What should your organization ask itself to determine whether retaining a tech vendor is the best move right now? We’re finding that some digital leaders, such as those in many B2C commerce organizations, are choosing to stay with the tech vendors that they already have in place. Why is that remarkable? It’s a shift from earlier years when they were more likely to be swept off their digital feet by other vendors’ promises of competitive ROI and sparkling innovation. Today, digital business leaders are instead prioritizing how they consolidate or reduce the software products in their ecosystems. One reason: Leaders who already have made the move to SaaS solutions often cannot justify the business case for replatforming. Plus, they don’t want incur the costs, disruption, and (inevitable) unexpected issues of a replacement project. But what if your current vendor isn’t innovating or providing the support that you feel your organization needs? To help determine whether your org is better off retaining your vendor or looking for a new solution, analyze your current vendor in light of the following questions: Can you — and your customers and employees — stomach the costs of staying put with your current vendor? Avoiding replatforming might seem less expensive because the transition itself brings inherent costs, but don’t discount the costs of staying with a vendor if you’re being left behind in terms of functionality that may create better customer and employee experiences. Think specifically about: The hard, direct costs of the current system. Analyze everything included: subscriptions, licensing, support, ongoing customizations, and potentially hosting. Factor in potential increases due to usage-based pricing, plus the costs of maintenance over time, as the solution ages. The hard, direct costs for a potential new vendor — plus the costs of the migration. These expenses may be recorded differently, but they’re still real costs of the change. Include training, potential new hires to bring in expertise, testing, and other costs of the transition. Whether your current solution will help you keep up with competitors. For each solution, analyze the impact on both customers and your employees. For example: Will you struggle to provide relevant search results and sacrifice conversion? Will customer experience suffer with poor post-purchase messaging and shipment tracking — and drive higher costs and volumes for customer care? Are competitors providing clear inventory availability in stores while you can’t? Will associates have a harder time fulfilling online orders for in-store pickup?   Are you getting enough of what you need from the current partnership? Culture fit is an often-overlooked factor in vendor selection and retention. You’ll know if it’s working. When it’s not, and if you’ve done all you can to communicate with your vendor about the relationship, it may be time to move on. Remember that: All strong vendors perpetually innovate. Do you receive inspiring new functionality from your vendor? Do you also feel that you have influence over its roadmap? Technology vendors in your ecosystem must be true partners to your organization. Is each of the vendors in your ecosystem an ally? What’s their history of delivering on promises and building trust with your teams? Is your organization able to work on improvements and innovative solutions alongside your partners? What does support look like, and is that working for your internal teams? If retention feels like a good fit for your organization and you want a strategy to “win” the renewal game, check out Forrester’s report, The Digital Leaders’ Guide To Acting As Your Own Tech Retention Advocate. For Forrester clients who want a buyers’ guide, please schedule an inquiry or guidance session with us here. (coauthored with Senior Research Associate Delilah Gonzalez) source

The survey asked Asian immigrants about their views of life in the United States and how it compares with their country of origin. Majorities say the U.S. is better on nearly all qualities asked about in the survey, and about three-quarters say they would choose to come to the U.S. if they could do it again. Still, the survey – conducted from July 2022 to January 2023 – found that two-thirds of Asian immigrants also feel dissatisfied with the way things are going in the country, and more than half say the country is generally headed in the wrong direction. Additionally, Asian immigrants generally view their children’s prospects in less optimistic terms than their own. This chapter explores Asian immigrants’ attitudes about life in the U.S. today. How Asian immigrants compare the U.S. with their origin countries Majorities of Asian American immigrants say the U.S. is better than their origin country on nearly all qualities asked about in the survey, including the opportunity to get ahead (77%), treatment of the poor (64%) and gender equality (62%), among others. The one quality that a majority of Asian immigrants say is better in their country of origin than in the U.S. is the strength of family ties (60%). By main reason for immigrating Among Asian immigrants who came to the U.S. due to conflict, about 80% or more say the U.S. is better than their origin country on nearly all qualities asked about. The only exception is the strength of family ties: 25% say family ties are stronger in the U.S., while 48% say this is better in their origin country and 23% say it’s equal in both. Among those who immigrated for other reasons, assessments of the U.S. are more mixed. For example, those who immigrated for educational opportunities (51%), family reunification (58%) or economic opportunities (65%) are less likely than those who immigrated due to conflict (84%) to say the U.S. is better for health care access than their country of origin. By ethnicity For these findings, “country of origin” is used to refer to the place respondents came from. Those of the same ethnicity may identify different places as their origin country, which can be influenced by ethnicity, birthplace, nationality, ancestry, or other social, cultural or political factors. For more information, refer to the terminology. 