Latest news with #SoftwareDevelopment
Forbes
3 days ago
- Business
- Forbes
Quantum Machine Learning: Here's How Business Leaders Can Prepare Now
Klaudia Zaika is the CEO of Apriorit, a software development company that provides engineering services globally to tech companies. The computational demands of today's AI systems are starting to outpace what classical hardware can deliver. How can we fix this? One possible solution is quantum machine learning (QML). QML introduces a fundamentally different approach to learning and optimization by applying quantum principles to AI algorithm design. This isn't a replacement, but rather an enhancement to classical AI and problem-solving. You may think that it's too early to prepare for QML, as most of today's quantum-related projects remain experimental and scientific. But not so long ago, in 2022, almost no one had heard about ChatGPT, and Google's Bard (now Gemini) wasn't even released yet. Now, as we've all seen, they are among the top go-to services for anything from organizing personal schedules to operating large enterprises. That's how fast these technologies evolve. This is why I believe that now is the perfect time to get ready and start building new infrastructures, developing new skills and planning your QML adoption strategy. What Makes Quantum Machine Learning Different QML isn't just about running existing AI models on faster hardware. It's about reimagining how learning itself happens. Classical AI systems rely on binary data structures and deterministic logic. These models weren't built to take advantage of quantum behavior and may lose efficiency or accuracy when forced into that environment. In contrast, QML models are designed to implement quantum principles like superposition and entanglement. These capabilities allow QML models to explore complex problem spaces more efficiently than non-quantum systems. As this field is still forming, there's no unified vision for the term 'quantum machine learning,' and it often refers to: • Quantum-enhanced machine learning, where quantum algorithms boost specific AI tasks, such as optimization or clustering. • Machine learning-enhanced processing of quantum data and the outputs from quantum experiments or simulations. • Hybrid quantum-classical models that combine two previous approaches and are often the most practical path forward in today's noisy intermediate-scale quantum (NISQ) era. With so much ambiguity, the path to industrial adoption of QML seems to be far from straightforward and is filled with lots of obstacles. Knowing and preparing for these obstacles will help your business avoid costly missteps when integrating this technology into your products, services and organizational processes. Barriers To Widespread QML Adoption From my company's experience with similar ground-breaking innovations, specifically cloud and AI, there are a few challenges that I believe leaders in this space should expect first. Take model size and complexity, for instance. The first and most important barrier to account for in this case is hardware. Most quantum systems today still fall into the NISQ category. They are a good fit for experimentation but won't be able to handle broad commercial deployment as they lack qubit stability, error correction and scalability. Next, if you look at today's software architectures, you'll see that they are also not ready for quantum adoption. In fact, there's still a lot of work for those who want to make legacy systems cloud- and AI-compatible, even in terms of building the right data architectures. And QML requires much more architectural modularity and flexibility than any other technology we can think of today. Hybrid systems are also a challenge of their own. You'll need to determine which functions to delegate to the quantum part and which are better left with the traditional, non-quantum side of your system. Designing and testing these hybrid environments, as well as integrating them with the existing software ecosystems, will require new thinking, especially in cybersecurity, data handling and orchestration. Last but not least, there's a deep talent gap. Quantum computing and machine learning are complex fields, and very few professionals are trained at the intersection of those two. When you listen to quantum enthusiasts, you may feel tempted to treat QML as an emerging silver bullet, which it isn't. Yet, it's a fundamental shift that will require prolonged, well-planned preparations from your business. How To Prepare For QML Today: A Strategic Roadmap QML adoption is a marathon, not a race. So if you want your business to use and benefit from QML, it's already time to prepare your teams, processes and software architecture for what's coming. Here's what you can start doing right now: 1. Educate your team. You don't need a quantum physicist on staff tomorrow, but your developers and architects should start building foundational literacy. Workshops, online courses and partnerships with academic programs can accelerate this process and help your business mitigate the risk of facing talent shortages in the future. 