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When Microsoft product leaders took to the stage at EPPC 2026, they outlined a unified vision for Power Automate’s future. The key takeaway was that native automation methods and newer AI-related capabilities aren’t competing with each other, but rather complementing one another.

The Power Platform has come to rely on Power Automate as a vital component, sitting between traditional deterministic automation and the increasingly important role of artificial intelligence in our daily work lives. Microsoft wants customers to know these different approaches can not only coexist but also enhance each other’s strengths.

Power Automate is getting a boost from AI capabilities being integrated into its core features: server connections for MCP servers, specialized agents developed with Copilot Studio or Foundry, and self-healing RPA tools. These advancements aim to improve automation processes without sacrificing the reliability of native methods.

With hundreds of thousands of active tenants and over 15 million users as of June 2026, Power Automate has established itself as a popular choice for businesses looking to automate repetitive tasks. And with more than 30 million flows created on the platform during this period, it’s clear that its user base is using it to get work done.

Principal product manager Costas Chamosfakides acknowledged that some customers are still concerned about the future of RPA and deterministic automation in a Power Automate world. However, he assured them that Microsoft’s roadmap aims to address these concerns head-on rather than replace traditional methods.

A major shift is underway in the world of finance and tax, driven by ongoing vendor consolidation and the increasing integration of artificial intelligence across the entire office of the CFO. Lucanet has responded to this trend with a family of AI agents designed to automate critical processes such as planning, closing, reporting, and environmental, social, and governance (ESG) analysis.

Elias Apel, CEO at Lucanet, notes that these new agents are part of a broader effort to empower financial leadership with intelligence, speed, and confidence. By streamlining complex workflows, Lucanet aims to enable fast, clear, and strategic decision-making, ultimately paving the way toward autonomous finance and tax operations.

The launch marks a paradigm shift in how mid-market and enterprise finance and tax teams manage their end-to-end workflows. The new platform combines 25-plus years of expertise with cutting-edge AI agents to automate high-friction, high-frequency tasks that have long been a source of frustration for financial professionals.

Lucanet’s Analyst Agent is one example of this technology in action. By performing detailed variance and driver analysis across financial and operational data, the Analyst Agent helps reduce the monthly reporting cycle time by surfacing insightful information and automating complex calculations.

The Close Agent accelerates and automates the complexities of the financial close process, including month-end data imports and validation. This agent is particularly useful for companies with multiple subsidiaries or locations, where manual processes can be slow and error-prone.

Other specialized agents include the Modeler Agent, which allows complex financial and operational models to be rapidly built using natural language; the Tagger Agent, which elevates Lucanet’s XBRL and regulatory tagging capabilities to a fully autonomous level, reducing effort by up to 95%; and the Report Agent, which drafts, enhances, and translates annual reports while checking consistency of language.

The ESG Emission Agent is another key component of the platform. This agent generates greenhouse gas (GHG) footprints up to five times faster than manual processes, providing companies with a more accurate picture of their environmental impact.

Lucanet Lume serves as the conversational layer for these agents, allowing users to describe what they need in natural language and routing tasks intelligently to the appropriate agent. This interface is designed to be user-friendly and accessible, even for those without extensive technical expertise.

The division of labor between deterministic calculations and AI-driven reasoning is a crucial aspect of Lucanet’s platform. According to Kevin Smith, CTO at Lucanet, ‘AI is probabilistic in nature and so not suitable to perform deterministic calculations.’ Instead, the core platform handles these tasks while the AI layer manages interpretation, explanation, and natural language processing.

The ultimate goal of this technology is to provide intelligence inside finance and tax that is trustworthy, traceable, and defensible. As Smith notes, ‘intelligence must be trustworthy in front of an auditor,’ a sentiment echoed by Apel’s emphasis on empowering financial leadership with confidence.

Cao Liangliang, director at Google DeepMind and IEEE Fellow, has returned to Hong Kong after a twenty-year absence. This is a significant boost for the city’s artificial intelligence community.

As an undergraduate student in China, Cao began exploring AI and working under renowned experts in computer vision and machine learning at the University of Science and Technology of China (USTC).

After graduating from USTC, Cao met Tang Xiao’ou, who was recruiting students for his lab. Many graduates from this lab have gone on to become prominent figures in China’s AI industry.