75% of Chinese immigrants say lack of state censorship is better in the U.S. than in their country of origin, but about half say the same about women’s rights (50%) and health care access (48%). For Filipino immigrants, about 80% or more say opportunities to get ahead and access to health care are better in the U.S. than in their origin country. Some 53% say the same about women’s rights.  79% of Indian immigrants say opportunities to get ahead are better in the U.S., while 52% say the same about health care access. Among Korean immigrants, roughly 70% or more say the U.S. is better than their origin country in opportunities to get ahead, conditions for raising children and women’s rights. On the other hand, 66% say health care access in better in their origin country than in the U.S. And 45% say regularly held honest elections are about equal in both places.  About 75% or more of Vietnamese immigrants say the U.S. is better than their country of origin across all qualities asked about except the strength of family ties. By citizenship Asian immigrants who are U.S. citizens are more likely than noncitizens to say the U.S. is better than their origin country on access to health care services (63% vs. 39%) and treatment of the poor (67% vs. 57%). For more on how Asian immigrants’ views of the U.S. compared with their country of origin varies across these subgroups, refer to Appendix A. Asian immigrants’ views of their own standard of living, and that of their children A large majority of Asian immigrants in the U.S. (77%) say their standard of living is much or somewhat better than their parents’ standard of living when they were the same age. Meanwhile, 13% say their standard of living is much or somewhat worse, and 9% say it’s about the same as that of their parents. The survey also finds Asian immigrants are less optimistic about their children’s prospects than their own. Among Asian immigrants who have children, about half (48%) say their children’s standard of living will be much or somewhat better than their own. Some 37% say their children’s standard of living will be much or somewhat worse, and 15% say it will be about the same as their own. Among Asian immigrants with children, expectations about the next generation’s standard of living vary by: Ethnicity: More than half of Vietnamese immigrants (58%) say their children’s standard of living will be better than their own, a higher share than among other ethnic groups. Citizenship: 58% of Asian immigrants who are not U.S. citizens say their children’s standard of living will be better, compared with 44% of those who have U.S. citizenship. Years in the U.S.: 64% of Asian immigrants who have lived in the U.S. for 10 years or less say their children’s standard of living will be better than their own, versus 49% of those in the U.S. for 11 to 20 years and 38% of those in the U.S. for more than 20 years. Education: Asian immigrants without a college degree are more likely than those with a bachelor’s degree or higher to say their children’s standard of living will be better than their own. Overall, immigrant Asian adults have a more positive outlook about their own and their children’s prospects than U.S.-born Asian adults: 77% of Asian immigrants say their standard of living is better than that of their parents, compared with 60% of U.S.-born Asian adults who hold the same view. 48% of Asian immigrants with children say their children’s standard of living will be better than their own, compared with 29% of U.S.-born Asian adults with children.

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The newly launched Swarm framework from developers at OpenAI is an experimental tool designed to orchestrate networks of AI agents, and it’s been making waves in the tech community. Unlike other multi-agent frameworks, Swarm aims to provide a blend of simplicity, flexibility and control that sets it apart. Although still in its early stages, Swarm offers a fresh take on agent collaboration, with core concepts like “routines” and “handoffs” to guide agents through collaborative tasks. While Swarm is not an official OpenAI product nor is intended as a production-ready tool, it provides valuable insights into the potential of multi-agent systems in enterprise automation. Its key focus is on simplifying agent interactions, which is achieved through the Chat Completions API. This stateless design means agents do not retain memory between interactions, contributing to Swarm’s simplicity but limiting its use for complex decision-making tasks that require contextual memory.  Instead, developers need to implement their own memory solutions, which offer both challenges and opportunities for customization. This balance of simplicity and control is a major point of attraction for developers interested in learning about or building multi-agent orchestration systems. A lightweight approach for developers Swarm is distinct in its lightweight design, focusing on ease of understanding and implementation. This approach gives developers more granular control over execution steps and tool calls, making it easier to experiment with agent interactions and orchestrations. Compared to other frameworks like LangChain or CrewAI, Swarm’s stateless model is easier to grasp, which makes it accessible for those who are new to multi-agent systems. However, the lack of built-in memory management is a noted limitation. To achieve more sophisticated agent behavior, developers must implement external memory solutions. Despite this, Swarm’s emphasis on transparency and modularity has been praised for enabling developers to tailor agent behaviors and extend the framework based on their needs Guiding collaboration with routines and handoffs At the heart of Swarm are the concepts of “routines” and “handoffs,” which are mechanisms designed to help agents carry out collaborative tasks in an organized manner. A routine is a set of instructions that agents follow to complete specific actions, while handoffs allow for seamless transitions between agents, each specializing in particular functions.  