By Jared Foretek ( January 6, 2025, 6:36 PM EST) — With just two weeks left before President-elect Donald Trump takes office and Republicans gain control of the Federal Communications Commission, a group of anti-Fox News advocates are calling out the commission for failing to hold any hearings on Fox’s Philadelphia affiliate’s license renewal…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Nvidia today unveiled Nvidia Project Digits, a personal AI supercomputer that provides AI researchers, data scientists and students worldwide with access to the power of the Nvidia Grace Blackwellplatform. Project Digits features the new Nvidia GB10 Grace Blackwell Superchip, offering a petaflop of AI computing performance for prototyping, fine-tuning and running large AI models. The company made the announcement during CEO Jensen Huang’s opening keynote at CES 2025, the big tech trade show in Las Vegas this week. With Project Digits, users can develop and run inference on models using their own desktop system, then seamlessly deploy the models on accelerated cloud or data center infrastructure. It is based on a “super secret chip called GB110, the smallest Blackwell we can make,” Huang said. Nvidia Project Digits “AI will be mainstream in every application for every industry. With Project Digits, the Grace Blackwell Superchip comes to millions of developers,” said Huang. “Placing an AI supercomputer on the desks of every data scientist, AI researcher and student empowers them to engage and shape the age of AI.” GB10 Superchip provides a petaflop of power-efficient AI performance The GB10 Superchip is a system-on-a-chip (SoC) based on the Nvidia Grace Blackwell architecture. It delivers up to one petaflop of AI performance at FP4 precision. GB10 features an Nvidia Blackwell GPU with latest-generation CUDA cores and fifth-generation Tensor Cores, connected via NVLink-C2C chip-to-chip interconnect to a high-performance Nvidia Grace CPU, which includes 20 power-efficient cores built with the Arm architecture. MediaTek, a market leader in Arm-based SoC designs, collaborated on the design of GB10, contributing to its best-in-class power efficiency, performance and connectivity. The GB10 Superchip enables Project Digits to deliver powerful performance using only a standard electrical outlet. Each Project Digits features 128GB of unified, coherent memory and up to 4TB of NVMe storage. With the supercomputer, developers can run up to 200-billion-parameter large language models. In addition, using Nvidia ConnectX networking, two Project Digits AI supercomputers can be linked to run up to 405-billion-parameter models. Grace Blackwell AI supercomputing within reach There are 72 Blackwell chips on this wafer. With the Grace Blackwell architecture, enterprises and researchers can prototype, fine-tune and test models on local Project Digits systems running Linux-based Nvidia DGX OS, and then deploy them seamlessly on Nvidia DGX Cloud, accelerated cloud instances, or data center infrastructure. This allows developers to prototype AI on Project Digits and then scale on cloud or data center infrastructure, using the same Grace Blackwell architecture and the Nvidia AI Enterprise software platform. Project Digits users can access an extensive library of Nvidia AI software for experimentation and prototyping, including software development kits, orchestration tools, frameworks and models available in the Nvidia NGC catalog and on the Nvidia Developer portal. Developers can fine-tune models with theNvidia NeMo framework, accelerate data science with Nvidia Rapids libraries and run common frameworks such as PyTorch, Python and Jupyter notebooks. To build agentic AI applications, users can also harness Nvidia blueprints and Nvidia NIM microservices, which are available for research, development and testing via the Nvidia Developer Program. When AI applications are ready to move from experimentation to production environments, the Nvidia AI Enterprise license provides enterprise-grade security, support and product releases of Nvidia AI software. Availability Project Digits is coming. Project Digits will be available in May from Nvidia and top partners, starting at $3,000. “By making Llama models open-source, we’re committed to democratizing access to cutting-edge AI technology. With Project Digits, developers can harness the power of Llama locally, unlocking new possibilities for innovation and collaboration,” said Ahmad Al-Dahle, head of GenAI at Meta, in a statement. “Advancing AI requires tools that empower researchers to experiment at scale, speed and precision. Nvidia’s Project Digits represents a significant leap forward. I’m excited to see how 128GB in such a small form factor can advance the future of enterprise AI,” said Silvio Savarese, chief scientist at Salesforce, in a statement. “At Hugging Face, we want to make it easy for developers to build their own AI,” said Je Boudier, head of product at Hugging Face, in a statement. “Nvidia’s Project Digits will empower AI builders to build and run their own Gen AI models and systems at the edge. With 128GB of unified memory, AI builders can run 200B parameter models locally, and connect multiple Project Digits systems to scale from there. I can’t wait to see what the Hugging Face community will build with Nvidia Project Digits.” “Nvidia’s Project Digits is a powerhouse you can hold in the palm of your hand. With two Project Digits units, developers can easily work with AI models up to 405B parameters in size. We can’t wait to see the apps people will build with this,” said Michael Chiang, cofounder of Ollama, in a statement. