AI For Real

Tag: artificial intelligence

  • A New Model (AI) Is Launched

    A New Model (AI) Is Launched

    Former OpenAI Chief Technology Officer Mira Murati has taken a major step in challenging the dominance of the artificial intelligence (AI) industry’s biggest players with the release of the first AI model from her startup, “Thinking Machines Lab”.

    The company unveiled “Inkling”, an open-weight foundation model designed to give developers greater flexibility to customize AI systems using their own data, rather than relying on proprietary models controlled by companies such as OpenAI and Anthropic.

    The model contains 975 billion parameters, but activates only 41 billion during any given task, a design intended to improve efficiency by reducing computing costs without sacrificing performance. Which means: The AI model has a vast amount of knowledge stored inside it, but it doesn’t use all of it every time you ask a question. Instead, it activates only the parts that are relevant to the task at hand. This makes the system faster and cheaper to run while still delivering high-quality answers.

    Thinking Machines Lab said its strategy focuses on balancing affordability and adaptability instead of competing solely on raw computing power. Alongside Inkling, the company is promoting “Tinker”, a cloud-based fine-tuning platform that enables developers to customize large AI models without managing complex infrastructure.

    Here’s What You Need To Really Know

    Unlike fully closed AI systems, Inkling’s open-weight design allows organizations to adapt the model to specialized tasks while retaining greater control over their data and applications.

    So what makes Inkling different is that it gives users more freedom. Most popular AI models are like rented apartments. You can use them, but you cannot change how they are built. Inkling is more like buying a house: businesses and developers can modify it to suit their own needs. It is also designed to work more efficiently by using only the parts of its “brain” needed for a task, making it faster and cheaper to run. This means companies can build AI tools tailored to their business without spending as much on computing or depending entirely on big tech firms.

    Inkling was trained using text, images, audio and video data and includes safeguards against misuse, including protections against cyberattacks and biological threats. The company also announced that the model was developed using Nvidia hardware under a multiyear partnership with the chipmaker.

    With its first model release, Thinking Machines Lab is positioning itself as a key contender in the rapidly evolving AI market, betting that openness, customization and lower costs will appeal to enterprises seeking alternatives to today’s dominant AI platforms.

    Image credit: Thinking Machines Lab

  • 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.

  • Legal Profession Grapples With AI Boom As Reliance Grows

    Legal Profession Grapples With AI Boom As Reliance Grows

    A decision by the University of Chicago Law School to ban laptops and phones in first year classrooms has become a defining example of how deeply artificial intelligence (AI) has entered legal education.

    The move is not an attempt to reject technology altogether. Instead, it reflects growing concern that future lawyers are becoming overly dependent on AI tools before mastering the fundamentals of legal reasoning, research and advocacy.

    The school will continue teaching students how to use AI responsibly, but only after they have built core analytical skills without digital assistance.

    Signals a Broader Shift

    The development highlights a broader shift taking place across legal fraternities worldwide. Law schools, law firms and courts are rapidly embracing generative AI for drafting contracts, summarising case law, reviewing documents, conducting legal research and even preparing litigation strategies.

    Tools such as Harvey, Legora and AI powered research platforms are becoming increasingly common in legal practice, promising significant gains in speed and efficiency.

    Yet the rapid adoption has also exposed serious risks. Several lawyers in the United States and elsewhere have faced court sanctions after submitting filings containing fictitious cases generated by AI chatbots. These incidents have fuelled concerns about “hallucinations”, where AI confidently produces inaccurate legal information.

  • Music Industry Introduces AI Labels To Boost Transparency

    Music Industry Introduces AI Labels To Boost Transparency

    The global music industry has introduced a new labelling system aimed at helping listeners distinguish between music created entirely by artificial intelligence and songs made by human artists with AI assistance.

    The initiative seeks to promote greater transparency as AI-generated content becomes increasingly common on streaming platforms.

    The voluntary framework has been developed jointly by leading music industry organisations, including the Recording Industry Association of America (RIAA), the International Federation of the Phonographic Industry (IFPI), the Grammys, SAG-AFTRA, and several independent music associations.

    Under the new system, tracks will carry one of two labels: “AI-generated” for songs in which vocals or major instrumental performances are produced by artificial intelligence, and “AI-assisted” for music created primarily by humans using AI tools during production.

    The move comes amid growing concerns over the rapid rise of AI-generated music and its impact on artists, copyright, and listener trust. Industry leaders believe the labels will provide consumers with greater clarity about how songs are created while allowing AI to remain a creative tool rather than replacing human artistry.