This structured approach to agent interactions allows developers to create dynamic, multi-step processes where tasks are handled by the agent best suited for each step. Examples include customer service systems where triage agents manage initial contact before passing on specific queries to agents specialized in sales, support or refunds. This adaptability makes Swarm particularly useful for building applications that require multiple, specialized capabilities to work together. Addressing limitations: The role of state and memory Despite its promising features, Swarm’s lack of internal support for state and memory limits its effectiveness in complex decision-making based on past interactions. For instance, in a sales scenario, a stateful system would allow agents to track customer history across interactions—a capability that Swarm, in its current form, does not provide. The release of Swarm has also sparked ethical discussions about its potential impact on the workforce and the broader implications of AI-driven automation. While Swarm aims to make sophisticated multi-agent systems more accessible, its capability to replace human tasks raises concerns about job displacement and fairness. Security experts have also highlighted the need for robust safeguards to prevent misuse or malfunction within these autonomous agent networks. However, the decision to open-source Swarm has created an opportunity for community-driven development, potentially leading to novel uses and improvements. As developers experiment with Swarm, they contribute to the growing understanding of how multi-agent orchestration can be leveraged to solve real-world problems, particularly in enterprise environments where automation can drive efficiency and allow human workers to focus on more strategic initiatives. source

In the dynamic world of technology, startups and growing tech vendors are constantly seeking innovative ways to stay ahead of the curve. The rise of generative AI (GenAI) offers a transformative opportunity, but leveraging its full potential requires more than just adoption—it necessitates a strategic approach called GenAI Engineering. This blog post delves into why GenAI Engineering matters for tech vendors and startups and how it can be a cornerstone of your growth strategy.  The GenAI Boom: A Catalyst for Innovation  Since the launch of ChatGPT in November 2022, the potential of GenAI has become evident across industries. From GitHub CoPilot to DALL-E and Google Bard, GenAI applications have showcased incredible capabilities in automating tasks, enhancing creativity, and improving decision-making processes. This surge in GenAI adoption is particularly relevant for tech startups and vendors who are uniquely positioned to harness these advancements for rapid innovation and market differentiation.  The Pitfalls of Consumer-Focused GenAI  While consumer-focused GenAI services have ignited interest, they often fall short in addressing the specific needs of enterprises, especially those in the tech sector. Startups and tech vendors require GenAI solutions that align with business objectives like scalability, accuracy, privacy, and cost-efficiency. For instance, concerns about data security, intellectual property, and the accuracy of GenAI outputs are paramount for these organizations.  What Is GenAI Engineering?  GenAI Engineering integrates concepts and decision-making between three overlapping and interdependent domains: Data Domain: High-quality data is the bedrock of successful GenAI projects. Startups must focus on data sourcing, quality, and privacy. Questions like where the data is sourced, its appropriateness for the intended outcomes, and its security are crucial.  AI Models Domain: Selecting and customizing the right GenAI models is essential. Startups need to consider the types of models that best suit their needs, how to fine-tune these models, and ensure their outputs are reliable and high-quality.  Outcomes Domain: GenAI implementations must be outcome-driven. This involves choosing the right implementation approach, determining the degree of autonomy for AI components, and selecting appropriate infrastructure platforms.  Three Reasons Why GenAI Engineering is Critical for Startups and Tech Vendors  GenAI Engineering is the disciplined approach to implementing GenAI technologies in a way that aligns with business goals and maximizes value. For startups and growing tech vendors, this means:  Strategic Implementation: GenAI Engineering bridges the gap between strategy and execution, ensuring that GenAI projects are aligned with business outcomes, resources, and constraints.  Scalability and Flexibility: By systematically applying clear business and technology principles, startups can scale their GenAI implementations efficiently, adapting to changing market demands and opportunities.  Innovation and Competitive Edge: GenAI Engineering empowers startups to innovate rapidly, offering customized solutions that differentiate them from competitors and appeal to their target markets.  Governing Factors in GenAI Engineering   For tech startups, the following factors are critical:  Value: Focus on outcomes that improve productivity, enhance product offerings, and drive growth. Startups need to evaluate the potential ROI of GenAI projects.  Resources: Assess available resources, including data, skills, tools, and infrastructure. Startups often operate with limited resources, making strategic resource allocation vital.  Constraints: Navigate industry regulations, internal policies, and risk management. Understanding these constraints helps in developing responsible and compliant GenAI solutions.  Collaboration: The Heart of GenAI Engineering  Effective GenAI Engineering ideally involves collaboration across various roles, such as CISOs, CDOs, data engineers, data scientists, developers, and non-technical domain experts. However, startups often lack the resources to have all these roles in-house. Here are practical steps for startups to initiate GenAI engineering:  Leverage Partnerships: Collaborate with universities, research institutions, and other tech startups. These partnerships can provide access to expertise, resources, and infrastructure that may be beyond the reach of a startup.  