In the quest to reach the full potential of artificial intelligence (AI) and machine learning (ML), there’s no substitute for readily accessible, high-quality data. If the data volume is insufficient, it’s impossible to build robust ML algorithms. If the data quality is poor, the generated outcomes will be useless. Data silos, lack of standardization, and uncertainty over compliance with privacy regulations can limit accessibility and compromise data quality, but modern data management can overcome those challenges. By partnering with industry leaders, businesses can acquire the resources needed for efficient data discovery, multi-environment management, and strong data protection. To fully leverage AI and analytics for achieving key business objectives and maximizing return on investment (ROI), modern data management is essential. The power of modern data management Modern data management integrates the technologies, governance frameworks, and business processes needed to ensure the safety and security of data from collection to storage and analysis. It enables organizations to efficiently derive real-time insights for effective strategic decision-making. Some of the key applications of modern data management are to assess quality, identify gaps, and organize data for AI model building. It’s also useful in countering the pressing IT talent shortage, in many cases providing the deep and broad expertise that few organizations can maintain in house. Partnering for greater value generation SAS and Intel customers have found that the strengths of each company – SAS’s advanced analytics and Intel’s high-performance computing – are magnified through their “better together” approach. Together, they offer complementary tools and services to achieve data discovery, gain access to real-time insights, implement multi-environment data management, and embed data protection at the chip level. “Tasks such as data analysis, machine learning, and predictive analytics require high performance, which Intel’s latest processors provide,” noted Bruno Domingues, CTO for Intel’s financial services industry practice. “The faster data is processed, the quicker actionable insights can be generated.” And that processing speed need not be hampered by the quest for perfection. The goal of modern data management is not to make data pristine. “It’s impossible,” says Shadi Shahin, Vice President of Product Strategy at SAS. “Trying to clean the data and make it perfect is not going to work. Understanding the use of the data is critical – it must be fit for purpose.” Achieving ROI from AI requires both high-performance data management technology and a focused business strategy. Organizations that are determined to control costs, minimize risk, and maximize productivity in their execution of an AI strategy should start small, leverage state-of-the-art technology, and work with trusted partners. Getting trusted results There’s no need for any organization to rely on traditional data management, data prep, and algorithms. “You can get value out of data much faster,” notes Shahin, “whether through recommendation engines, automated machine learning pipelines, or other modern techniques designed to solve legacy problems.” Together, SAS and Intel accelerate the journey to value realization. “You can start quickly and show value quickly,” adds Shahin. “You don’t need a multiyear project to show value in your data.” Check out this webinar to learn more tips and strategies for building a data foundation for AI-driven business growth. source

There is no doubt that project management solutions can help streamline workflows and boost team productivity. While the advanced features available have a wide variety of use cases, for many people a basic or free version is enough to get the job done. A solopreneur or small team may opt-in for the free version of a project management tool as a way to get organized on a budget. There is also benefit to business leaders or project managers trying out a free version as a way to test workflows before upgrading to a paid plan This review highlights the best free project management tools, including who and what they are best for, their core features, and the limitations of the free tiers. You will also find information on the benefits and challenges of using free software for project management and my methodology for compiling this list. 1 monday.com Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Any Company Size Any Company Size Features Agile Development, Analytics / Reports, API, and more 2 Wrike Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Medium (250-999 Employees), Enterprise (5,000+ Employees), Large (1,000-4,999 Employees) Medium, Enterprise, Large Features Agile Development, Analytics / Reports, API, and more 3 ClickUp Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Micro (0-49 Employees), Medium (250-999 Employees), Enterprise (5,000+ Employees), Large (1,000-4,999 Employees), Small (50-249 Employees) Micro, Medium, Enterprise, Large, Small Features Analytics / Reports, API, Billing / Invoicing, and more Top free project management software and tools comparison This table compares the top free project management tools in terms of relevant features, support, user limit, and storage for free plans, plus the starting price for paid plans if you decide to upgrade. Star rating User limit (free plan) Starting price (per user, per month) Gantt charts Free plan storage 24/7 customer support ClickUp 5/5 Unlimited $10 Yes 100MB Yes Wrike 4.6/5 Unlimited ​​$9.80 (billed annually) Only with paid plans 2GB per account No monday.com 5/5 2 $12 Yes 500MB Yes Notion 4.5/5 10 guest collaborators $12 Limited (Gantt chart template) 5MB No Smartsheet 3.8/5 1 user and 2 editors $9 Yes 500MB Yes MeisterTask 5/5 3 projects and 5 notes $9 Limited (Timeline feature) 20 MB per file Yes Trello 4.5/5 Unlimited $6 Not available Unlimited storage (10MB/file) No Asana 3.9/5 10 $13.49 Only with paid plans Unlimited storage (100MB per file) Yes Airtable 3.8/5 ​​5 editors $24 Only with paid plans 1GB of attachments per base No Teamwork 3.7/5 5 $13.99 Yes 100MB Yes Image: ClickUp ClickUp: Best overall ClickUp is a popular project management tool known for its extensive features. Even the free version of ClickUp is loaded with useful tools for project management. This includes unlimited users, activity views and custom fields. The free plan allows you to create up to five spaces—one for each project flow. Why I chose ClickUp ClickUp is a multifaceted project management tool, even in its free tier. It offers most standard views you’d expect from a great project management tool in the free version, including the task List, Board, Calendar, Table, Doc, and Chat views. All the other views have restrictions or are unavailable for free, but they are options if you choose to pay for a higher tier plan later. ClickUp is highly customizable and has lots of integrations, and the free version gives you a taste of workload management, dashboards, reminders, mind maps, whiteboards, and custom fields. For more information, check out our full ClickUp review. Standout features of the free version ClickUp Docs: You can create documents and connect them to workflows. You can also edit and share the documents in real time and set access permission rules for each document. ClickUp Goals: With this feature, you can create trackable objectives for each task or project. You can also assign various objectives to a single goal. Integrations: ClickUp boasts over 1,000 tools, with some of the top native ones including ClickUp API, Slack, GitHub, GitLab, HubSpot, Toggl, Harvest, Google Drive, Figma, and Microsoft Teams. ClickUp Agile board view. Image: ClickUp ClickUp pros and cons Pros Cons Feature-rich free plan. Maximum of 100 uses of custom fields. Unlimited free plan users. Free plan file storage is limited to 100MB. Easy to create and customize reports. What you get when you upgrade to the paid plans The table below includes the core features you will get when you subscribe to any of ClickUp’s paid plans. Unlimited Business Enterprise Monthly billing price per user $10 $19 Price available upon request Annual billing price per user $7 per month $12 per month Price available upon request Top features Everything in Free Forever, plus:– Unlimited storage– Unlimited integrations– Unlimited dashboards– Guests with permissions– Unlimited Gantt charts– Unlimited custom fields– Column calculations– Email in ClickUp– Teams (user groups)– Native time tracking– Goals & portfolios– Form view Everything in Unlimited, plus:– Google SSO– Unlimited teams– Custom exporting– Advanced public sharing– Advanced automations– Advanced project dashboard features– Advanced time tracking– Granular time estimates– Timesheets– Workload management– Timelines & mind maps– Goal folders Everything in Business, plus:– White labeling– Advanced permissions– Conditional logic in forms– Enterprise API– Unlimited custom roles– Team sharing for spaces– Universal search– Default personal views– MSA & HIPAA available– Single sign-on (SSO)– Custom capacity in workload– Live onboarding training When to use an alternative to ClickUp You need more file storage Though ClickUp offers a generous free plan, it only provides 100MB of file storage. If you need more storage space, you may need to consider using a ClickUp alternative that offers more storage capacity. Best ClickUp alternative: Asana The Asana free plan gives you unlimited file storage with a limit of 100MB per file. Image: Wrike Wrike: Best for individuals Wrike is an adaptable project management software suitable for all industries. It’s also highly customizable and offers robust collaboration tools. The free plan is ideal for solopreneurs and small businesses. Users

By Dorothy Atkins ( November 15, 2024, 9:55 PM EST) — X Corp. filed a lawsuit in California federal court seeking to block a new Golden State law aimed at combating artificial intelligence-generated deepfake political ads, claiming the regulation that takes effect in January is unconstitutional and violates Section 230 of the Communications Decency Act…. 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