    The proposed labels are expected to function similarly to existing explicit-content warnings on streaming services. However, implementation will rely on voluntary disclosures by artists, record labels, and distributors.

    Some platforms have already begun adopting similar measures. Apple Music has introduced AI transparency tags, while Spotify allows artists to disclose AI involvement through song credits.

    The organisations behind the initiative said the framework is designed to evolve alongside advances in AI technology.

  • AI Driven Cybersecurity Monitoring Gains Momentum As EY India Launches Cyber Risk Platform

    AI Driven Cybersecurity Monitoring Gains Momentum As EY India Launches Cyber Risk Platform

    Artificial intelligence (AI) is moving beyond threat detection and into continuous cybersecurity monitoring.

    EY India is launching an AI-powered Cyber Performance Management platform that aims to help enterprises measure cyber risk in real time rather than relying on periodic security reviews.

    What this indicates is a major shift in enterprise security, where AI is increasingly being used to monitor, analyse and prioritise cyber threats across complex digital environments.

    The new platform, said the company, brings together data from an organization’s cybersecurity tools to provide a “live” view of its security posture.

    Instead of presenting security teams with thousands of alerts, the AI engine evaluates vulnerabilities, identifies the most critical risks and links them to potential business impact. This allows security teams and business leaders to understand which threats require immediate attention.

    Traditional cybersecurity programmes often depend on manual assessments and periodic audits, making it difficult to keep pace with rapidly evolving threats. EY’s platform is designed to replace that approach with continuous AI-driven monitoring that can detect changes in an organisation’s risk profile as they happen.

    The company said the platform combines cybersecurity posture, threat exposure, detection and response into a single system. AI analyses large volumes of security data, helping organisations quantify cyber risk in financial and operational terms rather than relying solely on technical metrics. This gives executives a clearer understanding of how cyber incidents could affect business operations, compliance and revenue.

    The launch comes as enterprises continue expanding cloud infrastructure, connected devices and AI applications, creating larger attack surfaces that are increasingly difficult to monitor manually. As cyber threats become more sophisticated, organisations are looking to AI to automate routine monitoring, speed up threat prioritisation and improve response times.

    The announcement highlights a growing trend in enterprise security, where AI is no longer focused only on identifying attacks but on providing continuous cyber performance monitoring. As businesses face increasing regulatory pressure and more frequent cyber threats, real-time AI monitoring is becoming an essential part of enterprise risk management rather than an optional security upgrade.

    Image credit: EY/Facebook

  • What Are Wearable AI Devices

    What Are Wearable AI Devices

    Imagine having an AI assistant that’s always with you, not just on your phone, but on your wrist, in your glasses, or even in your earbuds. That’s the promise of wearable AI.

    What Are Wearable AI Devices?

    Wearable AI devices are gadgets you wear on your body that use artificial intelligence to understand what’s happening around you and help you in real time.

    Unlike traditional wearables that simply collect data, AI-powered wearables can analyze information, answer questions, make suggestions, and even anticipate your needs.

    Think of them as smart companions that are designed to make everyday tasks easier.

  • Why Your AI Isn’t Replacing You Yet

    Why Your AI Isn’t Replacing You Yet

    We’ve all seen the marketing demos. A sleek AI assistant effortlessly cruises through a complex workflow: it checks your calendar, reads your emails, updates your project management board, and fires off a summary report. All while you sip your morning coffee.

    It looks like magic. But for many of us, the reality of “Living With AI” feels a lot less like a magic show and more like trying to teach a brilliant, hyper-fast toddler to organize a library.

    You’ve likely asked an AI a question about your work and gotten a brilliant, concise answer. But when you asked it to actually do something across your company’s software ecosystem, it hit a wall. Why can your AI explain a concept in seconds, but struggle to move a simple task from your inbox to your task tracker?

    The answer lies in a problem as old as computers themselves: the “Digital Silo”.

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  • AI Tools May Be Eroding Professionals’ Skills, Researchers Warn

    AI Tools May Be Eroding Professionals’ Skills, Researchers Warn

    Artificial intelligence (AI) tools are helping professionals work faster, but growing evidence suggests they could also weaken the very skills people have spent years developing, prompting researchers to call for urgent studies on how to prevent AI driven deskilling.

    The concern is particularly strong in medicine. A survey of US health care workers published this month in nature found that 70% of nurses and 77% of physicians worry that over reliance on AI systems could reduce their clinical skills.