2. Explore low-risk pilot projects. Optimization, anomaly detection and pattern recognition are great starting points, especially when executed using quantum simulators or early-access platforms. These efforts can help your teams build hands-on familiarity without overcommitting. On the other hand, if you engage early as a business, whether through vendor exploration or academic partnerships, you can gain better visibility into what's coming and where your organization fits. 3. Build adaptable software architectures. Flexible, non-monolithic architectures are already gaining traction. But to prepare your software for QML or post-quantum cryptography (PQC), you need to bring it to a whole new level of agility. This means making your software architecture mostly API-driven, modular and as loosely coupled as possible. 4. Evolve your cybersecurity measures. Even if QML doesn't fit your business, you still need to think about ways to protect your data and systems from upcoming quantum threats. In particular, make sure to keep track of the US National Quantum Initiative and the latest PQC news from NIST. 5. Plan for the long run. QML isn't an investment that can offer a short-term ROI for most companies. Yet, just like cloud and AI before it, I believe this technology will fundamentally reshape how software is developed and deployed. The shift to quantum machine learning won't happen overnight. That means you still have time to prepare your business for the adoption of this promising technology—from considering fitting use cases and determining where and how QML will benefit you the most to deciding on the first implementation steps. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Yahoo
17-07-2025
- Business
- Yahoo
Joget Named in Everest Group Innovation Watch for Generative AI Apps in Software Development
Recognized for combined maturity, scale, partnerships, and investments in Generative AI Applications for Software Development COLUMBIA, Md., July 17, 2025 /PRNewswire/ -- Joget Inc., a global innovator in open-source AI-powered no-code/low-code development platform, is proud to announce its inclusion as a Fast Follower in the Everest Group Innovation Watch for Generative AI Applications in Software Development 2025. This recognition places Joget alongside industry giants such as Google, Meta, and Salesforce, highlighting its leadership in driving innovation through custom off-the-shelf software solutions. The Everest Group Innovation Watch report evaluates companies based on their market performance, maturity, scale, partnerships, and investments. Joget was specifically acknowledged for its contributions to reshaping enterprise software strategy through Joget Intelligence, its integrated suite of AI capabilities. Inside Joget Intelligence Designed for both developers and business users, Joget Intelligence delivers four key tools that simplify and accelerate intelligent app development: AI Designer: Visually build and refine apps using natural language prompts, documents, or images AI Agent Builder: Create smart automation agents that act on business logic, complete with human oversight AI Bundle: Extend platform capabilities with curated AI plugins AI Assistant: Get real-time guidance while designing workflows Together, these innovations empower both highly technical and non-technical users to design, adapt, and scale enterprise applications quickly, without sacrificing control or flexibility. This positioning aligns with a broader industry shift: enterprises are moving away from one-size-fits-all software toward modular, customizable, and AI-enabled platforms that evolve with their needs. With Joget Intelligence, companies can now build smarter applications that grow with them faster, more efficiently, and without vendor lock-in. Empowering Open Innovation at the Right Speed and Cost "At Joget, our mission is simple: empower open innovation—at the right speed and cost," said Raveesh Dewan, President and CEO of Joget Inc. "We are witnessing a significant shift from commercial off-the-shelf (COTS) to custom off-the-shelf solutions, where enterprises can leverage AI to create modular, easily customizable applications. Our customers and partners are already making this leap, and we're excited to help more organizations accelerate their innovation journey." The Rise of Custom Off-the-Shelf Solutions According to Everest Group, AI-powered tools are lowering barriers to custom software development. Enterprises are increasingly adopting a modular, AI-enabled approach to software development, starting with pre-built components and rapidly tailoring them to fit their specific environments. This transformation not only addresses the limitations of traditional COTS and SaaS models but also empowers enterprises to become proactive builders rather than reactive buyers. Joget's platform plays a pivotal role in this shift by providing a flexible framework that allows organizations to co-create value with technology vendors, adapting apps without the need to build from scratch. Drive Real Outcomes with Joget Organizations across industries are turning to Joget to take control of their digital transformation, reducing