By Thomas Brooke, Danielle Garno and Paula Jimenez Nieva ( January 22, 2025, 5:00 PM EST) — The once harmonious realm of neutrals, beige and a minimalistic look recently took a contentious turn with an influencer contesting ownership over this popular aesthetic…. 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

WHSmith just launched a retail media network (RMN) to bring retail media to its airport stores. Like every other RMN launched since Amazon, Walmart, and Best Buy pioneered retail media over a decade ago, WHSmith promises “more exciting and engaging retail experiences for consumers” and is “tailored to the needs of … supplier brands.” Our take: WHSmith’s network is yet another addition to the long tail of Amazon Ads copycats — and Amazon Ads’ scale leads other RMNs to project unrealistic growth. In the US, Amazon Ads is larger than all other RMNs combined. It’s growing faster than others and now selling advertising technology as a service, signaling sustained dominance. For smaller RMNs, operational realities interfere with execution. Advertisers tell us that they lack media know-how, mask trade promotion as media spend, and struggle to prove performance. Here are the facts: When retail media grows, trade funding declines. More than half of retail media ad spend comes from existing trade and shopper marketing budgets. Rather than earning incremental revenue, RMNs divert dollars that would have funded temporary price reductions, featured endcaps, and in-store demos into advertising. Retailers obscure RMNs’ inability to tap into digital and national media budgets by consolidating trade and retail media when reporting revenue publicly. Cannibalizing co-op funds remains a chief concern of executives at large RMNs, especially for multicategory, multibrand retailers. RMN execution is weaker than it should be. RMNs struggle to demonstrate incrementality, power real-time results, and offer self-service platforms, making it difficult for brands and agencies to plan, buy, and optimize ads. In fact, most RMNs remain mostly manual. Furthermore, despite in-store ads earning more attention than any other format, according to Forrester’s Consumer Benchmark Survey, 2024, in-store ads remain constrained by their difficulty to buy and measure. The few retailers that have invested in smart carts and digital displays have yet to roll them out nationally due to the capital expenditure that they require and their uncertain return on ad spend. Going forward, RMNs should prioritize self-service. Retail media is run by several ex-agency staff hired by RMNs to manage campaigns. Each RMN has tons of advertisers, so when media management is manual, it creates a lot of low-level labor that could be better spent on capabilities such as analytics. Resource-intensive, white-glove service may satisfy retailers’ largest first-party sellers, but there’s a long tail of first- and third-party sellers interested in allocating performance media budgets to self-serve highly relevant, revenue-generating ads. When they’re more self-service, RMNs have bigger budgets for sales, marketing, product, and engineering to focus on maximizing onsite profitability, full-funnel measurement, and making retail media programmatic. To learn what else RMNs should prioritize, check out The State Of Retail Media, 2025, by Sucharita Kodali and myself. We clarify retail media’s potential and challenges and advise how retailers can sell more ads. As always, feel free to schedule time to discuss. source

（相）創新科技及工業局局長孫東教授, JP(前排左五)、香港互動市務商會會長方保僑先生(前排右四)、通訊事務管理局辦公室通訊事務總監梁仲賢先生, JP(前排左四)、數字政策辦公室數字政策專員黃志光先生, JP(前排右三)及主要嘉賓合照。 香港互動市務商會昨晚舉行第 9 屆「傳媒轉型大獎」(下稱「大獎」) 頒獎禮暨周年晚宴。「大獎」旨在表揚過去一年在運用科技轉型、以創新方式傳遞新聞和資訊上表現出色的本地媒體機構。香港互動市務商會會長方保僑表示:「『大獎』作為本港具認受性的嘉許傳媒機構奬項，歷年來見證本地主要媒體的數碼轉型成果，亦見證不少新媒體出現，百花齊放。近年生成式 AI 迅速發展，重塑媒體格局。今屆大奬以『歷久常新 開拓前路』為主題，寄語商會和傳媒機構有長足發展; 並勉勵傳媒朋友在科技變革浪潮帶來的全新挑戰當中，保持創新的熱誠，繼續擔當開拓者。今屆我們擴大評審範圍，包括新增五大專門範疇獎項，以表揚提供專門內容及發揮影響力的媒體。」 創新科技及工業局局長孫東教授, JP 致辭時表示:「『大獎』旨在表揚過去一年在運用科技轉型、以創新方式傳遞新聞和資訊上表現出色的本地媒體機構，我很榮幸見證各機構獲獎。香港特區政府一直致力推動數字經濟及智慧城市的發展，鼓勵各行各業透過科技升級轉型，力求在不斷變化的商業環境中保持競爭力。我希望傳媒行業在向前開拓的路上，能保持拼勁，持續創新，並繼續秉持專業和值得珍視的價值、內容和運作方式。香港互動市務商會過去一直支持創意等業界善用科技進行創新轉型，相信商會未來會繼續鼓勵及支持業界把握大數據及人工智能等科技所帶來的新機遇，為市民打造更優質、更快捷、更安全的資訊體驗。」 獎項與時並進 專業評審團與公眾評選表現傑出媒體 香港互動市務商會於去年邀請來自八所大專院校的市場行銷、數位行銷和新聞學的學者組成專業評審團，就本地不同類別的媒體機構在數碼轉型的表現評分。評審團為六大類別媒體，包括報章、網媒、雜誌、電視、電台和英文媒體評分，以資訊創新及創意應用、跨媒體兼容性、能充分使用數碼營銷、促進業務擴展，以及用戶體驗作評審準則，選出每個類別中分別在「網站」、「應用程式」 和「社交媒體」範疇中的金、銀、銅獎，而總分得分最高的十間媒體則獲得「十大傑出傳媒獎」。 此外，今年亦新增五大專門範疇獎項，分別是「最具 ESG 影響力獎」、「整體生活消閒資訊大獎」、「整體資訊娛樂大獎」、「整體科技資訊大獎」及「整體財經資訊大獎」。除了嚴謹的專業評審，我 們同時注重公眾的參與，我們於去年 10 月邀請公眾透過網上投票選出熱門媒體網站、社交媒體專 頁及 OTT 平台，反應熱烈。 商會謹此恭賀一眾榮獲殊榮的媒體機構，並祝願在新一年各媒體繼續不懈追求卓越和創新，在數碼轉型的路上持續領跑、開拓前路。 LinkedIn Email Facebook Twitter WhatsApp The post 香港互動市務商會第9屆「傳媒轉型大獎」名單出爐 appeared first on VeriMedia. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More DeepSeek R1’s Monday release has sent shockwaves through the AI community, disrupting assumptions about what’s required to achieve cutting-edge AI performance. Matching OpenAI’s o1 at just 3%-5% of the cost, this open-source model has not only captivated developers but also challenges enterprises to rethink their AI strategies. The model has rocketed to the top-trending model being downloaded on HuggingFace (109,000 times, as of this writing) – as developers rush to try it out and seek to understand what it means for their AI development. Users are commenting that DeepSeek’s accompanying search feature (which you can find at DeepSeek’s site) is now superior to competitors like OpenAI and Perplexity, and is only rivaled by Google’s Gemini Deep Research. The implications for enterprise AI strategies are profound: With reduced costs and open access, enterprises now have an alternative to costly proprietary models like OpenAI’s. DeepSeek’s release could democratize access to cutting-edge AI capabilities, enabling smaller organizations to compete effectively in the AI arms race. This story focuses on exactly how DeepSeek managed this feat, and what it means for the vast number of users of AI models. For enterprises developing AI-driven solutions, DeepSeek’s breakthrough challenges assumptions of OpenAI’s dominance — and offers a blueprint for cost-efficient innovation. It’s the “how” DeepSeek did what it did that should be the most educational here. DeepSeek’s breakthrough: Moving to pure reinforcement learning In November, DeepSeek made headlines with its announcement that it had achieved performance surpassing OpenAI’s o1, but at the time it only offered a limited R1-lite-preview model. With Monday’s full release of R1 and the accompanying technical paper, the company revealed a surprising innovation: a deliberate departure from the conventional supervised fine-tuning (SFT) process widely used in training large language models (LLMs). SFT, a standard step in AI development, involves training models on curated datasets to teach step-by-step reasoning, often referred to as chain-of-thought (CoT). It is considered essential for improving reasoning capabilities. However, DeepSeek challenged this assumption by skipping SFT entirely, opting instead to rely on reinforcement learning (RL) to train the model. This bold move forced DeepSeek-R1 to develop independent reasoning abilities, avoiding the brittleness often introduced by prescriptive datasets. While some flaws emerge – leading the team to reintroduce a limited amount of SFT during the final stages of building the model – the results confirmed the fundamental breakthrough: reinforcement learning alone could drive substantial performance gains. The company got much of the way using open source – a conventional and unsurprising way First, some background on how DeepSeek got to where it did. DeepSeek, a 2023 spin-off from Chinese hedge-fund High-Flyer Quant, began by developing AI models for its proprietary chatbot before releasing them for public use.  Little is known about the company’s exact approach, but it quickly open sourced its models, and it’s extremely likely that the company built upon the open projects produced by Meta, for example the Llama model, and ML library Pytorch.  To train its models, High-Flyer Quant secured over 10,000 Nvidia GPUs before U.S. export restrictions, and reportedly expanded to 50,000 GPUs through alternative supply routes, despite trade barriers. This pales compared to leading AI labs like OpenAI, Google, and Anthropic, which operate with more than 500,000 GPUs each.   DeepSeek’s ability to achieve competitive results with limited resources highlights how ingenuity and resourcefulness can challenge the high-cost paradigm of training state-of-the-art LLMs. Despite speculation, DeepSeek’s full budget is unknown DeepSeek reportedly trained its base model — called V3 — on a $5.58 million budget over two months, according to Nvidia engineer Jim Fan. While the company hasn’t divulged the exact training data it used (side note: critics say this means DeepSeek isn’t truly open-source), modern techniques make training on web and open datasets increasingly accessible. Estimating the total cost of training DeepSeek-R1 is challenging. While running 50,000 GPUs suggests significant expenditures (potentially hundreds of millions of dollars), precise figures remain speculative. What’s clear, though, is that DeepSeek has been very innovative from the get-go. Last year, reports emerged about some initial innovations it was making, around things like Mixture of Experts and Multi-Head Latent Attention. How DeepSeek-R1 got to the “aha moment” The journey to DeepSeek-R1’s final iteration began with an intermediate model, DeepSeek-R1-Zero, which was trained using pure reinforcement learning. By relying solely on RL, DeepSeek incentivized this model to think independently, rewarding both correct answers and the logical processes used to arrive at them. This approach led to an unexpected phenomenon: The model began allocating additional processing time to more complex problems, demonstrating an ability to prioritize tasks based on their difficulty. DeepSeek’s researchers described this as an “aha moment,” where the model itself identified and articulated novel solutions to challenging problems (see screenshot below). This milestone underscored the power of reinforcement learning to unlock advanced reasoning capabilities without relying on traditional training methods like SFT. Source: DeepSeek-R1 paper. Don’t let this graphic intimidate you. The key takeaway is the red line, where the model literally used the phrase “aha moment.” Researchers latched onto this as a striking example of the model’s ability to rethink problems in an anthropomorphic tone. For the researchers, they said it was their own “aha moment.” The researchers conclude: “It underscores the power and beauty of reinforcement learning: rather than explicitly teaching the model on how to solve a problem, we simply provide it with the right incentives, and it autonomously develops advanced problem-solving strategies.” More than RL However, it’s true that the model needed more than just RL. The paper goes on to talk about how despite the RL creating unexpected and powerful reasoning behaviors, this intermediate model DeepSeek-R1-Zero did face some challenges, including poor readability, and language mixing (starting in Chinese and switching over to English, for example). So only then did the team decide to create a new model, which would become the final DeepSeek-R1 model. This model, again based on the V3

Artificial intelligence is no longer a futuristic concept — it’s here, promising to revolutionize industries by unlocking unparalleled efficiency and innovation. Yet, despite this immense potential, AI adoption remains elusive for many organizations. Businesses are grappling with challenges like skill shortages, unpredictable cloud pricing, and high computing demands. These barriers have left AI out of reach for many companies, especially those with limited resources.    But the good news is that new technologies are changing this landscape, making AI more accessible and affordable than ever before. From edge computing to no-code platforms and AutoML, businesses are increasingly finding ways to democratize AI, allowing them to leverage its power without breaking the bank. Emerging technologies are paving the way for AI adoption, offering businesses new opportunities to leverage these advancements for greater efficiency and innovation.   Overcoming the Barriers to AI Adoption    The barriers to AI adoption are well-documented. For many organizations, the cost of high-performance computing hardware, such as GPUs, and the unpredictability of cloud pricing have made AI investment seem risky. Additionally, a growing skill gap is preventing companies from finding the talent to manage and implement these technologies effectively.   Related:Should AI-Generated Content Include a Warning Label? What’s more, as AI systems become more complex, the need for highly specialized knowledge and tools to manage them grows. Organizations need solutions that simplify AI development and make it more cost-effective to deploy — without the need for extensive technical expertise.   Technologies Making AI More Accessible   Several key technologies are stepping up to tackle these barriers, providing businesses with the tools to integrate AI effectively.   1. Edge computing   Edge computing brings AI capabilities closer to data sources, allowing businesses to process and analyze data in real time. This proximity reduces latency and improves decision-making speed — crucial for industries like manufacturing, healthcare, and retail that rely on real-time insights. By decentralizing data processing, edge computing lowers the demand for centralized cloud resources and reduces overall costs.    2. No-code/Low-code platforms   No-code and low-code platforms are a game-changer for businesses that lack deep technical expertise. These platforms empower non-technical users to create and deploy AI models without writing complex code, making AI development more accessible and enabling a wider range of businesses to participate in AI-driven innovation, even with limited resources.   Related:Why Enterprises Struggle to Drive Value with AI 3. AutoML   Automated machine learning (AutoML) simplifies the process of building AI models. AutoML tools automatically handle model selection, training, and optimization, allowing users to create high-performing AI systems without requiring data science expertise. By streamlining these tasks, the technology significantly lowers the barrier for businesses looking to integrate AI into their operations, making deployment easier and faster.   4. AI on CPUs   AI’s computational demands, especially for tasks like training large language models, have traditionally required expensive GPU hardware. However, recent innovations are making it possible to run some AI models on more affordable CPUs. Techniques like quantization and frameworks like MLX are enabling smaller AI models to run efficiently on CPUs, broadening AI’s accessibility and reducing the need for costly hardware investments.    