    Researchers say those fears are beginning to find support in real world evidence. The study published last October in The Lancet Gastroenterology and Hepatology found that experienced physicians became less effective at detecting precancerous intestinal growths when an AI tool they had grown accustomed to using was suddenly unavailable.

    The study followed physicians in Poland who specialized in endoscopy and had each performed at least 2,000 colonoscopies during their careers. Researchers introduced an AI system that analysed colonoscopy images in real time and highlighted adenomas, a type of precancerous lesion. The system was available only on certain days, allowing researchers to compare doctors’ performance with and without AI assistance.

    Before the technology was introduced, doctors detected at least one adenoma in 28.4% of colonoscopies. After three months of using the AI system, the detection rate during procedures performed without AI assistance fell to 22.4%.

    The findings suggest that even highly experienced clinicians may become less effective at tasks they routinely perform if they become dependent on AI, researchers said. The study authors argued that continuous exposure to AI tools could leave clinicians less motivated, less focused and less responsible when making decisions without technological support.

    The issue is not limited to medicine. Researchers at AI company Anthropic have also begun investigating whether software engineers lose skills when they rely heavily on AI coding assistants.

    In a randomized controlled trial involving 52 software engineers, participants completed a basic coding task with access to online resources, while half were also encouraged to use an AI assistant. The study is part of a broader effort to understand how AI affects long term expertise.

    As AI becomes a routine part of workplaces across industries, researchers say the challenge will be finding ways to capture the technology’s productivity benefits without allowing essential human expertise to fade.

  • Cisco To Roll Out AI Agents To All 90,000 Employees From August

    Cisco To Roll Out AI Agents To All 90,000 Employees From August

    It may well prove to be a first in the global corporate world insofar as sheer numbers are concerned.

    Cisco will begin deploying personalised AI agents to its entire workforce of around 90,000 employees starting this August, marking one of the largest enterprise-wide AI rollouts to date.

    The initiative is aimed at boosting productivity by giving every employee an AI assistant capable of answering questions, automating routine tasks, and routing requests to the most suitable AI model.

    According to Cisco Chief Financial Officer Mark Patterson, the company has designed the system to dynamically select the best AI model for each task, helping optimise both performance and costs. Much of the AI infrastructure will also run on Cisco’s own systems to improve control over data and reduce token usage.

    The rollout will be accompanied by employee upskilling programmes and is expected to support innovation across functions such as finance and operations. However, Cisco executives acknowledged that adopting AI at this scale will not be without challenges.

    But what makes this move historic? Many other companies before Cisco have done this. Like Microsoft and Salesforce.

    What makes the announcement notable is its scale. Around 90,000 employees will each get a personalized AI agent. Multiple reports describe it as “one of the largest” enterprise-wide AI agent deployments.

  • Cloudflare Launches AI Crawler Management Tools For Websites

    Cloudflare Launches AI Crawler Management Tools For Websites

    Here’s some good news for website owners globally. Cloudflare has expanded its tools for managing artificial intelligence (AI) web crawlers, giving website owners more control over how AI companies access their content as concerns grow over declining referral traffic and content monetization. The announcement marks the company’s second annual “Content Independence Day” initiative.

    The company said its previous approach, which allowed customers to block AI bots with a single setting, did not provide enough flexibility.

    Instead, website owners can now distinguish between three categories of AI traffic: Search crawlers that index content for search engines, Agent crawlers that retrieve information on behalf of AI assistants, and Training crawlers that collect data for developing AI models. Customers can choose which categories to allow or block, including those using Cloudflare’s free tier.

    Cloudflare also announced new default settings that will take effect on September 15, 2026. For new domains, Training and Agent crawlers will be blocked by default on pages displaying advertisements, while Search crawlers will remain allowed. The company argues that ad-supported pages are designed for human visitors and that unrestricted AI access could undermine publisher revenue. Existing customers will be able to opt out of the new defaults before they are applied.

    Another significant change targets AI companies that use a single crawler for multiple purposes. Under the updated system, multi-purpose crawlers will be evaluated based on all of their activities. This means a crawler used for both search indexing and AI training could be blocked if a website owner chooses to restrict training access. Cloudflare said it encourages AI companies to separate their crawlers to improve transparency for publishers.

    For enterprise customers, Cloudflare also introduced BotBase, a searchable database of verified bots and automated agents designed to improve visibility into automated traffic. The company said the new controls reflect a broader effort to help publishers balance AI innovation with sustainable business models for online content.