Cao earned a master’s degree from MMLab in 2005 and worked there as a research assistant until 2006. He then moved to the United States to pursue his PhD at the University of Illinois Urbana-Champaign under Thomas S. Huang, known for his work on Chinese computer vision.

Today Cao is recognized globally for leading roles that shaped Silicon Valley’s most advanced AI systems - including Gemini and Apple Intelligence. His work has made a significant impact on machine learning and its applications in real-world scenarios.

The director considers Hong Kong as home, having announced on his personal website: ‘This is a full-circle moment for me.’ Now he brings the expertise gained from Google DeepMind and other Silicon Valley companies back to his hometown. Cao’s experience with AI assistants will be valuable to local institutions that are looking to integrate these technologies into their academic programs.

Cao’s return could be just what Hong Kong’s academic ecosystem needs right now, especially PolyU where he has taken up a new role. His presence is expected to have a positive impact on the local industry and help foster collaboration between researchers and students.

Nomagic, an innovative company based in Warsaw, Poland, and Sandy Springs, Georgia, has made significant strides in the field of embodied artificial intelligence. The company’s approach to developing AI systems for robots is distinct from that of its competitors, focusing on creating models that excel at specific tasks right out of the box rather than general-purpose ‘robot brains.’ This strategy aims to eventually build towards a more comprehensive system by mastering individual tasks first.

The concept of embodied AI has gained traction in Silicon Valley, with investors betting big on systems that can interact with the physical world through robotic devices. Many startups are working on developing general-purpose AI models for robots, which would enable them to perform various tasks without extensive programming. However, these models often fall short of human-level accuracy and require significant task-specific training before achieving reliable results.

Nomagic’s approach is centered around creating highly accurate AI robot brains that can tackle specific challenges with ease. To pursue this goal, the company established an AI research lab led by Markus Wulfmeier, a former Google DeepMind robotics researcher who now serves as Nomagic’s chief scientist. This move marked a significant step towards developing more effective and efficient AI systems for robots.

Nomagic has recently announced that it has successfully deployed its first vision-language-action (VLA) model to paying customers. The VLA system is designed to perceive objects in the world, understand text-based instructions from humans, and take actions accordingly. This achievement makes Nomagic one of the pioneers in running VLAs in a live production environment rather than lab experiments or staged demos.

The early results of Nomagic’s VLA deployment are promising, with the company reporting that it has roughly halved the rate of robot-caused interventions in warehouse operations. The system is being used by Brack.Alltron, Switzerland’s second-largest e-commerce platform, which relies on robots from Nomagic to automate order picking and packing tasks.

Roland Brack, founder and owner of Brack.Alltron, expressed his enthusiasm for Nomagic’s VLA systems, stating that they have marked a significant step forward in the company’s automation efforts. According to Brack, the addition of these intelligent systems has enabled them to run autonomous shifts through nights and Sundays without increasing pressure on their human workforce.

Despite its success, Nomagic acknowledges that its VLA system is not yet perfect, with an accuracy rate below 99.9% in specific tasks. However, the company has developed a system around the VLA by integrating it with older ‘classical’ robotics software that acts as a safety net and error catcher. This approach ensures that the entire system can be trusted to operate reliably in customer warehouses.

Kacper Nowicki, Nomagic’s co-founder and CEO, explained that achieving high reliability is crucial for companies operating in the physical world. He emphasized that 99.9% accuracy is not just a marketing number but rather the minimum required to gain entry into most facilities. To address this challenge, Nomagic has developed a harness system that complements its VLA model, allowing it to operate safely and efficiently from day one.

Nomagic’s approach stands in contrast to many of its competitors, which focus on developing general-purpose AI models for robots. Markus Wulfmeier, chief scientist at Nomagic, highlighted the limitations of this strategy, stating that most companies are racing to build highly versatile robot brains without considering the practical challenges of mastering specific tasks.

Wulfmeier pointed out that the physical world is dominated by a long tail of rare situations, making it difficult for AI models to achieve high accuracy. He emphasized that relying on simulation or human teleoperation alone cannot economically close the remaining gap to the level required in real-world settings. Instead, Nomagic trains its VLA model using data from its existing fleet of robots deployed with customers.

