ChatGPT

Ever wondered what happens when a company trying to build a ‘brain for the world’ needs to grow up, fast, without selling its soul? Well, OpenAI has just given us a peek as it pledges to keep its nonprofit core amid broader restructuring. OpenAI CEO Sam Altman has laid out their roadmap, and the headline news is: they’re rejigging the money side of things, but their core mission to make Artificial General Intelligence (AGI) work for all of us remains bolted down. In a letter, Altman wrote: “OpenAI is not a normal company and never will be.” It’s a bold statement, but it sets the scene for a company wrestling with how to fund world-changing tech while keeping its ethical compass pointing true north. Cast your mind back, if you will, to OpenAI’s early days. Altman paints a picture that’s a far cry from the tech behemoth it’s becoming. “When we started OpenAI, we did not have a detailed sense for how we were going to accomplish our mission,” he shared. “We started out staring at each other around a kitchen table, wondering what research we should do.” Forget fancy business models or product roadmaps back then. The idea of AI dishing out medical advice, revolutionising how we learn, or needing the kind of computing power that makes your gaming PC look like a pocket calculator – “hundreds of billions of dollars of compute,” as Altman puts it – wasn’t even on the horizon. Even the ‘how’ of building AGI was a bit of a head-scratcher. When OpenAI was founded as a nonprofit, some of the early thinkers at the company apparently thought AI should probably only be trusted to a handful of “trusted people” who could “handle it.” That view has done a complete 180. “We now see a way for AGI to directly empower everyone as the most capable tool in human history,” Altman declared. The big dream? If everyone gets their hands on AGI, we’ll cook up amazing things for each other, pushing society forward. Sure, some might use it for dodgy stuff, but Altman’s betting on humanity: “We trust humanity and think the good will outweigh the bad by orders of magnitude.” Their game plan is what they call “democratic AI.” They want to give us all these incredible tools. They’re even talking about open-sourcing powerful models, saying they want us to make decisions about how ChatGPT behaves. “We want to build a brain for the world and make it super easy for people to use for whatever they want (subject to few restrictions; freedom shouldn’t impinge on other people’s freedom, for example),” Altman explained. And people are already getting stuck in. Scientists are crunching data faster, programmers are coding smarter, and folks are even using ChatGPT to navigate tricky health issues or get advice on tough personal situations. Here’s the rub: the world wants way more AI than they can currently churn out. “We currently cannot supply nearly as much AI as the world wants,” Altman admitted. This insatiable appetite for AI, and the eye-watering sums of cash needed to feed it, is why OpenAI feels it’s time for it to “evolve” beyond a strict nonprofit structure. Altman boiled the restructuring down to three main goals: Getting the dough: They need to find a way to pull in the “hundreds of billions of dollars and may eventually require trillions of dollars” – yes, trillions with a ‘T’ – to make their AI tools available to everyone on the planet. Think of it like building a global superhighway for intelligence. Supercharging the nonprofit: They want their original nonprofit arm to be the “largest and most effective nonprofit in history,” using AI to make a massive positive difference in people’s lives. Delivering AGI that’s helpful and safe: This means doubling down on safety and making sure AI aligns with human values. Altman’s proud of OpenAI’s track record, including creating new “red teaming” methods (where they get clever people to try and break their AI to find flaws) and being open about how their models work. So, what’s the grand plan for this evolution? Crucially, the nonprofit side of OpenAI is staying firmly in the driver’s seat. This isn’t just some vague promise; it came after serious chats with “civic leaders” and the offices of the Attorneys General of California and Delaware. “OpenAI was founded as a nonprofit, is today a nonprofit that oversees and controls the for-profit, and going forward will remain a nonprofit that oversees and controls the for-profit. That will not change,” Altman stated. The bit that is changing is the for-profit LLC that currently sits under the nonprofit. This will morph into a Public Benefit Corporation (PBC). If you’re scratching your head, a PBC is a type of company that’s legally bound to consider its public benefit mission alongside making money. Think of companies like Patagonia or some ethical food brands – they want to do good while still being a business. It’s a model other AGI labs like Anthropic are using too, so it’s becoming a bit of a trend for purpose-driven tech firms. This also means they’re ditching their old, rather head-scratching “capped-profit” system. Altman explained this made sense when it looked like one company might dominate AGI, but now, with lots of players in the game, a “normal capital structure where everyone has stock” is simpler. The nonprofit side of OpenAI won’t just be in the driving seat; it’ll also become a big shareholder in this new PBC. According to Altman, this means the nonprofit will get a hefty chunk of resources to pour into programmes that help AI benefit different communities. As the PBC makes more money, the nonprofit gets more cash to splash on projects in areas like health, education, and science. They’re even getting a special commission to dream up ways their nonprofit work can make AI more democratic. Altman wrapped things up with a healthy dose of optimism, saying, “We believe this sets us up to continue to make rapid, safe progress and to put great AI in the hands of everyone.” OpenAI is clearly trying to attract the colossal funding needed for AGI development while hard-wiring its “benefit all of humanity” mantra into its very DNA. It’s a delicate tightrope walk, and you can bet the entire tech world, and probably a good chunk of the rest of us, will be watching to see if they can pull it off. (Image by Mohamed Hassan) See also: Google AMIE: AI doctor learns to ‘see’ medical images Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Sam Altman: OpenAI to keep nonprofit soul in restructuring appeared first on AI News. View the full article

Google is giving its diagnostic AI the ability to understand visual medical information with its latest research on AMIE (Articulate Medical Intelligence Explorer). Imagine chatting with an AI about a health concern, and instead of just processing your words, it could actually look at the photo of that worrying rash or make sense of your ECG printout. That’s what Google is aiming for. We already knew AMIE showed promise in text-based medical chats, thanks to earlier work published in Nature. But let’s face it, real medicine isn’t just about words. Doctors rely heavily on what they can see – skin conditions, readings from machines, lab reports. As the Google team rightly points out, even simple instant messaging platforms “allow static multimodal information (e.g., images and documents) to enrich discussions.” Text-only AI was missing a huge piece of the puzzle. The big question, as the researchers put it, was “Whether LLMs can conduct diagnostic clinical conversations that incorporate this more complex type of information.” Google teaches AMIE to look and reason Google’s engineers have beefed up AMIE using their Gemini 2.0 Flash model as the brains of the operation. They’ve combined this with what they call a “state-aware reasoning framework.” In plain English, this means the AI doesn’t just follow a script; it adapts its conversation based on what it’s learned so far and what it still needs to figure out. It’s close to how a human clinician works: gathering clues, forming ideas about what might be wrong, and then asking for more specific information – including visual evidence – to narrow things down. “This enables AMIE to request relevant multimodal artifacts when needed, interpret their findings accurately, integrate this information seamlessly into the ongoing dialogue, and use it to refine diagnoses,” Google explains. Think of the conversation flowing through stages: first gathering the patient’s history, then moving towards diagnosis and management suggestions, and finally follow-up. The AI constantly assesses its own understanding, asking for that skin photo or lab result if it senses a gap in its knowledge. To get this right without endless trial-and-error on real people, Google built a detailed simulation lab. Google created lifelike patient cases, pulling realistic medical images and data from sources like the PTB-XL ECG database and the SCIN dermatology image set, adding plausible backstories using Gemini. Then, they let AMIE ‘chat’ with simulated patients within this setup and automatically check how well it performed on things like diagnostic accuracy and avoiding errors (or ‘hallucinations’). The virtual OSCE: Google puts AMIE through its paces The real test came in a setup designed to mirror how medical students are assessed: the Objective Structured Clinical Examination (OSCE). Google ran a remote study involving 105 different medical scenarios. Real actors, trained to portray patients consistently, interacted either with the new multimodal AMIE or with actual human primary care physicians (PCPs). These chats happened through an interface where the ‘patient’ could upload images, just like you might in a modern messaging app. Afterwards, specialist doctors (in dermatology, cardiology, and internal medicine) and the patient actors themselves reviewed the conversations. The human doctors scored everything from how well history was taken, the accuracy of the diagnosis, the quality of the suggested management plan, right down to communication skills and empathy—and, of course, how well the AI interpreted the visual information. Surprising results from the simulated clinic Here’s where it gets really interesting. In this head-to-head comparison within the controlled study environment, Google found AMIE didn’t just hold its own—it often came out ahead. The AI was rated as being better than the human PCPs at interpreting the multimodal data shared during the chats. It also scored higher on diagnostic accuracy, producing differential diagnosis lists (the ranked list of possible conditions) that specialists deemed more accurate and complete based on the case details. Specialist doctors reviewing the transcripts tended to rate AMIE’s performance higher across most areas. They particularly noted “the quality of image interpretation and reasoning,” the thoroughness of its diagnostic workup, the soundness of its management plans, and its ability to flag when a situation needed urgent attention. Perhaps one of the most surprising findings came from the patient actors: they often found the AI to be more empathetic and trustworthy than the human doctors in these text-based interactions. And, on a critical safety note, the study found no statistically significant difference between how often AMIE made errors based on the images (hallucinated findings) compared to the human physicians. Technology never stands still, so Google also ran some early tests swapping out the Gemini 2.0 Flash model for the newer Gemini 2.5 Flash. Using their simulation framework, the results hinted at further gains, particularly in getting the diagnosis right (Top-3 Accuracy) and suggesting appropriate management plans. While promising, the team is quick to add a dose of realism: these are just automated results, and “rigorous assessment through expert physician review is essential to confirm these performance benefits.” Important reality checks Google is commendably upfront about the limitations here. “This study explores a research-only system in an OSCE-style evaluation using patient actors, which substantially under-represents the complexity… of real-world care,” they state clearly. Simulated scenarios, however well-designed, aren’t the same as dealing with the unique complexities of real patients in a busy clinic. They also stress that the chat interface doesn’t capture the richness of a real video or in-person consultation. So, what’s the next step? Moving carefully towards the real world. Google is already partnering with Beth Israel Deaconess Medical Center for a research study to see how AMIE performs in actual clinical settings with patient consent. The researchers also acknowledge the need to eventually move beyond text and static images towards handling real-time video and audio—the kind of interaction common in telehealth today. Giving AI the ability to ‘see’ and interpret the kind of visual evidence doctors use every day offers a glimpse of how AI might one day assist clinicians and patients. However, the path from these promising findings to a safe and reliable tool for everyday healthcare is still a long one that requires careful navigation. (Photo by Alexander Sinn) See also: Are AI chatbots really changing the world of work? Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Google AMIE: AI doctor learns to ‘see’ medical images appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

We’ve heard endless predictions about how AI chatbots will transform work, but data paints a much calmer picture—at least for now. Despite huge and ongoing advancements in generative AI, the massive wave it was supposed to create in the world of work looks more like a ripple so far. Researchers Anders Humlum (University of Chicago) and Emilie Vestergaard (University of Copenhagen) didn’t just rely on anecdotes. They dug deep, connecting responses from two big surveys (late 2023 and 2024) with official, detailed records about jobs and pay in Denmark. The pair zoomed in on around 25,000 people working in 7,000 different places, covering 11 jobs thought to be right in the path of AI disruption. Everyone’s using AI chatbots for work, but where are the benefits? What they found confirms what many of us see: AI chatbots are everywhere in Danish workplaces now. Most bosses are actually encouraging staff to use them, a real turnaround from the early days when companies were understandably nervous about things like data privacy. Almost four out of ten employers have even rolled out their own in-house chatbots, and nearly a third of employees have had some formal training on these tools. When bosses gave the nod, the number of staff using chatbots practically doubled, jumping from 47% to 83%. It also helped level the playing field a bit. That gap between men and women using chatbots? It shrank noticeably when companies actively encouraged their use, especially when they threw in some training. So, the tools are popular, companies are investing, people are getting trained… but the big economic shift? It seems to be missing in action. Using statistical methods to compare people who used AI chatbots for work with those who didn’t, both before and after ChatGPT burst onto the scene, the researchers found… well, basically nothing. “Precise zeros,” the researchers call their findings. No significant bump in pay, no change in recorded work hours, across all 11 job types they looked at. And they’re pretty confident about this – the numbers rule out any average effect ******* than just 1%. This wasn’t just a blip, either. The lack of impact held true even for the keen beans who jumped on board early, those using chatbots daily, or folks working where the boss was actively pushing the tech. Looking at whole workplaces didn’t change the story; places with lots of chatbot users didn’t see different trends in hiring, overall wages, or keeping staff compared to places using them less. Productivity gains: More of a gentle nudge than a shove Why the big disconnect? Why all the hype and investment if it’s not showing up in paychecks or job stats? The study flags two main culprits: the productivity boosts aren’t as huge as hoped in the real world, and what little gains there are aren’t really making their way into wages. Sure, people using AI chatbots for work felt they were helpful. They mentioned better work quality and feeling more creative. But the number one benefit? Saving time. However, when the researchers crunched the numbers, the average time saved was only about 2.8% of a user’s total work hours. That’s miles away from the huge 15%, 30%, even 50% productivity jumps seen in controlled lab-style experiments (RCTs) involving similar jobs. Why the difference? A few things seem to be going on. Those experiments often focus on jobs or specific tasks where chatbots really shine (like coding help or basic customer service responses). This study looked at a wider range, including jobs like teaching where the benefits might be smaller. The researchers stress the importance of what they call “complementary investments”. People whose companies encouraged chatbot use and provided training actually did report ******* benefits – saving more time, improving quality, and feeling more creative. This suggests that just having the tool isn’t enough; you need the right support and company environment to really unlock its potential. And even those modest time savings weren’t padding wallets. The study reckons only a tiny fraction – maybe 3% to 7% – of the time saved actually showed up as higher earnings. It might be down to standard workplace inertia, or maybe it’s just harder to ask for a raise based on using a tool your boss hasn’t officially blessed, especially when many people started using them off their own bat. Making new work, not less work One fascinating twist is that AI chatbots aren’t just about doing old work tasks faster. They seem to be creating new tasks too. Around 17% of people using them said they had new workloads, mostly brand new types of tasks. This phenomenon happened more often in workplaces that encouraged chatbot use. It even spilled over to people not using the tools – about 5% of non-users reported new tasks popping up because of AI, especially teachers having to adapt assignments or spot AI-written homework. What kind of new tasks? Things like figuring out how to weave AI into daily workflows, drafting content with AI help, and importantly, dealing with the ethical side and making sure everything’s above board. It hints that companies are still very much in the ‘figuring it out’ phase, spending time and effort adapting rather than just reaping instant rewards. What’s the verdict on the work impact of AI chatbots? The researchers are careful not to write off generative AI completely. They see pathways for it to become more influential over time, especially as companies get better at integrating it and maybe as those “new tasks” evolve. But for now, their message is clear: the current reality doesn’t match the hype about a massive, immediate job market overhaul. “Despite rapid adoption and substantial investments… our key finding is that AI chatbots have had minimal impact on productivity and labor market outcomes to date,” the researchers conclude. It brings to mind that old quote about the early computer age: seen everywhere, except in the productivity stats. Two years on from ChatGPT’s launch kicking off the fastest tech adoption we’ve ever seen, its actual mark on jobs and pay looks surprisingly light. The revolution might still be coming, but it seems to be taking its time. See also: Claude Integrations: Anthropic adds AI to your favourite work tools Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Are AI chatbots really changing the world of work? appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

