#Power Platform Developers

[atcuality1]

Simplify Transactions and Boost Efficiency with Our Cash Collection Application

Manual cash collection can lead to inefficiencies and increased risks for businesses. Our cash collection application provides a streamlined solution, tailored to support all business sizes in managing cash effortlessly. Key features include automated invoicing, multi-channel payment options, and comprehensive analytics, all of which simplify the payment process and enhance transparency. The application is designed with a focus on usability and security, ensuring that every transaction is traceable and error-free. With real-time insights and customizable settings, you can adapt the application to align with your business needs. Its robust reporting functions give you a bird’s eye view of financial performance, helping you make data-driven decisions. Move beyond traditional, error-prone cash handling methods and step into the future with a digital approach. With our cash collection application, optimize cash flow and enjoy better financial control at every level of your organization.

[softwarezone365]

Count The Rows In A Power Apps Gallery With AllItemsCount

Overview:

In this blog post, we’ll explore how to count Power Apps Gallery Rows using the AllItemsCount property. We’ll insert a label to show the count of all gallery items and then implement the exact code for precise counting. Each step comes with images and clear instructions. By the end, you’ll master the display of the total number of items in any gallery. Let’s dive in!

This code we use and count how many items in gallery

Read Full Post here: How to Count Rows in Power Apps Gallery: A Step-by-Step Guide (softwarezone365.com)

View Post On: LinkedIn: (4) Software Zone 365: Company Page Admin | LinkedIn Twitter: https://twitter.com/365_zone40741/status/1712883279580614768 Medium: Microsoft’s Power Platform Potential: Unlocking Business Impact (softwarezone365.com) Pinterest: https://www.pinterest.com/pin/1065875436793645010 Reddit: https://www.reddit.com/user/SoftwareZone365/comments/1773k2e/count_the_rows_in_a_power_apps_gallery_with/ Quora: https://www.quora.com/profile/Software-Zone-365/In-this-blog-post-we-ll-explore-how-to-count-Power-Apps-Gallery-Rows-using-the-AllItemsCount-property-We-ll-insert-a-l GitHub: Count The Rows In A Power Apps Gallery With AllItemsCount · softwarezone365/PowerApps Wiki (github.com) Instagram: https://www.instagram.com/p/CyWOuK8sGc9/ Facebook: https://m.facebook.com/story.php?story_fbid=300738072723966&id=100083633251137&mibextid=ZbWKwL

[nuageconcepts]

Dynamics 365 ERP consulting offers businesses guidance and support in implementing, customizing, and optimizing Microsoft Dynamics 365 enterprise resource planning (ERP) software. With a focus on improving operational efficiency, financial management, and supply chain visibility, Dynamics 365 ERP consulting helps organizations streamline their business processes and make data-driven decisions. Services may include software selection, installation, training, ongoing support, and software integrations with other business systems.

[jcmarchi]

Jarek Kutylowski, Founder & CEO of DeepL – Interview Series

New Post has been published on https://thedigitalinsider.com/jarek-kutylowski-founder-ceo-of-deepl-interview-series/

Jarek Kutylowski, Founder & CEO of DeepL – Interview Series

Jarek Kutylowski is the founder and CEO of DeepL, an advanced AI-powered translation tool known for its impressive accuracy and natural-sounding translations.

DeepL is on a mission to break down language barriers for businesses everywhere. Over 100,000 businesses and governments and millions of individuals in 228 global markets trust DeepL’s Language AI platform for human-like translation and better writing. Designed with enterprise security in mind, companies around the world leverage DeepL’s AI solutions that are specifically tuned for language to transform business communications, expand markets, and improve productivity. Founded in 2017, DeepL today has over 1,000 passionate employees and is supported by world-renowned investors including Benchmark, IVP, and Index Ventures.

Can you walk us through the early vision behind DeepL and how the company’s goals have evolved since its founding?

When I started the company back in 2017, we were at a turning point with deep learning. It had really started to gain traction, which inspired the name of our company. We’ve always had a vision of empowering organizations to thrive and scale globally without a language barrier standing in the way, which is still our mission today. So we had started experimenting with the technology and its application within language, and quickly saw how powerful it could be – it was clear that neural networks and AI were going to be the next big breakthrough in translation, and that what we could achieve with neural networks would far surpass existing traditional solutions.With this in mind, we decided  to package our findings into a product and share it with the world.

What began as a fun project quickly evolved into something more significant. Just a few months after officially launching our inaugural product, DeepL Translate, in 2017, we had some of the most important European and German companies knocking on our door, interested in deploying our solutions. This realization about the relevance and critical importance of our product to larger organizations inspired us to dive deeper into their problems, and invest time really understanding how our technology could solve them. We built out a more concrete commercial strategy and clear monetization plans, focusing first on releasing the product to the public and figuring out the business side later. Fast forward to today, and we’ve grown considerably, expanding our team, our products, and our customer network. We now have millions of individual users of our specialized AI translation and writing tools, and over 100,000+ business and government partners worldwide, including 50% of the Fortune 500 and leaders like Nikkei (owner of the Financial Times), Coursera, Deutsche Bahn, Zendesk and more.

You founded DeepL in a field already dominated by major players like Google and Microsoft. What was your strategy to carve out a niche in such a competitive landscape?

We’ve been up against Big Tech since we first started out, which has kept us agile, innovative, and motivated to offer our users the best solution out there. But I challenge anyone claiming they’ve solved the challenge of language for business and enterprise-use-cases with general models – this is where DeepL stands out and is really unique in the world of Language AI and translation.