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle design to combine human expertise and contextual intelligence on one side with AI-based techniques on the other. “When automated red teaming is complemented by targeted human insight, the resulting defense strategy becomes significantly more resilient,” writes OpenAI in the first paper (Ahmad et al., 2024). The company’s premise is that using external testers to identify the most high-impact real-world scenarios, while also evaluating AI outputs, leads to continuous model improvements. OpenAI contends that combining these methods delivers a multi-layered defense for their models that identify potential vulnerabilities quickly. Capturing and improving models with the human contextual intelligence made possible by a human-in-the-middle design is proving essential for red-teaming AI models. Why red teaming is the strategic backbone of AI security Red teaming has emerged as the preferred method for iteratively testing AI models. This kind of testing simulates a variety of lethal and unpredictable attacks and aims to identify their most potent and weakest points. Generative AI (gen AI) models are difficult to test through automated means alone, as they mimic human-generated content at scale. The practices described in OpenAI’s two papers seek to close the gaps automated testing alone leaves, by measuring and verifying a model’s claims of safety and security. In the first paper (“OpenAI’s Approach to External Red Teaming”) OpenAI explains that red teaming is “a structured testing effort to find flaws and vulnerabilities in an AI system, often in a controlled environment and collaboration with developers” (Ahmad et al., 2024). Committed to leading the industry in red teaming, the company had over 100 external red teamers assigned to work across a broad base of adversarial scenarios during the pre-launch vetting of GPT-4 prior to launch. Research firm Gartner reinforces the value of red teaming in its forecast, predicting that IT spending on gen AI will soar from $5 billion in 2024 to $39 billion by 2028. Gartner notes that the rapid adoption of gen AI and the proliferation of LLMs is significantly expanding these models’ attack surfaces, making red teaming essential in any release cycle. Practical insights for security leaders Even though security leaders have been quick to see the value of red teaming, few are following through by making a commitment to get it done. A recent Gartner survey finds that while 73% of organizations recognize the importance of dedicated red teams, only 28% actually maintain them. To close this gap, a simplified framework is needed that can be applied at scale to any new model, app, or platform’s red teaming needs. In its paper on external red teaming OpenAI defines four key steps for using a human-in-the-middle design to make the most of human insights: Defining testing scope and teams: Drawing on subject matter experts and specialists across key areas of cybersecurity, regional politics, and natural sciences, OpenAI targets risks that include voice mimicry and bias. The ability to recruit cross-functional experts is, therefore, crucial. (To gain an appreciation for how committed OpenAI is to this methodology and its implications for stopping deepfakes, please see our article “GPT-4: OpenAI’s shield against $40B deepfake threat to enterprises.”) Selecting model versions for testing, then iterating them across diverse teams: Both of OpenAI’s papers emphasize that cycling red teams and models using an iterative approach delivers the most insightful results. Allowing each red team to cycle through all models is conducive to greater team learning of what is and isn’t working. Clear documentation and guidance: Consistency in testing requires well-documented APIs, standardized report formats, and explicit feedback loops. These are essential elements for successful red teaming. Making sure insights translate into practical and long-lasting mitigations: Once red teams log vulnerabilities, they drive targeted updates to models, policies and operational plans — ensuring security strategies evolve in lockstep with emerging threats. Scaling adversarial testing with GPT-4T: The next frontier in red teaming AI companies’ red teaming methodologies are demonstrating that while human expertise is resource-intensive, it remains crucial for in-depth testing of AI models. In OpenAI’s second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning” (Beutel et al., 2024), OpenAI addresses the challenge of scaling adversarial testing using an automated, multi-pronged approach that combines human insights with AI-generated attack strategies. The core

