logo
NFWare Named Network Virtualization Innovation Gold Winner in 2025 Juniper Research Telco Innovation Awards

NFWare Named Network Virtualization Innovation Gold Winner in 2025 Juniper Research Telco Innovation Awards

Award recognizes role NFWare's virtualization stack plays in delivering CGNAT solutions that set industry records by delivering more than 400 Gbps throughput
'Winning this award gives us another opportunity to show the industry that virtualized CGNAT can reach the highest possible performance levels.' — Alexandra Yartseva, NFWare CMO
WALNUT CREEK, CA, UNITED STATES, January 30, 2025 / EINPresswire.com / -- NFWare today announced it was named Gold Winner of the Network Virtualization Innovation of the Year award in the Juniper Research Telco Innovation Awards.
The Telco Innovation Awards was begun in 2020 to recognize excellence and innovation in the telecom ecosystem. Each award entry is assessed and then voted on by a panel of Juniper Research analysts.
Virtualization Stack Sets CGNAT Performance Record
NFWare submitted its virtualized CGNAT solution that has achieved an industry high 400 Gbps throughput on a single Intel® architecture-based server in real-world deployments, revolutionizing how operators handle high traffic volumes.
This performance level is due to NFWare's proprietary multi-core networking stack, a technology developed by the company. This virtualization foundation leverages advanced multi-core parallelization algorithms and acts as a scalable base for high-performance applications. Designed to run on x86 servers, its flexible architecture supports various NICs and CPUs, ensuring optimal application performance.
'Even now we get comments doubting the performance potential of virtualized applications. Winning this award gives us another opportunity to show the industry that virtualized CGNAT can reach the highest possible performance levels,' said Alexandra Yartseva, CMO of NFWare. 'We're honored to have our solution win the Gold Winner award in the face of industry leading competitors.'
'NFWare's groundbreaking performance really impressed the judges and made them a great choice for Gold Winner of the Network Virtualization Innovation of the Year,' said Sam Barker, VP of Telecoms Market Research at Juniper Research. 'The company's approach to virtualization embodies the spirit of innovation that is the reason we started these awards.'
About NFWare
NFWare, Inc. is an innovative network software vendor that supplies internet service providers, telecom operators and data centers with super-fast virtualized CGNAT solutions for their networks. NFWare software-based NFV technology provides a level of proprietary hardware. NFWare was established in 2014 by experienced professionals in telecommunications, computer networking, and virtualization technologies. For more information, visit www.nfware.com.
NFWare
X
Legal Disclaimer:

Orange background

Try Our AI Features

Explore what Daily8 AI can do for you:

Comments

No comments yet...

Related Articles

I Optimized My Self-Care Routine Using AI. It Had Some Interesting Ideas
I Optimized My Self-Care Routine Using AI. It Had Some Interesting Ideas