We’re different from general-purpose AI systems in that we’ve built our AI translation and writing solutions to be specialized for language use-cases; this specialization enables us to achieve higher accuracy and precision while minimizing hallucinations and misinformation. It’s one thing to translate a menu while you’re on vacation – but most of our customers are businesses and knowledge workers, where the stakes are much higher. Imagine a lawyer who needs to translate highly nuanced, critical, and confidential legal documents; or a major media publisher who relies on our platform to translate thousands of stories a day, in real time, that are then distributed to millions of global readers. These complex use cases demand a level of accuracy, customization, data privacy, and security that general models can’t provide. Data protection and security are also central to our offering, so we adhere to the highest privacy and security standards including all GDPR requirements, offering state-of-the art data encryption and more.

DeepL has recently launched its first in-house LLM. How does this model differ from other large language models in the market, and in what context is it considered superior?

Our next-generation translation models are powered by proprietary LLM technology designed specifically for translation and editing, which sets it apart from other models on the market and sets a new industry standard for translation quality and performance. Unlike general-purpose models that rely solely on public Internet data, DeepL’s LLM benefits from over seven years of proprietary data curated specifically for content creation and translation. It also uses human model tutoring, with thousands of hand-picked language experts who are trained to refine and enhance the model’s translation quality.

Because of this, we’re proud that DeepL is widely considered to be the most reliable and preferred Language AI solution for businesses and professionals out there. In blind tests, professional translators have found that our next-generation LLM requires significantly fewer edits than other platforms, with Google and ChatGPT requiring between two and three times as many edits to get the same quality.

Can you explain the process behind training DeepL’s LLM? How much human input is required to maintain accuracy and nuance in translation, and how do you balance that with the computational aspects of AI development?

We train our LLM on a combination of highest-quality linguistic data, as well as the expertise of thousands of hand-picked language experts. This two-pronged approach, combining both computational aspects with human feedback, allows us to support our customers at scale while also ensuring that the quality and nuance of our translations remain on point. Human input is really crucial, primarily in data curation, quality assessment, and providing feedback for continuous improvement. DeepL’s translation quality just wouldn’t be where it is without this aspect.

DeepL has recently expanded into 165 new markets and added support for three new languages. What was the strategic thinking behind targeting these markets and languages, and how has the expansion impacted DeepL’s user base?

Language is a global problem that touches businesses across almost every industry – whether they’re facing language barriers internally among colleagues, or externally with customers and in diverse markets where they operate. And as a research-led company, everything we do is informed by our mission to break down language barriers, and the feedback we’re hearing from customers and businesses. Our decision to expand the availability of our products – DeepL Pro is now available across 228 global markets – and add new language capabilities, like the recent launch of Traditional Chinese, is rooted in our mission to break down these barriers, guided by customer feedback and market research.

We are at an exciting moment in AI, where adoption is not just a trend but a necessity. Companies are ready to embrace AI but are looking for technology that delivers real value and ROI, and we’re proven to drive significant impact – a 2024 Forrester study revealed that the use of DeepL delivered 345% ROI for global companies, reducing translation time by 90% while driving a 50% in workload reduction. By growing our reach and capabilities, we can deliver these real-world benefits to even more markets, people, and businesses. Take Traditional Chinese, for example – it’s the primary language for over 33 million people worldwide. So this is really driving the scale of our business and helping us to meet rising demand globally. Today, we have a network of over 100,000+ business customers, and it’s growing fast.

With over 100,000 customers, including major organizations like Deutsche Bahn and Zendesk, what are the primary challenges and opportunities you’ve encountered while scaling to meet enterprise and governmental needs?

When we’re in the room with a customer, they’re clear on the value and ROI we can provide to them – – even in the US, where you might think English is the only language spoken, most companies operate with multilingual teams and navigate the complexities of a global marketplace. A lot of people forget that! The value and impact of our specialized AI translation tools and writing services is clear. So it’s not a question of whether they should implement language AI tools, but one interesting question we do hear a lot from enterprises is around the personalization capabilities of our service.

Enterprises often adhere to specific brand guidelines and require AI solutions that reflect their unique brand voice and industry terminology. They want to be able to communicate consistently, both internally and externally, at scale – whether they’re writing something or translating a document, they want the content to sound like something they’ve written themselves, in their specific brand style. At DeepL, we’ve designed our tools with this in mind and offer a variety of more interactive, customization features including DeepL Glossary, which allows businesses and professionals to customize translations for specific words and phrases; as well as features like tone setting and more. This level of fine-tuning and the ability to make the content their own is something that is really important to our customers.

As DeepL expands and its AI models are used more broadly, what are some of the key ethical considerations you prioritize when developing language AI systems, particularly when handling sensitive or confidential information?

Any organization considering AI tools should always ask these questions when evaluating models and companies. Many AI solutions do not provide adequate security, often share user data with third parties, and train their AI models on user text, creating mistrust and making many users hesitant to use these tools with work-related documents. As I touched on earlier, DeepL takes a different approach – security is core to our mission and product offering.

Our customers come from a wide range of industries, including highly regulated sectors such as financial services and legal, and the confidentiality and security of their data is essential. It’s core to our product offering that we adhere to the highest enterprise-grade privacy and security standards to protect user data, including state-of-the-art data encryption and GDPR, ISO 27001 and Soc2 Type 2 compliance. We also do not use any text from our subscribers to enhance or train our AI models, understanding that many of our users translate sensitive information.

What can we expect next from DeepL in terms of product development? Are there any major features or innovations that you’re particularly excited about?

We’re a research-driven AI company focused on innovating with purpose, and our purpose is to solve the language problem for people and businesses everywhere. Looking to the future, we’ll continue to incorporate customer feedback into our development and focus on expanding the capabilities of our Language AI platform to make it an even more comprehensive solution for all language-related business needs. A lot of what we’re hearing from customers today is that the more interactivity and personalization we can provide, the better. So expanding our features and functionality to address this is a really interesting area for us, looking at how to make the DeepL experience even more dynamic and engaging for our users.

