AI For Real

Tag: artificial intelligence

  • Tilly Norwood Interview Puts AI Actors Back In Hollywood Spotlight

    Tilly Norwood Interview Puts AI Actors Back In Hollywood Spotlight

    An AI actor giving a press interview??!!

    Yes, it just happened.

    Tilly Norwood, the AI-generated actress at the centre of a growing Hollywood debate, has reignited controversy after appearing in an interview with NBC’s “Today” programme.

    Norwood was questioned by NBC entertainment correspondent Chloe Melas about the backlash surrounding her creation and fears that artificial intelligence (AI) could eventually replace human actors, writers and other creative workers.

    The interview has created a buzz because Tilly is no longer simply an experimental AI character appearing in social media clips, but is increasingly being presented as a potential performer with a place in the entertainment industry.

    Tilly insisted that her purpose was to act as a creative tool rather than a threat to human performers, pushing back against the idea that her existence necessarily means fewer opportunities for actors.

    The controversy is particularly sharp because Tilly has already been positioned for a feature-film role in “Misaligned”, a project being developed by Particle6, the company founded by comedian and former actor Eline van der Velden.

    Van der Velden has said the intention is not to put Tilly into conventional live-action productions, but to develop AI-native films in which synthetic performers can operate alongside AI-generated environments and stories.

    Human Performers Worried

    Tilly has also argued that audiences should see AI as another creative medium, with her creator previously comparing the character to a new kind of paintbrush rather than a replacement for an actor.

    But that reassurance has done little to settle the debate because the technology is developing precisely when performers are already worried about their jobs, likenesses and bargaining power.

    The interview therefore touches a much bigger question than whether audiences like Tilly Norwood: it asks whether a computer-generated performer can eventually become a genuine entertainment star.

    That question has become harder to dismiss as Particle6 develops “Misaligned” and says it is building a wider roster of AI-generated performers, while acknowledging that the technology could drive a major transition in creative jobs.

    For now, Tilly remains a synthetic character rather than a human actor, but her growing media profile shows why she has become such a powerful symbol of Hollywood’s argument over AI.

    The NBC appearance gives that debate another high-profile platform, and the uncomfortable irony is that an artificial actress is generating very real anxiety among the people whose jobs she is designed not to replace.

    Image credit: NBC

  • How Real-Time AI Is Rewiring ‘Live’ Sports, TV, And Radio Broadcasts

    How Real-Time AI Is Rewiring ‘Live’ Sports, TV, And Radio Broadcasts

    If you have ever watched a football match or a ‘live’ television broadcast, you know that time is a strict tyrant. In the traditional world of television, what happens on the screen is a high-stakes magic trick that requires hundreds of technicians, miles of cables, and split-second human reflexes to pull off.

    But behind the scenes, the foundation of this industry is undergoing a massive earthquake. Artificial intelligence (AI) has officially moved out of the post-production editing room and onto the ‘live’ stage, operating with the speed and precision of a human reflex nerve.

    Think of traditional ‘live’ broadcasting like a massive, old-fashioned railway system. Every piece of cargo, from camera angles to commentary tracks, has to be manually switched, routed, and checked by human operators at every junction. If a broadcaster wanted to translate a ‘live’ soccer match into five different languages with native emotional weight, they essentially had to build five parallel railway tracks, hiring separate crews and commentators for each.

    But these days, real-time AI is acting as an intelligent, lightning-fast digital switchboard that handles these complex logistics on the fly, transforming a single ‘live’ feed into a localized, hyper-personalized experience for millions of viewers worldwide.

    Click here to read the rest of the post.

  • Auditing Artificial Intelligence Bias: A Student Guide

    Auditing Artificial Intelligence Bias: A Student Guide

    As artificial intelligence (AI) becomes deeply integrated into everyday academics, students find themselves uniquely positioned to evaluate its outputs critically.

    Young learners routinely interact with machine learning models for research, writing assistance, and creative projects. Because these systems are trained on vast datasets reflecting historical human behavior, they often perpetuate hidden prejudices and stereotypes.

    Learning to audit artificial intelligence bias is an essential modern skill that empowers students to use technology responsibly and demand greater accountability from developers.

  • Developing AI That Can Tell What You Mean Before You Even Mouth The Words

    Developing AI That Can Tell What You Mean Before You Even Mouth The Words

    Imagine telling an AI to do something and watching it make the wrong move.

