Fraunhofer FIT Generative AI Lab

🌟 The Rise of Text-to-Video AI: Tools, Benefits, and Challenges What is Text-to-Video AI? Text-to-Video AI transforms written content like articles or scripts into videos by extracting extracts key messages and generating videos with visuals, voiceovers, transitions, and background music. This automation reduces the time and effort needed for video production. Popular Text-to-Video AI Tools Sora: This AI Sora can convert text into high-quality, realistic videos with visuals, audio, and emotional expressions simulating cinematic scenes. Currently only available to selected visual artists, Sora is expected to revolutionize video production once it's more widely accessible. Lumen5: Lumen5 transforms written content such as blog posts and articles into engaging videos using customized templates. It’s a popular choice for marketers and businesses aiming to create video content quickly and efficiently. Synthesia: Synthesia generates videos using AI avatars speaking in over 120 languages. With a vast library of avatars to choose from, it is ideal for creating video presentations, tutorials, and marketing content. 💡 Why Use Text-to-Video AI? 🗣️ Enhanced Communication: Complex ideas are easier to understand when supported by visuals. Text-to-video simplifies messaging through dynamic videos, overcoming communication barriers. 📈Powerful Marketing: Product descriptions or promotional texts can be turned into captivating marketing videos that capture your audience’s attention across social media platforms. 🎓Effective Training & Education: Text-based learning materials can be transformed into engaging instructional videos, enhancing learning and knowledge retention. 🌍 Stronger Online Presence: Videos can get more engagement online. Regularly publishing AI-generated videos can boost brand visibility and reach. Challenges of Text-to-Video AI ⚠️ Content Accuracy: AI may struggle to capture text nuances, potentially leading to inaccuracies or misinterpretations in the video output. Reviewing the video ensures it aligns with the original intent. 🎨Style and Tone: The style and tone of the video must align with the brand or message being communicated. A consistent visual identity is essential to maintaining brand integrity across content formats. 💰 Cost Considerations: High-quality video production can require substantial investment in technology, software, and creative resources. ⏳Timeliness and Updates: Text-based content tends to evolve more rapidly than videos. Updating a video to reflect new information or changes can be a time-consuming process, especially if substantial re-editing is needed. ⚖️ Copyright and Ownership Concerns: AI-generate videos face similar copyright issues as text content. Ownership is unclear, and video producers may risk losing control over their work, as AI platforms could claim rights to the videos. This creates challenges for creators and businesses regarding content usage and attribution. 

🌟𝐏𝐮𝐬𝐡𝐢𝐧𝐠 𝐭𝐡𝐞 𝐅𝐫𝐨𝐧𝐭𝐢𝐞𝐫𝐬 𝐨𝐟 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐟𝐢𝐜 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐰𝐢𝐭𝐡 𝐀𝐈: 𝐌𝐞𝐞𝐭 𝐔𝐧𝐝𝐞𝐫𝐦𝐢𝐧𝐝🌟 Scientific research got a powerful upgrade. Undermind, an innovative deep scientific research assistant developed by Massachusetts Institute of Technology quantum physics PhDs Joshua Ramette and Thomas Hartke, is setting a new benchmark for efficiency and precision in research. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐔𝐧𝐝𝐞𝐫𝐦𝐢𝐧𝐝? Undermind leverages cutting-edge AI, including large language models, to emulate how human researchers retrieve and synthesize information. With the ability to scan hundreds of research papers on a given topic in just minutes, it generates a comprehensive report that includes: - A 𝐝𝐞𝐭𝐚𝐢𝐥𝐞𝐝 𝐬𝐮𝐦𝐦𝐚𝐫𝐲 of the topic - An 𝐚𝐧𝐚𝐥𝐲𝐳𝐞𝐝 𝐚𝐧𝐝 𝐜𝐮𝐫𝐚𝐭𝐞𝐝 𝐥𝐢𝐬𝐭 𝐨𝐟 𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬, complete with relevance scores and summaries This approach offers researchers a thorough overview of complex topics, saving countless hours while providing unparalleled depth and accuracy. 𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: Undermind is designed to outperform traditional tools like Google Scholar in terms of: - Relevance of results - Density of meaningful information - Ability to handle complex queries 𝐖𝐡𝐚𝐭 𝐒𝐞𝐭𝐬 𝐔𝐧𝐝𝐞𝐫𝐦𝐢𝐧𝐝 𝐀𝐩𝐚𝐫𝐭: The platform features a chat-based interface that enables users to refine their research objectives collaboratively. Through this iterative dialogue, users can craft precise prompts tailored to their goals. In just 2–3 minutes, Undermind delivers a detailed topic summary and a curated list of references, complete with relevance scores to help researchers prioritize effectively. 𝐇𝐨𝐰 𝐈𝐭 𝐖𝐨𝐫𝐤𝐬: Undermind's process mirrors a human-like research methodology through four iterative steps: 1️⃣ Basic Search: Identification of relevant papers using a custom algorithm. 2️⃣ Relevance Classification: Papers are evaluated based on their alignment with the search prompt using GPT-4. 3️⃣ Adaption & Exploration: Insights guide refinements, expanding the search intelligently. 4️⃣ Comprehensiveness Estimation: Progress is tracked to ensure a thorough search. At present, Undermind scans over 200 million articles from the Semantic Scholar database, covering every scientific domain. While it currently processes abstracts and metadata, plans to incorporate full-text searches promise even greater utility in the future. 🔗 Ready to experience the future of research? Find more information about Undermind in the authors’ whitepaper (https://lnkd.in/dBgtsnfk) and try Undermind (https://lnkd.in/dU2N65mV)! 👏 A huge shoutout to Joshua Ramette, Thomas Hartke, and the Undermind team for advancing the frontiers of scientific research. We can’t wait to see how this tool evolves! Fraunhofer-Institut für Angewandte Informationstechnik FIT FIM Forschungsinstitut für Informationsmanagement