Collaboration: The Key to AI Democratization    Organizations cannot travel alone on the journey to making AI accessible. Collaboration between businesses will be essential to overcoming the barriers to AI adoption. By pooling resources, sharing expertise, and developing tailored solutions, companies can reduce costs and streamline the integration of AI into their operations.    Related:Why Every Employee Will Need to Use AI in 2025 Moreover, collaboration is critical for ensuring AI is implemented ethically and safely. As AI’s role in society grows, organizations must work together to establish guidelines and best practices that foster trust and prevent misuse. Transparency in AI development and deployment will be key to its long-term success.    Upskilling the Workforce to Build Trust in AI    Another challenge that organizations face is the need to upskill their workforce. As AI systems become more prevalent, employees must have the skills to manage, work alongside, and trust these technologies. Upskilling workers will alleviate concerns about data privacy, security, and job displacement, allowing for smoother AI adoption.    Investing in training programs will not only help employees adapt to AI systems but also ensure that organizations maximize the benefits of these technologies. A skilled workforce can collaborate effectively with AI, leading to improved productivity and innovation. The broader IT skills shortage is expected to impact nine out of 10 organizations by 2026, leading to $5.5 trillion in delays, quality issues, and revenue loss, according to IDC.    Unlocking AI’s Potential Across Industries    The future of AI is bright, but its potential can only be fully realized when it becomes accessible to all. By leveraging technologies like edge computing, no-code platforms, and AutoML, businesses can overcome the barriers to AI adoption and unlock new opportunities for growth and innovation.    Business leaders who invest in these technologies and prioritize upskilling their workforce will be well-positioned to thrive in an AI-powered future. With collaboration and a commitment to ethical implementation, AI can become a transformative force across industries, reshaping how we work, communicate, and innovate.    It’s time to embrace AI’s possibilities and take the next step toward a more accessible, inclusive future.    source

Google and Apple are the first companies investigated for potential Strategic Market Status under the new U.K. Digital Markets, Competition and Consumers Act. If Google or Apple receives the designation, bespoke conduct requirements will be drafted for the company to follow, preventing anti-competitive practices. On Jan. 23, the Competition and Markets Authority announced it would be assessing the mobile ecosystems of Google and Apple, including the companies’ operating systems, app stores, and default browsers, to see if they have disproportionate influence over the market. SEE: UK Regulator Probes Apple’s Mobile Browser Dominance “Given the importance of mobile ecosystems to people, businesses and the economy, it is critical that competition works well,” the CMA said in a press release. “Effective competition could ensure consumers and businesses are treated fairly by Apple and Google in relation to the terms and conditions they impose. “Effective competition could also ensure open opportunities for businesses to innovate and deliver a range of content, services and technological developments to consumers on a mobile device.” The announcement comes less than two weeks after the first Strategic Market Status investigation was announced. This was also looking at Google, but within the realm of search and search advertising services, where an advertiser pays for its advert to appear next to the results from a user’s search. What’s hot at TechRepublic What is the DMCCA? The DMCCA, which came into force on Jan. 1, is designed to regulate the behaviour of major digital firms with significant market power in the country. It grants the CMA new powers to impose requirements on tech companies with Strategic Market Status, reminiscent of the “gatekeeper” organisations that must abide by the E.U.’s Digital Markets Act. For the mobile ecosystem investigations, the CMA will look at factors such as the extent Apple and Google’s competitors are able to offer rival products and services, whether Apple and Google are favouring their own apps and services within the iOS and Android ecosystems, and if developers are bound to unfair terms and conditions if they want to distribute apps in their respective app stores. For the investigation into Google’s influence in search and search advertising services, the CMA will look at whether it is using its position to prevent innovation by others, such as withholding resources or designing AI services to limit how competing search engines could create equivalent features. The CMA will also assess whether the tech giant is using its dominant position to prioritise its search services such as