dependency on bloated SaaS platforms, streamlining complex workflows, and accelerating innovation without costly development cycles. If you're ready to move from generic solutions to tailored applications that grow with your business, it's time to explore what Joget can do for you. Start building smarter today: Contact us. Media Contact: pr@ About Joget Joget offers an open-source, AI-powered platform that converges no-code/low-code development with Generative AI to rapidly build and customize enterprise applications at scale. By combining AI with visual app builders —not raw code—Joget makes app generation faster, safer, and more accessible for business users and developers. With Generative AI and Agentic AI capabilities, Joget Intelligence enables organizations to automate and enhance processes while maintaining oversight and compliance. Unlike typical AI code generation, Joget's visual-first approach ensures applications are maintainable and governed within collaborative human workflows. As an Application and Integration Fabric, Joget connects legacy and modern systems seamlessly. Its extensible, open-source core and plugin architecture offer unmatched flexibility, and its White Label solution allows OEMs and digital solution providers to fully rebrand the platform. Trusted by startups, global enterprises, and government agencies, Joget delivers the speed of AI with the control of visual development for scalable, intelligent digital transformation. Visit and follow us on LinkedIn, X, Facebook, or YouTube. View original content to download multimedia: SOURCE Joget
Zawya
15-07-2025
- Business
- Zawya
Saudi Azm, Obeikan Glass shares to stop trading on Nomu ahead of Main Market transfer
Software development firm Saudi Azm for Communication and Information Technology Co., along with Obeikan Glass Co., will cease trading on the kingdom's parallel market Nomu from July 15 to make a switch to the Tadawul Main Market, following regulatory approval. Saudi Exchange has confirmed the shares will cease trading for five days in order to complete the transfer procedures, following which a listing date will be announced. Saudi Azm will transfer with an authorised capital of 30 million riyals ($8 million) and 60 million shares, while Obeikan Glass will move with an authorised capital of SAR 320 million and 32 million shares. Saudi Azm's and Obeikan Glass' move to TASI (main market) comes six months after the Saudi Exchange had rejected the requests on December 31, following the two companies failing to meet all transition requirements stipulated in the listing rules. In April 2024, Saudi Azm first announced it had received board approval to transfer its shares to TASI and had appointed Al Rajhi Capital as a financial advisor to handle the transaction. Meanwhile, Obeikan Glass, one of the largest float glass manufacturers in Saudi, had received board approval in December 2023 to transfer its shares from Nomu to TASI. (Writing by Bindu Rai, editing by Brinda Darasha)
Geek Wire
03-06-2025
- Business
- Geek Wire
Reality check: Microsoft Azure CTO pushes back on AI vibe coding hype, sees upper limit long-term
Microsoft Azure CTO Mark Russinovich speaks at a Technology Alliance event Tuesday in Redmond. (GeekWire Photo / Todd Bishop) REDMOND, Wash. — Microsoft Azure CTO Mark Russinovich cautioned that 'vibe coding' and AI-driven software development tools aren't capable of replacing human programmers for complex software projects, contrary to the industry's most optimistic aspirations for artificial intelligence. Russinovich, giving the keynote Tuesday at a Technology Alliance startup and investor event, acknowledged the effectiveness of AI coding tools for simple web applications, basic database projects, and rapid prototyping, even when used by people with little or no programming experience. However, he said these tools often break down when handling the most complex software projects that span multiple files and folders, and where different parts of the code rely on each other in complicated ways — the kinds of real-world development work that many professional developers tackle daily. 'These things are right now still beyond the capabilities of our AI systems,' he said. 'You're going to see progress made. They're going to get better. But I think that there's an upper limit with the way that autoregressive transformers work that we just won't get past.' Even five years from now, he predicted, AI systems won't be independently building complex software on the highest level, or working with the most sophisticated code bases. Instead, he said, the future lies in AI-assisted coding, where AI helps developers write code but humans maintain oversight of architecture and complex decision-making. This is more in line with Microsoft's original vision of AI as a 'Copilot,' a term that originated with the company's GitHub Copilot AI-powered coding assistant. Russinovich, a longtime Microsoft technical and cloud leader, gave an insider's overview of the AI landscape, including reasoning models that can think through complex problems before responding; the decline in unit costs for