We just wrapped up the Christmas Edition of our CIO Roundtable 🎄🎅 Today’s focus was on NIS-2 and why a proactive approach, for example by starting with implementing ISO 27001, is very beneficial for many companies. To this end, Thomas Schott from RAPA Gruppe shared valuable experience from his CIO and CISO work. After that, Florian Weiß moderated a lively podium discussion on good practices with Stefan König, Dr. Alexander Sänn, Markus Vogler, and Thomas Schott again. Finally, Niklas Gutheil and Valentin Mayer presented the Fraunhofer FIT Generative AI Lab. Thank you to all involved people and Merry Christmas. Universität Bayreuth, Fraunhofer-Institut für Angewandte Informationstechnik FIT, FIM Forschungsinstitut für Informationsmanagement, BF/M-Bayreuth

💡 𝐖𝐡𝐚𝐭’𝐬 𝐃𝐫𝐢𝐯𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝐆𝐚𝐢𝐧𝐬 𝐢𝐧 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐖𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈? Recent empirical studies provide new insights into how GenAI tools impact workforce performance. For instance, Brynjolfsson et al. (2023) observed productivity boosts of up to 34%, while Dell’Acqua et al. (2023) reported a 12.2% rise in completion rates, a 25.1% reduction in task time, and a 40% improvement in output quality. Yet, these gains are not uniform: some experienced or senior employees did not benefit at all. 𝐒𝐨, 𝐰𝐡𝐨 𝐠𝐚𝐢𝐧𝐬 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐟𝐫𝐨𝐦 𝐆𝐞𝐧𝐀𝐈 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧? Our findings indicate that: 𝐄𝐚𝐫𝐥𝐲-𝐜𝐚𝐫𝐞𝐞𝐫 𝐞𝐦𝐩𝐥𝐨𝐲𝐞𝐞𝐬 leverage GenAI to significantly shorten onboarding, from eight months down to three. 𝐋𝐨𝐰𝐞𝐫-𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐰𝐨𝐫𝐤𝐞𝐫𝐬 see productivity surges, sometimes achieving performance levels similar to their top-performing peers. In contrast, high performers show little to no improvement. 𝐘𝐨𝐮𝐧𝐠𝐞𝐫 𝐞𝐦𝐩𝐥𝐨𝐲𝐞𝐞𝐬 tend to reap more advantages than senior counterparts, potentially due to lower perceived adoption barriers. 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬 These patterns suggest GenAI may narrow performance gaps, raising strategic questions around HR policies, outsourcing decisions, and compensation models. Moreover, productivity outcomes are influenced not only by worker characteristics but also by task complexity, organizational culture, and technology design choices. 𝐖𝐡𝐚𝐭 𝐀𝐫𝐞 𝐘𝐨𝐮𝐫 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐨𝐧𝐬? We invite leaders, researchers, and practitioners to share their own experiences: ❓ How has GenAI reshaped your team’s productivity? ❓ What policy changes and workforce planning strategies does this shift demand? Please share your thoughts and insights in the comments. Sources: Brynjolfsson, E., Li, D., & Raymond, L. (2023). Generative AI at Work (No. w31161; S. w31161). National Bureau of Economic Research. https://lnkd.in/eGunHKKJ Dell’Acqua, F., McFowland III, E., Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2023). Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality (SSRN Scholarly Paper No. 4573321). https://lnkd.in/eW5bpUav Humlum, A., & Vestergaard, E. (2024). The Adoption of ChatGPT. SSRN Electronic Journal. https://lnkd.in/eBaVYhPA

