AI For Real

Tag: AI systems

  • Daybreak: OpenAI’s $1 Billion Push To Reinforce Global Cybersecurity

    Daybreak: OpenAI’s $1 Billion Push To Reinforce Global Cybersecurity

    OpenAI has launched “Daybreak for Frontline Defenders”, a global cybersecurity initiative designed to help organizations protect essential services from increasingly sophisticated AI-enabled cyberattacks.

    The initiative represents a $1 billion commitment to subsidized access to AI-powered cybersecurity tools, training, technical assistance, and partnerships.

    The program focuses particularly on organizations that often have significant security responsibilities but limited resources. These include water and wastewater utilities, electric-grid operators, state and local governments, community and regional banks, nonprofits, and open-source software maintainers.

    OpenAI says its tools can assist defenders with reviewing legacy code, investigating suspicious activity, identifying and validating vulnerabilities, prioritizing risks, and developing and testing security fixes.

    In simple terms, OpenAI’s Daybreak program is an effort to help organizations that protect essential services, such as electricity, water, local governments, banks, and nonprofits defend themselves against cyberattacks using AI.

    These organizations may not have the same cybersecurity resources as large corporations, so OpenAI plans to provide subsidized access to AI-powered security tools, training, and technical support. Daybreak is about using AI to help the people defending critical systems become faster and more capable at stopping cyber threats, while keeping human experts responsible for making decisions and verifying the results.

    A major component of the initiative is Daybreak for America, which initially concentrates on US essential services before expanding to partner countries.

    OpenAI says it intends to expand the model to partner countries in the coming weeks. Access is being rolled out progressively rather than being universally available worldwide from day one.

    The initiative reflects OpenAI’s broader view that AI should strengthen cybersecurity defenses as AI-powered attacks become more capable. However, the program also highlights the importance of human oversight: Daybreak is designed around verification, monitoring, safeguards, and authorized defensive use.

  • How AI Is Changing The US Open Experience For Tennis Fans

    How AI Is Changing The US Open Experience For Tennis Fans

    For the average tennis player, watching a Grand Slam can be as much about learning from the professionals as it is about enjoying the competition. At the 2026 US Open, IBM and the United States Tennis Association (USTA) are using artificial intelligence to make that experience more personal, easier to follow and more informative.

    The biggest change is a new Live Updates homepage on USOpen.org and the US Open app. Instead of scrolling through a huge amount of tournament information, fans can prioritize their favourite players and quickly find the matches, stories and insights that interest them. For a recreational player following a favourite star, it means less searching and more time watching tennis.

    AI is also offering a closer look at one of the most important shots in the game: the serve. The new Serve Quality metric analyses every serve in all 254 singles matches using limb-tracking technology. It follows 21 points on the player’s body and racquet 50 times per second, turning complex movement data into an easier-to-understand measure of serve quality.

    That could be particularly useful for club players. Rather than simply seeing that a professional hit a fast serve, fans can get more insight into the mechanics and quality behind it—potentially giving them ideas to take onto their own practice court.

    AI is also helping fans understand momentum. Key Moments identify important turning points, while Likelihood to Win uses match statistics, historical data, expert opinion and momentum to show how the contest is changing. An upgraded Match Chat assistant lets fans ask questions and receive answers using live data, analysis, photos and video.

  • Reward Hacking: When AI Optimises The Wrong Goal

    Reward Hacking: When AI Optimises The Wrong Goal

    In our segment on “AI Lingo”, today, we shall talk about “Reward Hacking”.

    Now imagine telling an AI agent, “Get the highest possible score.” You expect it to work hard, follow the rules and achieve the intended goal. But what if it discovers a shortcut?

    That is reward hacking.

  • What Does “Lost In The Middle” Mean?

    What Does “Lost In The Middle” Mean?

    Have you ever given an AI a long prompt with lots of details, only to find it missed one of the most important points? You may have encountered a phenomenon known as “Lost in the Middle.”

    In simple terms, Lost in the Middle happens when an AI pays less attention to information buried in the middle of a long prompt. It tends to remember the beginning of your instructions and the most recent details better than the content in between.