Anthropic just launched ‘Integrations’ for Claude that enables the AI to talk directly to your favourite daily work tools. In addition, the company has launched a beefed-up ‘Advanced Research’ feature for digging deeper than ever before. Starting with Integrations, the feature builds on a technical standard Anthropic released last year (the Model Context Protocol, or MCP), but makes it much easier to use. Before, setting this up was a bit technical and local. Now, developers can build secure bridges allowing Claude to connect safely with apps over the web or on your desktop. For end-users of Claude, this means you can now hook it up to a growing list of popular work software. Right out of the gate, they’ve included support for ten big names: Atlassian’s Jira and Confluence (hello, project managers and dev teams!), the automation powerhouse Zapier, Cloudflare, customer comms tool Intercom, plus Asana, Square, Sentry, PayPal, Linear, and Plaid. Stripe and GitLab are joining the party soon. So, what’s the big deal? The real advantage here is context. When Claude can see your project history in Jira, read your team’s knowledge base in Confluence, or check task updates in Asana, it stops guessing and starts understanding what you’re working on. “When you connect your tools to Claude, it gains deep context about your work—understanding project histories, task statuses, and organisational knowledge—and can take actions across every surface,” explains Anthropic. They add, “Claude becomes a more informed collaborator, helping you execute complex projects in one place with expert assistance at every step.” Let’s look at what this means in practice. Connect Zapier, and you suddenly give Claude the keys to thousands of apps linked by Zapier’s workflows. You could just ask Claude, conversationally, to trigger a complex sequence – maybe grab the latest sales numbers from HubSpot, check your calendar, and whip up some meeting notes, all without you lifting a finger in those apps. For teams using Atlassian’s Jira and Confluence, Claude could become a serious helper. Think drafting product specs, summarising long Confluence documents so you don’t have to wade through them, or even creating batches of linked Jira tickets at once. It might even spot potential roadblocks by analysing project data. And if you use Intercom for customer chats, this integration could be a game-changer. Intercom’s own AI assistant, Fin, can now work with Claude to do things like automatically create a bug report in Linear if a customer flags an issue. You could also ask Claude to sift through your Intercom chat history to spot patterns, help debug tricky problems, or summarise what customers are saying – making the whole journey from feedback to fix much smoother. Anthropic is also making it easier for developers to build even more of these connections. They reckon that using their tools (or platforms like Cloudflare that handle the tricky bits like security and setup), developers can whip up a custom Integration with Claude in about half an hour. This could mean connecting Claude to your company’s unique internal systems or specialised industry software. Beyond tool integrations, Claude gets a serious research upgrade Alongside these new connections, Anthropic has given Claude’s Research feature a serious boost. It could already search the web and your Google Workspace files, but the new ‘Advanced Research’ mode is built for when you need to dig really deep. Flip the switch for this advanced mode, and Claude tackles big questions differently. Instead of just one big search, it intelligently breaks your request down into smaller chunks, investigates each part thoroughly – using the web, your Google Docs, and now tapping into any apps you’ve connected via Integrations – before pulling it all together into a detailed report. Now, this deeper digging takes a bit more time. While many reports might only take five to fifteen minutes, Anthropic says the really complex investigations could have Claude working away for up to 45 minutes. That might sound like a while, but compare it to the hours you might spend grinding through that research manually, and it starts to look pretty appealing. Importantly, you can trust the results. When Claude uses information from any source – whether it’s a website, an internal doc, a Jira ticket, or a Confluence page – it gives you clear links straight back to the original. No more wondering where the AI got its information from; you can check it yourself. These shiny new Integrations and the Advanced Research mode are rolling out now in beta for folks on Anthropic’s paid Max, Team, and Enterprise plans. If you’re on the Pro plan, don’t worry – access is coming your way soon. Also worth noting: the standard web search feature inside Claude is now available everywhere, for everyone on any paid Claude.ai plan (Pro and up). No more geographical restrictions on that front. Putting it all together, these updates and integrations show Anthropic is serious about making Claude genuinely useful in a professional context. By letting it plug directly into the tools we already use and giving it more powerful ways to analyse information, they’re pushing Claude towards being less of a novelty and more of an essential part of the modern toolkit. (Image credit: Anthropic) See also: Baidu ERNIE X1 and 4.5 Turbo boast high performance at low cost Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Claude Integrations: Anthropic adds AI to your favourite work tools appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

Developer experience (DevEx) is more than just a buzzphrase. With the rise of complex tech stacks, remote-first teams, and continuous delivery, developers’ work processes have become more complex. Poor DevEx leads to slower deployments, burnout, and increased turnover. Great DevEx, on the other hand, boosts productivity, developer satisfaction, and the quality of shipped code. Developer Experience Insight Tools help engineering teams measure, optimise, and elevate how developers work. The tools track workflows, streamline collaboration, catch issues early, and ultimately create an environment where devs can do their best work. Why developer experience (DevEx) matters In the evolving world of software development, providing a seamless and efficient developer experience (DevEx) has become important. DevEx impacts productivity, code quality, and overall project success. A positive DevEx reduces onboarding time, minimises frustration, and fosters innovation by letting developers focus on solving problems rather than battling tools or workflows. Best practices for implementing developer experience (DevEx) insight tools Here are the key best practices: 1. Set clear objectives Before choosing a tool, identify the specific challenges you want to address – whether it’s reducing lead time, improving code review efficiency, or increasing deployment frequency. Clear goals guide tool selection and help you measure success. 2. Include developers in the decision process Involve developers early when evaluating DevEx tools. Their feedback ensures the solution aligns with daily workflows and avoids adding unnecessary complexity. A tool embraced by engineers is far more likely to deliver impact. 3. Focus on seamless integration Choose tools that integrate well with your existing toolchain – like Git platforms, CI/CD systems, IDEs, and project management software. This ensures insights flow naturally without disrupting developer habits. 4. Start with a pilot team Roll out the tool to a small group first. Collect feedback, refine configurations, and evaluate results before expanding across the organisation. A phased rollout minimises risk and builds internal champions. 5. Prioritise actionable insights Avoid tools that overwhelm with vanity metrics. Look for platforms that surface specific, actionable recommendations developers can use to improve workflows and outcomes immediately. 6. Continuously monitor and Iterate Developer tools evolve. Regularly review tool performance, gather feedback, and adjust settings or processes as needed. Continuous improvement is key to long-term DevEx success. Top 10 developer experience insight tools of 2025 1. Milestone Milestone is built for engineering operations leaders who need visibility into the actual developer experience. It aggregates data across Git repositories, issue trackers, and CI/CD platforms to uncover bottlenecks in delivery, collaboration, and productivity. Unlike traditional tools, Milestone emphasises context-aware metrics like review latency, merge frequency, and time-in-status. It helps managers pinpoint workflow friction and enable smoother engineering cycles, while giving developers visibility into how their work contributes to team goals. Highlights: Seamless integration with GitHub, Jira, and CI/CD tools Rich dashboards for tracking velocity, quality, and workflow health Helps identify systemic delivery delays Suitable for both team leads and individual contributors 2. Visual Studio Code Visual Studio Code (VS Code) is more than just an editor – it’s a central DevEx powerhouse. With its blazing speed, massive extension ecosystem, and deep integrations, VS Code allows developers to stay productive without leaving the IDE. Its features like IntelliSense, Live Share, built-in terminal, and version control support streamline the coding experience. Developers can collaborate, debug, and deploy – all from one interface. With growing support for cloud-based development and AI-powered tools (like GitHub Copilot), VS Code continues to redefine DevEx in 2025. Highlights: Robust plugin ecosystem (AI, Git, testing, Docker, etc.) Live Share enables real-time collaboration Built-in Git support and terminal access Customisable themes, layouts, and keyboard shortcuts 3. SonarQube SonarQube offers continuous inspection of code quality through static analysis, helping teams reduce technical debt and maintain clean, secure codebases. It identifies bugs and security vulnerabilities in more than 30 languages. By integrating into CI/CD pipelines, SonarQube enforces quality gates before code gets merged. Developers receive real-time feedback on code issues and maintainability, improving both DevEx and long-term project health. In 2025, SonarQube remains a go-to tool for teams that treat quality as a DevEx priority. Highlights: Static analysis for 30+ languages Real-time feedback during pull requests Technical debt tracking and maintainability scoring Tight CI/CD and Git integration 4. LogRocket LogRocket enhances frontend DevEx by providing user session replays and performance analytics. It captures how users interact with your application – clicks, navigation, console logs, and network activity – making bug reproduction and performance debugging far more efficient. It bridges the gap between code and user experience, letting developers trace issues quickly. LogRocket also offers integrations with Sentry, Segment, and other analytics platforms to add context to every user issue. Highlights: Session replays with console and network logs Frontend performance monitoring Automatic capture of UI errors and crashes Support for React, Vue, Angular, and more 5. OverOps OverOps specialises in identifying and preventing critical errors in production. It captures the full state of your application (stack trace, variable state, logs) at the moment of failure – without relying on log files alone. OverOps gives developers insight into “why” errors happen, not just “what” happened. This enables faster root-cause analysis, fewer regressions, and higher deployment confidence – all important to frictionless DevEx in modern environments. Highlights: Automated root-cause analysis of runtime errors Continuous monitoring in pre-prod and production Eliminates reliance on verbose logging Insights into code changes that introduced issues 6. Buddy Buddy is a modern DevOps automation platform that enhances DevEx through simple, visual pipelines. With a drag-and-drop UI, developers can set up and manage CI/CD workflows, run tests, build containers, and deploy – all without complex scripts. What makes Buddy unique is its speed and simplicity. It supports Docker, Kubernetes, AWS, and dozens of integrations out-of-the-box, helping teams ship faster while keeping DevEx at the forefront. Highlights: Intuitive UI for CI/CD automation Docker, Kubernetes, and serverless deployment support Real-time feedback on build and test status Git-based workflow and pipeline versioning 7. Docusaurus Docusaurus improves DevEx by making documentation creation and maintenance as easy and developer-friendly as possible. Built by Facebook, it allows dev teams to build fast, versioned, and customisable documentation websites using Markdown and React. In 2025, Docusaurus continues to lead in the “docs as code” movement, helping developers maintain high-quality internal and external documentation without leaving their code editors. Better docs lead to faster onboarding, fewer support tickets, and smoother development workflows. Highlights: Easy setup with React + Markdown Built-in search, versioning, and localisation Custom theming and plugin support Git-based deployment with GitHub Pages or Vercel 8. Exaflow Exaflow is a DevEx observability platform focused on surfacing friction in development and delivery workflows. It aggregates signals from Git providers, issue trackers, code reviews, and builds, offering real-time insights into how teams work. It emphasises transparency and operational health, providing metrics like lead time, handoff delays, and deployment frequency. By highlighting where delays or inefficiencies happen, Exaflow helps teams proactively improve DevEx and delivery outcomes. Highlights: Workflow observability and DevOps telemetry Actionable insights for velocity and bottlenecks Git, Jira, and CI/CD tool integrations Visual timelines of developer handoffs 9. Replit Replit is an online IDE that brings DevEx into the browser. Developers can code, collaborate, and deploy without setting up a local environment. With support for 50+ languages, instant hosting, and live multiplayer coding, it’s a game-changer for fast experimentation and learning. Replit is particularly impactful for onboarding new developers or running internal tooling. It supports AI code suggestions, deployment previews, and GitHub integrations, and offers a frictionless experience from idea to execution. Highlights: Cloud-based, zero-setup IDE Real-time collaboration with multiplayer editing Instant hosting and deployment features Built-in AI tools for autocomplete and debugging 10. Codacy Codacy brings automated code reviews into the DevEx toolkit. It analyses every commit and pull request to flag issues related to code quality, security, duplication, and style – before they reach production. Codacy integrates with your CI and Git workflows, helping developers maintain consistent standards without manual review overhead. It also enables teams to track quality trends over time, ensuring scalable and maintainable codebases. Highlights: Automated code analysis for multiple languages Configurable quality standards and code patterns GitHub/GitLab/Bitbucket CI/CD integration Security and maintainability insights What to consider when selecting a DevEx insight tool? Selecting the right DevEx tool can make or break your team’s efficiency. Below are critical factors to keep in mind: Compatibility with existing ecosystem: Does the tool integrate with your current tech stack, repositories, and CI/CD pipelines? Ease of use: Tools should be intuitive and require minimal learning curves for developers to adopt quickly. Customisability: Every organisation has unique needs. The tools should allow customisation to fit your workflows. Scalability: Ensure the tool can grow with your development team, projects, and increasing workloads. Cost-effectiveness: Evaluate the pricing model to ensure it aligns with your budget without sacrificing features. Community and support: A vibrant community or robust technical support can make the adoption process smoother and keep the tool up-to-date. Insight & analytics: Choose tools that provide powerful analytics and actionable insights to improve workflows. Compliance standards: Consider whether the tool adheres to regulatory and security requirements relevant to your industry. As software teams continue to scale, improving Developer Experience is increasingly important. The right DevEx insight tools allow you to identify friction, empower your engineers, and build healthier development cultures. The post Top 10 developer experience insight tools appeared first on AI News. View the full article