for shopping or travel, collecting and using consumer data without informed consent, and using publisher content without fair terms and conditions. SEE: Google Abusing Dominant Position in Ad Tech Sector, Says U.K. Government The DMCCA gives new enforcement powers to a group established inside the CMA called the Digital Markets Unit. It will draft a unique set of conduct requirements for each company designated as Strategic Market Status, which they must abide by even before exhibiting anti-competitive practices to prevent them from occurring. Additionally, the DMU can make “pro-competition interventions” that will actively address a company’s adverse effects on competition that stem from its disproportionate market power. Conduct requirements for Google and Apple in the realm of mobile ecosystems could include requiring the companies to provide third-party apps with the functionality needed to operate on iPhone or Android devices, or making it easier for users to download apps and pay for in-app content outside of Apple’s and Google’s own app stores. The CMA can continue to amend them even after completing the SMS investigation. Requirements for Google in the realm of search and search advertising might include forcing the company to make the user data it collects available to competitors or giving publishers more control over how their data is used, including in Google’s AI services. SMS-designated firms must have substantial market power in digital activity, strategic significance, and either a global turnover of more than £25 billion or a U.K. turnover of more than £1 billion. The CMA will conduct investigations into each firm before applying for SMS status, which usually takes about nine months. Mobile ecosystems and search and search advertising services represent the first two areas of digital activity that the CMA has launched SMS investigations into. Decisions will be made by the end of October. SEE: Regulator CMA to Scrutinize Microsoft and Other Cloud Service Providers in the UK E.U. and U.S. also take issue with Google’s anticompetitive practices in Search In March 2024, Google temporarily removed some Search widgets, such as Google Flights, to allow more access to individual businesses in response to the E.U.’s Digital Markets Act coming into force. However, just a few weeks later, the E.U. opened an ongoing non-compliance investigation, as regulators claim it is promoting its own services above third parties’ in search results. In December, Google announced several more changes to its Search features to comply with the DMA. In September 2024, the European Court of Justice upheld a €2.42 billion fine against Google for violating E.U. antitrust rules by favouring its own comparison shopping service, Google Shopping, in search results. Additionally, in August 2024, a federal judge ruled that the tech company monopolizes general search services and text ads, breaking U.S. antitrust law. However, Google is not going down without a fight. The tech company successfully overturned a €1.5 billion antitrust fine it received from the European Commission in 2019 for preventing third parties using its AdSense platform from displaying competitor ads next to Google search results. Google was also handed a €4.34 billion fine from the European Commission in 2018 for abusing its dominance by pre-installing Google Search into Android devices but has since escalated an appeal to the European Court of Justice. source

How many times have you created a new user on a Linux machine, only to find out that the new user doesn’t have sudo privileges? Without the ability to use sudo, that user is limited in what they can do. This, of course, is by design, because you certainly don’t want every user on your system to have admin privileges. However, users who want to enjoy admin rights must be able to use the sudo command. SEE: Debian vs Ubuntu: Which Linux Distro Fits Your Needs Best? (TechRepublic) How to give users sudo privileges Most modern Linux distributions have a user group that grants sudo privileges simply by being a member of that group. While sudo configurations allow individual accounts to have sudo privileges, this is not encouraged because it leads to user management headaches, especially if a user ID is changed or if that user’s account is removed or deactivated. You can determine which group this is by looking at the /etc/sudoers file. You can safely view the contents of this file using the command: sudo less /etc/sudoers In Fedora and Red Hat, this group is usually the wheel group: ## Allows people in group wheel to run all commands%wheel   ALL=(ALL)         ALL In Ubuntu and Kali, this group is usually the sudo group, not to be confused with the sudo command: # Allow members of group sudo to execute any command%sudo    ALL=(ALL:ALL)  ALL This means all members of the admin group have full sudo privileges. To add your user to the admin group, you would issue the command (as a user who already has full sudo privileges): sudo usermod -a -G sudo USERNAME