By Andrew Karpan ( November 19, 2024, 10:00 PM EST) — A Texas federal judge shot down Indian tech company Infosys Ltd.’s efforts to seal nondisclosure agreements involved in a trade secrets case over healthcare software, ruling that there was “nothing commercially sensitive” about them…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Come the new year, the incoming Trump administration is expected to make many changes to existing policies, and AI regulation will not be exempt. This will likely include repealing an AI executive order by current President Joe Biden. The Biden order established government oversight offices and encouraged model developers to implement safety standards. While the Biden AI executive order rules focus on model developers, its repeal could present some challenges for enterprises to overcome. Some companies, like Trump-ally Elon Musk’s xAI, could benefit from a repeal of the order, while others are expected to face some issues. This could include having to deal with a patchwork of regulations, less open sharing of data sources, less government-funded research and more emphasis on voluntary responsible AI programs.  Patchwork of local rules Before the EO’s signing, policymakers held several listening tours and hearings with industry leaders to determine how best to regulate technology appropriately. Under the Democratic-controlled Senate, there was a strong possibility AI regulations could move forward, but insiders believe the appetite for federal rules around AI has cooled significantly.  Gaurab Bansal, executive director of Responsible Innovation Labs, said during the ScaleUp: AI conference in New York that the lack of federal oversight of AI could lead states to write their policies.  “There’s a sense that both parties in Congress will not be regulating AI, so it will be states who may run the same playbook as California’s SB 1047,” Bansal said. “Enterprises need standards for consistency, but it’s going to be bad when there’s a patchwork of standards in different areas.”  California state legislators pushed SB 1047 — which would have mandated a “kill switch” to models among other government controls — with the bill landing on Gov. Gavin Newsom’s desk. Newsom’s veto of the bill was celebrated by industry luminaries like Meta’s Yann Le Cunn. Bansal said states are more likely to pass similar bills.  Dean Ball, a research fellow at George Mason University’s Mercatus Center, said companies may have difficulty navigating different regulations.  “Those laws may well create complex compliance regimes and a patchwork of laws for both AI developers and companies hoping to use AI; how a Republican Congress will respond to this potential challenge is unclear,” Ball said.  Voluntary responsible AI  Industry-led responsible AI has always existed. However, the burden on companies to be more proactive in being accountable and fair may heighten because their customers demand a focus on safety. Model developers and enterprise users should spend time implementing responsible AI policies and building standards that meet laws like the European Union’s AI Act.  During the ScaleUp: AI conference, Microsoft Chief Product Officer for Responsible AI Sarah Bird said many developers and their customers, including Microsoft, are readying their systems for the EU’s AI act.  But even if no sprawling law governs AI, Bird said it’s always good practice to bake responsible AI and safety into the models and applications from the onset.  “This will be helpful for start-ups, a lot of the high level of what the AI act is asking you to do is just good sense,” Bird said. “If you’re building models, you should govern the data going into them; you should test them. For smaller organizations, compliance becomes easier if you’re doing it from scratch, so invest in a solution that will govern your data as it grows.” However, understanding what is in the data used to train large language models (LLMs) that enterprises use might be harder. Jason Corso, a professor of robotics at the University of Michigan and a co-founder of computer vision company Voxel51, told VentureBeat the Biden EO encouraged a lot of openness from model developers.  “We can’t fully know the impact of one sample on a model that presents a high degree of potential bias risk, right? So model users’ businesses could be at stake if there’s no governance around the use of these models and the data that went in,” Corso said. Fewer research dollars  AI companies enjoy significant investor interest right now. However, the government has often supported research that some investors feel is too risky. Corso noted that the new Trump administration might choose not to invest in AI research to save on costs.  “I just worry about not having the government resources to put it behind those types of high-risk, early-stage projects,” Corso said. However, a new administration does not mean money will not be allocated to AI. While it’s unclear if the Trump administration will abolish the newly created AI Safety Institute and other AI oversight offices, the Biden administration did guarantee budgets until 2025. “A pending question that must color Trump’s replacement for the Biden EO is how to organize the authorities and allocate the dollars appropriated under the AI Initiative Act. This bill is the source for many of the authorities and activities Biden has tasked to agencies such as NIST and funding is set to continue in 2025. With these dollars already allocated, many activities will likely continue in some form. What that form looks like, however, has yet to be revealed,” Mercatus Center research fellow Matt Mittelsteadt said.  We’ll know how the next administration sees AI policy in January, but enterprises should prepare for whatever comes next.  source

defis-next-frontier-trident LinkedIn Email Facebook Twitter WhatsApp The post 行業研究報告: DeFi 下一個前沿 – 全面深度報告 appeared first on VeriMedia. source

Generative AI (GenAI) is rapidly emerging as a game changer for enterprises, but turning its potential into measurable value remains a significant challenge. According to a recent IDC study (Future Enterprise Resiliency and Spending Survey, Wave 4, IDC, April 2024), companies are conducting an average of 37 GenAI proofs of concept (POCs), with only five advancing to production. Of those, just three are considered successful. This stark contrast between experimentation and execution underscores the difficulties in harnessing AI’s transformative power. To bridge this gap, CIOs and technology leaders must not only identify the barriers but also adopt strategic approaches to improve the success rate and deliver real business value from GenAI initiatives. Let’s discuss the barriers and solutions for them. Data privacy and compliance issues Failing: Mismanagement of internal data with external models can lead to privacy breaches and non-compliance with regulations. Solution: Implement robust data governance frameworks and ensure compliance with regulations like GDPR and CCPA. Use anonymization and encryption techniques to protect sensitive data. Key takeaway: Prioritize data privacy and compliance to build trust and avoid legal repercussions. Bias and hallucinations Failing: GenAI models often produce biased or inaccurate outputs, leading to misinformation and potential legal issues. Solution: Regularly audit and retrain models using diverse and representative data sets. Implement bias detection and mitigation tools. Key takeaway: Continuous monitoring and updating of AI models are essential to minimize bias and improve accuracy. Provide transparency back to the original data source to allow verification of information. High costs Failing: The infrastructure and computational costs for training and running GenAI models are significant. Solution: Optimize models for efficiency, leveraging cloud-based solutions. But don’t forget to assess whether a private cloud option or a small language model will address your concerns. Key takeaway: Cost management strategies are crucial for sustainable AI deployment. We’ve already seen people struggle with cloud budgets; we are seeing a similar pattern with GenAI. Integration challenges Failing: Integrating AI into existing systems can be technically and operationally challenging. Solution: Develop a clear integration road map, invest in middleware solutions, and ensure cross-functional collaboration. Key takeaway: A well-planned integration strategy can smooth the transition and maximize AI benefits. Scalability issues Failing: AI solutions that work in controlled environments may struggle to scale effectively in real-world conditions. Solution: Conduct thorough scalability testing and use modular architectures to facilitate easier scaling. Key takeaway: Scalability should be a core consideration from the outset to ensure long-term success. Lack of clear use cases Failing: Difficulty in identifying specific business needs that GenAI can address. Solution: Engage stakeholders to identify pain points and opportunities where AI can add value. Pilot projects can help validate use cases. Key takeaway: Clear, well-defined use cases are essential for demonstrating AI’s value. Look for super use cases that address multiple opportunities rather than point solutions. Trust and oversight Failing: Lack of transparency and explainability in AI models can erode trust among users and stakeholders. Solution: Use explainable AI (XAI) techniques and maintain clear documentation of AI decision-making processes. Key takeaway: Transparency and explainability are key to building and maintaining trust in AI systems. Intellectual property risks Failing: GenAI can inadvertently use copyrighted material, leading to legal complications. Solution: Implement strict content sourcing policies and use AI tools that can verify the originality of generated content. Key takeaway: Protecting intellectual property is essential to avoid legal issues and maintain ethical standards. Conclusion GenAI offers transformative possibilities, but unlocking its true value demands more than just enthusiasm; it