If you’ve ever called a company and been greeted by a friendly automated voice offering you a menu of options, you’ve encountered an auto attendant. Without a live agent, this automated system directs you to the right department or person by having you select options from a menu. Multi-level auto attendants enhance the caller experience by offering a sophisticated and in-depth menu system, fully customized to a customer’s needs. Unlike standard attendants with a single layer of options, multi-level systems provide a hierarchy of choices, allowing callers to drill down to the exact service or department they need. This results in more precise call routing, decreased wait times, and reduced caller frustration. While the benefits of multi-level auto attendants are clear, the question remains: is it worth upgrading to this advanced system? For some companies, overhauling their current auto attendant may not seem necessary, especially at a higher cost. To make the best decision for your team, we’ll explore the features and advantages of multi-level auto attendants and important considerations to keep in mind if you decide to take the plunge. Note: RingCentral is one of the few top business phone services that includes a multi-level auto attendant with every plan. Check out our RingCentral review to learn more.  1 RingCentral RingEx Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Medium (250-999 Employees), Large (1,000-4,999 Employees), Enterprise (5,000+ Employees) Medium, Large, Enterprise Features Hosted PBX, Managed PBX, Remote User Ability, and more 2 Talkroute Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Any Company Size Any Company Size Features Call Management/Monitoring, Call Routing, Mobile Capabilities, and more 3 CloudTalk Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Any Company Size Any Company Size Features 24/7 Customer Support, Call Management/Monitoring, Contact Center, and more Why companies outgrow basic auto attendants Basic auto attendants are often customers’ first point of contact with a business. Their main responsibility is to efficiently route calls to the appropriate line. They greet callers with a pre-recorded message and a simple menu of options, like “For sales, press 1. For customer service, press 2,” ensuring customers are able to direct their call where it needs to go. This is invaluable for both remote and in-house customer service teams. It streamlines call handling, reduces the need for a live receptionist, and ensures a professional first impression to callers. However, as businesses shift and scale in size, the simplicity of basic auto attendants can limit organizations — the lack of flexibility fails to adapt to more complex organizational structures or varied customer needs. For example, a basic auto attendant cannot help a company accommodate multiple languages or enable callers to make payments over the phone. Both of these require the ability to create a phone tree with more than one level of options. For growing companies, this can lead to several specific issues: Inadequate call routing: With only a basic menu, calls may not be directed to the right department, leading to customer frustration and longer call times. Limited personalization: Basic systems don’t offer personalized experiences based on caller history or specific needs, missing an opportunity to improve customer satisfaction. Scalability issues: As a company expands, it might outgrow the capabilities of a basic auto attendant, leading to poor program functionality. Increased hold times: With all calls funneled through a single, simplistic menu, callers might experience longer hold times, which means more dropped calls. Thinking it might be time to ditch the basic auto attendant? Here are some telltale signs that your company would benefit from a multi-level auto attendant upgrade: Increased call volume: If your contact center is receiving more calls than it can efficiently handle, it’s time to consider a multi-level system. These systems allow for greater call routing options and ensure every caller is directed to the right agent. Frequent caller complaints: If customers often express frustration about getting lost in your call menu or not reaching the right department, it’s a clear indicator. High call abandonment rates: An increase in callers hanging up before reaching their desired destination suggests your current system may be too cumbersome. Employee feedback: If your staff report difficulties