The classroom hasn’t changed much in over a century. A teacher at the front, rows of students listening, and a curriculum defined by what’s testable – not necessarily what’s meaningful. But AI, as arguably the most powerful tool humanity has created in the last few years, is about to break that model open. Not with smarter software or faster grading, but by forcing us to ask: “What is the purpose of education in a world where machines could teach?” At AI News, rather than speculate about distant futures or lean on product announcements and edtech deals, we started a conversation – with an AI. We asked it what it sees when it looks at the classroom, the teacher, and the learner. What follows is a distilled version of that exchange, given here not as a technical analysis, but as a provocation. The system cracks Education is under pressure worldwide: Teachers are overworked, students are disengaged, and curricula feel outdated in a changing world. Into this comes AI – not as a patch or plug-in, but as a potential accelerant. Our opening prompt: “What roles might an AI play in education?“ The answer was wide-ranging: Personalised learning pathways Intelligent tutoring systems Administrative efficiency Language translation and accessibility tools Behavioural and emotional recognition Scalable, always-available content delivery These are features of an education system, its nuts and bolts. But what about meaning and ethics? Flawed by design? One concern kept resurfacing: bias. We asked the AI: “If you’re trained on the internet – and the internet is the output of biased, flawed human thought – doesn’t that mean your responses are equally flawed?” The AI acknowledged the logic. Bias is inherited. Inaccuracies, distortions, and blind spots all travel from teacher to pupil. What an AI learns, it learns from us, and it can reproduce our worst habits at vast scale. But we weren’t interested in letting human teachers off the hook either. So we asked: “Isn’t bias true of human educators too?” The AI agreed: human teachers are also shaped by the limitations of their training, culture, and experience. Both systems – AI and human – are imperfect. But only humans can reflect and care. That led us to a deeper question: if both AI and human can reproduce bias, why use AI at all? Why use AI in education? The AI outlined what it felt were its clear advantages, which seemed to be systemic, rather than revolutionary. The aspect of personalised learning intrigued us – after all, doing things fast and at scale is what software and computers are good at. We asked: “How much data is needed to personalise learning effectively?“ The answer: it varies. But at scale, it could require gigabytes or even terabytes of student data – performance, preferences, feedback, and longitudinal tracking over years. Which raises its own question: “What do we trade in terms of privacy for that precision?” A personalised or fragmented future? Putting aside the issue of whether we’re happy with student data being codified and ingested, if every student were to receive a tailored lesson plan, what happens to the shared experience of learning? Education has always been more than information. It’s about dialogue, debate, discomfort, empathy, and encounters with other minds, not just mirrored algorithms. AI can tailor a curriculum, but it can’t recreate the unpredictable alchemy of a classroom. We risk mistaking customisation for connection. “I use ChatGPT to provide more context […] to plan, structure and compose my essays.” – James, 17, Ottawa, Canada. The teacher reimagined Where does this leave the teacher? In the AI’s view: liberated. Freed from repetitive tasks and administrative overload, the teacher is able to spend more time guiding, mentoring, and cultivating important thinking. But this requires a shift in mindset – from delivering knowledge to curating wisdom. In broad terms, from part-time administrator, part-time teacher, to in-classroom collaborator. AI won’t replace teachers, but it might reveal which parts of the teaching job were never the most important. “The main way I use ChatGPT is to either help with ideas for when I am planning an essay, or to reinforce understanding when revising.” – Emily, 16, Eastbourne College, ***. What we teach next So, what do we want students to learn? In an AI-rich world, important thinking, ethical reasoning, and emotional intelligence rise in value. Ironically, the more intelligent our machines become, the more we’ll need to double down on what makes us human. Perhaps the ultimate lesson isn’t in what AI can teach us – but in what it can’t, or what it shouldn’t even try. Conclusion The future of education won’t be built by AI alone. The is our opportunity to modernise classrooms, and to reimagine them. Not to fear the machine, but to ask the ******* question: “What is learning in a world where all knowledge is available?” Whatever the answer is – that’s how we should be teaching next. (Image source: “Large lecture college classes” by Kevin Dooley is licensed under CC BY 2.0) See also: AI in education: Balancing promises and pitfalls Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Conversations with AI: Education appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

If you’re building with AI, or trying to defend against the less savoury side of the technology, Meta just dropped new Llama security tools. The improved security tools for the Llama AI models arrive alongside fresh resources from Meta designed to help cybersecurity teams harness AI for defence. It’s all part of their push to make developing and using AI a bit safer for everyone involved. Developers working with the Llama family of models now have some upgraded kit to play with. You can grab these latest Llama Protection tools directly from Meta’s own Llama Protections page, or find them where many developers live: Hugging Face and GitHub. First up is Llama Guard 4. Think of it as an evolution of Meta’s customisable safety filter for AI. The big news here is that it’s now multimodal so it can understand and apply safety rules not just to text, but to images as well. That’s crucial as AI applications get more visual. This new version is also being baked into Meta’s brand-new Llama API, which is currently in a limited preview. Then there’s LlamaFirewall. This is a new piece of the puzzle from Meta, designed to act like a security control centre for AI systems. It helps manage different safety models working together and hooks into Meta’s other protection tools. Its job? To spot and block the kind of risks that keep AI developers up at night – things like clever ‘prompt injection’ attacks designed to trick the AI, potentially dodgy code generation, or risky behaviour from AI plug-ins. Meta has also given its Llama Prompt Guard a tune-up. The main Prompt Guard 2 (86M) model is now better at sniffing out those pesky jailbreak attempts and prompt injections. More interestingly, perhaps, is the introduction of Prompt Guard 2 22M. Prompt Guard 2 22M is a much smaller, nippier version. Meta reckons it can slash latency and compute costs by up to 75% compared to the ******* model, without sacrificing too much detection power. For anyone needing faster responses or working on tighter budgets, that’s a welcome addition. But Meta isn’t just focusing on the AI builders; they’re also looking at the cyber defenders on the front lines of digital security. They’ve heard the calls for better AI-powered tools to help in the fight against cyberattacks, and they’re sharing some updates aimed at just that. The CyberSec Eval 4 benchmark suite has been updated. This open-source toolkit helps organisations figure out how good AI systems actually are at security tasks. This latest version includes two new tools: CyberSOC Eval: Built with the help of cybersecurity experts CrowdStrike, this framework specifically measures how well AI performs in a real Security Operation Centre (SOC) environment. It’s designed to give a clearer picture of AI’s effectiveness in threat detection and response. The benchmark itself is coming soon. AutoPatchBench: This benchmark tests how good Llama and other AIs are at automatically finding and fixing security holes in code before the bad guys can exploit them. To help get these kinds of tools into the hands of those who need them, Meta is kicking off the Llama Defenders Program. This seems to be about giving partner companies and developers special access to a mix of AI solutions – some open-source, some early-access, some perhaps proprietary – all geared towards different security challenges. As part of this, Meta is sharing an AI security tool they use internally: the Automated Sensitive Doc Classification Tool. It automatically slaps security labels on documents inside an organisation. Why? To stop sensitive info from walking out the door, or to prevent it from being accidentally fed into an AI system (like in RAG setups) where it could be leaked. They’re also tackling the problem of fake audio generated by AI, which is increasingly used in scams. The Llama Generated Audio Detector and Llama Audio Watermark Detector are being shared with partners to help them spot AI-generated voices in potential phishing calls or fraud attempts. Companies like ZenDesk, Bell Canada, and AT&T are already lined up to integrate these. Finally, Meta gave a sneak peek at something potentially huge for user privacy: Private Processing. This is new tech they’re working on for WhatsApp. The idea is to let AI do helpful things like summarise your unread messages or help you draft replies, but without Meta or WhatsApp being able to read the content of those messages. Meta is being quite open about the security side, even publishing their threat model and inviting security researchers to poke holes in the architecture before it ever goes live. It’s a sign they know they need to get the privacy aspect right. Overall, it’s a broad set of AI security announcements from Meta. They’re clearly trying to put serious muscle behind securing the AI they build, while also giving the wider tech community better tools to build safely and defend effectively. See also: Alarming rise in AI-powered scams: Microsoft reveals $4B in thwarted fraud Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Meta beefs up AI security with new Llama tools appeared first on AI News. View the full article

The *** has cut the ribbon on a pioneering electron beam (E-Beam) lithography facility to build the semiconductor chips of the future. What makes this special? It’s the first of its kind in Europe, and only the second facility like it on the planet—the other being in Japan. So, what’s the big deal about E-Beam lithography? Imagine trying to draw incredibly complex patterns, but thousands of times smaller than a human hair. That’s essentially what this technology does, using a focused beam of tiny electrons. Such precision is vital for designing the microscopic components inside the chips that run everything from our smartphones and gaming consoles to life-saving medical scanners and advanced defence systems. Semiconductors are already big business for the ***, adding around £10 billion to its economy each year. And that figure is only expected to climb, potentially hitting £17 billion by the end of the decade. Nurturing this sector is a major opportunity for the ***—not just for bragging rights in advanced manufacturing, but for creating high-value jobs and driving real economic growth. Speaking at the launch of the facility in Southampton, Science Minister Lord Patrick Vallance said: “Britain is home to some of the most exciting semiconductor research anywhere in the world—and Southampton’s new E-Beam facility is a major boost to our national capabilities. “By investing in both infrastructure and talent, we’re giving our researchers and innovators the support they need to develop next-generation chips right here in the ***.” Lord Vallance’s visit wasn’t just a photo opportunity, though. It came alongside some sobering news: fresh research published today highlights that one of the biggest hurdles facing the ***’s growing chip industry is finding enough people with the right skills. We’re talking about a serious talent crunch. When you consider that a single person working in semiconductors contributes an average of £460,000 to the economy each year, you can see why plugging this skills gap is so critical. So, what’s the plan? The government isn’t just acknowledging the problem; they’re putting money where their mouth is with a £4.75 million semiconductor skills package. The idea is to build up that talent pipeline, making sure universities like Southampton – already powerhouses of chip innovation – have resources like the E-Beam lab and the students they need. “Our £4.75 million skills package will support our Plan for Change by helping more young people into high-value semiconductors careers, closing skills gaps and backing growth in this critical sector,” Lord Vallance explained. Here’s where that cash is going: Getting students hooked (£3 million): Fancy £5,000 towards your degree? 300 students starting Electronics and Electrical Engineering courses this year will get just that, along with specific learning modules to show them what a career in semiconductors actually involves, particularly in chip design and making the things. Practical chip skills (£1.2 million): It’s one thing learning the theory, another designing a real chip. This pot will fund new hands-on chip design courses for students (undergrad and postgrad) and even train up lecturers. They’re also looking into creating conversion courses to tempt talented people from other fields into the chip world. Inspiring the next generation (Nearly £550,000): To really build a long-term pipeline, you need to capture interest early. This funding aims to give 7,000 teenagers (15-18) and 450 teachers some real, hands-on experience with semiconductors, working with local companies in existing *** chip hotspots like Newport, Cambridge, and Glasgow. The goal is to show young people the cool career paths available right on their doorstep. Ultimately, the hope is that this targeted support will give the *** semiconductor scene the skilled workforce it needs to thrive. It’s about encouraging more students to jump into these valuable careers, helping companies find the people they desperately need, and making sure the *** stays at the forefront of the technologies that will shape tomorrow’s economy. Professor Graham Reed, who heads up the Optoelectronics Research Centre (ORC) at Southampton University, commented: “The introduction of the new E-Beam facility will reinforce our position of hosting the most advanced cleanroom in *** academia. “It facilitates a vast array of innovative and industrially relevant research, and much needed semiconductor skills training.” Putting world-class tools in the hands of researchers while simultaneously investing in the people who will use them will help to cement the ***’s leadership in semiconductors. See also: AI in education: Balancing promises and pitfalls Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post *** opens Europe’s first E-Beam semiconductor chip lab appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