Utilize Open Source Tools: Take advantage of open-source GenAI tools and platforms. Communities like Hugging Face and GitHub host a multitude of projects that can accelerate your development efforts without significant upfront costs.  Engage with GenAI Platforms: Use AI platforms provided by major cloud providers like AWS, Google Cloud, and Azure. These platforms offer ready-to-use models, development tools, and infrastructure support that can help startups implement GenAI solutions quickly and cost-effectively.  Hire Freelancers and Consultants: Bring in external experts on a project basis. Freelancers and consultants can provide the specialized skills needed for specific tasks without the long-term financial commitment of full-time hires.  Build a Cross-Functional Core Team: Assemble a small, cross-functional team with diverse skills. Even with limited resources, having a core team that includes data engineers, developers, and business analysts can drive GenAI projects forward.  Invest in Training: Upskill existing employees through training programs focused on GenAI technologies. Online courses, workshops, and certifications can equip your team with the knowledge needed to implement GenAI solutions effectively.  Establishing a GenAI Center of Excellence (CoE)  For many startups, creating a GenAI Center of Excellence (CoE) can be a strategic move. A GenAI CoE can:  Centralize Expertise: Bring together experts from various domains to drive GenAI initiatives.  Promote Best Practices: Share success stories, establish standards, and ensure consistent application of GenAI Engineering principles.  Drive Innovation: Act as a hub for exploring new GenAI opportunities and developing cutting-edge solutions.  Practical Steps   Start with Data: Ensure you have a solid foundation of high-quality data. Implement robust data governance practices to maintain data integrity and privacy.  Choose the Right Models: Evaluate different GenAI models and select those that best align with your business goals. Consider fine-tuning and customizing models to meet specific needs.  Focus on Outcomes: Define clear business outcomes for your GenAI projects. Ensure that every implementation is aligned with these outcomes to maximize value.  Invest in Skills: Build a team with the necessary skills and expertise. Invest in training and development to keep your team updated on the latest GenAI advancements.  Foster Collaboration: Encourage collaboration across different roles and teams. Establish clear communication channels and collaborative tools to facilitate teamwork.  Key GenAI Use Cases for Startups and Growing Tech Vendors Understanding the potential use cases for GenAI can help startups identify where to focus their efforts:  Task Productivity: Simple tasks like summarizing reports, generating job descriptions,

Presented by SAP In the midst of the AI revolution, fake podcasts and chatbots may get the most hype, but the transformative power of AI lies in the heart of the business. It’s in the day-to-day operations where businesses can realize the potential of AI: automating and improving workflows, gaining intelligent insights and driving efficiency.  SAP, the global powerhouse behind many leading enterprise applications and business AI, is one of the pioneers in applying the latest AI technology to the challenges of running an enterprise. They are folding the technology into their stack across all levels including SAP S/4HANA Cloud Private Edition, a cloud-based ERP (Enterprise Resource Planning) solution that is customizable to meet unique business needs. ERP tools have long allowed business leaders to manage models of their business processes and now they’ll have the option to leverage time-saving assistance from AI. Joule, SAP’s generative AI copilot, is differentiated by direct integration across SAP’s broad set of business applications. Consider the basic example of a sales manager who oversees customer orders.  Since Joule is integrated across SAP’s business applications, the sales manager will be able to use Joule to check inventory status and potential supply chain issues causing a delay and ask Joule to monitor and resolve the issue quickly, improving workflow and customer experience. An AI copilot named Joule “Joule is the copilot throughout SAP’s cloud portfolio, including ERP,” explained Vinay V, cloud ERP solution expert for SAP. “Joule is the front end to the vast amounts of data and rich process information that resides within SAP. Joule will help to uncover and provide those insights for the end users.” The potential of Joule can be found throughout SAP’s solutions. A finance team, for instance, can automate fiscal decisions by using the AI to analyze past results, compare projected and actual financial performance, and make actionable recommendations for budget management. Supply chain management teams can ask Joule for smart predictions to optimize inventories and order fulfillment.  