3D printer manufacturer Bambu Lab has issued a new update after an early fix was withdrawn. Termed a critical calibration bug, the company has acted swiftly to deliver new code to its many users. The Shenzhen-based company has now released firmware version V01.01.02.07 for its H2D 3D printer through its Public Beta Program. Rolled out on May 23, this update introduces a comprehensive set of new features, performance enhancements, and critical bug fixes designed to elevate print quality, expand hardware compatibility, and offer users greater control. The release builds on feedback gathered from earlier beta phases. The Bambu Lab H2D Laser Full Combo in a workshop. Image via Bambu Lab. Features and Improvements Firmware V01.01.02.07 adds native support for the CyberBrick time-lapse kit. It also expands the H2D’s onboard AI failure detection system, now giving users the ability to individually toggle detection functions for nozzle clumping, spaghetti printing, air printing, and purge chute pile-ups from the printer’s interface. Hardware compatibility has been further extended. The AMS 2 Pro and AMS HT systems now support RFID-based automatic matching of drying parameters and can perform drying operations without rotating spools. Additionally, the Laser & Cut module can now initiate tasks directly from USB drive files, improving workflow support. Performance updates include improved foreign object detection on the smooth PEI plate, better regulation of heatbed temperatures, enhanced first-layer quality, more reliable chamber temperature checks before printing begins, and improved accuracy of laser module flame detection. The update also enhances the accuracy of nozzle clumping and nozzle camera dirty detection, while optimizing the pre-purging strategy. A collision issue between the nozzle flow blocker and nozzle wiper—previously triggered during flow dynamics calibration—has been resolved. Calibration reliability for the liveview camera has also improved, and issues with pre-extrusion lines sticking to prints during layer transitions have been addressed. Bambu Lab H2D Launch. Image via Bambu Lab. However, two known issues remain in this beta release: detection of filament PTFE tube detachment is currently disabled, and users cannot adjust heatbed temperature via the Bambu Handy app. The latter is expected to be fixed in a future app update. This version replaces V01.01.02.04, which was briefly released on May 20 before being withdrawn due to a critical calibration bug. That earlier version caused the right nozzle to crash into the wiper during left-nozzle calibration, damaging the printer. The firmware also temporarily disabled filament detachment detection. Bambu Lab quickly pulled the update and advised users to revert to the previous stable firmware while working on a corrected release—now realized in version V01.01.02.07. Accessing the Firmware To access the beta firmware, users can opt into the Public Beta Program through the Bambu Handy app by navigating to the “Me” section and selecting “Beta Firmware Program.” Once enrolled, the update will be rolled out gradually. Participants can leave the program at any time and revert to the most recent stable firmware version. Bambu Lab recommends updating Bambu Studio Presets before installing the firmware to ensure full compatibility. Full technical documentation and the official changelog are available on Bambu Lab’s website. Bambu Lab Hardware Line: H2D and Beyond The new firmware update applies to the H2D 3D printer, Bambu Lab’s flagship desktop manufacturing system unveiled in March 2025. Designed for professional users, the H2D offers the company’s largest build volume to date—350 x 320 x 325 mm—and includes two new AMS systems with integrated filament drying. Dual-nozzle extrusion and servo-driven precision deliver high accuracy, while a 350°C hotend and 65°C heated chamber allow reliable printing with high-performance, fiber-reinforced materials. With a toolhead speed of up to 1000 mm/s and acceleration of 20,000 mm/s², the H2D is built for productivity without compromising quality. The Bambu Lab H2D’s digital cutter. Image via Bambu Lab. Bambu Lab’s broader portfolio also includes the X1E, released in 2023 as an enterprise-grade upgrade to its X1 series. Developed with professional and engineering applications in mind, the X1E features LAN-only connectivity for secure, offline operation, enhanced air filtration, and precise thermal regulation. An increased maximum nozzle temperature expands its material compatibility, making it suitable for demanding industrial applications. At its core, the X1E builds on the proven performance of the X1 Carbon, extending the system’s capabilities for use in sensitive or regulated environments. Take the 3DPI Reader Survey — shape the future of AM reporting in under 5 minutes. Who won the 2024 3D Printing Industry Awards? Subscribe to the3D Printing Industry newsletter to keep up with the latest 3D printing news. You can also follow us on LinkedIn, and subscribe to the 3D Printing Industry Youtube channel to access more exclusive content. Featured image shows Bambu Lab H2D Launch. Image via Bambu Lab. Paloma Duran Paloma Duran holds a BA in International Relations and an MA in Journalism. Specializing in writing, podcasting, and content and event creation, she works across politics, energy, mining, and technology. With a passion for global trends, Paloma is particularly interested in the impact of technology like 3D printing on shaping our future.