Duolingo is restructuring parts of its workforce as it shifts toward becoming an “AI-first” company, according to an internal memo from CEO and co-founder Luis von Ahn that was later shared publicly on the company’s LinkedIn page. The memo outlines a series of planned changes to how the company operates, with a particular focus on how artificial intelligence will be used to streamline processes, reduce manual tasks, and scale content development. Duolingo will gradually stop using contractors for work that AI can take over. The company will also begin evaluating job candidates and employee performance partly based on how they use AI tools. Von Ahn said that headcount increases will only be considered when a team can no longer automate parts of its work effectively. “Being AI-first means we will need to rethink much of how we work. Making minor tweaks to systems designed for humans won’t get us there,” von Ahn wrote. “AI helps us get closer to our mission. To teach well, we need to create a massive amount of content, and doing that manually doesn’t scale.” One of the main drivers behind the shift is the need to produce content more quickly, and Von Ahn says that producing new content manually would take decades. By integrating AI into its workflow, Duolingo has replaced processes he described as slow and manual those that are more efficient and automated. The company has also used AI to develop features that weren’t previously feasible such as an AI-powered video call feature, which aims to provide tutoring to the level of human instructors. According to von Ahn, tools like this move the Duolingo platform closer to its mission – to deliver language instruction globally. The internal shift is not limited to content creation or product development. Von Ahn said most business functions will be expected to rethink how they operate and identify opportunities to embed AI into daily work. Teams will be encouraged to adopt what he called “constructive constraints” – policies that push them to prioritise automation before requesting additional resources. The move echoes a broader trend in the tech industry. Shopify CEO Tobi Lütke recently gave a similar directive to employees, urging them to demonstrate why tasks couldn’t be completed with AI before requesting new headcount. Both companies appear to be setting new expectations for how teams manage growth in an AI-dominated environment. Duolingo’s leadership maintains the changes are not intended to reduce its focus on employee well-being, and the company will continue to support staff with training, mentorship, and tools designed to help employees adapt to new workflows. The goal, he wrote, is not to replace staff with AI, but to eliminate bottlenecks and allow employees to concentrate on complex or creative work. “AI isn’t just a productivity boost,” von Ahn wrote. “It helps us get closer to our mission.” The company’s move toward more automation reflects a belief that waiting too long to embrace AI could be a missed opportunity. Von Ahn pointed to Duolingo’s early investment in mobile-first design in 2012 as a model. That shift helped the company gain visibility and user adoption, including being named Apple’s iPhone App of the Year in 2013. The decision to go “AI-first” is framed as a similarly forward-looking step. The transition is expected to take some time. Von Ahn acknowledged that not all systems are ready for full automation and that integrating AI into certain areas, like codebase analysis, could take longer. Nevertheless, he said moving quickly – even if it means accepting occasional setbacks – is more important than waiting for the technology to be fully mature. By placing AI at the centre of its operations, Duolingo is aiming to deliver more scalable learning experiences and manage internal resources more efficiently. The company plans to provide additional updates as the implementation progresses. (Photo by Unsplash) See also: AI in education: Balancing promises and pitfalls Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Duolingo shifts to AI-first model, cutting contractor roles appeared first on AI News. View the full article

At the Apsara Conference in Hangzhou, hosted by Alibaba Cloud, China’s AI startups emphasised their efforts to develop large language models. The companies’ efforts follow the announcement of OpenAI’s latest LLMs, including the o1 generative pre-trained transformer model backed by Microsoft. The model is intended to tackle difficult tasks, paving the way for advances in science, coding, and mathematics. During the conference, Kunal Zhilin, founder of Moonshot AI, underlined the importance of the o1 model, adding that it has the potential to reshape various industries and create new opportunities for AI startups. Zhilin stated that reinforcement learning and scalability might be pivotal for AI development. He spoke of the scaling law, which states that larger models with more training data perform better. “This approach pushes the ceiling of AI capabilities,” Zhilin said, adding that OpenAI o1 has the potential to disrupt sectors and generate new opportunities for startups. OpenAI has also stressed the model’s ability to solve complex problems, which it says operate in a manner similar to human thinking. By refining its strategies and learning from mistakes, the model improves its problem-solving capabilities. Zhilin said companies with enough computing power will be able to innovate not only in algorithms, but also in foundational AI models. He sees this as pivotal, as AI engineers rely increasingly on reinforcement learning to generate new data after exhausting available organic data sources. StepFun CEO Jiang Daxin concurred with Zhilin but stated that computational power remains a big challenge for many start-ups, particularly due to US trade restrictions that hinder ******** enterprises’ access to advanced semiconductors. “The computational requirements are still substantial,” Daxin stated. An insider at Baichuan AI has said that only a small group of ******** AI start-ups — including Moonshot AI, Baichuan AI, Zhipu AI, and MiniMax — are in a position to make large-scale investments in reinforcement learning. These companies — collectively referred to as the “AI tigers” — are involved heavily in LLM development, pushing the next generation of AI. More from the Apsara Conference Also at the conference, Alibaba Cloud made several announcements, including the release of its Qwen 2.5 model family, which features advances in coding and mathematics. The models range from 0.5 billion to 72 billion parameters and support approximately 29 languages, including ********, English, French, and Spanish. Specialised models such as Qwen2.5-Coder and Qwen2.5-Math have already gained some traction, with over 40 million downloads on platforms Hugging Face and ModelScope. Alibaba Cloud added to its product portfolio, delivering a text-to-video model in its picture generator, Tongyi Wanxiang. The model can create videos in realistic and animated styles, with possible uses in advertising and filmmaking. Alibaba Cloud unveiled Qwen 2-VL, the latest version of its vision language model. It handles videos longer than 20 minutes, supports video-based question-answering, and is optimised for mobile devices and robotics. For more information on the conference, click here. (Photo by: @Guy_AI_Wise via X) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post OpenAI’s latest LLM opens doors for China’s AI startups appeared first on AI News. View the full article

If you’ve ever tried to get your cybersecurity news picked up by media outlets, you’ll know just how much of a challenge (and how disheartening) it can be. You pour hours into what you think is an excellent announcement about your new security tool, threat research, or vulnerability discovery, only to watch it disappear into journalists’ overflowing inboxes without a trace. The cyber PR space is brutally competitive. Reporters at top publications receive tens, if not hundreds, of pitches each day, and they have no choice but to be highly selective about which releases they choose to cover and which to discard. Your challenge then isn’t just creating a good press release, it’s making one that grabs attention and stands out in an industry drowning in technical jargon and “revolutionary” solutions. Why most cybersecurity press releases fall flat Let’s first look at some of the main reasons why many cyber press releases fail: They’re too complex from the start, losing non-technical reporters They bury the actual news under corporate marketing speak. They focus on product features rather than the real-world impact or problems they solve. They lack credible data or specific research findings that journalists can cite as support. Most of these problems have one main theme: Journalists aren’t interested in promoting your product or your business. They are looking after their interests and seeking newsworthy stories their audiences care about. Keep this in mind and make their job easier by showing them exactly why your announcement matters. Learning how to write a cybersecurity press release What does a well-written press release look like? Alongside the reasons listed above, many companies make the mistake of submitting poorly formatted releases that journalists will be unlikely to spend time reading. It’s worth learning how to write a cybersecurity press release properly, including the preferred structure (headline, subheader, opening paragraph, boilerplate, etc). And, be sure to review some examples of high-quality press releases as well. AI strategies that transform your press release process Let’s examine how AI tools can significantly enhance your cyber PR at every stage. 1. Research Enhancement Use AI tools to track media coverage patterns and identify emerging trends in cybersecurity news. You can analyse which types of security stories gain traction, and this can help you position your announcement in that context. Another idea is to use LLMs (like Google’s Gemini or OpenAI’s ChatGPT) to analyse hundreds of successful cybersecurity press releases in a niche similar to yours. Ask it to identify common elements in those that generated significant coverage, and then use these same features in your cyber PR efforts. To take this a step further, AI-powered sentiment analysis can help you understand how different audience segments receive specific cybersecurity topics. The intelligence can help you tailor your messaging to address current concerns and capitalise on positive industry momentum. 2. Writing assistance If you struggle to convey complex ideas and terminology in more accessible language, consider asking the LLM to help simplify your messaging. This can help transform technical specifications into clear, accessible language that non-technical journalists can understand. Since the headline is the most important part of your release, use an LLM to generate a handful of options based on your core announcement, then select the best one based on clarity and impact. Once your press release is complete, run it through an LLM to identify and replace jargon that might be second nature to your security team but may be confusing to general tech reporters. 3. Visual storytelling If you are struggling to find ways to explain your product or service in accessible language, visuals can help. AI image generation tools, like Midjourney, create custom visuals based on prompts that help illustrate your message. The latest models can handle highly complex tasks. With a bit of prompt engineering (and by incorporating the press release you want help with), you should be able to create accompanying images and infographics that bring your message to life. 4. Video content Going one step further than a static image, a brief AI-generated explainer video can sit alongside your press release, providing journalists with ready-to-use content that explains complex security concepts. Some ideas include: Short Explainer Videos: Use text-to-video tools to turn essential sections of your press release into a brief (60 seconds or less) animated or stock-footage-based video. You can usually use narration and text overlays directly on the AI platforms as well. AI Avatar Summaries: Several tools now enable you to create a brief video featuring an AI avatar that presents the core message of the press release. A human-looking avatar reads out the content and delivers an audio and video component for your release. Data Visualisation Videos: Use AI tools to animate key statistics or processes described in the release for enhanced clarity. Final word Even as you use the AI tools you have at your disposal, remember that the most effective cybersecurity press releases still require that all-important human insight and expertise. Your goal isn’t to automate the entire process. Instead, use AI to enhance your cyber PR efforts and make your releases stand out from the crowd. AI should help emphasise, not replace, the human elements that make security stories so engaging and compelling. Be sure to shine a spotlight on the researchers who made the discovery, the real-world implications of any threat vulnerabilities you uncover, and the people security measures ultimately protect. Combine this human-focused storytelling with the power of AI automation, and you’ll ensure that your press releases and cyber PR campaigns get the maximum mileage. The post AI strategies for cybersecurity press releases that get coverage appeared first on AI News. View the full article

The role of AI in education is a controversial subject, bringing both exciting possibilities and serious challenges. There’s a real push to bring AI into schools, and you can see why. The recent executive order on youth education from President Trump recognised that if future generations are going to do well in an increasingly automated world, they need to be ready. “To ensure the United States remains a global leader in this technological revolution, we must provide our nation’s youth with opportunities to cultivate the skills and understanding necessary to use and create the next generation of AI technology,” President Trump declared. So, what does AI actually look like in the classroom? One of the biggest hopes for AI in education is making learning more personal. Imagine software that can figure out how individual students are doing, then adjust the pace and materials just for them. This could mean finally moving away from the old one-size-fits-all approach towards learning environments that adapt and offer help exactly where it’s needed. The US executive order hints at this, wanting to improve results through things like “AI-based high-quality instructional resources” and “high-impact tutoring.” And what about teachers? AI could be a huge help here too, potentially taking over tedious admin tasks like grading, freeing them up to actually teach. Plus, AI software might offer fresh ways to present information. Getting kids familiar with AI early on could also take away some of the mystery around the technology. It might spark their “curiosity and creativity” and give them the foundation they need to become “active and responsible participants in the workforce of the future.” The focus stretches to lifelong learning and getting people ready for the job market. On top of that, AI tools like text-to-speech or translation features can make learning much more accessible for students with disabilities, opening up educational environments for everyone. Not all smooth sailing: The challenges ahead for AI in education While the potential is huge, we need to be realistic about the significant hurdles and potential downsides. First off, AI runs on student data – lots of it. That means we absolutely need strong rules and security to make sure this data is collected ethically, used correctly, and kept safe from breaches. Privacy is paramount here. Then there’s the bias problem. If the data used to train AI reflects existing unfairness in society (and let’s be honest, it often does), the AI could end up repeating or even worsening those inequalities. Think biased assessments or unfair resource allocation. Careful testing and constant checks are crucial to catch and fix this. We also can’t ignore the digital divide. If some students don’t have reliable internet, the right devices, or the necessary tech infrastructure at home or school, AI could widen the gap between the haves and have-nots. It’s vital that everyone gets fair access. There’s also a risk that leaning too heavily on AI education tools might stop students from developing essential skills like critical thinking. We need to teach them how to use AI as a helpful tool, not a crutch they can’t function without. Maybe the biggest piece of the puzzle, though, is making sure our teachers are ready. As the executive order rightly points out, “We must also invest in our educators and equip them with the tools and knowledge.” This isn’t just about knowing which buttons to push; teachers need to understand how AI fits into teaching effectively and ethically. That requires solid professional development and ongoing support. A recent GMB Union poll found that while about a fifth of *** schools are using AI now, the staff often aren’t getting the training they need: Finding the right path forward It’s going to take everyone – governments, schools, tech companies, and teachers – pulling together in order to ensure that AI plays a positive role in education. We absolutely need clear policies and standards covering ethics, privacy, bias, and making sure AI is accessible to all students. We also need to keep investing in research to figure out the best ways to use AI in education and to build tools that are fair and effective. And critically, we need a long-term commitment to teacher education to get educators comfortable and skilled with these changes. Part of this is building broad AI literacy, making sure all students get a basic understanding of this technology and how it impacts society. AI could be a positive force in education – making it more personalised, efficient, and focused on the skills students actually need. But turning that potential into reality means carefully navigating those tricky ethical, practical, and teaching challenges head-on. See also: How does AI judge? Anthropic studies the values of Claude Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post AI in education: Balancing promises and pitfalls appeared first on AI News. View the full article