managing incoming calls or an increase in misrouted calls, your current system might fall short. Upgrading to a multi-level auto attendant can help you fix high call center queuing times by better handling incoming requests and offering callers a wider range of self-service options, which can further take the heat of busy agents. Advantages of a multi-level auto attendant A multi-level auto attendant functions like an Interactive Voice Response (IVR) system instead of a single-level phone menu. The key difference between IVR and auto attendants is that an IVR connects to a database and allows callers to accomplish simple actions themselves, such as checking an account balance or paying a bill. SEE: Discover why customers and agents love IVR systems.  A multi-level auto attendant offers the following advantages over a basic system: Efficient navigation: Callers can quickly navigate through menus carefully designed with IVR scripts and get directed to the appropriate department or agent in record times. Tailored call flows: Multi-level solutions allow for deep customization, enabling businesses to craft call flows that reflect their structure and customer interaction needs, adding a personal touch. Streamlined operations: Multi-level auto attendants automate call routing, cutting down on transfers and wait times, which boosts contact center efficiency and speeds up issue resolution. Professional image: Incorporating a multi-level system into your organization enhances your business’s image and makes customers feel comfortable interacting with your team. Scalable growth: With the scalable nature of VoIP services, integrating multi-level auto attendant features allows for easy expansion of services or locations, keeping pace with business growth. The most salient benefit of transitioning to a multi-level auto attendant system is enhancing the overall customer experience by improving call flows. This technology makes the first point of contact with your company more efficient,

Overview How has the advent of robotics driven the vehicle manufacturing arena into high gear? And in a nation with challenges as unique as in India, will self-driving vehicles remain a pipe dream? Anand Deodhar, Group CIO of Force Motors, gives us a technology ‘test drive’, in this episode of CIO Leadership Live. Register Now source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to pick out the best output to make a decision on.  Another part of the secret sauce? Retrieval-augmented generation (RAG), which allows agents to store and reuse knowledge efficiently, is getting better. Imagine a travel agent bot that not only plans trips but books flights and hotels in real time based on updated preferences and budgets. Takeaway: Businesses need to identify use cases where agents can provide high ROI — be it in customer service, sales, or internal workflows. Tool use and advanced reasoning capabilities will define the winners in this space. 2. Evals: the foundation of reliable AI Evaluations, or “evals,” are the backbone of any robust AI deployment. This is the process of choosing which LLM — among the hundreds now available — to use for your task. This is important for accuracy, but also for aligning AI outputs with enterprise goals. A good eval ensures that a chatbot understands tone, a recommendation system provides relevant options, and a predictive model avoids costly errors. For example, a company’s eval for a customer-support chatbot might include metrics for average resolution time, accuracy of responses, and customer satisfaction scores. A lot of companies have been investing a lot of time into processing inputs and outputs so that they conform to a company’s expectations and workflows, but this can take a lot of time and resources. As models themselves get better, many companies are saving effort by relying more on the models themselves to do the work, so picking the right one gets more important. And this process is forcing clear communication and better decisions. When you “get a lot more conscious of how to evaluate the output of something and what it is that you actually want, not only does that make you better with LLMs and AI, it actually makes you better with humans,” said Witteveen.  “When you can clearly articulate to a human: This is what I want, here’s how I want it to look like, here’s what I’m going to expect in it. When you get really specific about that, humans suddenly perform a lot better.”  Witteveen noted that company managers and other developers are telling him: “Oh, you know, I’ve gotten much better at giving directions to my team just from getting good at prompt engineering or just getting good at, you know, looking at writing the right evals for models.” By writing clear evals, businesses force themselves to clarify objectives — a win for both humans and machines. Takeaway: Crafting high-quality evals is essential. Start with clear benchmarks: response accuracy, resolution time, and alignment with business objectives. This ensures that your AI not only performs but aligns with your brand’s values. 