The power of AI is even more apparent when it can reach across disciplines, divisions and silos in organizations and coordinate the response. These cross-disciplinary opportunities may offer the greatest advantages for enterprises because they can unlock synergies that weren’t being tapped before. “We’re seeing and developing multiple use cases across each of these application areas,” explained V. “This is where we see there is a huge potential for making an impact and liberating the technology to help, running through these processes much more efficiently. We see this across finance, in supply chain, including manufacturing, delivery management, production-related activities and also in asset management.” While many of these complex opportunities are just beginning to be explored, AI will have obvious applications for the front-line teams that manage customers, both existing and future. SAP wants to marry their deep reservoir of transaction data with the ability of LLMs to add a more human layer to personalized responses. The ability of large language models to work with human languages opens up the possibilities for humans to work more naturally and efficiently with the underlying SAP systems as well. “With Joule, the end users, irrespective of their know-how, irrespective of their background or their familiarity with the systems, can use natural language to interact.” said V. “That’s a significant change in the way that end users will not only be able to interact, but be able to uncover greater insights on what’s happening within the system.” Embracing the cloud In essence, SAP is driving a foundational change for its customers. While end-user AI applications may be the most visible, SAP is also using generative AI to help customers migrate to the cloud faster. Moving to the cloud can help simplify operations and reduce costs for businesses. At SAP’s annual TechEd event, the company announced a new generative AI functionality to encourage and support customers to speed up this transition.  “Customers moving their ERP systems from on-premise to the cloud is a significant endeavor. No jokes about that,” explained Pratibha Kumar Sood, vice president of cloud ERP product marketing at SAP. That is why SAP is investing in generative AI capabilities to help customers get proactive guidance and task automation to help them on their journey. While this generative AI capability is still in beta, SAP plans to roll out this feature more broadly in Q1 of 2025 through its RISE with SAP program and has developed a rigorous process to help customers along the path of embracing the cloud — and any of the AI that’s available there. “This is where the RISE with SAP methodology comes in to help with SAP expert support, tools and best-practice guidance” said Sood.  “The methodology is not just theory — here are the steps and here are the phases — but it’s SAP providing expert guidance. We will have our architects, our SAP team of advisors to support the RISE customers right from day one.” SAP is also enhancing tools like SAP Build with generative AI capabilities that allow both developers and business users to create scalable, secure and stable extensions to their cloud ERP. These capabilities are designed to promote developer productivity with tools for automated code generation and code explanation. AI is also strengthening the process of data governance in organizations by bringing continuity to their business. “We’re leveraging AI to flag and quickly summarize the various changes that are made, or that are pending for a particular master data object,” explained V.  In the end, all of the seemingly magical powers of AI depend entirely on the quality of the data. The enterprises that rely upon SAP have been using it as a stable platform for back-office tasks like recording transactions or tracking inventory. Now they’re able to unlock new answers sealed away in this data to uncover trends and patterns with the complex analytical AI algorithms. To learn more, register for SAP’s RISE Into the Future virtual event taking place on October 22, 2024. Sponsored articles are content produced by a company that

This year feels different. Global geopolitical, economic, and technological shifts are compelling businesses in the Asia Pacific region to adapt like never before. In our June forecast, we captured how tech spending in APAC is transforming in response to this evolving landscape. We discussed how enterprises across the region are embedding adaptivity into the core of their business processes and, naturally, their IT capabilities as they respond to an ever-changing world — and boy is the world changing! AI Sets Us On A New S-Curve AI remains the elephant in the room. Last year’s unbridled hype around large language models and generative AI has given way to more pragmatism. Around 75% of my client interactions thus far in 2024 have had at least some AI flavor. Engaging with generative AI has grown more sophisticated, enabling developers to move past simple techniques like prompting over public APIs. They’re now creating advanced generative AI applications using emerging techniques such as retrieval-augmented generation and agentic AI. This has allowed organizations to unlock a whole new range of use cases for generative AI, surpassing the first generation of prompt-based chatbots and embedding AI more deeply in business operations. Many of today’s tech leaders began their careers during a similarly transformative period: the rise of digital. Over the past two decades, a first wave of digital technologies reshaped industries, empowering early adopters to create unprecedented value for customers and shareholders. As digital became mainstream, however, its impact plateaued. Once a competitive advantage, digital innovation is now seen as a basic requirement. The explosive growth flattened from a hockey-stick trajectory to a steady S-curve. Now, a new inflection point is upon us. Emerging AI and automation technologies promise to redefine business value by making processes smarter and more autonomous. We are at the beginning of a fresh S-curve, poised for another era of exponential growth and transformative impact. You Need Adaptive IT Capabilities As companies ready themselves for these transformational changes, they need to ensure that their IT capabilities are adaptive. Today’s organizations are often caught up in a perpetual balancing act between managing the costs of legacy on one hand and setting themselves up for innovation and value creation on the other. Forrester’s high-performance IT framework offers a methodology for technology leaders to apply to manage the transition to AI-infused, autonomous IT capabilities while staying nimble and pragmatic. In my session at Technology & Innovation Summit APAC 2024, I will be talking about: The key characteristics of an adaptive mindset. How to ensure that your tech investments remain agile and responsive to business needs in times of radical change. Real-world examples of companies that have successfully adopted an adaptive mindset along their journey to AI-driven transformation. Join Us In Sydney! Don’t miss this opportunity to join me, my fabulous colleagues, and your executive peers at this event. Register now for Forrester’s Technology & Innovation Summit APAC and take the first step toward transforming your business with the power of tech, talent, and AI. Reserve your place today and be part of a community that’s shaping the future of technology. For more details and to register, visit the event website. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Nvidia quietly unveiled a new artificial intelligence model on Tuesday that outperforms offerings from industry leaders OpenAI and Anthropic, marking a significant shift in the company’s AI strategy and potentially reshaping the competitive landscape of the field. The model, named Llama-3.1-Nemotron-70B-Instruct, appeared on the popular AI platform Hugging Face without fanfare, quickly drawing attention for its exceptional performance across multiple benchmark tests. Nvidia reports that their new offering achieves top scores in key evaluations, including 85.0 on the Arena Hard benchmark, 57.6 on AlpacaEval 2 LC, and 8.98 on the GPT-4-Turbo MT-Bench. These scores surpass those of highly regarded models like OpenAI’s GPT-4o and Anthropic’s Claude 3.5 Sonnet, catapulting Nvidia to the forefront of AI language understanding and generation. Nvidia’s AI gambit: From GPU powerhouse to language model pioneer This release represents a pivotal moment for Nvidia. Known primarily as the dominant force in graphics processing units (GPUs) that power AI systems, the company now demonstrates its capability to develop sophisticated AI software. This move signals a strategic expansion that could alter the dynamics of the AI industry, challenging the traditional dominance of software-focused companies in large language model development. Nvidia’s approach to creating Llama-3.1-Nemotron-70B-Instruct involved refining Meta’s open-source Llama 3.1 model using advanced training techniques, including Reinforcement Learning from Human Feedback (RLHF). This method allows the AI to learn from human preferences, potentially leading to more natural and contextually appropriate responses. With its superior performance, the model has the potential to offer businesses a more capable and cost-efficient alternative to some of the most advanced models on the market. The model’s ability to handle complex queries without additional prompting or specialized tokens is what sets it apart. In a demonstration, it correctly answered the question “How many r’s are in strawberry?” with a detailed and accurate response, showcasing a nuanced understanding of language and an ability to provide clear explanations. What makes these results particularly significant is the emphasis on “alignment,” a term in AI research that refers to how well a model’s output matches the needs and preferences of its users. For enterprises, this translates into fewer errors, more helpful responses, and ultimately, better customer satisfaction. How Nvidia’s new model could reshape business and research For businesses and organizations exploring AI solutions, Nvidia’s model presents a compelling new option. The company offers free hosted inference through its build.nvidia.com platform, complete with an OpenAI-compatible API interface. This accessibility makes advanced AI technology more readily available, allowing a broader range of companies to experiment with and implement advanced language models. The release also highlights a growing shift in the AI landscape toward models that are not only powerful but also customizable. Enterprises today need AI that can be tailored to their specific needs, whether that’s handling customer service inquiries or generating complex reports. Nvidia’s model offers that flexibility, along with top-tier performance, making it a compelling option for businesses across industries. However, with this power comes responsibility. Like any AI system, Llama-3.1-Nemotron-70B-Instruct is not immune to risks. Nvidia has cautioned that the model has not been tuned for specialized domains like math or legal reasoning, where accuracy is critical. Enterprises will need to ensure they are using the model appropriately and implementing safeguards to prevent errors or misuse. The AI arms race heats up: Nvidia’s bold move challenges tech giants Nvidia’s latest model release signals just how fast the AI landscape is shifting. While the long-term impact of Llama-3.1-Nemotron-70B-Instruct remains uncertain, its release marks a clear inflection point in the competition to build the most advanced AI systems. By moving from hardware into high-performance AI software, Nvidia is forcing other players to reconsider their strategies and accelerate their own R&D. This comes on the heels of the company’s introduction of the NVLM 1.0 family of multimodal models, including the 72-billion-parameter NVLM-D-72B. These recent releases, particularly the open-source NVLM project, have shown that Nvidia’s AI ambitions go beyond just competing—they are challenging the dominance of proprietary systems like GPT-4o in areas ranging from image interpretation to solving complex problems. The rapid succession of these releases underscores Nvidia’s ambitious push into AI software development. By offering both multimodal and text-only models that compete with industry leaders, Nvidia is positioning itself as a comprehensive AI solutions provider, leveraging its hardware expertise to create powerful, accessible software tools. Nvidia’s strategy seems clear: it’s positioning itself as a full-service AI provider, combining its hardware expertise with accessible, high-performance software. This move could reshape the industry, pushing rivals to innovate faster and potentially sparking more open-source collaboration across the field. As developers test Llama-3.1-Nemotron-70B-Instruct, we’re likely to see new applications emerge across sectors like healthcare, finance, education, and beyond. Its success will ultimately depend on whether it can turn impressive benchmark scores into real-world solutions. In the coming months, the AI community will closely watch how Llama-3.1-Nemotron-70B-Instruct performs in real-world applications beyond benchmark tests. Its ability to translate high scores into practical, valuable solutions will ultimately determine its long-term impact on the industry and society at large. Nvidia’s deeper dive into AI model development has intensified the competition. If this is the beginning of a new era in artificial intelligence, it’s one where fully integrated solutions may set the pace for future breakthroughs. source