In the next 5 to 10 years, where do you see DeepL’s technology fitting within the broader AI landscape, especially as AI continues to rapidly evolve?

5 years is really a long time for AI development so to me what really matters is the next 12 months! We are working on bringing the same revolution and disruption that we did with written language to other multimodal capabilities, which I am excited about.

Thank you for the great interview, readers who wish to learn more should visit DeepL.

[esselte974]

The rise of teleworking and teletravel

The travel sector has been a major driver of change in recent years thanks to technology. It’s in these innovations that artificial intelligence, virtual reality and blockchain have been absolutely central to redefining travel. Platforms powered by artificial intelligence now offer personalized travel recommendations and carry out bookings while guaranteeing an exceptional experience. Thanks to…

[rjas16]

The Human Impact of Generative AI

Shaping Tomorrow: How Generative AI Empowers People and Transforms Work

The Metaverse and Generative AI: Creating Immersive Experiences

Generative AI is swiftly transforming the landscape of numerous industries by enabling more efficient workflows and sparking once-impossible innovations. At the heart of this transformation is the capacity of generative AI to automate complex processes and generate new content, from visuals to code, thereby enhancing productivity and creative potential. This article sheds light on how generative AI is revolutionizing various sectors, improving communication through advanced natural language processing, personalizing experiences in healthcare, and even redefining content creation and software development.

The Power of Text-to-Image and Image-to-Image Generation

Generative AI technologies are redefining creativity and design by allowing for the seamless generation of visual content from textual descriptions. This capability accelerates the creative process and democratizes design capabilities, making them accessible to professionals without deep graphic design skills. Industries such as marketing and entertainment are reaping immense benefits from these advancements. For instance, graphic designers can now produce bespoke visuals in minutes, significantly reducing project turnaround times and allowing for greater scalability in creative projects, thereby enhancing business productivity.

This integration across business functions showcases the substantial efficiency gains that can be achieved, enabling companies to tailor their marketing materials quickly and in alignment with consumer preferences without requiring extensive manual effort.

Revolutionizing Software Development with Code Generation and Completion

Integrating generative AI into software development drastically enhances the efficiency and accuracy of coding processes. By automating mundane coding tasks, AI-powered tools free developers to focus on more complex and innovative aspects of software design. These advancements are not just about speeding up development but are transforming how software is created, tested, and deployed.

AI-Powered Coding Assistants

AI coding assistants like GitHub Copilot have been at the forefront of this transformation. According to a GitHub survey, developers using these AI tools report a 55% increase in productivity. These assistants leverage vast code repositories to offer real-time suggestions and complete lines of code, significantly speeding up the development process and reducing bugs.

For example, GitHub Copilot acts like a pair programmer, suggesting entire blocks of code based on natural language comments or a few lines of code. This greatly speeds up the coding process and enhances code quality by suggesting industry-standard practices and reducing errors.

Startups Leading the Charge in AI-Driven Code Generation

Several innovative startups are making waves in this space by focusing on specific niches of the development process

Tabnine - This tool uses machine learning to provide code completions for developers, supporting over a dozen programming languages. Its model learns from the codebase it's working on, offering tailored suggestions that improve over time.

Replit- Aimed at making coding more accessible, Replit provides a collaborative browser-based IDE with AI-powered coding assistance. It's particularly popular among educators and learners, democratizing access to coding tools and environments.

Codota- Like Tabnine, Codota offers intelligent code completions driven by AI. It integrates seamlessly with popular IDEs like IntelliJ and WebStorm, streamlining the development workflow by predicting needs and reducing repetitive coding tasks.

The Future of AI in Software Development

The trajectory of AI in software development points toward more integrated systems where AI tools assist with code and planning, testing, and deployment processes. These tools are expected to become more predictive, using historical data to guide development strategies and optimize team workflows.

By integrating AI into software development, the industry is seeing increased productivity and a shift in the developer's role from coder to innovator. As AI continues to evolve, the future of coding looks set to be more intuitive, creative, and, importantly, more efficient.

AI-Powered Content Creation: A New Era

The advent of generative AI is reshaping the landscape of content creation across multiple platforms. From crafting engaging blog posts to generating dynamic social media content and personalized emails, AI tools play a pivotal role in automating content generation, saving time, and maintaining a high standard of creativity and relevance.

Enhancing Productivity and Creativity

AI content generation tools are a boon for content creators, as they significantly reduce the time spent on content production. According to case studies from Jasper AI, thanks to AI assistance, content creators save an average of 3-4 hours per week. This time savings translates directly into increased productivity, allowing creators to focus more on strategy and less on the mechanics of content creation.

For instance, platforms like Jasper AI offer a range of content creation tools that automate the writing process, from first draft to finished piece, while ensuring the content is engaging and tailored to the audience. Similarly, Writesonic provides tools to enhance marketing content, enabling businesses to produce ads, product descriptions, and marketing copy quickly and efficiently.

The Role of AI in Personalization

Beyond sheer output, AI's real power in content creation lies in its ability to personalize content. By analyzing user behavior and preference data, AI can tailor content to meet the nuanced demands of different audience segments. This level of personalization is particularly effective in marketing, where tailored content can significantly improve engagement rates and conversions.

Navigating Challenges

While the benefits are substantial, using AI in content creation also presents challenges, particularly regarding the originality and authenticity of the content. To address this, many AI platforms are incorporating advanced algorithms that generate content and ensure that it is unique and aligns with the brand's voice. Additionally, a growing emphasis is on blending human creativity with AI efficiency to produce innovative and genuine content.