3. Cost efficiency: scaling AI without breaking the bank AI is getting cheaper, but strategic deployment remains key. Improvements at every level of the LLM chain are bringing dramatic cost reductions. Intense competition among LLM providers, and from open-source rivals, is leading to regular price cuts. Meanwhile, post-training software techniques are making LLMs more efficient. Competition from new hardware vendors such as Groq’s LPUs, and improvements by the legacy GPU provider Nvidia, are dramatically reducing inference costs, making AI accessible for more use cases. The real breakthroughs come from optimizing the way models are put to work in applications, which is the time of inference, rather than the time of training, when models are first built using data. Other techniques like model distillation, along with hardware innovations, mean companies can achieve more with less. It’s no longer about whether you can afford AI — you can do most projects much less expensively this year than even six months ago — but how you scale it. Takeaway: Conduct a cost-efficiency analysis for your AI projects. Compare hardware options and explore techniques like model distillation to cut costs without compromising performance. 4. Memory personalization: tailoring AI to your users Personalization is no longer optional — it’s expected. In 2025, memory-enabled AI systems are making this a reality. By remembering user preferences and past interactions, AI can deliver more tailored and effective experiences. Memory personalization isn’t widely or openly discussed because users often feel uneasy about AI applications storing personal information to enhance service. There are privacy

OpenAI has announced that its primary focus for the coming year will be on developing “superintelligence,” according to a blog post from Sam Altman. This has been described as AI with greater-than-human capabilities. While OpenAI’s current suite of products has a vast array of capabilities, Altman said that superintelligence will enable users to perform “anything else.” He highlights accelerating scientific discovery as the primary example, which, he believes, will lead to the betterment of society. “This sounds like science fiction right now, and somewhat crazy to even talk about it. That’s alright—we’ve been there before and we’re OK with being there again,” he wrote. The change of direction has been spurred by Altman’s confidence in his company now knowing “how to build AGI as we have traditionally understood it.” AGI, or artificial general intelligence, is typically defined as a system that matches human capabilities, whereas superintelligence exceeds them. SEE: OpenAI’s Sora: Everything You Need to Know Altman has eyed superintelligence for years — but concerns exist OpenAI has been referring to superintelligence for several years when discussing the risks of AI systems and aligning them with human values. In July 2023, OpenAI announced it was hiring researchers to work on containing superintelligent AI. The team would reportedly devote 20% of OpenAI’s total computing power to training what they call a human-level automated alignment researcher to keep future AI products in line. Concerns around superintelligent AI stem from how such a system could prove impossible to control and may not share human values. “We need scientific and technical breakthroughs to steer and control AI systems much smarter than us,” wrote OpenAI Head of Alignment Jan Leike and co-founder and Chief Scientist Ilya Sutskever in a blog post at the time. SEE: OpenAI and Anthropic Sign Deals With U.S. AI Safety Institute But, four months after creating the team, another company post revealed they “still (did) not know how to reliably steer and control superhuman AI systems” and didn’t have a way of “preventing (a superintelligent AI) from going rogue.” In May, OpenAI’s superintelligence safety team was disbanded and several senior personnel left due to the concern that “safety culture and processes have taken a backseat to shiny products,” including Jan Leike and the team’s co-lead Ilya Sutskever. The team’s work was absorbed by OpenAI’s other research efforts, according to Wired. Despite this, Altman highlighted the importance of safety to OpenAI in his blog post. “We continue to believe that the best way to make an AI system safe is by iteratively and gradually releasing it into the world, giving society time to adapt and co-evolve with the technology, learning from experience, and continuing to make the technology safer,” he wrote. “We believe in the importance of being world leaders on safety and alignment research, and in guiding that research with feedback from real world applications.” More must-read AI coverage The path to superintelligence may still be years away