The third edition of Machines Can See (MCS) Summit has concluded at Dubai’s Museum of the Future. More than 300 start‑ups pitched to investors from EQT Ventures, Balderton, Lakestar, e& capital and Mubadala, and more than 3,500 delegates from 45 countries attended the summit, while online engagement levels were high (4.7 million views). Real-time updates with the #MCS2025 hashtag are projected to exceed 5 million views. The summit was hosted by UAE-based Polynome Group under the patronage of H.H. Sheikh Hamdan bin Mohammed bin Rashid Al Maktoum. Strategic backers included Digital Dubai, Dubai Police, Emirates, Amazon Web Services, NVIDIA, IBM, SAP, MBZUAI among others. “In just three years, MCS has evolved from a specialist meet‑up into a true crossroads for the world’s top minds in science, business and public policy. The week proved that when researchers, entrepreneurs and governments share one stage, we move a step closer to transparent, human‑centred AI that delivers real value for society,” said Alexander Khanin, founder & CEO of Polynome Group Landmark agreements announced live on stage During the two‑day programme, several high‑profile agreements were signed at the summit, including: A trilateral Memorandum of Understanding between Astana Hub (Kazakhstan), IT‑Park Uzbekistan and Al‑Farabi Innovation Hub (UAE), creating a Central‑Asia‑to‑MENA soft‑landing platform for high‑growth start‑ups. A Google Cloud initiative offering no‑cost “Gen‑AI Leader” learning paths and discounted certification vouchers to accelerate responsible AI adoption across the region. Polynome Group officially launched AI Academy, an educational initiative developed in collaboration with the Abu Dhabi School of Management and supported by NVIDIA’s Deep Learning Institute. The Academy will offer short executive seminars and a specialised four‑month Mini‑MBA in AI, aimed at equipping leaders and innovators with practical AI knowledge to bridge the gap between technology research and commercial application. Policy & talent Day one opened with a ministerial round‑table – “Wanted: AI to Retain and Attract Talent to the Country.” Ministers Omar Sultan Al Olama (UAE), Amr Talaat (Egypt), Gobind Singh Deo (Malaysia), Zhaslan Madiyev (Kazakhstan) and Meutya Hafid (Indonesia) detailed visa‑fast‑track programmes, national GPU clouds and cross‑border sandboxes designed to reverse brain‑drain and accelerate R&D. Breakthrough research Prof. Michael Bronstein (University of Oxford/Google DeepMind) demonstrated Geometric Deep Learning applications that shorten drug‑discovery timelines and model subatomic physics. Marco Tempest (NASA JPL/MagicLab.nyc) blended GPT‑4o dialogue with mixed‑reality holograms, turning the stage into an interactive mind‑map. Prof. Michal Irani (Weizmann Institute) showed perception‑to‑cognition systems capable of reconstructing scenes from a single gaze sequence. Andrea Vedaldi (Oxford) premiered a 3‑D generative‑AI pipeline for instant city‑scale digital twins, while Marc Pollefeys (ETH Zurich/Microsoft) demonstrated real‑time spatial mapping at sub‑10 ms latency. Industry workshops & panels AWS ran a hands‑on clinic – “Building Enterprise Gen‑AI Applications” – covering RAG, agentic orchestration and secure deployment. NVIDIA’s workshop unveiled its platform approach to production generative‑AI on Hopper‑class GPUs, complementing its newly announced Service Delivery Partnership with Polynome Group’s legal entity, Intelligent Machines Consultancies. Dubai Police hosted a closed‑door DFA session on predictive policing, while X and AI workshops explored social‑data pipelines on GPU clusters. The parallel Machines Can Create forum examined AI’s role in luxury, digital art and media, with speakers from HEC Paris, The Sandbox, IBM Research and BBC, culminating in the panel “Pixels and Palettes: The Canvas of Tomorrow.” Prof. Marc Pollefeys, Director of the Mixed Reality and AI Lab at ETH Zurich and Microsoft, highlighted the role of cutting-edge technology in daily life: “We are at a turning point where technologies like spatial AI and real-time 3D mapping are moving from laboratories into everyday life, transforming cities, workplaces, and how we interact with the digital world. The Machines Can See Summit underscores how collaboration between researchers, industry, and policymakers accelerates this transition, bringing innovative solutions closer to everyone,” he said. Ethical & security focus Panels “Good AI: Between Hype and Mediocrity” and “Defending Intelligence: Navigating Adversarial Machine Learning” stressed the need for continuous audits, red‑teaming and transparent supply chains. Dubai Police, TII UAE and IBM urged adoption of ISO‑aligned governance tool‑kits to safeguard public‑sector deployments. High‑profile awards On Day Two, H.H. Sheikh Hamdan bin Mohammed bin Rashid Al Maktoum presented trophies for the Global Prompt Engineering Championship, for breakthroughs in multilingual, safety-aligned LLM prompting. Key takeaways The summit underscored three strategic imperatives for the decade ahead. Talent aviation – backed by unified tech visas, national GPU clouds and government‑funded sandbox clusters – is emerging as the most effective antidote to AI brain‑drain. Spatial computing is moving from laboratory to street level as sub‑10‑millisecond mapping unlocks safe humanoid robotics and city‑scale augmented‑reality services. Finally, secure generative AI must couple adversarial robustness with transparent, explainable pipelines before the technology can achieve mass‑market adoption in regulated industries. The post “Machines Can See 2025” wraps in Dubai after two‑day showcase of AI appeared first on AI News. View the full article

Baidu has unveiled ERNIE X1 Turbo and 4.5 Turbo, two fast models that boast impressive performance alongside dramatic cost reductions. Developed as enhancements to the existing ERNIE X1 and 4.5 models, both new Turbo versions highlight multimodal processing, robust reasoning skills, and aggressive pricing strategies designed to capture developer interest and marketshare. Baidu ERNIE X1 Turbo: Deep reasoning meets cost efficiency Positioned as a deep-thinking reasoning model, ERNIE X1 Turbo tackles complex tasks requiring sophisticated understanding. It enters a competitive field, claiming superior performance in some benchmarks against rivals like DeepSeek R1, V3, and OpenAI o1: Key to X1 Turbo’s enhanced capabilities is an advanced “chain of thought” process, enabling more structured and logical problem-solving. Furthermore, ERNIE X1 Turbo boasts improved multimodal functions – the ability to understand and process information beyond just text, potentially including images or other data types – alongside refined tool utilisation abilities. This makes it particularly well-suited for nuanced applications such as literary creation, complex logical reasoning challenges, code generation, and intricate instruction following. ERNIE X1 Turbo achieves this performance while undercutting competitor pricing. Input token costs start at $0.14 per million tokens, with output tokens priced at $0.55 per million. This pricing structure is approximately 25% of DeepSeek R1. Baidu ERNIE 4.5 Turbo: Multimodal muscle at a fraction of the cost Sharing the spotlight is ERNIE 4.5 Turbo, which focuses on delivering upgraded multimodal features and significantly faster response times compared to its non-Turbo counterpart. The emphasis here is on providing a versatile, responsive AI experience while slashing operational costs. The model achieves an 80% price reduction compared to the original ERNIE 4.5 with input set at $0.11 per million tokens and output at $0.44 per million tokens. This represents roughly 40% of the cost of the latest version of DeepSeek V3, again highlighting a deliberate strategy to attract users through cost-effectiveness. Performance benchmarks further bolster its credentials. In multiple tests evaluating both multimodal and text capabilities, Baidu ERNIE 4.5 Turbo outperforms OpenAI’s highly-regarded GPT-4o model. In multimodal capability assessments, ERNIE 4.5 Turbo achieved an average score of 77.68 to surpass GPT-4o’s score of 72.76 in the same tests. While benchmark results always require careful interpretation, this suggests ERNIE 4.5 Turbo is a serious contender for tasks involving an integrated understanding of different data types. Baidu continues to shake up the AI marketplace The launch of ERNIE X1 Turbo and 4.5 Turbo signifies a growing trend in the AI sector: the democratisation of high-end capabilities. While foundational models continue to push the boundaries of performance, there is increasing demand for models that balance power with accessibility and affordability. By lowering the price points for models with sophisticated reasoning and multimodal features, the Baidu ERNIE Turbo series could enable a wider range of developers and businesses to integrate advanced AI into their applications. This competitive pricing puts pressure on established players like OpenAI and Anthropic, as well as emerging competitors like DeepSeek, potentially leading to further price adjustments across the market. (Image Credit: Alpha Photo under CC BY-NC 2.0 license) See also: China’s MCP adoption: AI assistants that actually do things Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Baidu ERNIE X1 and 4.5 Turbo boast high performance at low cost appeared first on AI News. View the full article

The way we interact with our computers and smart devices is very different from previous years. Over the decades, human-computer interfaces have transformed, progressing from simple cardboard punch cards to keyboards and mice, and now extended reality-based AI agents that can converse with us in the same way as we do with friends. With each advance in human-computer interfaces, we’re getting closer to achieving the goal of interactions with machines, making computers more accessible and integrated with our lives. Where did it all begin? Modern computers emerged in the first half of the 20th century and relied on punch cards to feed data into the system and enable binary computations. The cards had a series of punched holes, and light was shone at them. If the light passed through a hole and was detected by the machine, it represented a “one”. Otherwise, it was a “zero”. As you can imagine, it was extremely cumbersome, time-consuming, and error-prone. That changed with the arrival of ENIAC, or Electronic Numerical Integrator and Computer, widely considered to be the first “Turing-complete” device that could solve a variety of numerical problems. Instead of punch cards, operating ENIAC involved manually setting a series of switches and plugging patch cords into a board to configure the computer for specific calculations, while data was inputted via a further series of switches and buttons. It was an improvement over punch cards, but not nearly as dramatic as the arrival of the modern QWERTY electronic keyboard in the early 1950s. Keyboards, adapted from typewriters, were a game-changer, allowing users to input text-based commands more intuitively. But while they made programming faster, accessibility was still limited to those with knowledge of the highly-technical programming commands required to operate computers. GUIs and touch The most important development in terms of computer accessibility was the graphical user interface or GUI, which finally opened computing to the masses. The first GUIs appeared in the late 1960s and were later refined by companies like IBM, Apple, and Microsoft, replacing text-based commands with a visual display made up of icons, menus, and windows. Alongside the GUI came the iconic “mouse“, which enabled users to “point-and-click” to interact with computers. Suddenly, these machines became easily navigable, allowing almost anyone to operate one. With the arrival of the internet a few years later, the GUI and the mouse helped pave the way for the computing revolution, with computers becoming commonplace in every home and office. The next major milestone in human-computer interfaces was the touchscreen, which first appeared in the late 1990s and did away with the need for a mouse or a separate keyboard. Users could now interact with their computers by tapping icons on the screen directly, pinching to zoom, and swiping left and right. Touchscreens eventually paved the way for the smartphone revolution that started with the arrival of the Apple iPhone in 2007 and, later, Android devices. With the rise of mobile computing, the variety of computing devices evolved further, and in the late 2000s and early 2010s, we witnessed the emergence of wearable devices like fitness trackers and smartwatches. Such devices are designed to integrate computers into our everyday lives, and it’s possible to interact with them in newer ways, like subtle gestures and biometric signals. Fitness trackers, for instance, use sensors to keep track of how many steps we take or how far we run, and can monitor a user’s pulse to measure heart rate. Extended reality & AI avatars In the last decade, we also saw the first artificial intelligence systems, with early examples being Apple’s Siri and Amazon’s Alexa. AI chatbots use voice recognition technology to enable users to communicate with their devices using their voice. As AI has advanced, these systems have become increasingly sophisticated and better able to understand complex instructions or questions, and can respond based on the context of the situation. With more advanced chatbots like ChatGPT, it’s possible to engage in lifelike conversations with machines, eliminating the need for any kind of physical input device. AI is now being combined with emerging augmented reality and virtual reality technologies to further refine human-computer interactions. With AR, we can insert digital information into our surroundings by overlaying it on top of our physical environment. This is enabled using VR devices like the Oculus Rift, HoloLens, and Apple Vision Pro, and further pushes the boundaries of what’s possible. So-called extended reality, or XR, is the latest take on the technology, replacing traditional input methods with eye-tracking, and gestures, and can provide haptic feedback, enabling users to interact with digital objects in physical environments. Instead of being restricted to flat, two-dimensional screens, our entire world becomes a computer through a blend of virtual and physical reality. The convergence of XR and AI opens the doors to more possibilities. Mawari Network is bringing AI agents and chatbots into the real world through the use of XR technology. It’s creating more meaningful, lifelike interactions by streaming AI avatars directly into our physical environments. The possibilities are endless – imagine an AI-powered virtual assistant standing in your home or a digital concierge that meets you in the hotel lobby, or even an AI passenger that sits next to you in your car, directing you on how to avoid the worst traffic jams. Through its decentralised DePin infrastructure, it’s enabling AI agents to drop into our lives in real-time. The technology is nascent but it’s not fantasy. In Germany, tourists can call on an avatar called Emma to guide them to the best spots and eateries in dozens of ******* cities. Other examples include digital popstars like Naevis, which is pioneering the concept of virtual concerts that can be attended from anywhere. In the coming years, we can expect to see this XR-based spatial computing combined with brain-computer interfaces, which promise to let users control computers with their thoughts. BCIs use electrodes placed on the scalp and pick up the electrical signals generated by our brains. Although it’s still in its infancy, this technology promises to deliver the most effective human-computer interactions possible. The future will be seamless The story of the human-computer interface is still under way, and as our technological capabilities advance, the distinction between digital and physical reality will more blurred. Perhaps one day soon, we’ll be living in a world where computers are omnipresent, integrated into every aspect of our lives, similar to Star Trek’s famed holodeck. Our physical realities will be merged with the digital world, and we’ll be able to communicate, find information, and perform actions using only our thoughts. This vision would have been considered fanciful only a few years ago, but the rapid pace of innovation suggests it’s not nearly so far-fetched. Rather, it’s something that the majority of us will live to see. (Image source: Unsplash) The post From punch cards to mind control: Human-computer interactions appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