Nomagic’s unique advantage lies in its ability to gather vast amounts of real-world data from its operational robots. This data is used to train and refine the VLA model, which Wulfmeier describes as unusually rich and diverse. By leveraging this approach, Nomagic aims to develop more effective AI systems for robots that can automate tasks with greater accuracy.

Tristan d’Orgeval, co-founder and chief strategy officer at Nomagic, stressed the importance of deploying robots in real-world settings first rather than relying on lab experiments or simulations. He noted that this approach allows companies like Nomagic to develop capable AI systems that emerge from practical experience rather than theoretical models.

The company’s focus on automation has earned it recognition within the industry. Recently, Nomagic won the 2026 International Intralogistics and Forklift Truck of the Year (IFOY) Award for its Shoebox Picker device. This achievement highlights the potential of Nomagic’s AI systems to tackle complex challenges in warehouse automation.

Nomagic’s approach marks a significant departure from traditional methods used by many companies working on embodied AI. By focusing on developing highly accurate models that excel at specific tasks, Nomagic aims to create more effective and efficient AI systems for robots.

META has launched its first in-house image model, called Muse Image. The new tool is part of Meta’s Superintelligence Labs and can generate images from scratch or edit existing photos. It also allows users to blend multiple references into one shot and pull real-time context from the web.

The model is available now within the Meta AI chatbot and powers over 30 new AI effects in Instagram Stories, enabling image generation inside WhatsApp chats in select countries. Facebook and Messenger will follow suit soon.

Muse Image pairs with another tool called Muse Spark to plan a layout before drawing an image. It can also call search and coding tools to render legible text, working QR codes, and detailed infographics. Basic creation is free, but extra capacity comes with Meta’s subscription plans. Advertisers will gain access through Advantage+ in the coming weeks.

The model works differently from traditional one-shot generators. Instead of producing an image immediately, it reasons through a request first, planning the composition and reviewing its own output before showing it to users. This allows for more control over the final result.

One of the key features of Muse Image is its ability to personalize creations based on user input. A presets panel offers one-tap ideas, from restoring old family photos to reimagining users as claymation characters or 16-bit game heroes. Users can also @-mention Instagram accounts to bring public photos into a creation.

The tool includes a markup feature that lets users sketch or circle edits directly on the image. Meta AI keeps the full conversation in memory, allowing users to refine their creations without starting over. For example, snapping a photo of a room and redesigning it using real products from the web or Facebook Marketplace is now possible with Muse Image.

Muse Image arrives as Meta tries to close the gap left by Google and OpenAI’s early moves into consumer image tools. According to internal benchmarks, Muse Image trails behind OpenAI’s latest model but outperforms Google’s Nano Banana 2 on editing tasks. This marks a significant step for Meta in developing its own AI capabilities.

The stakes are commercial as much as technical, since digital advertising remains Meta’s largest revenue source and faster, cheaper creative production feeds that engine directly. The company framed the launch within a broader push toward personal superintelligence and detailed the agentic design and tool use in its technical breakdown of the two new models.

Meta also previewed Muse Video, a clip generator built on the same foundation as Muse Image. This tool includes native audio support and is set for public release soon. The rollout follows Meta’s earlier move into AI video feeds with its Vibes stream of AI-generated clips, and it lands as generative tools expand from flat pictures toward editable 3D assets.

For everyday users, the appeal lies in the same one that drives prompt-based image generators aimed at non-designers – now folded into apps billions of people already use daily. Startups such as venture-backed challengers chasing brand-ready output show how fierce this market has become.

A recent incident at Idaho State University highlights the limitations of AI detection tools. Chemistry undergraduate Lauren Jager had written her PhD application personal statement herself, but every tool she used to check for authenticity flagged it as almost 100 percent machine-generated. To pass the checks, she deliberately made her writing less polished and submitted a statement that she considered inferior to her original. She was later accepted into a PhD program at the University of Utah.

The experience reflects a growing crisis in academic integrity technology. Universities are deploying AI detection tools to police student submissions, but researchers have found these instruments to be unreliable, biased, and easily circumvented. A 2025 study on GPTZero, widely considered one of the most-used detectors, revealed a false-positive rate of around 16 percent on human-written essays.

Another study from 2023 showed that most AI detection tools performed inconsistently on human text and struggled more with output from advanced models like GPT-4 than older systems. Notably, even the US Declaration of Independence has been repeatedly flagged as between 95 and 100 percent machine-generated by these detectors.