Over the last 30 years, the mobile phone industry has been through two major revolutions. The first revolution began when the mobile phone emerged, transforming the way we communicate by introducing mobile communications into our lives. The second revolution emerged in the latter half of these three decades when smartphones disrupted everything else in our lives. Today, with 3.1 billion smartphones in use globally, these devices play a critical role in the world. The latest technological development that has taken the tech world by storm is the launch of AI-powered smartphones. Although AI is not new, it gained significant attention with the launch of ChatGPT and the capabilities of Generative AI (GenAI). Leveraging Large Language Models (LLMs), a new revolution is coming to the smartphone – intelligence. Defining AI Smartphones* According to IDC, Gen AI smartphones are defined as devices featuring a system-on-a-chip (SoC) capable of running on-device Generative (Gen AI) models more quickly and efficiently leveraging a neural processing unit (NPU) with 30 Tera Operations Per Second (TOPS) or more, using the int-8 data type. The smartphone SoCs being designed and marketed by silicon vendors with next-gen AI smartphones in mind will increase in the future as they continue to push forward the NPU technology. However, to date, here are a few that qualify based on the definition above: Apple A17 Pro MediaTek Dimensity 9300 Qualcomm Snapdragon 8 Gen 3 Samsung Exynos 2400 Market Opportunity The latest IDC forecast estimates that Gen AI smartphone shipments will grow 364% year-over-year in 2024, reaching 234.2 million units. Despite the current macroeconomic environment and the fact that consumers are keeping their devices longer, the potential of Gen AI on a smartphone is expected to drive significant demand over the coming years. This segment is projected to be the fastest-growing segment in the smartphone category during the forecast period, outperforming the non-AI-enabled smartphone segment. Growth will continue into 2025 with an expected increase of 73.1%, followed by moderate double-digit growth for the rest of the forecast period. By 2028, worldwide Gen AI smartphone shipments will reach 912 million units, resulting in a compound annual growth rate (CAGR) of 78.4% for 2023-2028. Reshaping the Mobile Experience AI will enable manufacturers to offer unique and intelligent features, experiences and even services to their users. Since the introduction of the first iPhone and Android smartphones in 2007 and 2008, and particularly after the introduction of app marketplaces by Apple and Google, users have become used to interacting with the smartphone by using apps. The more powerful the apps, the better it is. With AI, the fewer apps the phone will need and the more capable can use data contextually to assist the user, the better it will be. This “app-less” world will revolutionize the user experience, requiring the phone to better “know” its users while ensuring personal data remains private and secure. The interaction with the smartphone will shift from touch to voice, as “intelligent” voice assistants become our true personal digital assistants. These conversational digital assistants, fully integrated with the device, will be game-changers, providing compelling reasons for users to upgrade their smartphones. Although a full AI experience is still in development, less than 18 months after the introduction of ChatGPT, several vendors announced their AI strategies and devices showcasing some intelligent capabilities. These include: Samsung Galaxy S24 Ultra: Some of the key AI features include a transcription summariser built into the voice recorder, real-time voice translation, and Circle to Search, a tool developed by Google that allows users to draw a circle around anything on screen and search it on Google. Xiaomi 14 Ultra: Features AI-generated subtitles for video calls and an AI Portrait feature that lets users take a selfie and add a different background. Google Pixel 8 Pro: Offers features like summarising recorded conversations, suggesting replies to messages, and creating AI-generated wallpapers. The camera also benefits from AI with Magic Editor (moving or removing objects); Best Take (selecting the best shot), and Video Boost (enhancing video colour and lighting). Apple Intelligence: The new suite of AI features will come to the iPhone, iPad and Mac later this year with the latest OS versions announced at Apple Worldwide Developers Conference. The AI features will include rewriting text and proofreading, generating email replies, and content summarization. Users will be able to generate images from text based on note contents and remove objects from photos. Siri, Apple’s digital assistant will become more conversational. OPPO AI Strategy: Betting big on AI, OPPO aims to incorporate over 100 Gen AI features across its lineup of AI-enabled smartphones in 2024. Unlike other players, OPPO aims to democratize AI by introducing these features to more affordable price points. Honor Magic 6 Pro: The device promises AI-powered user experiences and it is the first Honor’s all-scenario strategy, featuring cross-OS collaboration and AI designed with a human-centric approach. Motorola Razr 50 Ultra: Will run Google Gemini as the main digital assistant, offering AI out of the box. Features include recognizing photos, summarizing text, answering voice queries, and