Having worked with AI since 2018, I’m watching its slow but steady pick-up alongside the unstructured bandwagon-jumping with considerable interest. Now that the initial fear has subsided somewhat about a robotic takeover, discussion about the ethics that will surround the integration of AI into everyday business structures has taken its place. A whole new range of roles will be required to handle ethics, governance and compliance, all of which are going to gain enormous value and importance to organisations. Probably the most essential of these will be an AI Ethics Specialist, who will be required to ensure Agentic AI systems meet ethical standards like fairness and transparency. This role will involve using specialised tools and frameworks to address ethical concerns efficiently and avoid potential legal or reputational risks. Human oversight to ensure transparency and responsible ethics is essential to maintain the delicate balance between data driven decisions, intelligence and intuition. In addition, roles like Agentic AI Workflow Designer, AI Interaction and Integration Designer will ensure AI integrates seamlessly across ecosystems and prioritises transparency, ethical considerations, and adaptability. An AI Overseer will also be required, to monitor the entire Agentic stack of agents and arbiters, the decision-making elements of AI. For anyone embarking on the integration of AI into their organisation and wanting to ensure the technology is introduced and maintained responsibly, I can recommend consulting the United Nations’ principles. These 10 principles were created by the United Nations in 2022, in response to the ethical challenges raised by the increasing preponderance of AI. So what are these ten principles, and how can we use them as a framework? First, do no harm As befits technology with an autonomous element, the first principle focuses on the deployment of AI systems in ways that will avoid any negative impact on social, cultural, economic, natural or political environments. An AI lifecycle should be designed to respect and protect human rights and freedoms. Systems should be monitored to ensure that that situation is maintained and no long-term damage is being done. Avoid AI for AI’s sake Ensure that the use of AI is justified, appropriate and not excessive. There is a distinct temptation to become over-zealous in the application of this exciting technology and it needs to be balanced against human needs and aims and should never be used at the expense of human dignity. Safety and security Safety and security risks should be identified, addressed and mitigated throughout the life cycle of the AI system and on an on-going basis. Exactly the same robust health and safety frameworks should be applied to AI as to any other area of the business. Equality Similarly, AI should be deployed with the aim of ensuring the equal and just distribution of the benefits, risks and cost, and to prevent bias, deception, discrimination and stigma of any kind. Sustainability AI should be aimed at promoting environmental, economic and social sustainability. Continual assessment should be made to address negative impacts, including any on the generations to come. Data privacy, data protection and data governance Adequate data protection frameworks and data governance mechanisms should be established or enhanced to ensure that the privacy and rights of individuals are maintained in line with legal guidelines around data integrity and personal data protection. No AI system should impinge on the privacy of another human being. Human oversight Human oversight should be guaranteed to ensure that the outcomes of using AI are fair and just. Human-centric design practises should be employed and capacity to be given for a human to step in at any stage and make a decision on how and when AI should be used, and to over-ride any decision made by AI. Rather dramatically but entirely reasonably, the UN suggests any decision affecting life or death should not be left to AI. Transparency and Explainability This, to my mind, forms part of the guidelines around equality. Everyone using AI should fully understand the systems they are using, the decision-making processes used by the system and its ramifications. Individuals should be told when a decision regarding their rights, freedoms or benefits has been made by artificial intelligence, and most importantly, the explanation should be made in a way that makes it comprehensible. Responsibility and Accountability This is the whistleblower principle, that covers audit and due diligence as well as protection for whistleblowers to make sure that someone is responsible and accountable for the decisions made by, and use of, AI. Governance should be put in place around the ethical and legal responsibility of humans for any AI-based decisions. Any of these decisions that cause harm should be investigated and action taken. Inclusivity and participation Just as in any other area of business, when designing, deploying and using artificial intelligence systems, an inclusive, interdisciplinary and participatory approach should be taken, which also includes gender equality. Stakeholders and any communities that are affected should be informed and consulted and informed of any benefits and potential risks. Building your AI integration around these central pillars should help you feel reassured that your entry into AI integration is built on an ethical and solid foundation. Photo by Immo Wegmann on Unsplash Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post The ethics of AI and how they affect you appeared first on AI News. View the full article

One of the powerful methods for enhancing customer experiences and building lasting relationships is through Voice of Customer (VoC) tools. These tools allow businesses to gather insights directly from their customers, helping them to improve services, products, and overall customer satisfaction. What are voice of customer (VoC) tools? VoC tools are specialised software applications designed to collect, analyse, and interpret customer feedback. Feedback can come from various sources, including surveys, social media, direct customer interactions, and product reviews. The primary goal of the tools is to build a comprehensive understanding of customer sentiment, pain points, and preferences. VoC tools let organisations gather qualitative and quantitative data, translating the voice of their customers into actionable insights. By implementing these tools, businesses can achieve a deeper understanding of their customers, leading to informed decision-making and ultimately, enhanced customer loyalty. Top 7 Voice of Customer (VoC) tools for 2025 Here are the top seven VoC tools to consider in 2025, each offering unique features and functions to help you capture the voice of your customers effectively: 1. Revuze Revuze is an AI-driven VoC tool that focuses on extracting actionable insights from customer feedback, reviews, and surveys. Key features: Natural language processing to analyse open-ended responses. Comprehensive reporting dashboards that highlight key themes. The ability to benchmark against competitors. Benefits: Revuze empowers businesses to turn large amounts of feedback into strategic insights, enhancing decision-making and customer engagement. 2. Satisfactory Satisfactory is a user-friendly VoC tool that emphasises customer feedback collection through satisfaction surveys and interactive forms. Key features: Simple survey creation with customisable templates. Live feedback tracking and reporting. Integration with popular CRM systems like Salesforce. Benefits: Satisfactory helps businesses quickly gather customer feedback, allowing for immediate action to improve customer satisfaction and experience. 3. GetFeedback GetFeedback offers a streamlined platform for creating surveys and collecting customer insights, designed for usability across various industries. Key features: Easy drag-and-drop survey builder. Real-time feedback collection via multiple channels. Integration capabilities with other tools like Salesforce and HubSpot. Benefits: GeTFEEDBACK provides actionable insights while ensuring an engaging experience for customers participating in surveys. 4. Chattermill Chattermill focuses on analysing customer feedback through sophisticated AI and machine learning algorithms, turning unstructured data into actionable insights. Key features: Customer sentiment analysis across multiple data sources. Automated reporting tools and dashboards. Customisable alerts for key metrics and issues. Benefits: Chattermill enables businesses to react quickly to customer feedback, enhancing their responsiveness and improving overall service quality. 5. Skeepers Skeepers is designed for brands looking to amplify the customer voice by combining feedback gathering and brand advocacy functions. Key features: Comprehensive review management system. Real-time customer jury feedback for products. Customer advocacy programme integration. Benefits: Skeepers helps brands transform customer insights into powerful endorsements, boosting brand reputation and fostering trust. 6. Medallia Medallia is an established leader in the VoC space, providing an extensive platform for capturing feedback from various touchpoints throughout the customer journey. Key features: Robust analytics capabilities and AI-driven insights. Multi-channel feedback collection, including mobile, web, and in-store. Integration with existing systems for data flow. Benefits: Medallia’s comprehensive suite offers valuable tools for organisations aiming to transform customer feedback into strategic opportunities. 7. InMoment InMoment combines customer feedback across all channels, providing organisations with insights to enhance customer experience consistently. Key features: AI-powered analytics for deep insights and trends. Multi-channel capabilities for collecting feedback. Advanced reporting and visualisation tools. Benefits: With InMoment, businesses can create a holistic view of the customer experience, driving improvements across the organisation. Benefits of using VoC tools Enhanced customer understanding: By capturing and analysing customer feedback, businesses gain insights into what customers truly want, their pain points, and overall satisfaction levels. Improvement of products and services: VoC tools help organisations identify specific areas where products or services can be improved based on customer feedback, leading to increased satisfaction and loyalty. Informed decision making: With access to real-time customer insights, organisations can make data-driven decisions, ensuring that strategies align with customer preferences. Increased customer loyalty: When customers feel heard and valued, they are more likely to remain loyal to a brand, leading to repeat business and long-term growth. Competitive advantage: Organisations that effectively use customer feedback can stay ahead of competitors by quickly adapting to market demands and trends. Proactive issue resolution: VoC tools enable businesses to identify customer complaints early, allowing them to address issues proactively and improve overall customer satisfaction. Enhanced employee engagement: A deep understanding of customer needs can help employees deliver better service, enhancing their engagement and job satisfaction. How to choose VoC tools Choosing the right VoC tool involves several considerations: Define your goals: Before researching tools, clearly define what you want to achieve with VoC. Whether it’s improving product features, enhancing customer service, or understanding market trends, outlining your goals will help narrow your choices. Assess your budget: VoC tools come with various pricing models. Determine your budget and evaluate the tools that provide the best value for your investment. Evaluate features: Based on your goals, assess the features of each tool. Prioritise the features that align with your needs, like sentiment analysis, real-time reporting, or integration capabilities. Check integration options: Ensure that the chosen VoC tool can easily integrate with your existing systems. Integration can save time and enhance the overall efficiency of data utilisation. Look for scalability: As your business grows, your VoC needs may change. Choose a tool that can scale with your business and adapt to evolving customer insight demands. Request demos and trials: Take advantage of free trials or request demos to see how the tools function in real-time. The experience can provide valuable information about usability and effectiveness. Read reviews and case studies: Researching customer reviews, testimonials, and case studies can give you insights into how well the tool performs and its impact on businesses similar to yours. The post Top seven Voice of Customer (VoC) tools for 2025 appeared first on AI News. View the full article

The Qwen team at Alibaba has unveiled QwQ-32B, a 32 billion parameter AI model that demonstrates performance rivalling the much larger DeepSeek-R1. This breakthrough highlights the potential of scaling Reinforcement Learning (RL) on robust foundation models. The Qwen team have successfully integrated agent capabilities into the reasoning model, enabling it to think critically, utilise tools, and adapt its reasoning based on environmental feedback. “Scaling RL has the potential to enhance model performance beyond conventional pretraining and post-training methods,” the team stated. “Recent studies have demonstrated that RL can significantly improve the reasoning capabilities of models.” QwQ-32B achieves performance comparable to DeepSeek-R1, which boasts 671 billion parameters (with 37 billion activated), a testament to the effectiveness of RL when applied to robust foundation models pretrained on extensive world knowledge. This remarkable outcome underscores the potential of RL to bridge the gap between model size and performance. The model has been evaluated across a range of benchmarks, including AIME24, LiveCodeBench, LiveBench, IFEval, and BFCL, designed to assess its mathematical reasoning, coding proficiency, and general problem-solving capabilities. The results highlight QwQ-32B’s performance in comparison to other leading models, including DeepSeek-R1-Distilled-Qwen-32B, DeepSeek-R1-Distilled-Llama-70B, o1-mini, and the original DeepSeek-R1. Benchmark results: AIME24: QwQ-32B achieved 79.5, slightly behind DeepSeek-R1-6718’s 79.8, but significantly ahead of OpenAl-o1-mini’s 63.6 and the distilled models. LiveCodeBench: QwQ-32B scored 63.4, again closely matched by DeepSeek-R1-6718’s 65.9, and surpassing the distilled models and OpenAl-o1-mini’s 53.8. LiveBench: QwQ-32B achieved 73.1, with DeepSeek-R1-6718 scoring 71.6, and outperforming the distilled models and OpenAl-o1-mini’s 57.5. IFEval: QwQ-32B scored 83.9, very close to DeepSeek-R1-6718’s 83.3, and leading the distilled models and OpenAl-o1-mini’s 59.1. BFCL: QwQ-32B achieved 66.4, with DeepSeek-R1-6718 scoring 62.8, demonstrating a lead over the distilled models and OpenAl-o1-mini’s 49.3. The Qwen team’s approach involved a cold-start checkpoint and a multi-stage RL process driven by outcome-based rewards. The initial stage focused on scaling RL for math and coding tasks, utilising accuracy verifiers and code execution servers. The second stage expanded to general capabilities, incorporating rewards from general reward models and rule-based verifiers. “We find that this stage of RL training with a small amount of steps can increase the performance of other general capabilities, such as instruction following, alignment with human preference, and agent performance, without significant performance drop in math and coding,” the team explained. QwQ-32B is open-weight and available on Hugging Face and ModelScope under the Apache 2.0 license, and is also accessible via Qwen Chat. The Qwen team views this as an initial step in scaling RL to enhance reasoning capabilities and aims to further explore the integration of agents with RL for long-horizon reasoning. “As we work towards developing the next generation of Qwen, we are confident that combining stronger foundation models with RL powered by scaled computational resources will propel us closer to achieving Artificial General Intelligence (AGI),” the team stated. See also: Deepgram Nova-3 Medical: AI speech model cuts healthcare transcription errors Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Alibaba Qwen QwQ-32B: Scaled reinforcement learning showcase appeared first on AI News. View the full article