Some experts argue that even reasonably accurate detectors should not be used in high-stakes decisions due to the risk of false positives. Mike Perkins, who researches AI’s impact on academia at British University Vietnam, warned that using such tools could lead to unfair outcomes for students. Marzena Karpinska from Simon Fraser University cautioned that while detectors can identify broad trends across large datasets, they cannot reliably determine individual authorship.

A particular concern is bias: a Stanford University study found that AI detectors incorrectly labelled more than half of essays written by non-native English speakers as machine-generated, with an average false-positive rate of 61.3 percent.

The proliferation of large language models (LLMs) has made it easier for malicious actors to spread spam across the internet. This has led to a significant increase in bot content and spam, which can be overwhelming for users who spend time online.

In response, Reddit has developed tools that utilize LLMs to combat this issue. The irony is not lost on the platform: by using AI-powered technology to fight against AI-generated spam, they’re essentially fighting fire with fire.

According to Reddit’s own statistics, their updated tools have proven effective in reducing spam rates. They claim to block 23 million spam views per day and catch around 25,000 new spam posts and comments daily.

The company notes that these LLM-powered tools are able to detect subtle patterns of fake behavior and artificial hype that older systems often missed. This has led to a significant reduction in users’ exposure to spam – by about 20% from January to March compared with the prior three months.

Reddit’s approach is not unique, as other social platforms like YouTube, Meta, and Instagram have also implemented automated tools to reduce spam. However, Reddit’s updated system appears to be catching spam at a higher rate than its predecessors.

The use of AI-generated content has become increasingly common on these platforms, with some allowing users to post such content provided they disclose it as artificial. TikTok takes this a step further by giving users the option to toggle how much AI-generated content they want to see.

While detecting and flagging AI-generated content can be beneficial in reducing spam and hate speech, experts emphasize that human moderation is still crucial for effective results.

The recent Automate show in Chicago has left a lasting impression on the industry. The event was marked by high energy levels, dense crowds, and an unmistakable sense of urgency among exhibitors. It’s clear that the automation landscape is undergoing significant changes, with many feeling compelled to adapt or risk being left behind.

One of the key takeaways from the show is that various factors are now converging to drive a US manufacturing renaissance. Policy pressure, onshoring, manufacturing investment, labor constraints, and usable AI with improved reliability are all reinforcing each other, rather than moving in separate directions. This convergence has created an environment where innovation can thrive.

The Monday keynote speakers at Automate 2026 provided valuable insights into the changing nature of manufacturing. Andre Marino from Schneider emphasized that the US cannot compete with major manufacturing centers like China solely on capacity or labor costs. To achieve a ‘manufacturing renaissance’ in the US, it must focus on efficiency, connectivity, and innovation. However, this requires more than just investing in new factories; it demands better factories.

Mike Cicco added to the argument by highlighting that the barrier to automation is decreasing as AI makes systems easier to deploy and use. When combined with North American onshoring pressure, everything starts to fall into place. The forces driving change are no longer theoretical but practical, visible, and increasingly urgent.

The old model of industrial automation is losing its grip. Matt Moschner described why this shift feels so significant: the complex systems of the past were only as good as the day they were deployed. They worked beautifully until the environment changed, the product changed, or the labor model changed – then there was an expensive rip-and-replace standing in the way of competitiveness.

Now, the promise is different. Systems are becoming simpler and more robust, capable of learning and improving over time. AI makes incremental training possible, allowing models to be refined without turning every change into a massive reintegration exercise. This doesn’t mean industrial automation has become easy; it means the old tradeoff between capability and flexibility is starting to break down.

The industry needs to stop treating AI as just another software feature. It’s increasingly becoming a way to make automation less static, with the real shift being that machines are becoming more adaptable in practice rather than simply ‘intelligent’ in theory.

Software-defined manufacturing was a recurring theme throughout the event. Wendy Tan framed it not as whether AI matters but how to make it useful in production. For too long, the industry struggled to move from AI discussion to industrial impact. Her argument is that the time has finally come for software to leverage existing assets and investments, making hardware do things it couldn’t before.