Where USERNAME is the name of the user to be added. Once the user logs out and logs back in, they will now enjoy full sudo privileges. If you were using Fedora or a Red Hat-based distribution, you would use the wheel group instead: sudo usermod -a -G wheel USERNAME Note that the user will continue to have sudo privileges as long as that user has this group assignment. To revoke sudo privileges, you will need to remove that user from that group. SEE: Top Commands Linux Admins Need to Know (TechRepublic Premium) Use with caution You do not want to add every user to the sudoers file or the admin group. Use this with caution — otherwise, you risk jeopardizing system security. But with care, you can easily manage what your users can and cannot do. Do more with sudo privileges Using your newfound sudo privileges, you can add a new user to your Linux system, list system services, and search for files from the command line. In addition, you’ll want to make administration easier by combining multiple commands into a single bash prompt. This article was originally published in August 2023. It was updated by Antony Peyton in January 2025. source

In today’s fast-paced digital landscape, organizations are under constant pressure to adopt new technologies quickly, manage costs effectively, and maintain robust security and compliance standards. They’re also under tremendous pressure to build, manage, and scale IT automation across the organization. The Red Hat Ansible Automation Platform, available as both self-managed and fully managed solutions, is available through the Amazon Web Services (AWS) Marketplace, enabling streamlined access to the solution, quick deployment and integration within existing AWS environments, flexible and scalable pricing options, and unified governance and compliance capabilities. The Benefits of Procuring Through AWS Marketplace   Organizations are bogged down by lengthy vendor negotiations in traditional procurement methods, complex billing, and decentralized purchasing processes. AWS Marketplace, however, enables an environment in which organizations of all sizes can find, try, buy, deploy, and manage solutions from AWS Partners within the AWS ecosystem. Procuring through AWS Marketplace has a number of benefits. For example, companies can optimize time-to-value with standardized contracts and flexible payment options, allowing them to test software, pay as they go, negotiate custom terms, and save with volume pricing. “Dealing with one vendor for your procurement versus dealing with potentially 5,000 is a major benefit,” says John Walter, senior partner solutions architect at AWS. Organizations procuring through AWS Marketplace reduce risk with centralized governance and control. Users can launch third-party solutions that meet their security and compliance standards. Businesses can also optimize costs by consolidating third-party spending with AWS billing. This enables companies to simplify invoicing, track spending, and manage budgets. “You’re able to negotiate pricing with these partners, and having a single bill is a major benefit to customers,” Walter says. The Advantage of Red Hat and AWS Marketplace The Red Hat Ansible Automation Platform, available from the AWS Marketplace, offers customers all the benefits of Ansible automation, deployed on their AWS cloud. This solution, available as a self-managed or fully managed solution, provides organizations the tools they need to deploy enterprise wide automation. The Red Hat Ansible Automation Platform integrates with native AWS services and the full Ansible collection for AWS, making it easier and faster to provision. Users get the complete Ansible Automation platform, including the full Ansible collection for AWS, as well as integration services such as EC2, CloudFormation, VPC, ALB, AMI, Security Groups, key pairs, EFS, EBS, S3, RDS, Lambda, AWS Secret, and more. Customers also have unlimited access to experienced technical support engineers. Though customers of both the self-managed and fully managed solution can expect these same features, there are key differences: The Ansible Automation Platform Service on AWS is a managed service that Red Hat deploys and fully manages. Upgrades, patches, and ongoing maintenance of the platform are performed by Red Hat, enabling customers to focus on automation. The Ansible Automation Platform is a self-managed version of the Ansible Automation Platform. Customers are responsible for deploying the platform via a simple, streamlined processes. Customers are also responsible for performing ongoing maintenance and upgrades to the platform. Both the fully managed and self-managed offerings integrate seamlessly with both AWS infrastructure and services, as well as hybrid cloud resources, Walter says. “The key benefit to deploying either a self-managed or fully managed version of Ansible is you’ll be able to get up and running faster,” Walter says. “You don’t need to spend time on the deployment, and you’ll be able to take advantage of working within an ecosystem you’re comfortable and familiar with.” For more Red Hat information, click here. To view Red Hat offerings on AWS Marketplace, click here. source