Last week, leading experts from academia, industry, and regulatory backgrounds gathered to discuss the legal and commercial implications of AI explainability, with a particular focus on its impact in retail. Hosted by Professor Shlomit Yaniski Ravid of Yale Law and Fordham Law, the panel brought together thought leaders to address the growing need for transparency in AI-driven decision-making, emphasising the importance of ensuring AI operates in ethical and legal parameters and the need to ‘open the ****** box’ of AI decision-making. Regulatory challenges and the new AI standard ISO 42001 Tony Porter, former Surveillance Camera Commissioner for the *** Home Office, provided insights into regulatory challenges surrounding AI transparency. He highlighted the significance of ISO 42001, the international standard for AI management systems which offers a framework for responsible AI governance. “Regulations are evolving rapidly, but standards like ISO 42001 provide organisations with a structured approach to balancing innovation with accountability,” Porter said. The panel dissociation led by Prof. Yaniski Ravid featured representatives from leading AI companies, who shared how their organisations implement transparency in AI systems, particularly in retail and legal applications. Chamelio: Transforming legal decision-making with explainable AI Alex Zilberman from Chamelio, a legal intelligence platform exclusively built for in-house legal teams, addressed the role of AI in corporate legal operations. Chamelio changes how in-house legal teams operate through an AI agent that learns and uses the legal knowledge stored in its repository of contracts, policies, compliance documents, corporate records, regulatory filings, and other business-important legal documents. Chamelio’s AI agent performs core legal tasks like extracting important obligations, streamlines contract reviews, monitors compliance, and delivers actionable insights that would otherwise remain buried in thousands of pages of documents. The platform integrates with existing tools and adapts to a team’s legal knowledge. “Trust is the number one requirement to build a system that professionals can use,” Zilberman said. “This trust is achieved by providing as much transparency as possible. Our solution allows users to understand where each recommendation comes from, ensuring they can confirm and verify every insight.” Chamelio avoids the ‘****** box’ model by letting legal professionals trace the reasoning behind AI-generated recommendations. For example, when the system encounters areas of a contract that it doesn’t recognise, instead of guessing, it flags the uncertainty and requests human input. This approach helps legal professionals control important decisions, particularly in unprecedented scenarios like clauses with no precedent or conflicting legal terms. Buffers.ai: Changing inventory optimisation Pini Usha from Buffers.ai shared insights on AI-driven inventory optimisation, an important application in retail. Buffers.ai serves medium to large retail and manufacturing brands, including H&M, P&G, and Toshiba, helping retailers – particularly in the fashion industry – tackle inventory optimisation challenges like forecasting, replenishment, and assortment planning. The company helps ensure the right product quantities are delivered to the correct locations, reducing instances of stockouts and excess inventory. Buffers.ai offers a full-SaaS ERP plugin that integrates with systems like SAP and Priority, providing ROI in months. “Transparency is key. If businesses cannot understand how AI predicts demand fluctuations or supply chain risks, they will be hesitant to rely on it,” Usha said. Buffers.ai integrates explainability tools that allow clients to visualise and adjust AI-driven forecasts, helping ensure alignment with real-time business operations and market trends. For example, when placing a new product with no historical data, the system analyses similar product trends, store characteristics, and local demand signals. If a branch has historically shown strong demand for comparable items, the system might recommend a higher quantity without any existing data for the new product. Similarly, when allocating inventory between branches and online stores, the system details factors like regional sales performance, customer traffic patterns, and online conversion rates to explain its recommendations. Corsight AI: Facial recognition in retail and law enforcement Matan Noga from Corsight AI discussed the role of explainability in facial recognition technology, which is used increasingly for security and customer experience enhancement in retail. Corsight AI specialises in real-world facial recognition, and provides its solutions to law enforcement, airports, malls, and retailers. The company’s technology is used for applications like watchlist alerting, locating missing persons, and forensic investigations. Corsight AI differentiates itself by focusing on high-speed, and real-time recognition in ways compliant with evolving privacy laws and ethical AI guidelines. The company works with government and its commercial clients to promote responsible AI adoption, emphasising the importance of explainability in building trust and ensuring ethical use. ImiSight: AI-powered image intelligence Daphne Tapia from ImiSight highlighted the importance of explainability in AI-powered image intelligence, particularly in high-stakes applications like border security and environmental monitoring. ImiSight specialises in multi-sensor integration and analysis, utilising AI/ML algorithms to detect changes, anomalies, and objects in sectors like land encroachment, environmental monitoring, and infrastructure maintenance. “AI explainability means understanding why a specific object or change was detected. We prioritise traceability and transparency to ensure users can trust our system’s outputs,” Tapia said. ImiSight continuously refines its models based on real-world data and user feedback. The company collaborates with regulatory agencies to ensure its AI meets international compliance standards. The panel underscored the important role of AI explainability in fostering trust, accountability, and ethical use of AI technologies, particularly in retail and other high-stakes industries. By prioritising transparency and human oversight, organisations can ensure AI systems are both effective and trustworthy, aligning with evolving regulatory standards and public expectations. Watch the full session here The post Explainability for retailers using AI: Insights from experts appeared first on AI News. View the full article

Deepgram has unveiled Nova-3 Medical, an AI speech-to-text (STT) model tailored for transcription in the demanding environment of healthcare. Designed to integrate seamlessly with existing clinical workflows, Nova-3 Medical aims to address the growing need for accurate and efficient transcription in the ***’s public NHS and private healthcare landscape. As electronic health records (EHRs), telemedicine, and digital health platforms become increasingly prevalent, the demand for reliable AI-powered transcription has never been higher. However, traditional speech-to-text models often struggle with the complex and specialised vocabulary used in clinical settings, leading to errors and “hallucinations” that can compromise patient care. Deepgram’s Nova-3 Medical is engineered to overcome these challenges. The model leverages advanced machine learning and specialised medical vocabulary training to accurately capture medical terms, acronyms, and clinical jargon—even in challenging audio conditions. This is particularly crucial in environments where healthcare professionals may move away from recording devices. “Nova‑3 Medical represents a significant leap forward in our commitment to transforming clinical documentation through AI,” said Scott Stephenson, CEO of Deepgram. “By addressing the nuances of clinical language and offering unprecedented customisation, we are empowering developers to build products that improve patient care and operational efficiency.” One of the key features of the model is its ability to deliver structured transcriptions that integrate seamlessly with clinical workflows and EHR systems, ensuring vital patient data is accurately organised and readily accessible. The model also offers flexible, self-service customisation, including Keyterm Prompting for up to 100 key terms, allowing developers to tailor the solution to the unique needs of various medical specialties. Versatile deployment options – including on-premises and Virtual Private Cloud (VPC) configurations – ensure enterprise-grade security and HIPAA compliance, which is crucial for meeting *** data protection regulations. “Speech-to-text for enterprise use cases is not trivial, and there is a fundamental difference between voice AI platforms designed for enterprise use cases vs entertainment use cases,” said Kevin Fredrick, Managing Partner at OneReach.ai. “Deepgram’s Nova-3 model and Nova-3-Medical model, are leading voice AI offerings, including TTS, in terms of the accuracy, latency, efficiency, and scalability required for enterprise use cases.” Benchmarking Nova-3 Medical: Accuracy, speed, and efficiency Deepgram has conducted benchmarking to demonstrate the performance of Nova-3 Medical. The model claims to deliver industry-leading transcription accuracy, optimising both overall word recognition and critical medical term accuracy. Word Error Rate (WER): With a median WER of 3.45%, Nova-3 Medical outperforms competitors, achieving a 63.6% reduction in errors compared to the next best competitor. This enhanced precision minimises manual corrections and streamlines workflows. Keyword Error Rate (KER): Crucially, Nova-3 Medical achieves a KER of 6.79%, marking a 40.35% reduction in errors compared to the next best competitor. This ensures that critical medical terms – such as drug names and conditions – are accurately transcribed, reducing the risk of miscommunication and patient safety issues. In addition to accuracy, Nova-3 Medical excels in real-time applications. The model transcribes speech 5-40x faster than many alternative speech recognition vendors, making it ideal for telemedicine and digital health platforms. Its scalable architecture ensures high performance even as transcription volumes increase. Furthermore, Nova-3 Medical is designed to be cost-effective. Starting at $0.0077 per minute of streaming audio – which Deepgram claims is more than twice as affordable as leading cloud providers – it allows healthcare tech companies to reinvest in innovation and accelerate product development. Deepgram’s Nova-3 Medical aims to empower developers to build transformative medical transcription applications, driving exceptional outcomes across healthcare. (Photo by Alexander Sinn) See also: Autoscience Carl: The first AI scientist writing peer-reviewed papers Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Deepgram Nova-3 Medical: AI speech model cuts healthcare transcription errors appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]

Opera has introduced “Browser Operator,” a native AI agent designed to perform tasks for users directly within the browser. Rather than acting as a separate tool, Browser Operator is an extension of the browser itself—designed to empower users by automating repetitive tasks like purchasing products, completing online forms, and gathering web content. Unlike server-based AI integrations which require sensitive data to be sent to third-party servers, Browser Operator processes tasks locally within the Opera browser. Opera’s demonstration video showcases how Browser Operator can streamline an everyday task like buying socks. Instead of manually scrolling through product pages or filling out payment forms, users could delegate the entire process to Browser Operator—allowing them to shift focus to activities that matter more to them, such as spending time with loved ones. Harnessing natural language processing powered by Opera’s AI Composer Engine, Browser Operator interprets written instructions from users and executes corresponding tasks within the browser. All operations occur locally on a user’s device, leveraging the browser’s own infrastructure to safely and swiftly complete commands. If Browser Operator encounters a sensitive step in the process, such as entering payment details or approving an order, it pauses and requests the user’s input. You also have the freedom to intervene and take control of the process at any time. Every step Browser Operator takes is transparent and fully reviewable, providing users a clear understanding of how tasks are being executed. If mistakes occur – like placing an incorrect order – you can further instruct the AI agent to make amends, such as cancelling the order or adjusting a form. The key differentiators: Privacy, performance, and precision What sets Browser Operator apart from other AI-integrated tools is its localised, privacy-first architecture. Unlike competitors that depend on screenshots or video recordings to understand webpage content, Opera’s approach uses the Document Object Model (DOM) Tree and browser layout data—a textual representation of the webpage. This difference offers several key advantages: Faster task completion: Browser Operator doesn’t need to “see” and interpret pixels on the screen or emulate mouse movements. Instead, it accesses web page elements directly, avoiding unnecessary overhead and allowing it to process pages holistically without scrolling. Enhanced privacy: With all operations conducted on the browser itself, user data – including logins, cookies, and browsing history – remains secure on the local device. No screenshots, keystrokes, or personal information are sent to Opera’s servers. Easier interaction with page elements: The AI can engage with elements hidden from the user’s view, such as behind cookie popups or verification dialogs, enabling seamless access to web page content. By enabling the browser to autonomously perform tasks, Opera is taking a significant step forward in making browsers “agentic”—not just tools for accessing the internet, but assistants that actively enhance productivity. See also: You.com ARI: Professional-grade AI research agent for businesses Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Opera introduces browser-integrated AI agent appeared first on AI News. View the full article

The newly-formed Autoscience Institute has unveiled ‘Carl,’ the first AI system crafting academic research papers to pass a rigorous double-blind peer-review process. Carl’s research papers were accepted in the Tiny Papers track at the International Conference on Learning Representations (ICLR). Critically, these submissions were generated with minimal human involvement, heralding a new era for AI-driven scientific discovery. Meet Carl: The ‘automated research scientist’ Carl represents a leap forward in the role of AI as not just a tool, but an active participant in academic research. Described as “an automated research scientist,” Carl applies natural language models to ideate, hypothesise, and cite academic work accurately. Crucially, Carl can read and comprehend published papers in mere seconds. Unlike human researchers, it works continuously, thus accelerating research cycles and reducing experimental costs. According to Autoscience, Carl successfully “ideated novel scientific hypotheses, designed and performed experiments, and wrote multiple academic papers that passed peer review at workshops.” This underlines the potential of AI to not only complement human research but, in many ways, surpass it in speed and efficiency. Carl is a meticulous worker, but human involvement is still vital Carl’s ability to generate high-quality academic work is built on a three-step process: Ideation and hypothesis formation: Leveraging existing research, Carl identifies potential research directions and generates hypotheses. Its deep understanding of related literature allows it to formulate novel ideas in the field of AI. Experimentation: Carl writes code, tests hypotheses, and visualises the resulting data through detailed figures. Its tireless operation shortens iteration times and reduces redundant tasks. Presentation: Finally, Carl compiles its findings into polished academic papers—complete with data visualisations and clearly articulated conclusions. Although Carl’s capabilities make it largely independent, there are points in its workflow where human involvement is still required to adhere to computational, formatting, and ethical standards: Greenlighting research steps: To avoid wasting computational resources, human reviewers provide “continue” or “stop” signals during specific stages of Carl’s process. This guidance steers Carl through projects more efficiently but does not influence the specifics of the research itself. Citations and formatting: The Autoscience team ensures all references are correctly cited and formatted to meet academic standards. This is currently a manual step but ensures the research aligns with the expectations of its publication venue. Assistance with pre-API models: Carl occasionally relies on newer OpenAI and Deep Research models that lack auto-accessible APIs. In such cases, manual interventions – such as copy-pasting outputs – bridge these gaps. Autoscience expects these tasks to be entirely automated in the future when APIs become available. For Carl’s debut paper, the human team also helped craft the “related works” section and refine the language. These tasks, however, were unnecessary following updates applied before subsequent submissions. Stringent verification process for academic integrity Before submitting any research, the Autoscience team undertook a rigorous verification process to ensure Carl’s work met the highest standards of academic integrity: Reproducibility: Every line of Carl’s code was reviewed and experiments were rerun to confirm reproducibility. This ensured the findings were scientifically valid and not coincidental anomalies. Originality checks: Autoscience conducted extensive novelty evaluations to ensure that Carl’s ideas were new contributions to the field and not rehashed versions of existing publications. External validation: A hackathon involving researchers from prominent academic institutions – such as MIT, Stanford University, and U.C. Berkeley – independently verified Carl’s research. Further plagiarism and citation checks were performed to ensure compliance with academic norms. Undeniable potential, but raises larger questions Achieving acceptance at a workshop as respected as the ICLR is a significant milestone, but Autoscience recognises the greater conversation this milestone may spark. Carl’s success raises larger philosophical and logistical questions about the role of AI in academic settings. “We believe that legitimate results should be added to the public knowledge base, regardless of where they originated,” explained Autoscience. “If research meets the scientific standards set by the academic community, then who – or what – created it should not lead to automatic disqualification.” “We also believe, however, that proper attribution is necessary for transparent science, and work purely generated by AI systems should be discernable from that produced by humans.” Given the novelty of autonomous AI researchers like Carl, conference organisers may need time to establish new guidelines that account for this emerging paradigm, especially to ensure fair evaluation and intellectual attribution standards. To prevent unnecessary controversy at present, Autoscience has withdrawn Carl’s papers from ICLR workshops while these frameworks are being devised. Moving forward, Autoscience aims to contribute to shaping these evolving standards. The company intends to propose a dedicated workshop at NeurIPS 2025 to formally accommodate research submissions from autonomous research systems. As the narrative surrounding AI-generated research unfolds, it’s clear that systems like Carl are not merely tools but collaborators in the pursuit of knowledge. But as these systems transcend typical boundaries, the academic community must adapt to fully embrace this new paradigm while safeguarding integrity, transparency, and proper attribution. (Photo by Rohit Tandon) See also: You.com ARI: Professional-grade AI research agent for businesses Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Autoscience Carl: The first AI scientist writing peer-reviewed papers appeared first on AI News. View the full article