CNET

timea day ago

  • CNET

I Optimized My Self-Care Routine Using AI. It Had Some Interesting Ideas

The term "self-care" may be one of the most overused buzz hyphenates on the internet. All sorts of activities can fall into the category of "self-care," casting a net wide enough to encompass everything from washing your face to finding ways to relax. Ironically, falling behind in your self-care activities can serve as a source of anxiety for the already stressed population, of which so many of us are well-worn card-carrying members, trying to quiet the riot inside our heads. Bubble bathing, face masking and attending trendy exercise classes in the pursuit of a better quality of life are common solutions, but keeping up with them is a whole separate battle. Here's how you can use AI to create a flexible, realistic self-care plan and keep yourself on track when you'd rather soothe via doom scrolling or consumption of substances that are not prepared, or conceived, with care in mind. Invest in feeling awesome Since the realm of self-care is so broad, using an AI tool to discover stuff that might enhance your calm with the push of a button can keep you focused. I used Gemini's Deep Research tool to give me a comprehensive list of products that claim to have self-care benefits instead of wading through wave after wave of targeted junk, or wondering which massager won't fly out of your hands when slicked up with calming essential oils. Deep Research was good for this one because I got a preview of the parameters under which the tool would create its findings, perfect when you need to be specific about exclusions like simple face wash. Deep Research allows for revisions on search parameters, no matter how creepy, before you start the results gathering process. Google Gemini / Screenshot by CNET This is not the primary use case of Deep Research, but when you're dealing with slippery essential oils, you can never have too much insight. Google Gemini / Screenshot by CNET Take a little look-see inside yourself Humans are notoriously lousy at unpracticed, unfocused introspection, but the cold, calculating zeroes and ones of an AI tool like ChatGPT can offer you an outside perspective on what you're capable of doing with any regularity in the self-care department. Provide an honest overview of your lifestyle, including any work, school or life commitments (like my hypothetical clown school in the example below), to give the tool the best possible chance at recommending strategies for self-care that keep you invested. ChatGPT / Screenshot by CNET Google Gemini / Screenshot by CNET When you can't keep an appointment to save your under-cared life A little self-care every once in a while is great, even if it's just sporadic, but what you really want for maximum relaxation impact is a ritual or at least a menu of activities designed to peak your well-being. ChatGPT gave me a breakdown of all the things I can do on a daily, weekly, monthly and yearly basis to take my self-care game to the next level over a gradual period of time, giving me seasonal self-care events to look forward to. The Winter schedule would be particularly useful if you're trying to make some positive changes for the new year. ChatGPT / Screenshot by CNET Self-care activities are best when they not only nourish your body and mind but also set your soul on fire. Give AI tools a chance to help you discover what gets you going, or if you're already aware, resist the urge to lie or be shy. BONUS: Anonymity and the ability to cast shame aside is one of the best things about using AI tools for private matters you might need to consult a human about, like a wellness coach or personal trainer. Letting your freak flag fly in the pursuit of happiness is a lot easier when you're unburdening yourself to a machine. You may have been holding back on starting a solid self-care routine activity you're embarrassed about because of what those other pesky humans might think. Screw 'em! The robot listens and doesn't judge. ChatGPT was more than up for the task. It also remembers whatever info you've fed into it already, so it matched up my clown schooling with some clown-themed activities: ChatGPT / Screenshot by CNET ChatGPT / Screenshot by CNET It must stay private.

Q4 Platform Voted ‘Favorite New Product: Financial Services' in 2025 American Business Awards
Q4 Platform Voted ‘Favorite New Product: Financial Services' in 2025 American Business Awards

Business Wire

time2 days ago

  • Business Wire

Q4 Platform Voted ‘Favorite New Product: Financial Services' in 2025 American Business Awards