Wendy also made another crucial point: the clunky user experience of traditional industrial automation systems needs to change. If industrial automation is going to scale broadly, it cannot remain an expert-only craft built on obscure workflows and heroic engineering effort. The next phase of adoption depends on simple workflows, ease of use, and a more standardized, software-driven path from intent to execution.

The Automate 2026 panel described a clear sense of urgency driven by tariffs, trade disruption, workforce shortages, and general economic instability. Their response? To become more agile and automate what can be automated – uncertainty is not a reason to wait out the storm but rather a reason to move now before it’s too late.

The old model tolerated long pilots, year-long science projects, and endless experimentation because the operating environment felt more stable. However, if the market is moving fast, policy is changing rapidly, and supply chains remain volatile, then speed and flexibility become part of the value proposition – a crucial aspect for manufacturers to consider in today’s landscape.

Andre Marino warned against underinvesting: there is still plenty of hype in the market, but waiting too long carries its own strategic cost. The industry needs to strike a balance between caution and timely investment to reap the benefits of automation and AI.

The big questions about AI are trust, reliability, safety, and lifecycle validation – crucial aspects that Mike Cicco highlighted as essential for industrial adoption. He emphasized that AI should be combined with hardcoded, reliable systems that the industry already trusts rather than trying to replace them entirely. This approach will enable AI to add value where it matters most.

The Automate 2026 event has left a lasting impact on the industry, highlighting the need for manufacturers to adapt and innovate in response to changing market conditions. As we move forward, it’s essential to address the challenges of automation and AI adoption while leveraging their potential benefits – driving growth, improving efficiency, and enhancing competitiveness.

The future of industrial robotics and physical intelligence will be crucial topics for discussion as the industry continues to evolve. Manufacturers must prioritize innovation, adaptability, and collaboration to stay ahead in today’s fast-paced landscape.

CyberProof Inc., a co-managed security services company, has introduced an agentic artificial intelligence service designed to automate up to two-thirds of security investigations. The new service, called CyberProof Agentic MXDR, pairs AI agents with human security experts across the detection, response, and exposure management lifecycle.

The service replaces manual security operations center workflows with a collaborative ecosystem of agents that work under human governance. This approach allows for faster alert triage and more accurate threat identification, which can lead to sharper investigation accuracy by as much as 30%. Unlike standalone AI tools or closed AI SOC platforms, CyberProof’s framework works seamlessly with models from various third-party vendors, including Microsoft Corp., Google LLC, Anthropic PBC, and others.

CyberProof Agentic MXDR is designed for large enterprises and mid-market organizations running complex hybrid cloud environments. The service includes a built-in quality control framework that continuously scores AI agents on effectiveness, speed, and cost. This allows chief information security officers to monitor accuracy versus compute spend as frontier model prices rise. CyberProof argues that its agentic approach can help bridge the gap between calendar-driven defense and the rapid pace of threat disclosures.

The company claims that generative AI has compressed the time attackers need to exploit new flaws, forcing organizations to operationalize threat intelligence faster than ever before. In a statement, Chief Executive Tony Velleca noted that collecting threat data is no longer a competitive advantage; instead, it’s about execution, reducing exposure, immediate detection, and containment. Legacy security operations metrics are becoming obsolete due to the speed gap between attackers and defenders.

CyberProof vice president Doron Davidson emphasized that the blend of AI agents and human expertise aims to deliver faster, more consistent investigations with fewer false positives. The 24/7 service reports outcomes through CyberProof’s Reveal360 dashboard and is available immediately for organizations weighing their operational maturity and stack compatibility. A complimentary readiness assessment is being offered to help teams evaluate their preparedness.

CyberProof delivers co-managed security operations built around Microsoft Azure and the wider Microsoft security stack, which it has been using since its founding in 2017. The company’s new agentic MXDR service is designed to streamline security investigations while maintaining human oversight and control. CyberProof Agentic MXDR can help organizations automate up to two-thirds of their security investigations, freeing up resources for more critical tasks.

The launch of CyberProof Agentic MXDR marks a significant step forward in the development of AI-powered security solutions. By combining the strengths of both humans and machines, this service has the potential to revolutionize the way organizations approach threat detection and response. As the cybersecurity landscape continues to evolve at an unprecedented pace, it’s essential for companies like CyberProof to innovate and adapt their strategies to stay ahead of emerging threats.