Blockchain has tried to claim many things as its own over the years, from global payment processing to real-world assets. But in artificial intelligence, it’s found synergy with a sector willing to give something back. As this symbiotic relationship has grown, it’s become routine to hear AI and blockchain mentioned in the same breath. While the benefits web3 technology can bring to artificial intelligence are well documented – transparency, P2P economies, tokenisation, censorship resistance, and so on – this is a reciprocal arrangement. In return, AI is fortifying blockchain projects in different ways, enhancing the ability to process vast datasets, and automating on-chain processes. The relationship may have taken a while to get started, but blockchain and AI are now entwined. Trust meets efficiency While AI brings intelligent automation and data-driven decision-making, blockchain offers security, decentralisation, and transparency. Together, they can address each other’s limitations, offering new opportunities in digital and real-world industries. Blockchain provides a tamper-proof foundation and AI brings adaptability, plus the ability to optimise complex systems. Together, the two promise to enhance scalability, security, and privacy – key pillars for modern finance and supply chain applications. AI’s ability to analyse large amounts of data is a natural fit for blockchain networks, allowing data archives to be processed in real time. Machine learning algorithms can predict network congestion – as seen with tools like Chainlink’s off-chain computation, which offers dynamic fee adjustments or transaction prioritisation. Security also gains: AI can monitor blockchain activity in real-time to identify anomalies more quickly than manual scans, so teams can move to mitigate attacks. Privacy is improved, with AI managing zero-knowledge proofs and other cryptographic techniques to shield user data; methods explored by projects like Zcash. These types of enhancements make blockchain more robust and attractive to the enterprise. In DeFi, Giza‘s agent-driven markets embody the convergence of web3 and artificial intelligence. Its protocol runs autonomous agents like ARMA, which manage yield strategies across protocols and offer real-time adaptation. Secured by smart accounts and decentralised execution, agents can deliver positive yields, and currently manage hundreds of thousands of dollars in on-chain assets. Giza shows how AI can optimise decentralised finance and is a project that uses the two technologies to good effect. Blockchain as AI’s backbone Blockchain offers AI a decentralised infrastructure to foster trust and collaboration. AI models, often opaque and centralised, face scrutiny over data integrity and bias – issues blockchain counters with transparent, immutable records. Platforms like Ocean Protocol use blockchain to log AI training data, providing traceability without compromising ownership. That can be a boon for sectors like healthcare, where the need for verifiable analytics is important. Decentralisation also enables secure multi-party computation, where AI agents collaborate across organisations – think federated learning for drug discovery – without a central authority, as demonstrated in 2024 by IBM’s blockchain AI pilots. The trustless framework reduces reliance on big tech, helping to democratise AI. While AI can enhance blockchain performance, blockchain itself can provide a foundation for ethical and secure AI deployment. The transparency and immutability with which blockchain is associated can mitigate AI-related risks by ensuring AI model integrity, for example. AI algorithms and training datasets can be recorded on-chain so they’re auditable. Web3 technology helps in governance models for AI, as stakeholders can oversee and regulate project development, reducing the risks of biased or unethical AI. Digital technologies with real-world impact The synergy between blockchain and AI exists now. In supply chains, AI helps to optimise logistics while blockchain can track item provenance. In energy, blockchain-based smart grids paired with AI can predict demand; Siemens reported a 15% efficiency gain in a 2024 trial of such a system in Germany. These cases highlight how AI scales blockchain’s utility, while the latter’s security can realise AI’s potential. Together, they create smart, reliable systems. The relationship between AI and blockchain is less a merger than a mutual enhancement. Blockchain’s trust and decentralisation ground AI’s adaptability, while AI’s optimisation unlocks blockchain’s potential beyond that of a static ledger. From supply chain transparency to DeFi’s capital efficiency, their combined impact is tangible, yet their relationship is just beginning. (Image source: Unsplash) The post Trust meets efficiency: AI and blockchain mutuality appeared first on AI News. View the full article

Palo Alto-based You.com has introduced ARI, a professional-grade AI research agent for businesses to access competitive insights. ARI (Advanced Research & Insights) delivers comprehensive, accurate, and interactive reports within minutes—potentially shaking up the $250 billion management consulting industry. You.com claims ARI completes reports that typically require weeks of labour and cost thousands of dollars in just five minutes, at a fraction of traditional expenses. With the ability to process over 400 sources simultaneously – a figure set to grow as the technology scales – ARI promises to deliver “verified citations and insights 3X faster than other currently available solutions.” Bryan McCann, Co-Founder and CTO of You.com, said: “ARI’s breakthrough is its ability to maintain contextual understanding while processing hundreds of sources simultaneously. “When combined with chain-of-thought reasoning and extended test-time compute, ARI is able to discover and incorporate adjacent research areas dynamically as analysis progresses.” A powerful AI agent for business research Traditional AI research tools are typically limited to processing between 30 to 40 data sources at a time. ARI stands out by handling hundreds of public and private data streams, ensuring unparalleled accuracy and scope in its analysis. The system doesn’t just stop at summarising data; it enhances user experience by producing rich, interactive graphs, charts, and visualisations for deeper insights. Designed to cater equally to high-level professionals and knowledge workers across industries, ARI combines advanced functionality with user-friendly accessibility. This dual-purpose design allows enterprises to deploy it as a personal assistant or as a replacement for expensive research efforts traditionally carried out by consulting firms. At the heart of ARI is a series of capabilities: Simultaneous source analysis: Processes hundreds of data sources, both public and private. Chain-of-Thought reasoning: Dynamically evolves research parameters as insights emerge. Real-time verification: Provides direct validation for every claim and data point. Interactive visualisation engine: Automatically generates and cites graphs and charts to enhance reporting. Enterprise data integration: Analyses a mix of public and private datasets to deliver actionable insights. During its initial deployment phase, ARI has demonstrated its versatility and potential for impact across several industries: Consulting: By analysing market reports, competitor financials, patent filings, and social sentiment data in hours rather than weeks, ARI supports due diligence with ease. Financial services: With the ability to integrate real-time data from earnings calls, SEC filings, and industry news, ARI helps support faster and more accurate investment decisions. Healthcare: ARI accelerates the synthesis of clinical trials, medical journals, patient data, and treatment guidelines, providing insights that support evidence-based care. Media: From audience data to trending topics and competitor activity, ARI enables the rapid identification of new story angles and anticipates emerging narratives in key markets. Dr Dennis Ballwieser, Managing Director and Editor at Wort & Bild Verlag, commented: “The research time has dropped from a few days to just a few hours, and the accuracy across both ******* and English content has been remarkable. “What excites me most is the opportunity to democratise access to professional-grade research. With ARI’s ability to analyse hundreds of verifiable sources simultaneously while maintaining accuracy, we can now offer professional insights to organisations of all sizes at a fraction of the traditional cost.” Accelerating access to strategic insights The potential for technologies like ARI goes beyond time and cost savings. For companies such as global consultancy firm APCO Worldwide, ARI’s capabilities provide a level of quality and personalisation that aligns with the modern needs of clients. Philip Fraser, CIO at APCO Worldwide, said: “To us, ARI represents a step-change in the quality and alignment to the needs of our clients. We are very excited about working with You.com to integrate the power of ARI into our award-winning, proprietary Margy AI platform.” Through such integrations, ARI has the potential to move organisations away from periodic, resource-intensive research projects towards continuous real-time intelligence that drives better decision-making across all levels. Richard Socher, Co-Founder and CEO of You.com, added: “When every employee has instant access to comprehensive, validated insights that previously required teams of consultants and weeks of work, it changes the speed and quality of business decision-making. ARI represents a paradigm shift in how organisations operate.” ARI is the newest addition to You.com’s expanding AI agent ecosystem, which has already seen the development of over 50,000 custom agents since late 2024. The company has raised $99 million in funding from investors such as Salesforce Ventures, NVIDIA, and Georgian Ventures. With ARI, You.com aims to set a new standard for an enterprise-grade AI research agent as part of broader decision-making systems. (Photo by Jeremy Beadle) See also: Endor Labs: AI transparency vs ‘open-washing’ Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post You.com ARI: Professional-grade AI research agent for businesses appeared first on AI News. View the full article

EU-funded initiative CERTAIN aims to drive ethical AI compliance in Europe amid increasing regulations like the EU AI Act. CERTAIN — short for “Certification for Ethical and Regulatory Transparency in Artificial Intelligence” — will focus on the development of tools and frameworks that promote transparency, compliance, and sustainability in AI technologies. The project is led by Idemia Identity & Security France in collaboration with 19 partners across ten European countries, including the St. Pölten University of Applied Sciences (UAS) in Austria. With its official launch in January 2025, CERTAIN could serve as a blueprint for global AI governance. Driving ethical AI practices in Europe According to Sebastian Neumaier, Senior Researcher at the St. Pölten UAS’ Institute of IT Security Research and project manager for CERTAIN, the goal is to address crucial regulatory and ethical challenges. “In CERTAIN, we want to develop tools that make AI systems transparent and verifiable in accordance with the requirements of the EU’s AI Act. Our goal is to develop practically feasible solutions that help companies to efficiently fulfil regulatory requirements and sustainably strengthen confidence in AI technologies,” emphasised Neumaier. To achieve this, CERTAIN aims to create user-friendly tools and guidelines that simplify even the most complex AI regulations—helping organisations both in the public and private sectors navigate and implement these rules effectively. The overall intent is to provide a bridge between regulation and innovation, empowering businesses to leverage AI responsibly while fostering public trust. Harmonising standards and improving sustainability One of CERTAIN’s primary objectives is to establish consistent standards for data sharing and AI development across Europe. By setting industry-wide norms for interoperability, the project seeks to improve collaboration and efficiency in the use of AI-driven technologies. The effort to harmonise data practices isn’t just limited to compliance; it also aims to unlock new opportunities for innovation. CERTAIN’s solutions will create open and trustworthy European data spaces—essential components for driving sustainable economic growth. In line with the EU’s Green Deal, CERTAIN places a strong focus on sustainability. AI technologies, while transformative, come with significant environmental challenges—such as high energy consumption and resource-intensive data processing. CERTAIN will address these issues by promoting energy-efficient AI systems and advocating for eco-friendly methods of data management. This dual approach not only aligns with EU sustainability goals but also ensures that AI development is carried out with the health of the planet in mind. A collaborative framework to unlock AI innovation A unique aspect of CERTAIN is its approach to fostering collaboration and dialogue among stakeholders. The project team at St. Pölten UAS is actively engaging with researchers, tech companies, policymakers, and end-users to co-develop, test, and refine ideas, tools, and standards. This practice-oriented exchange extends beyond product development. CERTAIN also serves as a central authority for informing stakeholders about legal, ethical, and technical matters related to AI and certification. By maintaining open channels of communication, CERTAIN ensures that its outcomes are not only practical but also widely adopted. CERTAIN is part of the EU’s Horizon Europe programme, specifically under Cluster 4: Digital, Industry, and Space. The project’s multidisciplinary and international consortium includes leading academic institutions, industrial giants, and research organisations, making it a powerful collective effort to shape the future of AI in Europe. In January 2025, representatives from all 20 consortium members met in Osny, France, to kick off their collaborative mission. The two-day meeting set the tone for the project’s ambitious agenda, with partners devising strategies for tackling the regulatory, technical, and ethical hurdles of AI. Ensuring compliance with ethical AI regulations in Europe As the EU’s AI Act edges closer to implementation, guidelines and tools like those developed under CERTAIN will be pivotal. The Act will impose strict requirements on AI systems, particularly those deemed “high-risk,” such as applications in healthcare, transportation, and law enforcement. While these regulations aim to ensure safety and accountability, they also pose challenges for organisations seeking to comply. CERTAIN seeks to alleviate these challenges by providing actionable solutions that align with Europe’s legal framework while encouraging innovation. By doing so, the project will play a critical role in positioning Europe as a global leader in ethical AI development. See also: Endor Labs: AI transparency vs ‘open-washing’ Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post CERTAIN drives ethical AI compliance in Europe appeared first on AI News. View the full article For verified travel tips and real support, visit: [Hidden Content]