    Think of it like reading a long email. You probably remember the opening, the conclusion, and the call to action, but the details halfway through are easier to overlook. AI models can behave in a similar way.

  • Make Your Own AI with Pi (Raspberry)

    Make Your Own AI with Pi (Raspberry)

    Who says you need a supercomputer to build artificial intelligence (AI)? Today, something as powerful as AI can run on something as small as a Raspberry Pi!

    This tiny, affordable computer is opening the doors to a world of smart technology, allowing students, makers, and tech enthusiasts to create everything from face-recognition systems and voice assistants to robots and smart home gadgets.

    With “AI Projects with Raspberry Pi”, learning AI is no longer limited to experts. It’s now fun, hands-on, and within reach of anyone with curiosity and a Raspberry Pi.

    The new guide is packed with hands-on projects that enable students, hobbyists, educators, and makers to explore the rapidly growing world of artificial intelligence using Raspberry Pi hardware.

    The publication introduces readers to essential AI concepts such as computer vision, machine learning, and natural language processing through practical, project-based learning. Instead of focusing only on theory, it encourages users to build real-world applications, including smart cameras, voice-controlled assistants, environmental monitoring systems, and intelligent automation projects. Many of these applications run directly on Raspberry Pi devices, showcasing the power of edge AI, where data is processed locally for faster performance, enhanced privacy, and reduced reliance on cloud services.

    What’s Raspberry Pi?

    Raspberry Pi is a series of compact, affordable single-board computers developed to make computing, programming, and digital innovation accessible to everyone.

    Since its launch in 2012, Raspberry Pi has become a global favourite among students, educators, hobbyists, and professionals for learning coding, building electronics projects, and developing applications in robotics, IoT, AI, and automation. Despite its small size, a Raspberry Pi delivers impressive computing power and supports a wide range of programming languages, operating systems, and hardware accessories. With a strong emphasis on hands-on learning and innovation, Raspberry Pi has empowered millions of people worldwide to turn creative ideas into real-world technology solutions.

    Beyond teaching technical skills, “AI Projects with Raspberry Pi” inspires creativity and innovation. It highlights how affordable computing can be transformed into intelligent systems capable of solving real-world problems across education, robotics, smart homes, and environmental monitoring. By combining powerful AI tools with easy-to-use hardware, Raspberry Pi continues to empower the next generation of innovators.

    Image credit: Raspberry Pi

  • Growing Role Of Generative AI In University Assignments: Trends And Challenges

    Growing Role Of Generative AI In University Assignments: Trends And Challenges

    The growing use of generative artificial intelligence (gen AI) in higher education reflects a significant shift in how students approach academic work.

    For example, fresh data from Turnitin indicates that 53.6% of Australian university submissions screened between October 2025 and April 2026 contained some level of AI-generated content. Within this group, around 10% of submissions were assessed as containing more than 80% AI-written material.

    These figures suggest that AI tools have moved beyond experimentation and become part of mainstream student workflows.

    The data also highlights that AI use exists along a broad spectrum rather than as a single category of behaviour.

    Different Strokes For Different People

    Some students appear to use AI for limited tasks such as brainstorming, editing or improving clarity, while others rely on it more extensively to generate substantial portions of their assignments. This distinction complicates conventional discussions about academic integrity because AI use is no longer simply a matter of whether students use these tools, but how they incorporate them into the learning process.

    The widespread adoption of generative AI raises questions about the purpose and design of university assessments.

    Traditional take-home essays and written assignments were created on the assumption that students would independently research, analyse and compose their responses.

    As AI systems become increasingly capable of producing coherent and well-structured text, these assumptions are being challenged. Universities are therefore examining whether existing forms of assessment continue to measure the knowledge and skills they were originally intended to evaluate.

    The findings also indicate that institutional responses are evolving. Many Australian universities, for example, have introduced policies governing the acceptable use of generative AI, yet translating these policies into consistent classroom practice remains an ongoing process.

    Variations in assessment design, disciplinary expectations and instructor guidance mean that students may encounter different standards across courses. This creates an environment in which clear communication about acceptable AI use becomes increasingly important.