Posted on : May 31, 2025 By Tech World Times Security Testing  Rate this post In a digital era where cyberattacks are increasing in frequency, complexity, and cost, organizations must stay one step ahead by investing in robust cybersecurity strategies. At the heart of this defense lies Cyber Security Threat Analysis, a process that helps businesses detect, understand, and respond to threats before they escalate. This comprehensive guide explores the fundamentals of threat analysis, the methodologies used in 2025, emerging trends, and how companies can implement an effective threat analysis framework to safeguard their digital assets. What is Cyber Security Threat Analysis? Cyber Security Threat Analysis is the process of identifying, assessing, and prioritizing potential and existing cybersecurity threats. It involves analyzing data from various sources to uncover vulnerabilities, detect malicious activity, and evaluate the potential impact on systems, networks, and data. The goal is to proactively defend against attacks rather than react to them after damage is done. Why Threat Analysis Matters in 2025 With the growing adoption of AI, IoT, cloud computing, and remote work, the digital landscape has expanded. This has also widened the attack surface for threat actors. According to recent studies, global cybercrime costs are projected to reach $10.5 trillion annually by 2025. Threat analysis is no longer optional; it’s a critical component of enterprise cybersecurity strategies. Key Components of Cyber Security Threat Analysis Threat Intelligence Gathering Collecting data from open-source intelligence (OSINT), internal systems, dark web monitoring, and threat intelligence platforms.Threat Identification Recognizing indicators of compromise (IOCs), such as malicious IP addresses, abnormal behavior, and unusual login attempts.Risk Assessment Evaluating the likelihood and potential impact of a threat on business operations.Vulnerability Management Identifying weaknesses in systems, applications, and networks that could be exploited.Incident Response Planning Developing action plans to quickly contain and remediate threats. Types of Cyber Threats in 2025 Threat actors continue to evolve, leveraging advanced techniques to breach even the most secure environments. Here are the most prominent threats organizations face in 2025: Ransomware-as-a-Service (RaaS): Cybercriminals offer ransomware toolkits to affiliates, enabling less-skilled attackers to launch sophisticated attacks. Phishing 3.0: AI-generated deepfake emails and voice messages make phishing harder to detect. Supply Chain Attacks: Attackers compromise third-party software or vendors to gain access to larger networks. Cloud Security Breaches: Misconfigured cloud environments remain a top vulnerability. IoT Exploits: Devices with weak security protocols are targeted to infiltrate larger systems. Insider Threats: Employees or contractors may intentionally or unintentionally expose systems to risk. Modern Threat Analysis Methodologies 1. MITRE ATT&CK Framework The MITRE ATT&CK framework maps the behavior and techniques of attackers, providing a structured method to analyze and predict threats. 2. Kill Chain Analysis Developed by Lockheed Martin, this method breaks down the stages of a cyberattack from reconnaissance to actions on objectives, allowing analysts to disrupt attacks early in the chain. 3. Threat Modeling Threat modeling involves identifying assets, understanding potential threats, and designing countermeasures. STRIDE (Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, Elevation of privilege) is a popular model used in 2025. 4. Behavior Analytics User and Entity Behavior Analytics (UEBA) uses machine learning to detect anomalies in user behavior that could indicate threats. The Role of AI and Automation in Threat Analysis Artificial Intelligence (AI) and automation are revolutionizing Cyber Security Threat Analysis in 2025. AI-driven analytics tools can: Correlate large volumes of data in real-time Detect zero-day vulnerabilities Predict attack patterns Automate incident response processes Platforms like IBM QRadar, Microsoft Sentinel, and Splunk integrate AI capabilities for enhanced threat detection and response. Building a Threat Analysis Framework in Your Organization Establish Objectives Define what you want to protect, the types of threats to look for, and the goals of your analysis.Choose the Right Tools Invest in threat intelligence platforms, SIEM systems, and endpoint detection and response (EDR) tools.Create a Skilled Team Assemble cybersecurity professionals including threat hunters, analysts, and incident responders.Integrate Data Sources Pull data from internal logs, external intelligence feeds, user activity, and cloud services.Run Simulations Regularly test your threat detection capabilities using red teaming and penetration testing.Review and Adapt Continuously update the threat model based on evolving threats and organizational changes. Metrics to Measure Threat Analysis Success Mean Time to Detect (MTTD): Time taken to identify a threat. Mean Time to Respond (MTTR): Time taken to neutralize the threat. False Positive Rate: Accuracy of alerts generated. Threat Coverage: Percentage of known threats the system can detect. Business Impact Score: How much value the threat analysis process adds to business continuity and risk mitigation. Challenges in Cyber Security Threat Analysis Data Overload: Managing and analyzing massive volumes of data can be overwhelming. Alert Fatigue: Too many alerts, including false positives, reduce response effectiveness. Talent Shortage: Skilled cybersecurity professionals are in high demand but short supply. Rapid Threat Evolution: Attack techniques evolve quickly, making it hard to maintain up-to-date defenses. Best Practices for Effective Threat Analysis Prioritize Critical Assets: Focus analysis efforts on high-value systems and data. Implement Zero Trust Security: Never trust, always verify; ensure robust identity and access controls. Automate Where Possible: Use automation to handle repetitive tasks and free up human resources for strategic analysis. Encourage a Security Culture: Train employees to recognize and report suspicious activity. Leverage Community Intelligence: Participate in threat intelligence sharing communities like ISACs (Information Sharing and Analysis Centers). Future of Threat Analysis Beyond 2025 The future of Cyber Security Threat Analysis will continue to evolve with: Quantum Computing Threats: New cryptographic challenges will require upgraded threat models. Decentralized Threat Intelligence: Blockchain-based threat sharing platforms could emerge. Autonomous Cyber Defense: AI systems capable of defending networks without human input. Conclusion Cyber Security Threat Analysis is an indispensable element of modern digital defense, especially in a hyper-connected 2025. With increasingly sophisticated threats on the horizon, businesses must adopt proactive threat analysis strategies to protect their digital environments. From leveraging AI tools to integrating structured methodologies like MITRE ATT&CK and STRIDE, a multi-layered approach can provide robust defense against cyber adversaries. Investing in skilled teams, up-to-date technologies, and continuous improvement is essential to building resilient cybersecurity infrastructure. FAQs 1. What is Cyber Security Threat Analysis? It is the process of identifying, evaluating, and mitigating potential cybersecurity threats to protect data, networks, and systems.2. Why is threat analysis important in 2025? With rising digital threats and complex attack vectors, proactive analysis helps businesses prevent breaches and minimize damage.3. Which tools are best for threat analysis? Popular tools include Splunk, IBM QRadar, Microsoft Sentinel, and CrowdStrike.4. How does AI help in threat analysis? AI helps by automating data analysis, detecting patterns, and predicting threats in real-time.5. What industries benefit most from threat analysis? Finance, healthcare, government, and tech sectors, where data protection and regulatory compliance are critical.Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More AI is advancing at a rapid clip for businesses, and that’s especially true of speech and voice AI models. Case in point: Today, ElevenLabs, the well-funded voice and AI sound effects startup founded by former Palantir engineers, debuted Conversational AI 2.0, a significant upgrade to its platform for building advanced voice agents for enterprise use cases, such as customer support, call centers, and outbound sales and marketing. This update introduces a host of new features designed to create more natural, intelligent, and secure interactions, making it well-suited for enterprise-level applications. The launch comes just four months after the debut of the original platform, reflecting ElevenLabs’ commitment to rapid development, and a day after rival voice AI startup Hume launched its own new, turn-based voice AI model, EVI 3. It also comes after new open source AI voice models hit the scene, prompting some AI influencers to declare ElevenLabs dead. It seems those declarations were, naturally, premature. According to Jozef Marko from ElevenLabs’ engineering team, Conversational AI 2.0 is substantially better than its predecessor, setting a new standard for voice-driven experiences. Enhancing naturalistic speech A key highlight of Conversational AI 2.0 is its state-of-the-art turn-taking model. This technology is designed to handle the nuances of human conversation, eliminating awkward pauses or interruptions that can occur in traditional voice systems. By analyzing conversational cues like hesitations and filler words in real-time, the agent can understand when to speak and when to listen. This feature is particularly relevant for applications such as customer service, where agents must balance quick responses with the natural rhythms of a conversation. Multilingual support Conversational AI 2.0 also introduces integrated language detection, enabling seamless multilingual discussions without the need for manual configuration. This capability ensures that the agent can recognize the language spoken by the user and respond accordingly within the same interaction. The feature caters to global enterprises seeking consistent service for diverse customer bases, removing language barriers and fostering more inclusive experiences. Enterprise-grade One of the more powerful additions is the built-in Retrieval-Augmented Generation (RAG) system. This feature allows the AI to access external knowledge bases and retrieve relevant information instantly, while maintaining minimal latency and strong privacy protections. For example, in healthcare settings, this means a medical assistant agent can pull up treatment guidelines directly from an institution’s database without delay. In customer support, agents can access up-to-date product details from internal documentation to assist users more effectively. Multimodality and alternate personas In addition to these core features, ElevenLabs’ new platform supports multimodality, meaning agents can communicate via voice, text, or a combination of both. This flexibility reduces the engineering burden on developers, as agents only need to be defined once to operate across different communication channels. Further enhancing agent expressiveness, Conversational AI 2.0 allows multi-character mode, enabling a single agent to switch between different personas. This capability could be valuable in scenarios such as creative content development, training simulations, or customer engagement campaigns. Batch outbound calling For enterprises looking to automate large-scale outreach, the platform now supports batch calls.\ Organizations can initiate multiple outbound calls simultaneously using Conversational AI agents, an approach well-suited for surveys, alerts, and personalized messages. This feature aims to increase both reach and operational efficiency, offering a more scalable alternative to manual outbound efforts. Enterprise-grade standards and pricing plans Beyond the features that enhance communication and engagement, Conversational AI 2.0 places a strong emphasis on trust and compliance. The platform is fully HIPAA-compliant, a critical requirement for healthcare applications that demand strict privacy and data protection. It also supports optional EU data residency, aligning with data sovereignty requirements in Europe. ElevenLabs reinforces these compliance-focused features with enterprise-grade security and reliability. Designed for high availability and integration with third-party systems, Conversational AI 2.0 is positioned as a secure and dependable choice for businesses operating in sensitive or regulated environments. As far as pricing is concerned, here are the available subscription plans that include Conversational AI currently listed on ElevenLabs’ website: Free: $0/month, includes 15 minutes, 4 concurrency limit, requires attribution and no commercial licensing. Starter: $5/month, includes 50 minutes, 6 concurrency limit. Creator: $11/month (discounted from $22), includes 250 minutes, 6 concurrency limit, ~$0.12 per additional minute. Pro: $99/month, includes 1,100 minutes, 10 concurrency limit, ~$0.11 per additional minute. Scale: $330/month, includes 3,600 minutes, 20 concurrency limit, ~$0.10 per additional minute. Business: $1,320/month, includes 13,750 minutes, 30 concurrency limit, ~$0.096 per additional minute. A new chapter in realistic, naturalistic AI voice interactions As stated in the company’s video introducing the new release, “The potential of conversational AI has never been greater. The time to build is now.” With Conversational AI 2.0, ElevenLabs aims to provide the tools and infrastructure for enterprises to create truly intelligent, context-aware voice agents that elevate the standard of digital interactions. For those interested in learning more, ElevenLabs encourages developers and organizations to explore its documentation, visit the developer portal, or reach out to the sales team to see how Conversational AI 2.0 can enhance their customer experiences. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

What just happened? An international team of researchers have discovered a cosmic anomaly unlike anything previously witnessed. The object in question, located roughly 15,000 light-years away in our very own Milky Way galaxy, has been observed emitting both radio waves and X-ray radiation. The celestial body, dubbed ASKAP J1832- 0911, was initially found by astronomers using the Australian Square Kilometer Array Pathfinder (ASKAP), a radio telescope located in Australia. Another look using NASA's Chandra X-Ray telescope found the object also emitted X-rays in an unusual yet predictable pattern. Every 44 minutes, the object flashes both radio waves and X-rays for two minutes straight. Team leader Zieng (Andy) Wang said the object is unlike anything they have seen before, although its origins are likely not as mysterious as one might initially think. Wang said the object could very well be what's left of a dead star with powerful magnetic fields, called a magnetar – or perhaps something as simple as a pair of stars in a binary system in which one of the two is a highly magnetized white dwarf. Then again, neither explanation fully explains exactly what astronomers are witnessing with its behavior so the jury is still out. Conversely, Wang added, the discovery could be a new type of physics or a fresh model of stellar evolution we haven't seen before. // Related Stories NASA's Chandra X-ray observatory launched into space way back in 1999 and has been orbiting Earth ever since. The ASKAP radio telescope array has been operational since 2012, and consists of 36 giant antennas – each measuring 39 feet in diameter – that work together as one tool. Further study will be needed to help astronomers better understand exactly what they are looking at. At 15,000 light-years away, the object is somewhat nearby in the grand scheme of the universe but still quite far away in actual distance. For reference, one light-year is roughly equal to six trillion miles. The team's research has been published in the journal Nature and is titled, "Detection of X-ray Emission from a Bright Long-Period Radio Transient."