TORONTO--(BUSINESS WIRE)--Q4 Inc., the leading provider of IR Ops software, has won a People's Choice Stevie ® Award in the 23rd annual American Business Awards ®, the U.S.A.'s premier business awards program. Q4 customers and other members of the public voted the AI-powered Q4 Platform as 'Favorite New Product: Financial Services,' for its ability to drive IR productivity and performance. 'This honor is especially meaningful because it represents the voice of our most valued audience: our customers,' said Q4 CEO Darrell Heaps. 'We're proud of the impact our AI is having — helping IR teams cut through complexity, uncover insights faster, and focus on what matters most: driving stronger investor relationships and long-term company value. The award celebrates our customers' successes and underscores our commitment to continued AI innovation.' More than 11,000 votes were cast in the People's Choice portion of the American Business Awards (ABAs) — honoring new solutions and services delivering real-world results. This recognition also adds to Q4's strong showing at this year's ABAs. Expert judges named the Q4 Platform a winner for 'New Product: Financial Services' and 'New Technology: AI Solution: Financial' — calling it a 'game-changer' and 'impressive AI-driven solution that addresses the complexities of investor relations.' Transforming IR with AI These wins come as Q4 further demonstrates how its AI helps IR teams reimagine their workflows and supercharge results. This week at NIRI2025, the premier event for IR professionals, Q4 previewed its latest agentic AI innovation. To learn more about how Q4's AI, purpose-built for IR, unleashes productivity and strengthens outcomes, please visit the Q4 site. About Q4 Inc. Q4 Inc. is the leading provider of IR Ops software with the world's largest set of proprietary investor data, purpose-built to remove obstacles between public companies and their investors. Q4 gives investor relations leaders, C-suite executives, and their teams the tools to attract, manage, and understand investors — all in one place. The AI-enabled Q4 Platform boasts applications for website and event management, engagement analytics, and overall lifecycle management, including AI Earnings Co-Pilot to generate draft scripts based on historical data, and AI earnings call summaries to understand peer sentiment. The Q4 Platform also includes a streamlined investor CRM and shareholder intelligence with enhanced metrics to elevate investor targeting strategies. Q4 delivers the data, insights, and workflows that give IR teams the power to focus on what really matters: strategy, relationships, and driving premium valuations for their companies. Headquartered in Toronto, with offices in New York and London, Q4 is a trusted partner to more than 2,600 public companies globally, including many of the most respected brands in the world. The company maintains an award-winning culture where team members grow and thrive. Learn more at

Future Forecasting The Yearly Path That Will Advance AI To Reach AGI By 2040
Future Forecasting The Yearly Path That Will Advance AI To Reach AGI By 2040