requires strategy, foresight, and resilience. To move from potential to impact, organizations must confront its unique challenges head-on with well-thought-out solutions. By zeroing in on critical lessons and proactively managing risks, businesses can not only mitigate the pitfalls but also position themselves to fully capitalize on the immense power of GenAI, driving innovation and delivering sustained value. Learn more about IDC’s research for technology leaders OR subscribe today to receive industry-leading research directly to your inbox. International Data Corporation (IDC) is the premier global provider of market intelligence, advisory services, and events for the technology markets. IDC is a wholly owned subsidiary of International Data Group (IDG Inc.), the world’s leading tech media, data, and marketing services company. Recently voted Analyst Firm of the Year for the third consecutive time, IDC’s Technology Leader Solutions provide you with expert guidance backed by our industry-leading research and advisory services, robust leadership and development programs, and best-in-class benchmarking and sourcing intelligence data from the industry’s most experienced advisors. Contact us today to learn more. Daniel Saroff is group vice president of consulting and research at IDC, where he is a senior practitioner in the end-user consulting practice. This practice provides support to boards, business leaders, and technology executives in their efforts to architect, benchmark, and optimize their organization’s information technology. IDC’s end-user consulting practice utilizes IDC’s extensive international IT data library, robust research base, and tailored consulting solutions to deliver unique business value through IT acceleration, performance management, cost optimization, and contextualized benchmarking capabilities. source

By Adam Lidgett ( November 18, 2024, 8:05 PM EST) — A U.S. International Trade Commission judge has found that claims in a pair of Ericsson patents were invalid, handing a win to Lenovo in a case over mobile phones, laptops and other related products…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Microsoft has quietly built the largest enterprise AI agent ecosystem, with over 100,000 organizations creating or editing AI agents through its Copilot Studio since launch – a milestone that positions the company ahead in one of enterprise tech’s most closely watched and exciting  segments. “That’s a lot faster than we thought, and it’s a lot faster than any other kind of cutting edge technology we’ve released,” Charles Lamanna, Microsoft’s executive responsible for the company’s agent vision, told VentureBeat. “And that was like a 2x growth in just a quarter.” The rapid adoption comes as Microsoft significantly expands its agent capabilities. At its Ignite conference starting today, the company announced it will allow enterprises to use any of the 1,800 large language models (LLMs) in the Azure catalog within these agents – a significant move beyond its exclusive reliance on OpenAI’s models. The company also unveiled autonomous agents that can work independently, detecting events and orchestrating complex workflows with minimal human oversight. (See our full coverage of today’s Microsoft’s agent announcements here.)  These AI agents – software that can reason and perform specific business tasks using generative AI – are emerging as a powerful tool for enterprise automation and productivity. Microsoft’s platform enables organizations to build these agents for tasks ranging from customer service to complex business process automation, while maintaining enterprise-grade security and governance. Building an enterprise-grade foundation Microsoft’s early lead in AI agents stems from its focus on enterprise requirements that often get overlooked in the AI hype cycle. While its new autonomous agents and LLM flexibility grabbed headlines at Ignite, the company’s real advantage lies in its enterprise infrastructure. The platform integrates with over 1,400 enterprise systems and data sources, from SAP to ServiceNow to SQL databases. This extensive connectivity lets organizations build agents that can access and act on data across their existing IT landscape. While enterprises can build custom agents from scratch, Microsoft has also launched ten pre-built autonomous agents targeting core business functions like sales, service, finance, and supply chain – to accelerate adoption for common enterprise use cases. The company did not provide any more detail about which types of agents customers are finding the most popular. But Lamanna said that aside from apps that IT departments are building for specific core tasks, there was a second category of apps that is more bottoms-up. This is where employees create Copilot agents to share their documents or presentations with their team or other partners, so that others can interact with the content and ask questions about it.  