State-of-the-art models show human-competitive accuracy on AIME, GPQA, MATH-500, and OlympiadBench, solving Olympiad-level problems. Recent multimodal foundation models have advanced benchmarks for disciplinary knowledge and mathematical reasoning. However, these evaluations miss a crucial aspect of machine intelligence: physical reasoning, which requires integrating disciplinary knowledge, symbolic operations, and real-world constraints. Physical problem-solving differs fundamentally from pure mathematical reasoning as it demands models to decode implicit conditions in questions. For example, interpreting “smooth surface” as zero friction coefficient, and maintaining physical consistency across reasoning chains because physical laws remain constant regardless of reasoning trajectories. MLLM shows excellent visual understanding by integrating visual and textual data across various tasks, motivating exploration of its reasoning abilities. However, uncertainty remains regarding whether these models possess genuine advanced reasoning capabilities for visual tasks, particularly in physical domains closer to real-world scenarios. Several LLM benchmarks have emerged to evaluate reasoning abilities, with PHYBench being most relevant for physics reasoning. MLLM scientific benchmarks, such as PhysReason and EMMA, contain multimodal physics problems with figures, however, they include only small physics subsets, which inadequately evaluate MLLMs’ capabilities for reasoning and solving advanced physics problems. Researchers from the University of Hong Kong, the University of Michigan, the University of Toronto, the University of Waterloo, and the Ohio State University have proposed PHYX, a novel benchmark to evaluate the physical reasoning capabilities of foundation models. It comprises 3,000 visually-grounded physics questions, precisely curated across six distinct physics domains: Mechanics, Electromagnetism, Thermodynamics, Wave/Acoustics, Optics, and Modern Physics. It evaluates physics-based reasoning via multimodal problem-solving with three core innovations: (a) 3,000 newly collected questions with realistic physical scenarios requiring integrated visual analysis and causal reasoning, (b) Expert-validated data design covering six fundamental physics domains, and (c) Strict unified three-step evaluation protocols. Researchers designed a four-stage data collection process to ensure high-quality data. The process begins with an in-depth survey of core physics disciplines to determine coverage across diverse domains and subfields, followed by the recruitment of STEM graduate students as expert annotators. They comply with copyright restrictions and avoid data contamination by selecting questions without answers that are immediately available. Moreover, quality control involves a three-stage cleaning process including duplicate detection through lexical overlap analysis with manual review by physics Ph.D. students, followed by filtering the shortest 10% of questions based on textual length, resulting in 3,000 high-quality questions from an initial collection of 3,300. PHYX presents significant challenges for current models, with even the worst-performing human experts achieving 75.6% accuracy, outperforming all evaluated models and showing a gap between human expertise and current model capabilities. The benchmark reveals that multiple-choice formats narrow performance gaps by allowing weaker models to rely on surface-level cues, but open-ended questions demand genuine reasoning and precise answer generation. Comparing GPT-4o’s performance on PHYX to previously reported results on MathVista and MATH-V (both 63.8%), lower accuracy in physical reasoning tasks emphasizes that physical reasoning requires deeper integration of abstract concepts and real-world knowledge, presenting greater challenges than purely mathematical contexts. In conclusion, researchers introduced PHYX, the first large-scale benchmark for evaluating physical reasoning in multimodal, visually grounded scenarios. Rigorous evaluation reveals that state-of-the-art models show limitations in physical reasoning, relying predominantly on memorized knowledge, mathematical formulas, and superficial visual patterns rather than genuine understanding of physical principles. The benchmark focuses exclusively on English-language prompts and annotations, limiting assessment of multilingual reasoning abilities. Also, while images depict physically realistic scenarios, they are often schematic or textbook-style rather than real-world photographs, which may not fully capture the complexity of perception in natural environments. Check out the Paper, Code and Project Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Sajjad AnsariSajjad Ansari is a final year undergraduate from IIT Kharagpur. As a Tech enthusiast, he delves into the practical applications of AI with a focus on understanding the impact of AI technologies and their real-world implications. He aims to articulate complex AI concepts in a clear and accessible manner.Sajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Meta AI Introduces Multi-SpatialMLLM: A Multi-Frame Spatial Understanding with Multi-modal Large Language ModelsSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Can LLMs Really Judge with Reasoning? Microsoft and Tsinghua Researchers Introduce Reward Reasoning Models to Dynamically Scale Test-Time Compute for Better AlignmentSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/NVIDIA AI Introduces AceReason-Nemotron for Advancing Math and Code Reasoning through Reinforcement LearningSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Researchers Introduce MMLONGBENCH: A Comprehensive Benchmark for Long-Context Vision-Language Models