CyberProof Agentic MXDR is available immediately, with a complimentary readiness assessment offered to help teams evaluate their preparedness. The service reports outcomes through CyberProof’s Reveal360 dashboard, providing real-time insights into security operations. With its agentic approach and built-in quality control framework, this new service has the potential to transform the way organizations manage their security investigations.

CyberProof Agentic MXDR is designed for large enterprises and mid-market organizations running complex hybrid cloud environments. The service can help bridge the gap between calendar-driven defense and the rapid pace of threat disclosures. By automating up to two-thirds of security investigations, this new service has the potential to free up resources for more critical tasks and improve overall investigation accuracy.

CyberProof Agentic MXDR is a significant development in the field of AI-powered security solutions. The company’s agentic approach combines human expertise with machine learning capabilities to deliver faster, more consistent investigations with fewer false positives. With its built-in quality control framework and seamless integration with various third-party vendors, this new service has the potential to revolutionize the way organizations manage their security operations.

Enterprise organizations often struggle with document-heavy processes that consume significant time and resources. The main challenge is extracting valuable information from documents, which are frequently trapped in PDFs, forms, and other formats. This problem persists despite advancements in automation technologies like AI. To address this issue, Nitro has developed a platform called Nitro Automate, designed to bridge the gap between documents and automation workflows.

Built for high-volume document processing, Nitro Automate is an intelligent document automation platform that integrates document processing, workflow automation, and AI-powered integrations. This solution enables teams to eliminate manual document tasks and automate business processes more efficiently. With its fast deployment capabilities and ability to work anywhere PDFs are handled, Nitro Automate can start delivering value almost immediately.

Nitro Automate extends the company’s existing document productivity solutions – Nitro PDF, Nitro Sign, and Nitro Smart Redact – by addressing the challenge of managing document operations across AI agents, workflows, and systems. Instead of relying on employees to manually move documents between systems or extract data from forms and documents, Nitro Automate automates tasks throughout the document lifecycle.

The platform’s capabilities include process automation, conversion, reshaping, transformation, compression, security, and integration with enterprise applications. It works at two levels: team and department level, where it combines document automation, AI-powered data extraction, and workflow orchestration to eliminate manual document work for high-volume processes; and individual level, where it accelerates essential document tasks that still require human judgment.

Across both levels, Nitro Automate’s enterprise integrations make document data accessible to every stakeholder. This enables teams to automate a process or work through it directly. The platform transforms document-intensive workflows in four key ways: eliminating many manual PDF tasks, automating eSignature workflows beyond the signature, unlocking data trapped in documents, and building document workflows that work in the AI era.

One of the primary challenges Nitro Automate addresses is the substantial operational overhead created by repetitive manual document steps. These activities may seem insignificant when viewed individually but can add up to hours of low-value manual work embedded inside otherwise automated workflows. By removing these manual document steps from core business workflows, teams can focus on higher-value work while improving process consistency and reducing errors.

Nitro Automate also helps organizations automate eSignature workflows beyond the signature itself. This involves integrating document preparation, routing, approvals, and Nitro Sign workflows into a single automated experience. Instead of managing signatures manually across disconnected tools, teams can create workflows that automatically move documents through each stage of the lifecycle, resulting in faster turnaround times, improved visibility, and fewer bottlenecks.

Another key capability of Nitro Automate is its ability to transform unstructured document content into actionable business data. Critical information stored inside contracts, invoices, applications, forms, and other documents can be accessed using AI-powered tools like Nitro Automate. This enables downstream systems and workflows to accelerate processes such as invoice and accounts payable automation, employee onboarding, contract management, customer intake, compliance reporting, claims, and case management.

As enterprises move beyond AI copilots that assist with individual tasks towards deploying AI agents capable of executing multi-step workflows autonomously, document automation becomes an increasingly important part of AI strategy. Nitro Automate addresses this challenge by providing flexible integration options supporting both human- and AI-driven workflows. Business teams can build their own automations using low-code and no-code tools, while developers can easily integrate Nitro Automate into existing applications and workflows using APIs.

Nitro Automate is compatible with the Model Context Protocol (MCP), an emerging open source standard that allows AI agents to securely access external tools and services. Through MCP, AI agents can use Nitro’s document automation solution to interact with documents inside business workflows, making Nitro Automate a document execution layer for enterprise AI initiatives.