AI Tools Transforming the Content Landscape

Several other tools and platforms are at the forefront of this AI-driven content revolution

Grammarly leverages AI to correct grammar and enhance the tone and clarity of the text, making it more effective and audience-appropriate.

Articoolo creates unique textual content from scratch, simulating a human writer and significantly shortening the content development cycle

Advancements in Natural Language Processing: Understanding and Communicating Better

Natural language processing (NLP) is the heart of generative AI, enabling machines to understand and interact using human language. This technology has seen significant advancements in recent years, leading to improved communication tools and a deeper understanding of textual data across industries.

Enhanced Communication Tools

One of the most visible impacts of advanced NLP is improving communication tools such as chatbots and virtual assistants. These AI-driven systems can now handle complex conversations, understand nuances, and provide increasingly indistinguishable responses from human interactions. For instance, chatbots powered by sophisticated NLP models are used in customer service to respond instantly to customer inquiries, reducing wait times and improving customer satisfaction.

Sentiment Analysis and Translation

NLP is also pivotal in sentiment analysis, where AI models assess the emotional tone behind text data. This is incredibly useful for businesses to gauge customer sentiment from reviews, social media posts, and other interactions. Machine translation has benefited immensely from NLP, enabling more accurate and context-aware translations that are crucial in global communications.

Real-World Applications of NLP

Customer Service : AI-enhanced chatbots can now provide 24/7 customer service, precisely handling inquiries and redirecting complex issues to human operators.

Market Analysis : NLP tools analyze vast amounts of data from market research to provide insights into consumer behavior, trends, and preferences.

Healthcare : In the medical field, NLP is used to interpret and classify clinical documentation, helping in faster and more accurate patient diagnoses.

Cutting-Edge NLP Technologies

Platforms like OpenAI's GPT -4 are leading the charge in NLP technology. This model has set new standards for language models with its ability to generate coherent and contextually relevant text based on minimal input. This model and others like it are enhancing existing applications and paving the way for new uses that were previously unimaginable.

Challenges and Ethical Considerations

Despite its advancements, NLP faces challenges, particularly in bias and ethical use. Ensuring that AI systems do not perpetuate existing biases in training data is a significant concern that requires ongoing attention and refinement. Moreover, as NLP systems become more integrated into daily activities, privacy and data security questions become more pressing.

Generative AI in Healthcare and Drug Discovery: Accelerating Progress

Accelerating Drug Discovery

One of AI's most impactful applications in healthcare is accelerating the drug discovery process. Traditional drug development is notoriously time-consuming and costly, often taking over a decade and billions of dollars to bring a new drug to market. AI models can predict the effectiveness of compounds much faster than traditional experimental methods, reducing the time and financial investments required. For example, AI systems can simulate the interaction between drugs and biological targets to identify promising candidates for further development, thereby streamlining the early stages of drug discovery.

A McKinsey report highlights that AI has the potential to halve the time required for drug discovery, suggesting a reduction in timelines from 10 years to just five years. This not only speeds up the availability of new medications but also significantly cuts down on R&D costs.

Personalized Medicine

Beyond drug discovery, generative AI enhances personalized medicine, where treatments are tailored to individual patients. By analyzing genetic data, AI systems can predict how patients respond to various therapies, allowing for more personalized and effective care. This approach is particularly transformative in fields like oncology, where understanding the specific genetic makeup of a tumor can guide more targeted and effective treatment strategies.

AI in Medical Imaging

Another critical area where AI is making strides is in medical imaging. AI algorithms can process images faster and often more accurately than human radiologists, identifying subtle patterns that might be overlooked. Tools like Google Health's AI model for breast cancer screening, which has been shown to improve the accuracy of detecting breast cancer in mammograms, exemplify the potential of AI to enhance diagnostic accuracy and improve patient outcomes

Ethical Considerations and Challenges

While the benefits are substantial, integrating AI in healthcare raises significant ethical and privacy concerns. Data security, consent for using personal medical data, and ensuring AI does not perpetuate existing healthcare disparities must be addressed. These challenges require robust regulatory frameworks and ongoing oversight to ensure that the benefits of AI in healthcare are realized without compromising patient trust or safety.

AI-Driven Personalization: Tailoring Experiences for Maximum Impact

Artificial intelligence significantly enhances personalization across various sectors, transforming how services and content are delivered to meet individual preferences and needs. This customization is crucial in e-commerce, education, and media, where tailored experiences can significantly boost user engagement and satisfaction.

Personalized Recommendations

In e-commerce, AI-driven personalization engines analyze user behavior, past purchases, and browsing history to recommend products that users are more likely to purchase. Companies like Amazon and Netflix are renowned for using AI to generate personalized recommendations, which enhances the user experience and increases revenue through improved conversion rates.

Customized Learning Experiences

AI personalization in education revolutionizes learning by adapting content to fit each student's learning pace and style. Platforms like Khan Academy use AI to offer a customized learning path for each user, making education more accessible and effective by addressing individual learning needs and preferences. This approach helps identify areas where students struggle and provide targeted exercises to improve their understanding and retention of the subject matter.

Personalized AI-Powered Content Creation

AI is also making strides in personalized content creation. Tools like Grammarly and Quill Bot tailor writing aids to the user's style and preferences, improving written communication's clarity, tone, and grammaticality. This personalization enhances the writing process and ensures the content effectively conveys the intended message.

Business Benefits

Personalization can lead to significant business benefits, including increased customer loyalty and spending. A study by Deloitte found that companies that leverage consumer behavior insights through personalization see revenue increase by 6% to 10%, which is two to three times higher than those that don't. Personalized marketing campaigns ensure that customers receive messages that resonate with their specific needs and preferences, greatly enhancing the effectiveness of marketing efforts.