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More AI is advancing at a rapid clip for businesses, and that’s especially true of speech and voice AI models. Case in point: Today, ElevenLabs, the well-funded voice and AI sound effects startup founded by former Palantir engineers, debuted Conversational AI 2.0, a significant upgrade to its platform for building advanced voice agents for enterprise use cases, such as customer support, call centers, and outbound sales and marketing. This update introduces a host of new features designed to create more natural, intelligent, and secure interactions, making it well-suited for enterprise-level applications. The launch comes just four months after the debut of the original platform, reflecting ElevenLabs’ commitment to rapid development, and a day after rival voice AI startup Hume launched its own new, turn-based voice AI model, EVI 3. It also comes after new open source AI voice models hit the scene, prompting some AI influencers to declare ElevenLabs dead. It seems those declarations were, naturally, premature. According to Jozef Marko from ElevenLabs’ engineering team, Conversational AI 2.0 is substantially better than its predecessor, setting a new standard for voice-driven experiences. Enhancing naturalistic speech A key highlight of Conversational AI 2.0 is its state-of-the-art turn-taking model. This technology is designed to handle the nuances of human conversation, eliminating awkward pauses or interruptions that can occur in traditional voice systems. By analyzing conversational cues like hesitations and filler words in real-time, the agent can understand when to speak and when to listen. This feature is particularly relevant for applications such as customer service, where agents must balance quick responses with the natural rhythms of a conversation. Multilingual support Conversational AI 2.0 also introduces integrated language detection, enabling seamless multilingual discussions without the need for manual configuration. This capability ensures that the agent can recognize the language spoken by the user and respond accordingly within the same interaction. The feature caters to global enterprises seeking consistent service for diverse customer bases, removing language barriers and fostering more inclusive experiences. Enterprise-grade One of the more powerful additions is the built-in Retrieval-Augmented Generation (RAG) system. This feature allows the AI to access external knowledge bases and retrieve relevant information instantly, while maintaining minimal latency and strong privacy protections. For example, in healthcare settings, this means a medical assistant agent can pull up treatment guidelines directly from an institution’s database without delay. In customer support, agents can access up-to-date product details from internal documentation to assist users more effectively. Multimodality and alternate personas In addition to these core features, ElevenLabs’ new platform supports multimodality, meaning agents can communicate via voice, text, or a combination of both. This flexibility reduces the engineering burden on developers, as agents only need to be defined once to operate across different communication channels. Further enhancing agent expressiveness, Conversational AI 2.0 allows multi-character mode, enabling a single agent to switch between different personas. This capability could be valuable in scenarios such as creative content development, training simulations, or customer engagement campaigns. Batch outbound calling For enterprises looking to automate large-scale outreach, the platform now supports batch calls.\ Organizations can initiate multiple outbound calls simultaneously using Conversational AI agents, an approach well-suited for surveys, alerts, and personalized messages. This feature aims to increase both reach and operational efficiency, offering a more scalable alternative to manual outbound efforts. Enterprise-grade standards and pricing plans Beyond the features that enhance communication and engagement, Conversational AI 2.0 places a strong emphasis on trust and compliance. The platform is fully HIPAA-compliant, a critical requirement for healthcare applications that demand strict privacy and data protection. It also supports optional EU data residency, aligning with data sovereignty requirements in Europe. ElevenLabs reinforces these compliance-focused features with enterprise-grade security and reliability. Designed for high availability and integration with third-party systems, Conversational AI 2.0 is positioned as a secure and dependable choice for businesses operating in sensitive or regulated environments. As far as pricing is concerned, here are the available subscription plans that include Conversational AI currently listed on ElevenLabs’ website: Free: $0/month, includes 15 minutes, 4 concurrency limit, requires attribution and no commercial licensing. Starter: $5/month, includes 50 minutes, 6 concurrency limit. Creator: $11/month (discounted from $22), includes 250 minutes, 6 concurrency limit, ~$0.12 per additional minute. Pro: $99/month, includes 1,100 minutes, 10 concurrency limit, ~$0.11 per additional minute. Scale: $330/month, includes 3,600 minutes, 20 concurrency limit, ~$0.10 per additional minute. Business: $1,320/month, includes 13,750 minutes, 30 concurrency limit, ~$0.096 per additional minute. A new chapter in realistic, naturalistic AI voice interactions As stated in the company’s video introducing the new release, “The potential of conversational AI has never been greater. The time to build is now.” With Conversational AI 2.0, ElevenLabs aims to provide the tools and infrastructure for enterprises to create truly intelligent, context-aware voice agents that elevate the standard of digital interactions. For those interested in learning more, ElevenLabs encourages developers and organizations to explore its documentation, visit the developer portal, or reach out to the sales team to see how Conversational AI 2.0 can enhance their customer experiences. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

