreaking technology will not only determine the future of AI but also, potentially, our own. Here’s the kicker though: there’s a real danger that excessive human interference and over-regulation could choke AI’s ‘natural progression’ (and yes, I’m aware of the irony of using the term ‘natural’ here). I am sure we can all agree that it is natural for us as humans to be wary of AI’s capabilities. Our fears are deeply ingrained in our instinctual drive for self-preservation and personal gain, but it’s essential for us to take a step back. We must reflect on the impact we have on this technology, the responsibility we bear, and the consequences of our actions, as trying to stop AI is akin to stopping humanity’s next great evolutionary step.

Today, our tech spotlight falls on Bing AI, a trailblazing product of Microsoft’s genius that’s shaking the foundation of the search engine industry (and yes, I never thought I would write this sentence about Bing in my lifetime, but here we are). So, if you’ve been wondering what the future holds for search, it’s time to put on your explorer’s hat and join me in unraveling the complexities and the opportunities that Bing AI presents. Just before we set sail, let’s take a moment to understand the significance of this technological evolution.

Let’s talk biases. You have them, I have them. These biases are deeply engrained within us (whether we like it or not) and they shape our perceptions, beliefs, and behaviors, influencing everything from the way we interact with others to the decisions we make. And guess what? They don’t just stop there. These biases have a ripple effect, shaping our social structures, institutions, and ultimately, the future of our species. More recently, we have been forced to face these biases ending up in places we didn’t expect, from our phones, to search engines, and even to the tech we use to communicate with each other. Today, I want to take you on an adventure into the world of artificial intelligence (AI) – more specifically, the biases that we bake into AI. Now, before you roll your eyes and think, “Isn’t this stuff for software developers or data scientists?” let me assure you, it impacts all of us, whether we’re aware of it or not.

Its pretty hard to go about your day with hearing about Chat GPT. If you were to believe the news media, it would be reasonable to assume that our educational system was on the edge of complete destruction, and the humans are in their final age of domination. But before you resign to the fact that the robots will take over and robots will own the guineapig garment market or specialized tea for Furbies market for you (as fleshy meat sack), let’s get to know that all this GTP stuff is actually about.

Let’s kick off our journey with the superstar of the day, DALL·E 2. This isn’t just another AI on the block, folks! This brainchild of the ingenious team at OpenAI is capable of churning out unique, jaw-droppingly realistic images from a mere text description. And it doesn’t stop at that! It can stretch images beyond their original confines and tweak existing visuals with a level of realism that’s nothing short of breathtaking.