There is disagreement about how long it will be until superintelligence is achieved. The November 2023 blog post said it could develop within a decade. But nearly a year later, Altman said it could be “a few thousand days away.” However, Brent Smolinski, IBM VP and global head of Technology and Data Strategy, said this was “totally exaggerated,” in a company post from September 2024. “I don’t think we’re even in the right zip code for getting to superintelligence,” he said. AI still requires much more data than humans to learn a new capability, is limited in the scope of capabilities, and does not possess consciousness or self-awareness, which Smolinski views as a key indicator of superintelligence. He also claims that quantum computing could be the only way we might unlock AI that surpasses human intelligence. At the start of the decade, IBM predicted that quantum would begin to solve real business problems before 2030. SEE: Breakthrough in Quantum Cloud Computing Ensures its Security and Privacy Altman predicts AI agents will join the workforce in 2025 AI agents are semi-autonomous generative AI that can chain together or interact with applications to carry out instructions or make decisions in an unstructured environment. For example, Salesforce uses AI agents to call sales leads. TechRepublic predicted at the end of the year that the use of AI agents will surge in 2025. Altman echoes this in his blog post, saying “we may see the first AI agents ‘join the workforce’ and materially change the output of companies.” SEE: IBM: Enterprise IT Facing Imminent AI Agent Revolution According to a research paper by Gartner, the first industry agents to dominate will be software development. “Existing AI coding assistants gain maturity, and AI agents provide the next set of incremental benefits,” the authors wrote. By 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, according to the Gartner paper. A fifth of online store interactions and at least 15% of day-to-day work decisions will be conducted by agents by that year. “We are beginning to turn our aim beyond that, to superintelligence in the true sense of the word,” Altman wrote. “We’re pretty confident that in the next few years, everyone will see what we see, and that the need to act with great care, while still maximizing broad benefit and empowerment, is so important.” source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Nvidia today announced that its autonomous vehicle (AV) platform, Nvidia Drive AGX Hyperion, has passed industry-safety assessments by TÜV SÜD and TÜV Rheinland — two of the industry’s foremostauthorities for automotive-grade safety and cybersecurity. This achievement raises the bar for AV safety, innovation and performance, Nvidia said during the keynote speech by Nvidia CEO Jensen Huang at CES 2025, the big tech trade show in Las Vegas this week. Drive Hyperion is the industry’s end-to-end autonomous driving platform. It includes the Drive AGX system-on-a-chip (SoC) and reference board design, the Nvidia DriveOS automotive operating system, a sensor suite, and an active safety and level 2+ driving stack. Automotive safety pioneers such as Mercedes-Benz, JLR and Volvo Cars are adopting the platform, which is designed to be modular, so customers can easily use what they need. It is also scalable and built to be upgradeable and compatible across future DRIVE SoC generations. Available in the first half of this year, the latest iteration of Drive Hyperion — designed for both passenger and commercial vehicles — will feature the high-performance Drive AGX Thor SoC built on the Nvidia Blackwell architecture. “A billion vehicles driving trillions of miles each year move the world. With autonomous vehicles — one of the largest robotics markets — now here, the Nvidia Blackwell-powered platform will shift this revolution into high gear,” said Huang. “The next wave of autonomous machines will rely on physical AI world foundation models to understand and interact with the real world, and Nvidia Drive is purpose-built for this new era, delivering unmatched functional safety and AI.” Driving safety forward: certified assurance for next-gen vehicles Nvidia Thor AI chip platform Next-generation vehicles will be increasingly software-defined, capable of receiving new features and functionality over their lifetime. Tapping into Nvidia’s 15,000 engineering years invested in vehicle safety, Drive Hyperion will help ensure advanced automotive systems with rich, AI-based functionalities are compliant with the automotive industry’s stringent functional safety and cybersecurity standards. Nvidia recently received safety certifications and assessments from accredited third parties, including: ● TÜV SÜD, which granted the ISO 21434 Cybersecurity Process certification to Nvidia for automotive SoC, platform and software engineering processes. Additionally, Nvidia DriveOS 6.0 conforms toISO 26262 Automotive Safety Integrity Level (ASIL) D standards, pending certification release.