Security and governance features, often afterthoughts in AI deployments, are built into Microsoft’s core architecture. The platform’s control system ensures agents operate within enterprise permissions and data governance frameworks. “We think it will show up everywhere,” Lamanna told VentureBeat, “because whenever you have a technology that makes something possible that was previously impossible, all of you kind of are always shocked by how broadly it ends up being used.” He compared it with the Internet, where connectivity extended from the browser to the operating system, and fundamentally changed client-server architecture.  The LLM made a big breakthrough, Lamanna explains, in that it understands unstructured content – language or video or audio – and has shown the beginnings of reasoning, to make conclusions or judgments based on this data, Lamanna said. “So the browser, word processor, the core operating system experience, and the way you do sales processes and customer support processes – they all have to be reevaluated now that this capability exists…I don’t think there’ll be really any part of the stack in computing that doesn’t have some component reimagined as a result of all the agent and AI capabilities.” Early adopters are already seeing results. McKinsey reduced its project intake workflows from 20 days to just 2 days using automated routing agents. Pets at Home deployed fraud prevention agents in under two weeks, saving millions annually. Other companies using Copilot Studio include Nsure, McKinsey, Standard Bank, Thomson Reuters, Virgin Money, Clifford Chance and Zurich, Microsoft told VentureBeat. The Agent mesh: Microsoft’s vision for enterprise AI At the heart of Microsoft’s strategy is what Lamanna calls the “agent mesh” – an interconnected system where AI agents collaborate to solve complex problems. Rather than operating in isolation, agents can pass tasks, messages, and knowledge seamlessly across the enterprise. Copilot Studio has been associated so far with agents that are triggered via chat, but now Microsoft is emphasizing any kind of actions. Imagine an enterprise where agents collaborate seamlessly: A sales agent triggers an inventory agent to check stock availability, which then notifies a customer service agent to update the client. This architecture includes: Autonomous agents that detect events and trigger actions without human oversight An orchestration layer that coordinates multiple specialized agents Real-time monitoring tools that provide transparency into agent workflows Microsoft’s research arm recently released the Magnetic-One system based on the company’s Autogen framework, which establishes a sophisticated agent hierarchy: a managing agent maintains task checklists in an “outer loop” while specialized agents execute work in an “inner loop.” This architecture could potentially soon embrace tools like Microsoft’s OmniParser that let agents interpret UI elements, and showcases Microsoft’s technical lead in computer-using agents — matching capabilities being developed by Anthropic and Google. The company said it is working to bring this research into production, but did not specify how and when. Image: Microsoft Research’s Magentic-One multi-agent system, aims to solve open-ended web and file-based tasks, using two loops, and outer loop and an inner loop.   Microsoft’s approach addresses a key enterprise challenge: scaling from hundreds to potentially millions of agents while maintaining control. The platform enables companies to coordinate multiple specialized agents through its orchestration capabilities – an approach that aligns with a broader industry trend toward multi-agent systems. The platform’s pricing model reflects this enterprise focus. Rather than charging per token like most AI providers, Microsoft

News influencers – People who regularly post about current events and civic issues on social media and have at least 100,000 followers on any of Facebook, Instagram, TikTok, X (formerly Twitter) or YouTube. Political orientation – A measure of a news influencer’s partisan or ideological views. A right-leaning news influencer is one who publicly expresses that they identify as a Republican or conservative or support Donald Trump in the 2024 presidential election. A left-leaning news influencer is one who publicly expresses that they identify as a Democrat, liberal or progressive or support Vice President Kamala Harris (or supported President Joe Biden before he dropped out of the race) in the 2024 election. This information was found in the bio, profile picture, banner image, pinned posts or recent posts on an influencer’s social media account, any personal website or professional page, and prominent media coverage. Values and identities – Language or imagery in the bio, profile picture, banner image or pinned posts on an influencer’s social media account that expresses specific beliefs or identities. News organization affiliation – An influencer with this affiliation is one who either currently works for or previously worked for a news organization, as well as freelancers who have regularly contributed to news organizations. A news organization can be any news