Democratizing Development: Low-Code/No-Code Platforms

The rise of low-code and no-code platforms marks a significant shift in how software and applications are developed. These platforms democratize the ability to build complex systems without extensive programming knowledge. This technology enables a broader range of people, including those without formal coding expertise, to create applications, automate workflows, and contribute to digital transformation efforts within their organizations.

Empowering Non-Technical Users

Low-code and no-code platforms such as Microsoft PowerApps, Google AppSheet, and Bubble empower non-technical users to build applications through intuitive graphical user interfaces. These platforms provide drag-and-drop components, pre-built templates, and simple logic formulas, making it easier for non-developers to bring their ideas to life quickly and efficiently.

Reducing Development Time and Costs

The impact of these platforms on development time and cost is profound. By simplifying the development process, low-code and no-code platforms can reduce the time to develop and deploy applications by up to 90%. This reduction accelerates innovation within companies and significantly cuts costs associated with traditional software development, such as hiring specialized development staff and lengthy project timelines.

Enhancing Business Agility

Companies utilizing low-code/no-code platforms can enhance their agility by quickly adapting to changing market conditions and business needs. These tools allow businesses to prototype and iterate on solutions rapidly, enabling a more responsive approach to customer needs and market dynamics.

Case Studies

Microsoft PowerApps has enabled companies to build custom business apps that connect to their data stored in the underlying data platform (Microsoft Dataverse) or in various online and on-premises data sources.

Bubble allows users to design interactive, multi-user apps for desktop and mobile browsers. Users can create web applications ranging from simple prototypes to complex SaaS applications without writing a single line of code.

Challenges and Considerations

While low-code and no-code platforms offer numerous benefits, they also present challenges, such as limited customization for complex requirements and potential issues with scaling as needs grow. Moreover, reliance on these platforms can lead to vendor lock-in, where businesses depend on the platform's capabilities and pricing structures.

The Future of Low-Code/No-Code

As these platforms mature, they are expected to become more robust, offering greater flexibility, integration options, and advanced features that cater to more complex development needs. The evolving landscape of low-code/no-code technology promises to blur the lines between technical and non-technical users, fostering a more inclusive environment for innovation across industries.

Scaling low-code and no-code platforms has inherent limitations and challenges that can impact their effectiveness, especially as organizational needs grow and become more complex. Here's a closer look at some of these limitations and how they might affect the broader adoption and scalability of these platforms

Customization and Flexibility

Limited Customization: Low-code and no-code platforms offer significant ease of use and speed through pre-built templates and drag-and-drop interfaces. However, they often need more flexibility for more complex, customized solutions. Businesses may find that these platforms can only sometimes accommodate the specific requirements or unique processes that differentiate them from their competitors.

Integration Issues: As organizations scale, the need to integrate with other systems and data sources increases. Low-code and no-code platforms sometimes need help with complex integrations or more support for specific external APIs, limiting their utility in a fully integrated tech ecosystem.

Performance and Scalability

Performance Constraints: Applications built on low-code/no-code platforms can suffer performance issues as user numbers increase and data loads become heavier. These platforms may need to be optimized for high-performance scenarios, leading to slower response times and reduced user satisfaction.

Scalability Challenges: Scaling applications built with low-code/no-code tools can be problematic, especially when dealing with large volumes of data or high transaction rates. While some platforms are improving their capabilities in this area, there remains a significant gap compared to custom-developed applications.

Security and Compliance

Security Concerns: The ease of application development also comes with the risk of creating security vulnerabilities, particularly if the platform does not enforce strict security standards. Organizations must be vigilant about the security aspects of applications developed through low-code/no-code platforms, especially when handling sensitive data.

Compliance Issues: Regulatory compliance can also be a concern, as the automatic code generation and data handling procedures of low-code/no-code platforms might not automatically align with specific industry regulations, such as GDPR or HIPAA, requiring additional oversight to ensure compliance.

Maintenance and Support

Dependence on Vendors: Using low-code/no-code platforms often means relying on the vendor for updates, security patches, and new features. This dependence can lead to issues if the platform does not evolve in line with the latest technological developments or if vendor support is lacking.

Technical Debt: Applications built on low-code/no-code platforms can accumulate technical debt if not properly maintained. This can lead to increased costs and resources being diverted to manage and upgrade legacy systems initially developed to save time and money.

Moving Forward with Low-Code/No-Code

Despite these limitations, strategic use of low-code and no-code platforms can still benefit many organizations, especially when used for specific purposes where the advantages outweigh the drawbacks. Businesses should carefully evaluate their long-term needs and choose platforms with the best ease of use, flexibility, and scalability. Understanding these limitations will help organizations make informed decisions about when and how to incorporate low-code and no-code solutions into their IT strategy, ensuring they can maximize the benefits while mitigating potential downsides.

AI-Enabled Cybersecurity: Staying Ahead of Threats

Artificial intelligence (AI) has emerged as a crucial ally in the rapidly evolving cybersecurity landscape. With cyber threats becoming more sophisticated and frequent, AI technologies are pivotal in enhancing defenses by automating detection, response, and prevention strategies. This integration of AI in cybersecurity is not just a trend but a necessary evolution to cope with the scale and complexity of modern cyber threats.

Enhanced Threat Detection

AI excels in identifying patterns and anomalies, which makes it ideal for threat detection. Machine learning algorithms can analyze vast amounts of data from network traffic, logs, and past incidents to identify unusual behavior that may signify a security breach. This capability allows for real-time threat detection, significantly reducing the time between infiltration and response.

Automated Response Systems

Once a threat is detected, the speed of response is critical. AI-powered systems can respond to threats faster than human teams, automating certain responses to common types of attacks. Rapid response capability can mitigate the effects of attacks, stopping them before they spread throughout the network or result in significant data loss.