By Jared Foretek ( January 24, 2025, 5:04 PM EST) — German software giant SAP is asking the Ninth Circuit to reconsider its revival of data analytics company Teradata’s trade secrets and tying suit against it, saying the panel wrongly applied per se antitrust treatment to a “highly innovative software market.”… 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

Buying dynamics are more complex than ever. Buying groups are getting larger, sales cycles are growing longer, and expectations for personalization have increased — all transforming how businesses purchase solutions. To succeed, B2B marketers must adopt strategies that are as dynamic and adaptable as their target audiences. Enter adaptive programs — a cutting-edge approach that equips marketers to utilize real-time data, engage stakeholders effectively, and optimize the entire customer lifecycle. Why Adaptive Programs Are Critical For B2B Success Traditional demand generation strategies often fall short in B2B, where lengthy purchase processes and group decision-making dominate. Adaptive programs address these challenges by allowing B2B frontline marketers to respond to real-time buyer signals, tailor their outreach, and align their efforts across multiple channels and touchpoints. Thes B2B marketers are then empowered to go beyond lead generation and focus on creating long-term value through meaningful engagement with all buying group members. At their core, adaptive programs revolve around five critical pillars: technology, actionable insights, buying group engagement, channel orchestration, and lifecycle support. These elements form a framework for data-driven, scalable, and highly effective B2B marketing programs. The Three Stages Of Transitioning To Adaptive Programs The path to adopting adaptive programs unfolds in three strategic stages: Optimize traditional methods. Refine existing practices to lay the groundwork for more advanced adaptive strategies. This includes integrating your CRM with marketing automation tools, improving data quality, and establishing foundational lead-scoring models. These steps enable B2B marketers to streamline processes and identify high-value accounts. Implement a hybrid approach. Gradually incorporate adaptive components, such as AI-driven tools for predictive analytics and real-time data processing. These technologies help marketers identify intent signals, prioritize accounts, and engage decision-makers with relevant content at the right time. Centralizing data through customer data platforms ensures better targeting and a unified view of buying group behaviors. Commit to full adaptivity. The final stage involves fully automating decision-making processes and leveraging advanced analytics to predict future customer needs. With adaptive programs, B2B marketers can orchestrate personalized interactions across multiple channels, aligning every touchpoint with the buyer’s journey. This complete integration drives efficiency and enables marketers to deliver tailored messaging that resonates with each stakeholder in the buying group. Benefits Beyond Demand Generation The benefits of adaptive programs extend beyond improving demand generation. By focusing on the entire customer lifecycle, B2B marketers can unlock upselling, cross-selling, and retention opportunities. For example, AI-powered insights can identify when an account is ready for expansion, enabling sales teams to act at the right time with the right offer. Additionally, adaptive programs foster better collaboration between marketing and sales teams. By sharing real-time insights and coordinated strategies, both functions can work harmoniously to deliver seamless buyer experiences and close deals faster and more effectively. Move Forward With Confidence Adopting adaptive programs is no longer an option for B2B marketers — it’s essential. The ability to pivot based on real-time insights and deliver highly personalized experiences is crucial. By investing in the right technology, training, and organizational alignment, businesses can stay ahead of the curve and meet the evolving expectations of B2B buyers. Success begins with small, strategic changes. Start by refining your existing programs, gradually incorporating adaptive elements, and scaling your efforts as you gain confidence in your approach. With adaptive programs, B2B marketers can unlock unprecedented opportunities for growth, strengthen relationships with buying groups, and position their businesses for long-term success. Want more detailed advice on how to make the shift? Join me and my colleague Barbara Winters on Wednesday, February 12, for a live webinar, Revolutionizing B2B Marketing With Adaptive Programs For Buying Groups. we’ll walk you through a simple, step-by-step framework for launching adaptive programs, whether you’re starting small or going all in. source