training and running AI models; and the growing importance of small language models that can run efficiently on edge devices. Mark Russinovich discusses the shift in resources from AI training to inference. (GeekWire Photo / Todd Bishop) He described the flip that has taken place in computing resources, from primarily training AI models in the past to now focusing more on AI inference, as usage of artificial intelligence has soared. He also discussed the emergence of agentic AI systems that can operate autonomously — reflecting a big push this year for Microsoft and other tech giants — as well as AI's growing contributions to scientific discoveries, such as the newly announced Microsoft Discovery. But Russinovich, who is also Azure's chief information security officer, kept gravitating back to AI limitations, offering a healthy reality check overall. He discussed his own AI safety research, including a technique that he and other Microsoft researchers developed called 'crescendo' that can trick AI models into providing information they'd otherwise refuse to give. The crescendo method works like a 'foot in the door' psychological attack, he explained, where someone starts with innocent questions about a forbidden topic and gradually pushes the AI to reveal more detailed information. Ironically, he noted, the crescendo technique was referenced in a recent research paper that made history as the first largely AI-generated research ever accepted into a tier-one scientific conference. Russinovich also delved extensively into ongoing AI hallucination problems — showing examples of Google and Microsoft Bing giving incorrect AI-generated answers to questions about the time of day in the Cook Islands, and the current year, respectively. 'AI is very unreliable. That's the takeaway here,' he said. 'And you've got to do what you can to control what goes into the model, ground it, and then also verify what comes out of the model.' Depending on the use case, Russinovich added, 'you need to be more rigorous or not, because of the implications of what's going to happen.'
Gizmodo
02-06-2025
- Business
- Gizmodo
Download CrossOver for macOS and Linux
The main attraction of CrossOver goes beyond its capability to execute Windows applications, according to users. The application operates in such a way that users feel like they never disconnected from their Mac or Linux environments. You're not creating virtual machines. There's no need for a computer reboot when you require a specific app through this software. The software enables you to maintain your position on your macOS or Linux desktop while gaining access to programs that were previously unavailable to you. Few professionals working across diverse environments will find this significant benefit particularly helpful. Your primary computer operates with Mac, but you require an accounting software that functions exclusively on Windows. If you switched to Linux, then your preferred design application does not provide support for your new operating system. The design of CrossOver allows users to avoid making sacrifices with their software choices. You can experience dual functionality since CrossOver operates between macOS and Linux desktops through a single interface. The several years of development have made the platform solid, so it remains resistant to breakage. About 75% of standard applications Americans wish to operate on have undergone testing, and developers have simplified the installation process with basic click commands. Simplicity stands out as the key distinguishing fact of this particular product. For decades, Wine demonstrated complete power while maintaining requirements that demand technical abilities. The sophisticated, complex functions of the program remain hidden behind an elegant user interface provided by CrossOver. The system functions best without needing to understand how everything operates within the program code. The application selection process is simple since you just need to choose the desired app to install, then watch CrossOver execute all operations automatically. Softness in user experience activates two significant benefits: first, it reduces the amount of time required to complete actions, and secondly, it conserves precious energy for other tasks. The process requires no debugging activities or searches on online forums. Moving forward with your work process just requires you to launch your selected software. Managers who work with productivity tools combined with older software or games without native system compatibility will experience a total shift in their work process because of this access method. The software removes the ongoing platform battle between different operating systems. It reduces friction. Open-source foundations enable this platform, while the provider delivers support through regular updates along with technical assistance that users need. The stable, practical functionality of CrossOver provides an excellent program solution for developers and gamers and business owners, and strictly functional users who require particular software without damaging their system operations.