outlet that has a staff and multiple bylines. Researchers considered affiliated influencers to have this background regardless of their news organization’s political orientation, audience size or primary publishing method (digital, TV, print, etc.). Major social media sites – The five primary sites we studied, chosen based on audience size and the presence of discussion about news: Facebook, Instagram, TikTok, X (formerly Twitter) and YouTube. On social media, many news consumers get news directly from journalists and news organizations, including both individual reporters and institutional feeds. But news influencers are mostly voices from beyond the newsroom: About three-quarters (77%) have no background or affiliation with a news organization. Roughly a quarter (23%) of news influencers work for a news organization (or did so in the past). To compare news influencers with experience producing journalism within the news industry and those who came to prominence outside of it, researchers looked for past or present affiliations with news media organizations. These groups have different career experiences and potentially different levels of formal journalistic training. News influencers with a news organization affiliation include former CNN analyst Chris Cillizza, who now has his own Substack newsletter, as well as people with a current affiliation like Fox News host Jesse Watters and local journalists like Olivia DiVenti. Those without an affiliation have a wide variety of backgrounds, including podcaster Mike Figueredo and activist Charlie Kirk. How we define ‘news organization affiliation’ Researchers categorized all news influencers based on whether they are currently or were previously employed by a news organization. The list of news organizations that people in this category work for (or used to work for) is broad, ranging from long-standing newspapers to cable news channels to newer digital outlets. News organizations are defined as all outlets with a staff and multiple bylines. Researchers considered affiliated influencers to have this background regardless of their news organization’s political orientation, audience size or primary publishing method (digital, TV, print, etc.). News influencers from the news industry are more reserved in expressing political views There are several differences between news influencers who have worked for a news organization and those who have not. For one, news influencers with a connection to the news industry are less likely to explicitly advertise their political orientations. About two-thirds of those who’ve worked for a news organization (64%) do not express a clear political orientation in their social media profile, posts, personal website or media coverage, compared with 44% of those without that background. Similar shares of news influencers with and without connections to news organizations identify as right-leaning (25% and 27%, respectively). But news influencers from news organizations are less likely to explicitly identify as left-leaning. Just 9% of news influencers with a current or former connection to a news organization say they are liberal or Democratic. Among those with no such background, 25% identify with the political left. It is also much more common for news influencers without a background in the news industry to openly link themselves to certain values or identities in their social media profiles. About one-in-five influencers who have not worked for a news organization (22%) do this, compared with just 2% of those who have worked for a news organization. In particular, news influencers not tied to the news industry are more likely to show support for LGBTQ+ rights or identify as LGBTQ+ (8%). None of those with news industry backgrounds express this appeal.  No news influencers in the sample who have worked for news organizations express a position on abortion or a pro-Ukraine stance in their accounts, while small shares of those with no affiliation with a news organization identify with these positions. News influencers who have not worked for a news organization are more likely to be on video sites and manage fan communities News influencers with and without links to news organizations also differ in where and how they engage with audiences. X is the most widely used social media site among all news influencers overall, but those who have been affiliated with news organizations are more likely to use the site than other news influencers (96% vs. 82%). A previous Pew Research Center survey also indicates that the site formerly known as Twitter ranks at the top of social media sites for work-related tasks among journalists. And U.S. adults who regularly get news on X also are more likely to say they get news from journalists and news organizations than those who regularly get news on other social media sites. By contrast, news influencers without a background in the news industry are more likely to be on YouTube and TikTok than those with that background. Half of these news influencers have a YouTube presence, roughly twice