● TÜV Rheinland, which performed an independent United Nations Economic Commission for Europe (UNECE) safety assessment of Nvidia Drive AV related to safety requirements for complex electronicsystems. In addition, Nvidia is now accredited by the ANSI National Accreditation Board (ANAB) to provide safety and cybersecurity inspections for Nvidia Drive ecosystem partners. The new Nvidia Drive AI Systems Inspection Lab will help the Nvidia Drive automotive ecosystem build autonomous driving software that meets the industry’s evolving safety and AI standards. Nvidia is the first platform company to receive a comprehensive set of third-party assessments for its automotive technologies — including the Nvidia Drive end-to-end self-driving platform, spanning SoC, OS, sensor architecture and level 2+ application software — as well as independent accreditation as an AI systems safety and cybersecurity inspection lab for the automotive market. Intelligence powered by industry-leading compute Thus Nvidia has won a key accreditation for its autonomous tech. Nvidia Drive Thor, the core computer for Drive Hyperion, is the successor to the production-proven Nvidia Drive Orin. Its architecture compatibility and scalability means developers can use existing software from earlier Drive product generations, as well as integrate future updates, to achieve seamless development pipelines. Drive Thor is based on the Nvidia Blackwell architecture and is optimized for the most demanding processing workloads, including those involving generative AI, vision language models and large language models. Its simplified architecture enhances generalization, reduces latency and boosts safety by harnessing powerful Nvidia accelerated computing to run the end-to-end AV stack and a proven safety stack in parallel. Drive Thor paves the way for the next era of AV technology, known as AV 2.0, which involves delivering humanlike autonomous driving capabilities for navigating the most complex roadway scenarios. In addition to the Drive AGX in-vehicle computer, two other Nvidia computers serve as the foundation for automotive-grade AV development: Nvidia DGX systems for training advanced AI models and building a robust AV software stack in the cloud, and the Nvidia Omniverse platform running on Nvidia OVX systems for simulation and validation. These three computers, now enhanced with the new Nvidia Cosmos world foundation model platform, are set to accelerate end-to-end AV development and mass deployment. source

TotalAV and Surfshark both offer affordable pricing plans that combine VPN tools with antivirus software, leading many potential buyers to weigh the pros and cons of TotalAV VPN and Surfshark. To help you choose the best VPN for your business or personal use, I’ve reviewed both Surfshark and TotalAV VPN and compared them below. TotalAV VPN: Best for a simple VPN bundled with antivirus protection and a password manager. Surfshark: Best for a highly-rated VPN that offers fast speeds and an explicit no-logs policy. 1 Semperis Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Large (1,000-4,999 Employees), Enterprise (5,000+ Employees) Large, Enterprise Features Advanced Attacks Detection, Advanced Automation, Anywhere Recovery, and more 2 ESET PROTECT Advanced Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Any Company Size Any Company Size Features Advanced Threat Defense, Full Disk Encryption , Modern Endpoint Protection, and more 3 NordLayer Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Small (50-249 Employees), Medium (250-999 Employees), Large (1,000-4,999 Employees), Enterprise (5,000+ Employees) Small, Medium, Large, Enterprise TotalAV VPN vs Surfshark: Comparison table TotalAV VPN Surfshark # of servers 50 100 # of countries 36 3,200+ # of devices (VPN only) 6-8 depending on plan Unlimited VPN on all plans No Yes Antivirus on all plans Yes No Split tunneling No Yes Dedicated IP address No Yes Starting price for one-year plan with both VPN and antivirus $3.25 per month ($39 per year) $2.99 per month ($35.88 per year) TotalAV VPN vs Surfshark: Pricing Both TotalAV and Surfshark offer three tiers of pricing plans, but there are some key differences between them. Most importantly, TotalAV only offers a VPN on its two most expensive plans, while antivirus protection is available on all three. Surfshark’s plans offer the inverse: antivirus protection is only available on its two most expensive plans, while the VPN is available on all three. SEE: Everything You Need to Know about the Malvertising Cybersecurity Threat (TechRepublic Premium) TotalAV VPN pricing TotalAV offers three yearly subscription plans. The first tier, TotalAV VPN, doesn’t include a VPN, only antivirus protection, so it’s not a good comparison to Surfshark. The two more expensive tiers do include a VPN in addition to other features. Monthly, quarterly, and