Organizations investing in automation and AI should recognize the significant opportunity for operational improvement presented by document-intensive processes. By bridging the gap between documents, workflows, enterprise systems, and AI agents, Nitro Automate brings together document processing, eSignature automation, data extraction, and workflow orchestration to create intelligent workflows that can scale alongside next-generation enterprise AI.

Document-heavy processes are often a major bottleneck in business operations. Nitro Automate helps organizations eliminate these bottlenecks by automating tasks such as manual PDF management, signature routing, and document approval. By doing so, teams can improve efficiency, reduce errors, and free up resources for higher-value work.

An ongoing arms race in writing has been gaining attention, with two sides vying for dominance. On one side are AI content detectors that have become increasingly sophisticated. Tools like GPTZero, Turnitin, Originality.ai, and ZeroGPT have made significant strides in identifying AI-generated text. Their earlier versions were relatively easy to bypass, but the newer models have developed a keen sense of what makes writing feel artificial. They can detect consistency in paragraph rhythm, the way language is hedged, vocabulary patterns that lean towards formality, and the absence of human-like tangents and personality.

The other side of this arms race involves writers, marketers, students, and professionals who use AI as part of their workflow. These individuals have been searching for ways to make AI-generated content pass muster with both detection tools and human readers. This is where tools like HumanizeAIText come in – they rewrite AI drafts to introduce variation, idiosyncrasy, and natural roughness that human writing typically exhibits.

To understand the difference between AI-written text and its human counterpart, try this experiment: pull up a piece of AI-generated content, such as something written by ChatGPT or Claude. Read it quickly once, then go back to examine the sentence lengths. Chances are most will fall within a 15-25 word range. Look at how each paragraph opens – often with a topic sentence announcing what’s to come, followed by supporting sentences and sometimes a brief summary or transition.

This structure is competent but recognizable as AI-generated once you’ve seen it enough times. Human writing doesn’t typically follow this pattern. People’s attention wanders mid-sentence; they make points only to immediately qualify them or wonder aloud if the point holds true. They use shorter bursts when something feels urgent and longer, more unwieldy constructions for complex ideas.

The imperfections in human writing aren’t bugs – they’re what makes it feel like someone rather than a machine wrote it. AI detectors have essentially learned to measure this. When writing is too consistent, structured, or evenly paced across a document, flags are raised. This is why raw AI output has become increasingly difficult to pass off as human writing without some kind of intervention.

The obvious response to the challenge posed by AI-generated content is editing it yourself. However, rewriting an AI draft to sound like you actually wrote it requires double attention: tracking what the content says while simultaneously listening for every sentence that sounds machine-like. This process takes time – and for those using AI precisely because they don’t have a lot of time, it can eat up most of the efficiency gains provided by the AI in the first place.

This is where tools like HumanizeAIText fill a practical gap for many people. They’re not replacing human judgment but doing a first pass at addressing mechanical problems: evening out sentence rhythm, breaking up too-perfect paragraph structures, and introducing natural variation that makes writing feel less generated. The user still needs to review the result and ensure it says what they intended.

The real issue with AI-generated content isn’t just whether it triggers a detector but whether it’s actually good at its job – persuading, informing, or connecting with readers. Content that sounds robotic fails not only at the detection level but also at engaging human readers. People can feel when something is off, even if they can’t pinpoint why.

The argument for using tools like HumanizeAIText isn’t just about avoiding detection; it’s about closing the gap between what AI produces and what actually works on a human reader. This involves more than just passing a detector – it requires writing that reads as though someone genuinely cared about its content.

A new report from OpenAI Economic Research has shed light on the potential impact of artificial intelligence (AI) on the European labor market. The study, titled ‘The AI Jobs Transition Framework for the EU’, examines how AI capabilities may translate into different kinds of near-term occupational change across EU member states. This is a crucial question, as jobs do not change in the same way that AI capabilities can cross borders quickly.

The world of AI video generation has seen a radical transformation in recent years. Gone are the days when creators focused solely on visually appealing backgrounds or cinematic landscapes. Today, there’s an emphasis on mastering hyper-personalized character consistency and emotional depth.

Our evaluation process involved putting each engine through identical tests to assess its performance in creating personalized short videos, social media content, and emotionally resonant narratives centered around the creator. We wanted to find out which tools could deliver high-quality results without breaking the bank or risking legal issues.