Forbes

time2 days ago

  • Forbes

Future Forecasting The Yearly Path That Will Advance AI To Reach AGI By 2040

Future forecasting the yearly path of advancing todays to AGI by 2040. In today's column, I am continuing my special series on the likely pathways that will get us from conventional AI to the avidly sought attainment of AGI (artificial general intelligence). AGI would be a type of AI that is fully on par with human intellect in all respects. I've previously outlined seven major paths that seem to be the most probable routes of advancing AI to reach AGI (see the link here). Here, I undertake an analytically speculative deep dive into one of those paths, namely I explore the year-by-year aspects of the considered most-expected route, the linear path. Other upcoming postings will cover each of the other remaining paths. The linear path consists of AI being advanced incrementally, one step at a time until we arrive at AGI. Let's talk about it. This analysis of an innovative AI breakthrough is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here). First, some fundamentals are required to set the stage for this weighty discussion. There is a great deal of research going on to further advance AI. The general goal is to either reach artificial general intelligence (AGI) or maybe even the outstretched possibility of achieving artificial superintelligence (ASI). AGI is AI that is considered on par with human intellect and can seemingly match our intelligence. ASI is AI that has gone beyond human intellect and would be superior in many if not all feasible ways. The idea is that ASI would be able to run circles around humans by outthinking us at every turn. For more details on the nature of conventional AI versus AGI and ASI, see my analysis at the link here. We have not yet attained AGI. In fact, it is unknown as to whether we will reach AGI, or that maybe AGI will be achievable in decades or perhaps centuries from now. The AGI attainment dates that are floating around are wildly varying and wildly unsubstantiated by any credible evidence or ironclad logic. ASI is even more beyond the pale when it comes to where we are currently with conventional AI. Right now, efforts to forecast when AGI is going to be attained consist principally of two paths. First, there are highly vocal AI luminaires making individualized brazen predictions. Their headiness makes outsized media headlines. Those prophecies seem to be coalescing toward the year 2030 as a targeted date for AGI. A somewhat quieter path is the advent of periodic surveys or polls of AI experts. This wisdom of the crowd approach is a form of scientific consensus. As I discuss at the link here, the latest polls seem to suggest that AI experts generally believe that we will reach AGI by the year 2040. Should you be swayed by the AI luminaries or more so by the AI experts and their scientific consensus? Historically, the use of scientific consensus as a method of understanding scientific postures has been relatively popular and construed as the standard way of doing things. If you rely on an individual scientist, they might have their own quirky view of the matter. The beauty of consensus is that a majority or more of those in a given realm are putting their collective weight behind whatever position is being espoused. The old adage is that two heads are better than one. In the case of scientific consensus, it might be dozens, hundreds, or thousands of heads that are better than one. For this discussion on the various pathways to AGI, I am going to proceed with the year 2040 as the consensus anticipated target date. Besides the scientific consensus of AI experts, another newer and more expansive approach to gauging when AGI will be achieved is known as AGI convergence-of-evidence or AGI consilience, which I discuss at the link here. As mentioned, in a previous posting I identified seven major pathways that AI is going to advance to become AGI (see the link here). The most often presumed path is the incremental progression trail. The AI industry tends to refer to this as the linear path. It is essentially slow and steady. Each of the other remaining major routes involves various twists and turns. Here's my list of all seven major pathways getting us from contemporary AI to the treasured AGI: You can apply those seven possible pathways to whatever AGI timeline that you want to come up with. Let's undertake a handy divide-and-conquer approach to identify what must presumably happen on a year-by-year basis to get from current AI to AGI. Here's how that goes. We are living in 2025 and somehow are supposed to arrive at AGI by the year 2040. That's essentially 15 years of elapsed time. In the particular case of the linear path, the key assumption is that AI is advancing in a stepwise fashion each year. There aren't any sudden breakthroughs or miracles that perchance arise. It is steady work and requires earnestly keeping our nose to the grind and getting the job done in those fifteen years ahead. The idea is to map out the next fifteen years and speculate what will happen with AI in each respective year. This can be done in a forward-looking mode and also a backward-looking mode. The forward-looking entails thinking about the progress of AI on a year-by-year basis, starting now and culminating in arriving at AGI in 2040. The backward-looking mode involves starting with 2040 as the deadline for AGI and then working back from that achievement on a year-by-year basis to arrive at the year 2025 (matching AI presently). This combination of forward and backward envisioning is a typical hallmark of futurecasting. Is this kind of a forecast of the future ironclad? Nope. If anyone could precisely lay out the next fifteen years of what will happen in AI, they probably would be as clairvoyant as Warren Buffett when it comes to predicting the stock market. Such a person could easily be awarded a Nobel Prize and ought to be one of the richest people ever. All in all, this strawman that I show here is primarily meant to get the juices flowing on how we can be future forecasting the state of AI. It is a conjecture. It is speculative. But at least it has a reasonable basis and is not entirely arbitrary or totally artificial. I went ahead and used the fifteen years of reaching AGI in 2040 as an illustrative example. It could