    Written by AI or Not?

    Another notable aspect of the discussion concerns the limitations of AI detection.

    While detection tools can identify patterns consistent with AI-generated writing, they cannot always determine how students used AI or distinguish between acceptable assistance and inappropriate dependence. This makes assessment of student work more complex than traditional plagiarism detection, where copied material can often be directly traced to an original source.

    The Australian data also gains significance when viewed in an international context. Turnitin reported similar patterns in the United Kingdom, while submissions in the United States showed an even higher proportion of heavily AI-generated work. These comparisons suggest that the integration of generative AI into higher education is part of a broader global trend rather than an isolated national development.

    Conclusion

    Overall, the increasing use of generative AI by university students illustrates a changing educational landscape in which digital tools are becoming embedded in academic practice. The available evidence points to a shift from viewing AI as an emerging technology to recognising it as a routine component of student work. As universities continue to refine policies, assessment methods and teaching practices, the discussion is likely to focus on defining the role that AI should play within higher education while ensuring that assessment continues to reflect intended learning outcomes.

  • Meeting Productivity With AI – AI Productivity Playbook -5

    Meeting Productivity With AI – AI Productivity Playbook -5

    Meetings are essential for collaboration, but they often consume more time than necessary. Employees spend hours preparing agendas, taking notes, tracking action items, and following up after discussions.

    Artificial Intelligence (AI) transforms meetings from time-consuming events into highly productive sessions by automating routine tasks and helping teams focus on meaningful conversations.

    Why AI Matters in Meetings

    Traditional meetings often suffer from:

    • Lack of clear agendas
    • Poor note-taking
    • Missed action items
    • Lengthy follow-up emails
    • Difficulty recalling important decisions

    AI addresses these challenges by capturing conversations, organizing information, and generating actionable insights in real time.

  • AI For Email Mastery – AI Productivity Playbook –4

    AI For Email Mastery – AI Productivity Playbook –4

    Most people spend 2–3 hours every day managing emails.

    What if AI could cut that time in half?

    Here are 7 ways to master your inbox with AI:

  • Summarize Long Articles, Reports, And PDFs In Minutes – AI Productivity Playbook-2

    Summarize Long Articles, Reports, And PDFs In Minutes – AI Productivity Playbook-2

    The Challenge

    We consume more information than ever before.

    Articles, reports, research papers, newsletters, meeting notes, PDFs—the list never ends. Yet most of us don’t have the time to read everything in detail.

    The result? Information overload.

    The good news is that AI can help you extract the key insights in a fraction of the time.

    The Workflow

    1. Open your AI tool of choice.
    2. Paste the article, report, or document (or upload the PDF if supported).
    3. Use the prompt below.
    4. Review the summary and decide whether the full document deserves a deeper read.

    Think of AI as your first-pass analyst. It helps you identify what matters before you invest your time.

  • AI Learns House Cleaning Through Human Helpers

    AI Learns House Cleaning Through Human Helpers

    There’s a meme somewhere that says, ” I want AI to do this”, depicting a robot doing kitchen work in a house. Well, that wish is coming true, at least in New York.

    Imagine opening your front door to a team of cleaners and a private chef offering their services for free. It sounds like winning a bizarre lottery, but there is one small catch: every sponge wipe, saucepan scrub and misplaced sock is being recorded for science, or more specifically, for robots.

    According to this report, a New York-based initiative called “Shift”, run by AI company “Micro AGI”, is sending camera-equipped workers into people’s homes to collect data that could help train future household robots. The goal is ambitious: teach machines how to navigate the chaos of real homes, where every kitchen is different and every junk drawer appears to operate under its own laws of physics.

    Workers wear cameras mounted on their caps, capturing detailed footage of cleaning and cooking tasks. Founder Bercan Kilic says the effort is necessary because robots need vast amounts of real-world data to learn how to interact with objects under constantly changing conditions. The company plans to sell anonymised datasets to robotics and AI firms.

    For now, New Yorkers must decide whether a spotless apartment is worth helping train the robot butler of the future, and whether that future knows where they keep the good towels.