🎥 𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐢𝐧𝐠 𝐕𝐢𝐝𝐞𝐨 𝐂𝐫𝐞𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐯𝐢𝐝𝐞𝐨-𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐀𝐈 The way we create visual contents is about to be revolutionized. AI Video generation tools are transforming content production by automating entire tasks, saving time, and enabling creators to produce high-quality visuals with minimal effort. Here’s a quick overview what these tools offer:  𝐖𝐡𝐚𝐭 𝐢𝐬 𝐯𝐢𝐝𝐞𝐨 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐀𝐈? Video generating AI uses methods like neural networks, Generative Adversarial Networks (GAN) or diffusion models to create or edit videos based on simple inputs like text descriptions or images.  𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐕𝐢𝐝𝐞𝐨-𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐀𝐈 👤𝐀𝐈 𝐚𝐯𝐚𝐭𝐚𝐫𝐬 allow the creation of realistic, animated characters that can deliver scripted content or specific tasks in videos. These avatars can simulate human-like interaction, making them ideal for presentations or customer service.  🎬𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐯𝐢𝐝𝐞𝐨 𝐞𝐝𝐢𝐭𝐢𝐧𝐠 tools automate tasks like cutting, transitioning, color correction, and audio syncing. These tools help reduce manual editing time and increase productivity.  📝 𝐓𝐞𝐱𝐭-𝐭𝐨-𝐯𝐢𝐝𝐞𝐨 tools can take content, such as blogs or articles, and automatically generate corresponding videos. AI processes the text and adds relevant visuals, transitions, and voiceovers, creating video content from simple text.   🖼️ 𝐈𝐦𝐚𝐠𝐞-𝐭𝐨-𝐯𝐢𝐝𝐞𝐨 tools allow creators to turn static images into videos. By adding motion, transitions, and effects these tools can create videos with minimal effort.   𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: ⚙️𝐋𝐚𝐜𝐤 𝐨𝐟 𝐇𝐮𝐦𝐚𝐧 𝐂𝐫𝐞𝐚𝐭𝐢𝐯𝐢𝐭𝐲: AI generated videos can sometimes lack the creative nuances that human producers might add. Elements like emotional depth still require human input.  🎙️𝐒𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜 𝐬𝐨𝐮𝐧𝐝 𝐨𝐟 𝐚𝐯𝐚𝐭𝐚𝐫𝐬: AI generated voice overs can sound synthetic and fail to convey emotions, which can affect the authenticity of the video.   🔗𝐓𝐨𝐨𝐥 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: Many AI video generators require multiple tools to complete a full video production process (e.g., combining text-to-speech with video editing).  𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬: 🌟 AI video generators allow businesses to create 𝐥𝐚𝐫𝐠𝐞 𝐯𝐨𝐥𝐮𝐦𝐞𝐬 𝐨𝐟 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐪𝐮𝐢𝐜𝐤𝐥𝐲, ideal for marketing campaigns, training materials, and social media posts.  💡With AI-driven voiceovers, it’s easy to create videos in 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞𝐬, making content more accessible to global audiences and improving inclusivity.  ⚙️ By automating the video creation process, AI not only reduces production time but also𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞𝐬 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲.  🎥 With video-generating AI, anyone can create professional-quality videos even 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐞𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞. 𝐅𝐢𝐧𝐝 𝐨𝐮𝐭 𝐦𝐨𝐫𝐞 𝐚𝐛𝐨𝐮𝐭 𝐜𝐞𝐫𝐭𝐚𝐢𝐧 𝐭𝐨𝐨𝐥𝐬 𝐧𝐞𝐱𝐭 𝐰𝐞𝐞𝐤! Fraunhofer-Institut für Angewandte Informationstechnik FIT