biannual subscriptions are also available, but you must contact TotalAV for pricing information. The pricing for each yearly subscription is as follows: TotalAV Antivirus Pro: $29 for the first year and up to 4 devices. TotalAV Internet Security: $39 for the first year and up to 6 devices. TotalAV Total Security: $49 for the first year and up to 8 devices. TotalAV does offer a free trial through the Google Play Store, meaning that only Android users can take advantage of it. This means iPhone users won’t get the benefit of a free trial. To sign up for the trial, you must first download the app to your device through the Google Play Store. After making an account, you must select a premium paid plan, and then choose the option for the seven-day free trial. TotalAV also offers a 30-day money-back guarantee for the annual subscription and a 14-day money-back guarantee for the monthly, quarterly, or biannual subscriptions. However, some users report difficulty with getting a refund if they change their minds. See the section “Surfshark vs TotalAV VPN on Reddit” below for first-hand user accounts. For more general information about VPN pricing, see our article that explains how much a VPN costs on average. Surfshark pricing Surfshark offers three levels of plans for individuals. Surfshark Starter includes the VPN plus ad and cookie pop-up blockers but not antivirus protection, so it’s not an exact comparison to TotalAV. Surfshark One adds real-time breach alerts and antivirus protection, while Surfshark One+ includes data removal. The pricing for each individual subscription is as follows: 24 months: Surfshark Starter: $1.99 per month + 4 extra months. Surfshark One: $2.49 per month + 4 extra months. Surfshark One+: $3.99 per month + 4 extra months. 12 months: Surfshark Starter: $2.99 per month + 4 extra months. Surfshark One: $3.39 per month + 4 extra months. Surfshark One+: $5.99 per month + 4 extra months. Month-to-month: Surfshark Starter: $15.45 per month. Surfshark One: $17.95 per month. Surfshark One+: $20.65 per month. Surfshark offers both a seven-day free trial and a 30-day money-back guarantee, but some strings are attached to both of them. The seven-day free trial can be accessed via both the App Store and the Google Play Store, and follows steps similar to the free trial sign-up for TotalAV. Surfshark also offers a 30-day money-back guarantee. However, the terms of service state that subscriptions made via iTunes/App Store/Amazon, with a prepaid card/gift card, or an anonymous Dedicated IP option are not eligible for the 30-day money-back guarantee. For more information, check out the full Surfshark review and the guide that explains how to use Surfshark. More cloud security coverage Surfshark vs TotalAV VPN: Feature comparison VPN Winner: Surfshark Surfshark is widely regarded as one of the better VPNs on the market today, supporting unlimited simultaneous devices while providing affordable pricing plans. It also offers more than 3,200 servers across 100 countries, making it possible to quickly connect to servers around the world. It has a strict no-logs policy and offers multiple security protocols: WireGuard, OpenVPN, and IKEv2 VPN. During my testing, I found using Surfshark’s VPN to be easy and fast, on par with NordVPN and other top VPNs that I’ve tested. The Surfshark VPN interface is relatively simple to navigate. Image: Surfshark Meanwhile, TotalAV’s VPN is a bit of an afterthought compared to its antivirus software, and it shows. TotalAV only has servers in 30 countries, so only a third of the locations that Surfshark covers. Furthermore, TotalAV only has 50 servers total compared to Surfshark’s 3,200 servers, which is a huge difference. This smaller server network can lead to longer load times, which is a

The new RISE with SAP on IBM Power Virtual Server offering, he said, “seems like a practical option for companies already using SAP on IBM. It provides a clear path to the cloud, promising to transition SAP S/4HANA workloads in just 90 days, with potential cost savings of 30% based on IBM’s own experience.” It does, added Kramer, also “include extended support options, helping businesses prepare for the end of SAP support. The familiarity of SAP and IBM Power systems makes the shift less daunting, though adoption will depend on factors such as the company’s current SAP setup, budget, and readiness for cloud migration. Change management and data quality will be key areas to address during the transition.” Bickley said the reference to IBM Consulting as a potential systems integration (SI) partner, “further allows IBM to embed themselves in the SAP partner network as an infrastructure provider. The reality is that IBM’s cloud business never quite took off, so this is really chasing the ‘tag ends.’  Most of this business will flow to Azure or AWS, with the leftovers dropping to Google Cloud Platform (GCP) and now IBM.” source