Macworld People have high hopes for the Apple Watch in 2025 after the Apple Watch Series 10 disappointed in 2024. Despite rumors of major design changes, the Apple Watch 10 turned out to be more or less identical to every Apple Watch sold over the last few years. The display was just a tad larger and the body was slightly thinner, but overall it was very similar to the Series 9. At this point you might look to the old adage: “If it ain’t broke don’t fix it”. Apple’s Watch follows a proven formula that has brought a lot of success. It should be no surprise that the company isn’t rocking the boat. That might be forgivable, but Apple didn’t even update the Apple Watch Ultra or Apple Watch SE in 2024, leaving both with an older processor generation. Luckily this year it looks like Apple will deliver some long awaited Apple Watch features. Read on to find out what’s in store for Apple Watch Series 11 in the fall of 2025? Here’s what we’ve heard so far about the 2025 Apple Watch. We’ll run though what’s expected for each Apple Watch model below, starting with the Apple Watch Series 11. Apple Watch Series 11: Release date Launch at September iPhone event: Tuesday September 9 (probable) Pre-order from September 12 On sale from September 19 For years now, Apple has announced the new Apple Watch together with the new iPhone at a September event, with both products going on sale the following week. We haven’t heard anything to make us think that won’t be the case again this year. The most likely date is the second Tuesday of September, which is September 9. Pre-orders for the Apple Watch Series 11 will probably begin that Friday (September 12) with general availability in stores a week later (September 19). Apple Watch Series 11: Price Likely to start at $399, unless Apple has to raise prices Ever since the Apple Watch Series 3, Apple has kept starting prices the same for the aluminum Apple Watch and we don’t expect a price hike this year. This means that the Series 11 will start at $399 and the larger version will cost $429, with an extra $100 for cellular capability and higher prices for various bands. With the Series 10, Apple also offered a titanium case for $699/$749, which includes LTE like the Ultra. Rumors suggest Apple will keep that option around with the Series 11, likely at the same price. However, no discussion of future Apple product pricing is complete without mentioning U.S. tariffs, which are changing rapidly and could have a big impact on pricing. It’s simply too difficult to predict whether tariffs will increase the Apple Watch’s price, the odds are significant. Apple Watch Series 11: Design The Apple Watch Series 11 is likely to look nearly identical to the Series 10.Dominik Tomaszewski / Foundry Rumors throughout 2024 told us to expect more significant changes to the Apple Watch design last year, and then the Series 10 debuted and it looked like a very slightly flattened Series 9. This year, there aren’t even any rumors of big design changes. Currently, all signs point to an Apple Watch that is identical in appearance to the Series 10, with the possible exception of new colors, possibly sky blue to match the MacBook Air and iPhone 17. Apple Watch SE 3: Release date, price and design Launch at September iPhone event: Tuesday September 9 (probable) Pre-order from September 12 On sale from September 19 Apple hasn’t updated the Apple Watch SE since 2022 and that update was an extremely minor one, bringing a new chip and crash detection with a lower price of $249. It was initially rumored that 2024 would bring a substantial update to the budget watch with a plastic redesign, but that didn’t happen. Then Mark Gurman of Bloomberg reported that a plastic SE redesign would arrive in 2025 alongside the Series 11. However, a rumor in March claimed that the new Apple Watch SE had run into production issues and was in “serious jeopardy.” So Apple may need to switch to an aluminum design for the new SE or stick with the current model for another year. The Apple Watch SE hasn’t been updated since 2022.Henry Burrell / Foundry Apple Watch Ultra 3: Release date, price and design Launch at September iPhone event: Tuesday September 9 (probable) Pre-order from September 12 On sale from September 19 We do expect that there will be an Apple Watch Ultra 3 in 2025. Rumors so far point to 5G and satellite texting capabilities, according to Bloomberg’s Mark Gurman. The Apple Watch Ultra 2025 might also be slimmer. And yes, the display might be even larger. In addition to that you can expect all the features coming to the Apple Watch Series 11. Apple Watch 2025: Features coming to the Apple Watch Apple has been working on incorporating a microLED display on the Apple Watch for several years, according to various leaks and rumors, but it’s not expected to debut on the Series 11 this year. Some think this display shift will come to Apple Watch Ultra first, but even that isn’t expected in 2025. Health We’re not expecting any new health sensors. Apple’s blood glucose monitoring initiative is still years from ending up in a watch, it seems, and while there have been rumors of high blood pressure monitoring, that’s apparently not quite ready to appear this year, either. Specs So what will be new? You can bet on a new S11 SiP (system in package), offering somewhat better performance. Don’t expect a big boost in battery life, though—that has been a near constant 18 hours for nearly the entire lifespan of the watch. Apple Intelligence and the new Siri could be one of the new features of watchOS 12 and the Apple Watch Series 11.Michael Simon/IDG Apple Intelligence AI is a key aspect of almost every new Apple product now, and rumors suggest Apple is working on bringing some AI features to the Apple Watch. The actual AI models probably won’t be able to run on the watch; rather, the watch will gather data, the processing will be done on your iPhone, and then the watch will display results or notifications. Many of the new AI features will likely debut as part of watchOS 12, but some might be kept as Series 11 exclusives in order to help differentiate the hardware. 5G and Satelite It’s also rumored that the Apple Watch Ultra 3 will support 5G cellular and satellite messaging, which could also come to the Series 11. Apple debuted a wireless chip in the iPhone 16e, but it’s not clear whether it’s small or efficient enough for the Apple Watch yet. No changes? The Series 11 could just be a ‘Series 10s’ We criticized the Apple Watch Series 8 for being largely the same as the Series 7, and the Series 9 for being almost identical to the Series 8. This looks like it could be another one of those years, where there are only very minor changes to the Apple Watch that you won’t even notice in everyday use. That doesn’t mean it’s a bad watch, or that Apple is in trouble. There is virtually no competition for the Apple Watch, and as long as Apple keeps pressing forward with watchOS software features, it’s likely to keep selling a lot of Apple Watches. But if you’re hoping for a major change to the Apple Watch hardware, you’ll probably have to wait another year or more.