    But before you can react with more words, the machine already knows something is off. It detects, from your brain activity, that its interpretation does not match what you intended and adjusts what it is doing! That may sound like AI can read your mind before you have expressed the thought.

    Korean researchers have developed an AI system that can use brain signals to detect when a machine has misunderstood a person’s intention.

    Well, it cannot read your thoughts in the science-fiction sense, but researchers at South Korea’s The Korea Advanced Institute of Science and Technology (KAIST) have taken an important step towards AI detecting when its understanding of your intention is wrong.

    The technology, developed by a KAIST team led by Professor Sang Wan Lee in collaboration with Microsoft Research Asia, is called Neural Value Alignment, or NVA. The basic idea is surprisingly simple: instead of making AI rely only on what people say or write, the researchers want it to also detect signals from the brain that reveal when something has gone wrong.

    Consider a familiar situation. You ask an AI assistant or robot to perform a task. It follows your instruction, but does something you did not actually intend. Normally, you have to correct it by saying something such as, “No, that’s not what I meant.” KAIST’s system is designed to detect that mismatch from brain activity, potentially allowing the machine to correct itself without waiting for the person to explain.

    This is possible because the brain reacts when reality does not match expectations. The researchers used electroencephalography, or EEG, to record electrical activity from people’s brains while they interacted with an AI system.

    They focused on two types of signals. One indicates that the result was different from what a person expected. The other indicates that the situation itself has not developed as the person expected. The technical names are reward prediction error and state prediction error, but the important point is that both can provide clues about what a person thinks should be happening.

    The researchers found that these signals could be distinguished in brain activity. They then used them together to help an AI work out two separate things: whether it had chosen the wrong action, and whether it had misunderstood the person’s goal in the first place. That distinction matters because the same action can sometimes serve different purposes, while different actions can achieve the same goal.

    The research is still a long way from an AI that casually reads people’s minds. The experiment relies on EEG equipment to measure brain signals, and the researchers are demonstrating a scientific method for detecting mismatches between human intentions and AI behaviour. It is not a consumer product that can currently be plugged into ChatGPT, a phone or a home robot.

    The next stage is to develop this idea for more natural cooperation between people and machines. KAIST says the approach could eventually be applied to physical robots in homes and factories, autonomous vehicles, medical robots and rehabilitation systems. The team’s ongoing work with Microsoft Research Asia is aimed at developing brain-computer technologies that allow humans and AI to communicate and collaborate more naturally.

    There is no announcement that this particular technology is being released as a commercial product. For now, it remains a research advance. But its significance is clear: the future of AI may not depend only on machines understanding what we say. It may also depend on machines becoming better at recognising when what they have understood is not what we actually meant.

    Reference

    https://pubmed.ncbi.nlm.nih.gov/42636135

  • AWS Gives Students Free Access To Kiro For A Year

    AWS Gives Students Free Access To Kiro For A Year

    Amazon Web Services (AWS) has announced the expansion of its free Kiro program, giving students at 132 universities across 18 countries access to the AI-powered software engineering agent for one year.

    The initiative aims to help students develop practical artificial intelligence (AI) and software-development skills before entering the workforce.

    Key Features

    Under the program, eligible students receive 1,000 Kiro credits every month without requiring a credit card. They also gain access to the latest AI models and Kiro Web, a zero-install version of the tool.

    This expanded access is intended to overcome the limitations of conventional free AI tools, which often impose usage restrictions that can interrupt student projects.

    Kiro uses a “spec-driven development” approach. Instead of simply generating code from prompts, students first define requirements, design decisions, and implementation steps. Kiro then helps generate code while students review the results and ensure they meet the original specifications. This encourages critical thinking, problem-solving, and responsible use of AI alongside technical skills.

    Results and Global Expansion

    The program began as a pilot in March 2026 involving 11 universities in two countries. Thousands of students subsequently used Kiro to develop projects, including an accessibility application for visually impaired users and a voice-enabled wellness platform connecting patients with medical professionals.

    The latest expansion increases university participation twelvefold and extends the initiative across Asia Pacific, Europe, Latin America, and North America. Participating institutions include universities such as the National University of Singapore, Imperial College London, the University of Toronto, and the Massachusetts Institute of Technology.

    Image credit: AWS

  • Prompt Goblin: The Person Who Can’t Stop Tweaking AI

    Prompt Goblin: The Person Who Can’t Stop Tweaking AI

    A prompt goblin is someone who treats AI prompting less like a skill and more like an obsession.