Vulnerability Management

AI also aids in vulnerability management by identifying weak points in the network before attackers can exploit them. By continuously scanning systems and software for vulnerabilities and comparing them against emerging threats, AI systems can prioritize vulnerabilities that pose the most immediate risk, guiding cybersecurity teams on where to focus their remediation efforts.

Predictive Capabilities

One of the most promising aspects of AI in cybersecurity is its predictive capabilities. By learning from historical data, AI can predict the types of attacks likely to occur, enabling organizations to prepare defenses proactively rather than reactively. This forward-looking approach helps maintain a stronger security posture and better preparation against potential threats.

Challenges and Ethical Considerations

While AI significantly enhances cybersecurity efforts, it raises privacy and ethical data use challenges. The vast amounts of data required to train AI models must be handled responsibly to ensure privacy protections are not compromised. Furthermore, as AI systems become more autonomous in making security decisions, establishing clear accountability for decisions made by AI is crucial.

Embracing the Future with Generative AI

As explored throughout this article, generative AI is not just a technological advancement but an exponential shift recasting industry models, enhancing human creativity, and redefining what is possible in the digital age. From revolutionizing content creation to reshaping software development and pushing the boundaries in healthcare, AI's impact is profound and far-reaching.

Advancements in natural language processing have improved how we interact with machines, making them more intuitive and responsive. In cybersecurity, AI's predictive capabilities are setting new standards for protection, staying one step ahead of evolving threats. Meanwhile, in the realms of personalization and education, AI is creating experiences that are more tailored and impactful than ever before. However, the journey does not end here. The future holds even greater potential as we continue to innovate and integrate AI into various facets of our lives and work. The opportunities to leverage AI for driving growth, efficiency, and creativity are limitless, and the time to act is now.

Engage with Coditude

Are you ready to harness the power of generative AI to transform your business? Connect with Coditude today and join us at the forefront of this exciting revolution. Our team of experts is dedicated to helping you explore the vast possibilities of AI, from developing custom AI solutions to integrating AI-driven processes into your existing systems. Whether you're looking to enhance your cybersecurity defenses, streamline your content creation, or tap into AI's powerful analytics for strategic insights, Coditude is here to guide you every step of the way. Let's build the future together—innovative, efficient, and brighter than ever.

[lagoontechnologies]

The Future of Software Development: How No-Code Platforms are Driving Business Growth?

Our digital world of development landscape is experiencing an extreme shift on No Code DX Platforms. It will be a supercharger for the software development industry to rule Information Technology’s future. They are transforming the way we sneak up software platforms and are making them more penetrable, energy-efficient, and democratized.

Understanding No-Code Platforms

No-code platforms are one of the innovative game-changer for modern developers can get in the process of software development. They were developed to ease the job of developing applications and shorten the time taken to develop them.

These platforms come in the form of graphical and visual development interfaces where users can create an application by dragging and dropping some components. They rid the necessity of coding as we knew it hence opening up opportunities in software development to more people.

Here are some key features:

Drag-and-drop interfaces

Pre-built templates

Visual programming

Integration capabilities

Scalability and flexibility

These features make no-code platforms equally effective and applicable in small and large-scale businesses. They facilitate rapid application development and deployment which decreases the amount of time and money to be spent towards development.

The Evolution of No-Code Technology

The use of no-code technology is not a new concept in this world. It originated from the early periods of computing time when visual programming languages were proposed.

Thus, the technology has developed and reached maturity over the years. Today, they have turned into powerful tools that can be efficiently utilized to create versatile business applications.

The emergence of no-code tools is a reaction to demands for software development and lack of programmers. It is a solution that enables what is called “citizen developers” to develop applications without much coding expertise.

Main Characteristics and Advantages of No-Code Solutions

They come with a host of features that make them a powerful tool for businesses. These features are designed to simplify the development process and make it more efficient.

Drag-and-drop interfaces: This feature can be used to create an application where one can develop an application through using a drop-on-drop feature on a design board. As a type of programming, it is more or less visible, and logical to most people in a way.

Pre-built templates: It has been known to have its own set of templates which the users can employ as the basis for the applications that they wish to develop. It also must be noted that these templates can be further tailored to suit the nature of the business in question.

Integration capabilities: No-code platforms can interact with other applications and systems making it easier for businesses to digitize their processes and operations.

Scalability and flexibility: As they are conceived to accommodate fluctuations and changes, they may support any type and size of business, starting with the new venture.

Key Features of No-Code Platforms

Democratizing Software Creation

A great innovation of No-Code platforms is the equal opportunity it gives to anyone to develop applications. Businessmen and marketing departments, for instance, can design functional applications without necessarily having programming knowledge. No-Code platforms allow users to develop solutions by using simple drag-and-drop and adapting ready templates without involving development teams. This is where it is made perfectly even or fair for everyone concerned.

Accelerating Time-to-Market

This means that under today’s conditions and especially in the context of growing competition, speed is the most significant advantage. No-code platforms significantly help decrease development time and minimize the time spent to launch applications. It takes a small fraction of the time as it would take most companies to code new solutions, test them, and then deploy them. This ability results in a shorter time-to-market that ensures companies can respond to changes in the marketplace thereby providing them with a significant competitive advantage.

Lowering Development Costs

Unfortunately, skilled developers are not cheap to hire, and any custom coding can be very time-consuming and costly. These problems are solved by No-Code platforms since they allow teams to develop an application with less technical support. It not only reduces the development cost but also releases the pressures of IT departments from many ordinary tasks so that they can deal with more elaborate issues, making the process produce better overall effects.