changing message tones before sending. It includes Motorola’s AI tool, Style Sync, which creates a wallpaper based on the colors and patterns of a specific photo. Use Cases Gen AI smartphones are expected to disrupt different aspects of our lives. IDC identified several use cases that can drive adoption and have a positive impact: Work Environment: According to an IDC survey, employees view the smartphone as one of the most important tools in the workspace. AI will streamline tasks by summarizing content from meetings and documents, allowing users to focus on discussions. AI will also summarize email threads, suggest replies based on the conversation, and help manage calendars based on requests received via email or messages. Healthcare and Wellbeing: Smartphones have become central to various wearables (through apps) that collect data on vital body signals, such as blood pressure, heart rate, and blood oxygen. AI will monitor this data from all sensors, alerting users to potential risks and suggesting dietary and exercise plans

One of the oldest security technologies — the venerable enterprise firewall — continues to thrive, as highlighted in the recently published report, The Forrester Wave™: Enterprise Firewall Solutions, Q4 2024. Contrary to expectations that this space might have little left to offer, enterprise firewall vendors have done well to keep this technology relevant for modern cybersecurity needs. They have made significant progress in keeping up with rapid innovations while supporting clients in securing dispersed and hybrid enterprise architectures. While enterprise firewalls continue to be delivered in the same manner, vendors have made the move to offer these capabilities as part of other “platform” initiatives such as Zero Trust edge/secure access service edge (ZTE/SASE) to not only make security enterprise firewalls more accessible to improve their adoption but to also increase value retention, not just for large enterprises but also for small- and medium-sized enterprises. Consolidate, Centralize, And Deliver A Unified Management Experience Clients require a consistent and streamlined method for managing various deployments of enterprise firewall solutions. This involves having a unified UX/UI across physical, virtual, and cloud deployments and recognizing the need to support adjacent efforts like ZTE/SASE. Consequently, leading enterprise firewall solutions now offer integrated and unified management for data center, branch, and edge use cases, which include: As-a-service offerings. Zero Trust network access (ZTNA). Software-defined wide-area networks (SD-WAN). With this unified approach, clients can derive greater value from their enterprise firewall investments, enabling them to address use cases that secure both north-south and east-west traffic regardless of environment. Clients can streamline policies across various enforcement points, strategically creating and orchestrating policies at different levels of the transit path for multiple transient connections without having to navigate multiple administrative consoles. Common policy construct and centralized visibility, enhanced with built-in AI/ML, also improve policy optimization for enhanced incident response. Part Of The Bigger Picture It’s no surprise that the industry continues to push for cloud migration, prompting organizations to evaluate enterprise firewalls to ensure that they meet modern challenges and requirements without adding costs or complexity. The reality is that enterprises will have hybrid topologies for the foreseeable future, consisting of a mix of cloud, virtual, and physical environments, all of which need security. To advance toward a more mature Zero Trust architecture, it’s crucial for organizations to see the big picture and choose the right solutions for the long term. Enterprise firewall vendors have not only enhanced capabilities but also improved consumption models, making these solutions viable for securing cloud workloads, facilitating secure connectivity with integrated SD-WAN and ZTNA, and creating microperimeters. That last use case is a big deal, too, since 61% of global respondents in large enterprises view enterprise firewalls as essential for supporting a microsegmentation strategy, according to Forrester’s most recent Security Survey. The advancements in enterprise firewalls are transforming them from single-purpose tools into adaptable security solutions that can flexibly support an organization’s digital transformation journey. Shared Mission, Shared Outcomes The ZTE/SASE market is rapidly expanding, with many organizations seeing it as the ideal starting point for a Zero Trust architecture journey. And why not? As my colleague Andre Kindness highlights in his blog, this market is both disruptive and transformative. It allows organizations to replace legacy solutions with a consumable product as a service, merging networking and security stacks. But what if you want to keep your firewall investment? Enterprise firewall vendors are addressing this by converging and consolidating their solutions to support and integrate ZTE/SASE. This approach simplifies adoption while preserving the value of existing deployments for organizations with ongoing on-premises needs. Whether the future involves moving to the cloud or not, the mission remains the same: Maintain comprehensive security everywhere, at all times. While the leaders in this space have advanced this strategy, other vendors are not too far behind and are poised to offer cost-effective offerings for smaller enterprises and other organizations. You can read more about my findings and view each vendor’s strengths and weaknesses in the Wave report. Forrester clients, please reach out to schedule guidance sessions or inquiries with me to discuss our findings. If you’re feeling bold, join me at Forrester’s Security & Risk Summit in Baltimore on December 9–11, where I will host two sessions on Zero Trust that include a workshop and a panel discussion for getting your Zero Trust journey to the next level. Hope to see you there! source