be that 2050 is the date for AGI instead, and thus this journey will play out over 25 years. The timeline and mapping would then have 25 years to deal with rather than fifteen. If 2030 is going to be the AGI arrival year, the pathway would need to be markedly compressed. I opted to identify AI technological advancements for each of the years and added some brief thoughts on the societal implications too. Here's why. AI ethics and AI law are bound to become increasingly vital and will to some degree foster AI advances and in other ways possibly dampen some AI advances, see my in-depth coverage of such tensions at the link here. Here then is a strawman futures forecast year-by-year roadmap from 2025 to 2040 of a linear path getting us to AGI: Year 2025: AI multi-modal models finally become robust and fully integrated into LLMs. Significant improvements in AI real-time reasoning, sensorimotor integration, and grounded language understanding occur. The use of AI in professional domains such as law, medicine, and the like rachet up. Regulatory frameworks remain sporadic and generally unadopted. Year 2026: Agentic AI starts to blossom and become practical and widespread. AI systems with memory and planning capabilities achieve competence in open-ended tasks in simulation environments. Public interest in governing AI increases. Year 2027: The use of AI large-scale world models spurs substantially improved AI capabilities. AI can now computationally improve from fewer examples via advancements in AI meta-learning. Some of these advances allow AI to be employed in white-collar jobs that have a mild displacement economically, but only to a minor degree. Year 2028: AI agents have gained wide acceptance and are capable of executing multi-step tasks semi-autonomously in digital and physical domains, including robotics. AI becomes a key element as taught in schools and as used in education, co-teaching jointly with human teachers. Year 2029: AI is advanced sufficiently to have a generalized understanding of physical causality and real-world constraints through embodied learning. Concerns about AI as a job displacer reach heightened attention. Year 2030: Self-improving AI systems begin modifying their own code under controlled conditions, improving efficiency without human input. This is an important underpinning. Some claim that AGI is now just a year or two away, but this is premature, and ten more years will first take place. Year 2031: Hybrid AI consisting of integrated cognitive architectures unifying symbolic reasoning, neural networks, and probabilistic models has become the new accepted approach to AI. Infighting among AI developers as to whether hybrid AI was the way to go has now evaporated. AI-based tutors fully surpass human teachers in personalization and subject mastery, putting human teachers at great job risk. Year 2032: AI agents achieve human-level performance across most cognitive benchmarks, including abstraction, theory of mind (ToM), and cross-domain learning. This immensely exceeds prior versions of AI that did well on those metrics but not nearly to this degree. Industries begin to radically restructure and rethink their businesses with an AI-first mindset. Year 2033: AI scalability alignment protocols improve in terms of human-AI values alignment. This opens the door to faster adoption of AI due to a belief that AI safety is getting stronger. Trust in AI grows. But so is societal dependence on AI. Year 2034: AI interaction appears to be indistinguishable from human-to-human interaction, even as tested by those who are versed in tricking AI into revealing itself. The role of non-human intelligence and how AI stretches our understanding of philosophy, religion, and human psychology has become a high priority. Year 2035: AI systems exhibit bona fide signs of self-reflection, not just routinized mimicry or parroting. Advances occur in having AI computationally learn from failure across domains and optimizing for long-term utility functions. Debates over some form of UBI (universal basic income) lead to various trials of the approach to aid human labor displacements due to AI. Year 2036: AI advancement has led to fluid generalization across a wide swath of domains. Heated arguments take place about whether AGI is emerging, some say it is, and others insist that a scaling wall is about to be hit and that this is the best that AI will be. Nations begin to covet their AI and set up barriers to prevent other nations from stealing or copying the early AGI systems. Year 2037: Advances in AI showcase human-like situational adaptability and innovation. New inventions and scientific discoveries are being led by AI. Questions arise about whether this pre-AGI has sufficient moral reasoning and human goal alignment. Year 2038: AI systems now embody persistent identities, seemingly able to reflect on experiences across time. Experts believe we are on the cusp of AI reaching cognitive coherence akin to humans. Worldwide discourse on the legal personhood and rights of AI intensifies. Year 2039: Some of the last barriers to acceptance of AI as nearing AGI are overcome when AI demonstrates creativity, emotional nuance, and abstract reasoning in diverse contexts. This was one of the last straws on the camel's back. Existential risks and utopian visions fully dominate public apprehensions. Year 2040: General agreement occurs that AGI has now been attained, though it is still early days of AGI and some are not yet convinced that AGI is truly achieved. Society enters a transitional phase: post-scarcity economics, redefinition of human purpose, and consideration of co-evolution with AGI. Mull over the strawman timeline and consider where you will be and what you will be doing during each of those fifteen years. One viewpoint is that we are all along for the ride and there isn't much that anyone can individually do. I don't agree with that sentiment. Any of us can make a difference in how AI plays out and what the trajectory and impact of reaching AGI is going to be. As per the famous words of Abraham Lincoln: 'The most reliable way to predict the future is to create it.'

DOWNLOAD THE APP

Get Started Now: Download the App

Ready to dive into the world of global news and events? Download our app today from your preferred app store and start exploring.
app-storeplay-store