One tool that really stood out from the rest is CoupleLens - an AI video generator with exceptional character fidelity tests. When we uploaded a static reference photo, it seamlessly integrated individual portraits into cohesive and dynamic scenes. The company claims their model preserves 98% of facial geometric features and natural micro-expressions even when applying its vast library of styles and filters.

Another standout tool is Runway - a top choice for technical directors who require total mastery over camera physics and background layering. Using Motion Brush Pro 2.0, Runway showed breathtaking control in isolated background adjustments. However, setting up consistent character tracking requires extensive advanced prompt weighting - something that’s not exactly beginner-friendly.

For indie filmmakers on a tight budget, Kling is an excellent option for generating long-duration B-roll content. It handled continuous 10-to-15 second blocks with impressive physical accuracy and features a great multi-language native audio generator that auto-syncs lip movements to generated characters. This feature alone makes it a valuable asset for any filmmaker working on a shoestring budget.

Veo is another top performer in our evaluation - particularly when generating text-to-video scenes that look like natural documentaries complete with ambient soundscapes. Its built-in spatial audio logic seamlessly generates localized sound effects based entirely on what’s moving in the frame. The result is an immersive viewing experience that feels authentic and engaging.

For corporate creative teams who cannot afford even a 1% risk of a copyright lawsuit, Firefly is an essential tool for image and video generation. Integrated directly into Adobe Premiere Pro timeline, it effortlessly handles clean texture generations, object additions, and minor transitions using assets that are entirely safe for public distribution - no risk of plagiarism or infringement.

AI tools like these can be a game-changer for creators who want to produce high-quality content without breaking the bank. Whether you’re looking for an AI video generator with exceptional character fidelity or one that excels at generating realistic text-to-video scenes, there’s a tool on this list designed to help you succeed.

The right AI video generator depends on your specific creative goals - not just whether you’re solo creator bringing memories to life or corporate team building a brand. We recommend testing the free tiers of CoupleLens first to see which engine best fits your style and needs, so you can get started creating amazing content with ease.

The film Late Night with the Devil has been making waves in the entertainment industry, but not for its plot or cast. Instead, it’s caught heat online due to allegations of using AI-generated imagery. The movie, which stars David Dastmalchian as a late-night TV host trying to save his show with a Halloween special gone wrong, was initially praised by critics, including Mashable’s review at SXSW 2023. However, some viewers have taken issue with the film’s use of artificial intelligence in its visuals.

The controversy began when user ‘based gizmo’ posted a one-star review on Letterboxd on March 19, stating: ‘There’s AI all over this… Don’t let this be the start of accepting this shit in your entertainment.’ This sparked a wave of discussion online, with users taking to X (formerly Twitter) to share screenshots from the film’s trailer and dissect the allegations. The focus was particularly on interstitials throughout the fictional live TV broadcast, which included illustrations such as a skeleton dancing in a pumpkin patch.

As the conversation around AI-generated images grew, Mashable reached out to Shudder for comment. In response, co-writers and co-directors Cameron Cairnes and Colin Cairnes confirmed that their film did indeed use AI. According to them, they experimented with artificial intelligence for three still images, which were then edited further and appeared as brief interstitials in the movie. They credited their graphics and production design team for helping create a 70s aesthetic.

The statement from Late Night with the Devil’s creators echoes recent trends in film and TV using AI-generated imagery. Marvel’s Secret Invasion used AI to create its opening credits last year, while True Detective: Night Country faced criticism earlier this year over background posters that looked suspiciously like they were created by a machine. This pushback comes at a time when Hollywood is still reeling from the WGA and SAG strikes, during which both unions fought for protections against AI replacing human work.

Late Night with the Devil premieres in theaters on March 22 and will be available to stream on Shudder starting April 19. The controversy surrounding its use of AI-generated imagery has sparked a wider conversation about the role of artificial intelligence in filmmaking, leaving audiences and industry professionals alike wondering where this trend is headed.

HP Inc.’s journey to enterprise transformation began with small teams proving a new way of working was possible, and it’s now scaling up its successful pilots across different areas through the OpenAI Frontier strategic partnership. This move extends how HP is deploying frontier capabilities to enhance customer-facing experiences and accelerate transformation across its operations globally.