Michael Giannou, Global General Manager, HoneywellMay 29, 20254 Min ReadAndriy Popov via Alamy StockData centers are quickly becoming the backbone of our information-driven world. At the same time, the increasing sophistication of cybersecurity attacks, combined with the growing frequency of extreme climate events, means there is also greater operational risk than ever before, as bad actors have begun targeting cooling centers to purposefully compromise the equipment, causing irreversible loss and damage.  The best defense against these threats is an integrated system centered around situational awareness and security. By taking steps to safeguard key areas, data center operators can enhance the protection of their facility and data, helping prevent costly threats and downtime.  Seeing the Big Picture Developing a comprehensive awareness and monitoring system serves as a critical first step to protecting data centers. This is especially important as data centers begin to welcome more tenants into shared space, requiring vendors to consider each tenant individually and as part of the broader system. A threat to one tenant can quickly become a threat to all tenants. Centralizing all information in one system provides a single location for operators to view and analyze real-time data, allowing them to instantly access critical information, monitor incidents and respond quickly with pre-defined incident workflows. An intelligent system will integrate all security events -- including video recordings, access point clearance and data reporting -- together in one place to reduce coverage gaps and information silos.  Related:Another benefit of having one comprehensive system is the ability to integrate separate aspects of the system to improve response time. For example, a centralized security system could be configured to ensure that any fire or intruder alarm immediately triggers the CCTV cameras in the vicinity of the alarm, so the security team can quickly and efficiently respond to the situation. Close partnering between systems that transcend departments such as security, IT and the management of employees, contractors and visitors is key to protecting the facility and its data, both in low-friction (e.g. office space) and high-friction (e.g. server space) areas.  Addressing the Gaps Once a centralized security system is in place, operators can address the cybersecurity gaps where the data center is most vulnerable to bad actors. A strong, always-on cybersecurity program should be tailored to the specific facility and its compliance needs, often including:  Data encryption: Whether data is stored in the system or just passing through, encryption is key to preventing unauthorized access. A strong encryption process goes beyond thwarting attacks -- it is critical for establishing trust, ensuring the authenticity of data exchanged, guaranteeing the integrity of commands to smart devices and maintaining secrecy where it is needed most.  Related:Network security: Data center operators can help prevent unauthorized access and cyberattacks by developing strong intrusion detection/prevention systems, firewalls and network segmentation. Facility protection: By integrating technologies such as electronic access control, biometrics, CCTV and perimeter detection, operators can maintain security around the physical facility. Security also requires vendors to adhere to standard operating procedures, often overlooked in today’s technology-focused environment, such as enforcing visitor security policies and requiring visitors to have escorts.  Regular audits and updates: It may seem to be a lower priority than the often-urgent concerns set forth above, but out-of-date firmware carries a significant cybersecurity risk. Proactive attention and system maintenance can reduce operating costs in the long run and help avoid costly downtime. Related:Looking Ahead With so many current considerations to focus on, data center operators must also look ahead to future-proof their facility. As quickly as the industry has grown in recent years, the momentum will likely continue to accelerate. One new frontier emerging is quantum security: using quantum-enhanced randomness to deliver truly unpredictable key generation and safeguard sensitive information. This enables the system to develop armor that evolves just as quickly as cybersecurity threats. As the largest companies make significant investment in data centers -- for example, Microsoft’s plan to invest approximately $80 billion in AI-enabled datacenters in FY25 -- many in the industry are watching to see how these companies’ actions and investments shape the future of both data centers and building security overall.  Another forward-looking trend is military-grade solutions entering the commercial and industrial marketplace. It is clear to understand how a system hardened for integrated perimeter security in harsh environments can also fit the security and resilience needs of a data center. In addition, those solutions have often been certified through rigorous testing and evaluation, giving operators confidence their system can withstand almost all third-party attacks.  Finally, the industry will begin to prioritize modularity -- meaning systems that can be added to in the future, will work with third-party solutions and are both user-friendly and energy-efficient. This allows operators to expand their facilities to include the latest and greatest technology without a costly overhaul of their existing infrastructure. By integrating with their business systems and leaning into wider stakeholder influence, organizations can more effectively monitor and manage their facilities using modular systems. Cybersecurity risks can never truly be considered resolved -- it is constantly evolving. But by continuously revisiting the areas detailed above, data center operators can enhance their facility and systems protections, helping to protect their data now and in the future.  About the AuthorMichael GiannouGlobal General Manager, HoneywellMichael Giannou is a global sales executive with over 15 years of experience leading high-performing teams and driving growth in the data center and technology sectors. As Global General Manager of Data Centers at Honeywell, he built and led a global sales team, delivering double-digit growth and now leads the company’s global data center vertical. Previously, at Schneider Electric, he grew division sales from $70M to $350M over six years. Known for transforming underperforming programs and developing trusted customer relationships, Michael is a strategic, growth-focused leader passionate about mentoring enterprise sales professionals.  See more from Michael GiannouWebinarsMore WebinarsReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like

The Netherlands is facing a growing cyber security crisis, with a staggering 66% of Dutch businesses lacking adequate cyber resilience, according to academic research.   As geopolitical tensions rise and digital threats escalate, Rick van der Kleij, a psychologist and professor in Cyber Resilient Organisations at Avans University of Applied Sciences, who also conducts research at TNO, says that traditional approaches have failed and a paradigm shift is urgently needed.  Van der Kleij suggests that cyber security provides the illusion of safety rather than actual protection for many Dutch organisations. His stark assessment is that the Netherlands’ traditional approach to cyber risk is fundamentally broken.  “We need to stop thinking in terms of cyber security. It’s a model that has demonstrably failed,” he says. “Despite years of investment in cyber security measures, the frequency and impact of incidents continue to increase rapidly across Dutch businesses.”  This reflects the central argument of his recent inaugural lecture “Now that security is no more”, where he called for a paradigm shift in how Dutch organisations approach cyber risks.  Van der Kleij describes “the great digital dilemma” of balancing openness and security in a country with one of Europe’s most advanced digital infrastructures. “How can entrepreneurs remain open and connected without having to completely lock down their businesses?” he asks.  