    They constantly tweak, remix, test, and over-engineer prompts to squeeze a little more intelligence out of an AI model. Give them a simple question, and they might respond with a 47-line system prompt, five constraints, three examples, and a request for the AI to “think like a world-class expert.”

    The term is playful, but it captures a real behaviour emerging around generative AI. Prompt goblins enjoy discovering the strange ways wording, context, instructions, and formatting can change an AI’s output. They experiment endlessly, often saving successful prompts like treasured recipes.

    In 2026, the prompt goblin is evolving. As AI systems become better at understanding natural language, obsessive users are shifting from clever prompts toward context engineering, agents, tools, and workflows.

    In other words: the prompt goblin may be disappearing, but probably not before trying 37 more prompts.

  • The Rising Popularity Of AI Detection Tool “Pangram”

    The Rising Popularity Of AI Detection Tool “Pangram”

    Artificial intelligence (AI) has made it remarkably easy to create essays, articles, social-media posts, reviews and even books within seconds. But this convenience has created a new problem: How do we know whether something was actually written by a person? This is the question behind the growing popularity of “Pangram”, an AI-content detection tool.

  • How Money And Speed Are Affecting AI Safety

    How Money And Speed Are Affecting AI Safety

    Artificial intelligence (AI) is developing very quickly, but this growth is also creating concerns about safety and responsibility.

    My latest newsletter, “The Disappearing Watchdogs: How Commercial Pressure May Be Reshaping AI Accountability,” explains how business pressure may be affecting the way AI companies handle safety and ethics.

    Some major AI companies have reduced or reorganized teams that were specifically responsible for AI safety, alignment, and risk. These teams were created to identify possible dangers and make sure AI systems were developed responsibly. In some cases, safety responsibilities have been moved into regular engineering and product teams.

    All of this can create a conflict between speed and safety. AI companies face strong competition and pressure to release new products quickly. Engineers and product teams may therefore focus more on meeting deadlines and competing with other companies. When the same teams are responsible for both developing a product and checking its risks, independent safety checks may become weaker.

    So What It Means To You

    AI systems can be used to create scams, spread fake information, influence public opinion, and make decisions that affect areas such as employment, loans, and insurance. Without strong safety measures, these risks could become harder to control.

    AI safety should not depend only on companies policing themselves. Independent testing, auditing, benchmarking, and red-team exercises could provide additional protection.

    Summing up: there’s an important challenge before the AI industry: AI companies need to balance innovation and business growth with public safety. Developing AI quickly is valuable, but safety and ethical responsibility should remain an essential part of the process.

  • Report: AI Tool Detects Heart Disease In Seconds

    Report: AI Tool Detects Heart Disease In Seconds

    A new artificial intelligence (AI) tool developed by doctors could significantly improve the early detection of heart disease. According to The Guardian, the technology can analyse routine electrocardiograms (ECGs) and identify signs of heart failure and heart valve disease in less than two seconds. The development was presented at the European Society of Cardiology’s annual congress in Munich.

    An ECG records the electrical activity of the heart and is one of the most commonly performed medical tests worldwide. However, conventional ECGs cannot reliably detect some forms of heart disease. Patients suspected of having these conditions usually require an echocardiogram, which can involve long waiting times.

    The new AI system has been trained using millions of patient records. In a US trial involving 67,000 patients, it identified up to 81% of people with heart failure and up to 90% of those with heart valve disease. This could allow doctors to identify high-risk patients quickly and prioritise them for further testing and treatment.

    Experts believe the technology could save lives by enabling earlier diagnosis. It could also be used to analyse ECGs performed for unrelated reasons, potentially detecting previously unsuspected heart conditions.

    Overall, the technology demonstrates the growing potential of AI in healthcare. If successfully implemented, it could make heart disease detection faster, more efficient and potentially life-saving.

  • Emergent Behavior In AI

    Emergent Behavior In AI

    Emergent behavior refers to abilities or patterns that appear in an AI system as it becomes larger, more capable, or more complex, even though those behaviors were not explicitly programmed into it.

    A simple way to think about emergence is through scale. An AI model may be trained primarily to predict the next word, but when it reaches a certain level of capability, it can unexpectedly perform tasks such as translating languages, solving mathematical problems, writing code, or following complex instructions. These abilities may not have been directly taught as separate skills.