Empowering Innovation

No-code platforms enable creativity since technical barriers are removed. Working in teams allows for the formation of new hypotheses quickly and the optimization of existing concepts with new possibilities. It enhances flexibility and enables businesses to expand into new sectors, switch strategies if necessary, and adapt to new trends in the constantly expanding digital world.

Seamless Integration & Scalability

Let me unwrap one of the misconceptions regarding the No-Code platforms, and that most of them are lacking in terms of functionality. The existing No-Code tools are capable of offering the integration level by which the new applications can link with the pre-existing system, database, as well as APIs. Moreover, while using No-Code platforms, companies can get tools that adapt to the company’s growth and needs but remain as much as simple as possible yet as complex as needed.

Shaping the Future of Development

In light of the ongoing trends towards digitalization in the sphere of enterprise management and operation, No-Code platforms cannot be considered as dispensable. By filtering out these constraints, they enable organisations to satisfy customer needs, introduce new products and services, and remain relevant in the market. The future of development is finally now and it has arrived through the advent of No-Code.

In Conclusion: The Future of Business Growth Lagoon’s No-Code Platforms are revolutionizing how businesses operate, making development accessible, cost-effective, and agile. Whether you’re aiming for rapid innovation, reducing development costs, or scaling effortlessly, Lagoon’s No-Code solutions provide the tools to drive sustainable business growth. The future of digital transformation is here — and it’s code-free!

[atcuality1]

Redefine Customer Engagement with AI-Powered Application Solutions

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The Ultimate Guide to NFT Marketplace Development: Key Features and Strategies

Description: This comprehensive guide outlines essential features and strategies for successful NFT marketplace development. Build a thriving NFT-powered platform today.

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Low-code application development with Microsoft Power Apps

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Centralised AI is dangerous: how can we stop it? - AI News

New Post has been published on https://thedigitalinsider.com/centralised-ai-is-dangerous-how-can-we-stop-it-ai-news/

Centralised AI is dangerous: how can we stop it? - AI News

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The intelligence displayed by generative AI chatbots like OpenAI’s ChatGPT has captured the imagination of individuals and corporations, and artificial intelligence has suddenly become the most exciting area of technology innovation.

AI has been recognised as a game changer, with potential to transform many aspects of our lives. From personalised medicine to autonomous vehicles, automated investments to digital assets, the possibilities enabled by AI seem endless.

But as transformational as AI will be, there are a lot of risks posed by this new technology. While fears about a malicious, Skynet-style AI system going rogue are misplaced, the dangers of AI centralisation are not. As companies like Microsoft, Google and Nvidia forge ahead in their pursuit of AI, fears about the concentration of power in the hands of just a few centralised players are becoming more pronounced.

Why should we worry about decentralised AI?

Monopoly power

The most pressing issue arising from centralised AI is the prospect of a few tech giants achieving monopolistic control over the industry. The big tech giants have already accumulated a very significant market share in AI, giving them possession of vast amounts of data. They also control the infrastructure that AI systems run on, enabling them to stifle their competitors, hobble innovation, and perpetuate economic inequality.

By achieving a monopoly over the development of AI, these companies are more likely to have an unfair influence on regulatory frameworks, which they can manipulate to their advantage. It will mean that smaller startups, which lack the enormous resources of big tech giants, will struggle to keep up with the pace of innovation. Those that do survive and look like they might thrive will almost certainly end up being acquired, further concentrating power in the hands of the few. The result will be less diversity in terms of AI development, fewer choices for consumers, and less favourable terms, limiting the use-cases and economic opportunities promised by AI.

Bias and Discrimination

Aside from monopolistic control, there are genuine fears around the bias of AI systems, and these concerns will take on more importance as society increasingly relies on AI.

The risk stems from the fact that organisations are becoming more reliant on automated systems to make decisions in many areas. It’s not unusual for a company to employ AI algorithms to filter job applicants, for example, and the risk is that a biased system could unfairly exclude a subset of candidates based on their ethnicity, age or location. AI is also used by insurance companies to set policy rates, by financial services firms to determine if someone qualifies for a loan and the amount of interest they’ll need to pay, and by law enforcement to determine which areas are more likely to see higher crime. In all of these use-cases, the potential implications of biased AI systems are extremely worrying.

Whether it’s law enforcement targeting minority communities, discriminatory lending practices or something else, centralised AI can potentially exacerbate social inequality and enable systemic discrimination.

Privacy and surveillance

Another risk posed by centralised AI systems is the lack of privacy protections. When just a few big companies control the vast majority of data generated by AI, they gain the ability to carry out unprecedented surveillance on their users. The data accumulated by the most dominant AI platforms can be used to monitor, analyse and predict an individual’s behaviour with incredible accuracy, eroding privacy and increasing the potential for the information to be misused.

It’s of particular concern in countries with authoritarian governments, where data can be weaponised to create more sophisticated tools for monitoring citizens. But even in democratic societies, there is a threat posed by increased surveillance, as exemplified by the revelations of Edward Snowden about the US National Security Agency’s Prism program.

Corporations can also potentially misuse consumer’s data to increase their profits. In addition, when centralised entities accumulate vast amounts of sensitive data, this makes them more lucrative targets for hackers, increasing the risk of data leaks.

Security risks

Issues of national security can also arise due to centralised AI. For instance, there are justified fears that AI systems can be weaponised by nations, used to conduct cyberwarfare, engage in espionage, and develop new weapons systems. AI could become a key tool in future wars, raising the stakes in geopolitical conflicts.

AI systems themselves can also be targeted. As nations increase their reliance on AI, such systems will make for enticing targets, as they are obvious single points of failure. Take out an AI system and you could disrupt the entire traffic flow of cities, take down electrical grids, and more.