The statistics are stark. Van der Kleij’s study found that 66% of Dutch businesses are inadequately prepared for cyber threats. Recent ABN Amro research confirms the crisis: one in five businesses suffered cyber crime damage last year, rising to nearly 30% among large companies. For the first time, SMEs (80%) are more frequently targeted than large corporations (75%), marking a significant shift in cyber criminal strategy.  Despite the numbers, a perception gap persists. Van der Kleij identifies ‘the overconfident’ – Dutch businesses believing their cyber security is adequate when it isn’t. While SME attack rates soar, their risk perception remains static, whereas large organisations show marked awareness increases (from 41% to 64%). This creates a “waterbed effect” – as large companies strengthen defences, cyber criminals shift to less-prepared SMEs which are paradoxically reducing cyber security investments.  Van der Kleij emphasises a crucial distinction: while cyber security focuses on preventing incidents, cyber resilience acknowledges that incidents will happen. “It’s about having the capacity to react appropriately, recover from incidents, and learn from what went wrong to emerge stronger,” he says.  This requires four capabilities – prepare, respond, recover and adapt – yet most Dutch organisations focus only on preparation. The ABN Amro findings confirm this: many SMEs have firewalls but lack intrusion detection or incident response plans. Large companies take a more balanced approach, combining technology with training, response capabilities and insurance.  Uber’s experience illustrates the weakness of purely technical approaches. After a 2016 hack, they implemented two-factor authentication – yet were hacked again in 2022 by an 18-year-old using WhatsApp social engineering. “This shows that investing only in technology without addressing human factors creates fundamental weakness, which is particularly relevant for Dutch businesses that prioritise technological solutions,” van der Kleij adds.  Van der Kleij challenges the persistent myth that humans are cyber security’s weakest link. “People are often blamed when things go wrong, but the actual vulnerabilities typically lie elsewhere in the system, often in the design itself,” he says.  The misdirection is reflected in spending: 85% of cyber security investments go toward technology, 14% toward processes and just 1% toward the human component. Yet the ABN Amro research shows phishing – which succeeds through psychological manipulation rather than sophisticated technology – affects 71% of Dutch businesses.  “We’ve known for decades that people aren’t equipped to remember complex passwords across dozens of accounts, yet we continue demanding this and then express surprise when they create workarounds,” van der Kleij says. “Rather than blaming users, we should design systems that make secure behaviour easier. In the Netherlands, we need more human awareness in security teams, not more security awareness training for end users.”  Why do so many Dutch SMEs fail to invest in cyber resilience despite evident risks? Van der Kleij believes it’s about behaviour, not business size. “It’s not primarily about size or industry – it’s about behaviour and beliefs,” he says.  Common limiting beliefs among Dutch entrepreneurs include “I’m too small to be a target” or “I don’t have confidential information”. Remarkably, even suffering a cyber attack doesn’t change this mindset. “Studies show that when businesses are hacked, it doesn’t automatically lead them to better secure their operations afterward,” van der Kleij says.  The challenge is reaching those who need help most. “We have vouchers, we have arrangements where entrepreneurs can get help at a significantly reduced fee from cyber security professionals, but uptake remains negligible,” van der Kleij says. “It’s always the same parties who come to the government’s door – the large companies who are already mature. The small ones, we just can’t seem to reach them.”  Van der Kleij sees “relational capital” – resources generated through partnerships – as key to enhancing Dutch cyber resilience. “You can become more cyber resilient by establishing partnerships,” he says, pointing to government-encouraged initiatives like Information Sharing and Analysis Centers.   The ABN Amro research reveals why collaboration matters: 39% of large companies experienced cyber incidents originating with suppliers or partners, compared with 25% of smaller firms. This supply chain vulnerability drives major Dutch organisations to demand higher standards from partners through initiatives such as Big Helps Small.  European regulations reinforce this trend. The new NIS2 directive will expand coverage from hundreds to several thousand Dutch companies, yet only 11% have adequately prepared. Among SMEs, approximately half have done little preparation – despite Dutch police warnings about increasingly frequent ransomware attacks where criminals threaten to release stolen data publicly.  Van der Kleij’s current research at Avans University focuses on identifying barriers to cyber resilience investment through focus groups with Dutch entrepreneurs. “When we understand these barriers – which are more likely motivational than knowledge-related – we can design targeted interventions,” he says.  Van der Kleij’s message is stark: “The question isn’t whether your organisation will face a cyber incident, but when – and how effectively you’ll respond. Cyber resilience encompasses cyber security while adding crucial capabilities for response, recovery and adaptation. It’s time for a new paradigm in the Netherlands.”  Read more about Dutch cyber security

Location: Springfield, VA / St. Louis, MO / Dayton, OH Salary: $180k-$250k (TS/SCI w Poly Required) Job Description: We are seeking an experienced Remote Sensing Algorithm Engineer who specializes in multiple modalities, sensor technologies and spectral phenomenology such as EO/IR, MSI, and HSI to support a large R&D program that is focused on providing advanced intelligence analysis for the benefit of the DoD and IC communities. Please note that we are not looking for an imagery analyst. The right candidate will have software engineering/software development skills coupled with an in depth background and understanding of sensor capabilities and expertise in processing collected data to derive meaningful insights. Responsibilities: collaborate with analysts and engineers to create innovative solutions that improve data exploitation capabilities. design and implement algorithms that enhance target detection, material identification, and geospatial analysis focus on integrating and optimizing software tools for end-users in the GEOINT and IMINT analytic community produce technical documentation Required Skills: Active TS/SCI Security Clearance Ability to develop, test, and validate remote sensing algorithms for geospatial applications. Strong programming skills in Python, ENVI/IDL, C++ and Matlab Background in remote sensing techniques, especially with hyperspectral image processing techniques and data calibration. Demonstrated experience developing tools for hyperspectral data analysis Effective collaboration skills to work with interdisciplinary teams across the DoD and IC Desired Bonus Skills: Advanced mathematical expertise in areas such as machine learning for spectral data. Background in physics-based modeling and signal processing for remote sensing applications. Familiarity with hyperspectral image calibration and preprocessing techniques. Hands-on experience with portable or handheld field instrumentation devices such as FTIRs, Hyperspectral imaging cameras, spectroradiometers, or spectrometers (e.g. ASD, SVC, D&P, Exoscan, Bayspec - OCI Series, Spectral Evolution - PSR Series, Headwall Photonics - Nano-Hyperspec, Micro-Hyperspec )Apply NowLet's start your dream job Apply now Meet JobCopilot: Your Personal AI Job HunterAutomatically Apply to Remote Full-Stack Programming JobsJust set your preferences and Job Copilot will do the rest-finding, filtering, and applying while you focus on what matters. Activate JobCopilot