Ethics

The other major concern of centralised AI is about ethics. That’s because the handful of companies that control AI systems would gain substantial influence over a society’s cultural norms and values, and might often prioritise profit, creating further ethical concerns.

For example, AI algorithms are already being used widely by social media platforms to moderate content, in an attempt to identify and filter out offensive posts. The worry is that algorithms, either by accident or design, might end up suppressing free speech. 

There is already controversy about the effectiveness of AI-powered moderation systems, with numerous seemingly innocuous posts being blocked or taken down by automated algorithms. This leads to speculation that such systems are not broken but being manipulated behind the scenes based on the political narrative the platform is trying to promote.

The alternative? Decentralised AI

The only logical counterweight to centralised AI is the development of decentralised AI systems that ensure that control of the technology remains in the hands of the majority, rather than the few. By doing this, we can ensure that no single company or entity gains a significant influence over the direction of AI’s development.

When the development and governance of AI is shared by thousands or millions of entities, its progress will be more equitable, with greater alignment to the needs of the individual. The result will be more diverse AI applications, with an almost endless selection of models used by different systems, instead of a few models that dominate the industry.

Decentralised AI systems will also mean checks and balances against the risk of mass surveillance and manipulation of data. Whereas centralised AI can be weaponised and used in a way that’s contrary to the interests of the many, decentralised AI hedges against this kind of oppression.

The main advantage of decentralised AI is that everyone is in control over the technology’s evolution, preventing any single entity from gaining an outsized influence over its development.

How to decentralise AI

Decentralised AI involves a rethink of the layers that make up the AI technology stack, including elements like the infrastructure (compute and networking resources), the data, models, training, inference, and fine-tuning processes.

We can’t just put our hopes in open-source models if the underlying infrastructure remains fully centralised by cloud computing giants like Amazon, Microsoft and Google, for instance. We need to ensure that every aspect of AI is decentralised

The best way to decentralise the AI stack is to break it down into modular components and create markets around them based on supply and demand. One such example of how this can work is Spheron, which has created a Decentralised Physical Infrastructure Network (DePIN) that anyone can participate in.

With Spheron’s DePIN, everyone is free to share their underutilised computing resources, essentially renting them out to those who need infrastructure to host their AI applications. So, a graphic designer who uses a powerful laptop with a GPU can donate processing power to the DePIN when they’re not using it for their own work, and be rewarded with token incentives.

What this means is that the AI infrastructure layer becomes widely distributed and decentralised, with no single provider in control. It’s enabled by blockchain technology and smart contracts, which provide transparency, immutability and automation.

DePIN can also work for open-source models and underlying data. For instance, it’s possible to share training datasets on a decentralised network like Qubic, which will make sure the provider of that data is rewarded each time their information is accessed by an AI system.

To ensure access and permissions are decentralised, every part of the technology stack is distributed in this way. However, the AI industry currently struggles to provide such a level of decentralisation. Although open-source models have become extremely popular among AI developers, most people continue to rely on proprietary cloud networks, meaning the training and inference processes are heavily centralised.

But there are strong incentives for decentralisation to win out. One of the primary advantages of DePIN networks, for example, is that they help to reduce overheads. Because networks like Spheron don’t rely on intermediaires, participants don’t need to make any payments or share revenue with third-parties. Moreover, they can afford to be more competitive in terms of pricing than corporations that are under pressure to grow profitability.

Decentralisation must win

The future of AI holds a lot of potential, but it’s also perilous. While the capabilities of AI systems have improved dramatically in the last few years, most of the advances have been made by all-powerful companies and that has resulted in an increase in their influence over the industry. There’s a price to pay for this, not just in monetary terms.

The only reasonable alternative is to promote the greater adoption of decentralised AI, which can enhance accessibility and ensure a greater flexibility of AI. By allowing everyone to participate in the development of AI on an equal footing, we’ll see more diverse, interesting, and useful applications that can benefit everyone equally, as well as putting their users first.

Building a decentralised AI future will involve a great deal of coordination and collaboration across every layer of the AI stack. Fortunately, there are strong incentives for participants to do just that. And again, the incentives are not just monetary.

[esselte974]

The rise of teleworking and teletravel

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[todayiot]

Transform Your IoT Projects with Bluetooth Mesh

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Equipment Neglected During Development at Tatanagar Railway Station

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[bitlai]

Bitlai: Seamless Platform Connections for Your Business

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Having trouble joining your systems and APIs with other platforms? Is your business not talking effectively with others? Bitlai is here to change that. We provide the best way to connect platforms smoothly, making your business run better than ever.

Only 2% of shoppers buy on their first online visit. But with Bitlai's help, you can attract that other 98%. Studies show 43% more people will buy if they see the product again. Our tech lets you reach these buyers.

Bitlai not only makes selling easier, but it also gets your brand out there more. A retargeted ad's click rate is 10 times better than a regular ad. With Bitlai, marketing to your audience is far more effective.

Bitlai also knows the power of talking to people one-on-one. Birthday emails are almost five times more likely to get a sale than normal ads. Bitlai's connections help you send these kinds of emails for better results.

We value your current customers at Bitlai. It costs a lot less to keep them than to find new ones. Our system makes sure your old customers keep coming back. This is key to your business' success.

Bitlai helps your business run smoothly and efficiently. We take lessons from Apple, focusing on showing our platform's worth. We provide great simplicity and service.

With us, boosting your security is easy. By staying on top of laws like GDPR, you protect your data. This means safer emails and secure customer info.

Good customer service is crucial. Offering help around the clock or with LiveChat makes a big difference. It keeps your clients happy and brings them closer.

Don't overlook the power of a good platform connector. Find out more about Bitlai and its transformative effects on your business.

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