💡🔍𝐅𝐫𝐨𝐦 𝐑𝐢𝐬𝐤 𝐭𝐨 𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲: 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐀𝐠𝐞 𝐨𝐟 (𝐆𝐞𝐧)𝐀𝐈🔍💡 (Generative) artificial intelligence offers tremendous potential for innovation and efficiency, but its integration into business processes comes with 𝐮𝐧𝐢𝐪𝐮𝐞 𝐫𝐢𝐬𝐤𝐬 𝐚𝐧𝐝 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬. This is where (Gen)AI Governance becomes essential. At Fraunhofer FIT Generative AI Lab, we’ve developed a 𝐜𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐦𝐞𝐭𝐡𝐨𝐝 𝐭𝐨 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦 𝐞𝐱𝐢𝐬𝐭𝐢𝐧𝐠 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 𝐢𝐧𝐭𝐨 (𝐆𝐞𝐧)𝐀𝐈-𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐬𝐲𝐬𝐭𝐞𝐦𝐬. This approach helps organizations not only address the technical, ethical, and social risks of AI but also maximize its value while ensuring compliance and trust. 🤔 𝐖𝐡𝐲 𝐝𝐨 𝐰𝐞 𝐧𝐞𝐞𝐝 𝐀𝐈 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞?: (Gen)AI introduces complexities like algorithmic opacity (the "black box"), biases, and regulatory uncertainties. Traditional governance mechanisms often fall short in addressing these, necessitating a tailored approach to manage these risks effectively and sustainably. 🔧 𝐎𝐮𝐫 𝐦𝐞𝐭𝐡𝐨𝐝 𝐩𝐫𝐨𝐯𝐢𝐝𝐞𝐬 𝐚𝐧 𝐢𝐭𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐟𝐨𝐫 (𝐆𝐞𝐧)𝐀𝐈 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞: 1️⃣ Develop an (Gen)AI strategy aligned with organizational values. 2️⃣ Build a roadmap to integrate AI across processes. 3️⃣ Identify use cases and engage stakeholders. 4️⃣ Iteratively analyze risks, regulatory requirements, and corporate governance structures. 5️⃣ Evaluate and ensure secure (Gen)AI utilization. 6️⃣ Integrate tailored governance mechanisms into existing systems. 7️⃣ Continuously monitor and adapt the governance framework. This dynamic approach ensures that (Gen)AI governance evolves alongside technology, maintaining alignment with organizational goals and regulatory standards. 💡 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲 𝐟𝐨𝐫 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬: 📢 Start with a clear (Gen)AI strategy and roadmap. 📢 Integrate (Gen)AI governance with existing frameworks, avoiding siloed initiatives. 📢 Embrace an iterative process to adapt to emerging risks and innovations. Governance is not just about mitigating risks; it’s about unlocking the full potential of (Gen)AI responsibly. Let’s make (Gen)AI a strategic enabler, not just a technological tool. Read the full paper here: https://lnkd.in/eBNEfxqy 𝐇𝐨𝐰 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐚𝐝𝐝𝐫𝐞𝐬𝐬𝐢𝐧𝐠 (𝐆𝐞𝐧)𝐀𝐈 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞? 𝐋𝐞𝐭’𝐬 𝐝𝐢𝐬𝐜𝐮𝐬𝐬! 👇 Dominik Protschky, Moritz Schüll, Nils Urbach Fraunhofer-Institut für Angewandte Informationstechnik FIT FIM Forschungsinstitut für Informationsmanagement #AIGovernance #FraunhoferFIT #ResponsibleAI #Innovation #Leadership

💡📚𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐢𝐧𝐠 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐀𝐫𝐭𝐢𝐜𝐥𝐞 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐒𝐓𝐎𝐑𝐌 𝐛𝐲 𝐒𝐭𝐚𝐧𝐟𝐨𝐫𝐝 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲📚💡 Stanford University has unveiled an innovative AI-powered writing system, STORM (Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking). Designed to produce foundational research articles with depth and accuracy akin to Wikipedia pages, STORM is set to transform how we synthesize and present complex information. 🔧𝐇𝐨𝐰 𝐒𝐓𝐎𝐑𝐌 𝐖𝐨𝐫𝐤𝐬: STORM employs a sophisticated 𝐦𝐮𝐥𝐭𝐢-𝐚𝐠𝐞𝐧𝐭 𝐬𝐲𝐬𝐭𝐞𝐦 that extracts, organizes, and evaluates relevant data. It ensures that the generated articles are both accurate and up-to-date. Here's how it unfolds: - 𝐏𝐫𝐞-𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐒𝐭𝐚𝐠𝐞: The system aggregates information from a diverse array of sources, including scientific publications, news articles, and online databases. These findings are then analyzed and debated through simulated discussions involving multiple AI agents, including a domain-specific expert agent. 𝐓𝐡𝐞 𝐨𝐮𝐭𝐜𝐨𝐦𝐞? 𝐀 𝐜𝐫𝐚𝐟𝐭𝐞𝐝 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 𝐨𝐮𝐭𝐥𝐢𝐧𝐞 𝐠𝐫𝐨𝐮𝐧𝐝𝐞𝐝 𝐢𝐧 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐝𝐚𝐭𝐚. - 𝐖𝐫𝐢𝐭𝐢𝐧𝐠 𝐒𝐭𝐚𝐠𝐞: STORM generates well-organized articles with comprehensive content and clear citations, mirroring the style and depth of Wikipedia. What sets STORM apart is its commitment to transparency. Each generated article includes detailed references to its sources, enabling users to review and verify the information with ease. 🔛𝐓𝐰𝐨 𝐌𝐨𝐝𝐞𝐬 𝐨𝐟 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧: 1️⃣𝐒𝐓𝐎𝐑𝐌 (Fully AI-Autonomous): Generates articles independently. 2️⃣𝐂𝐨-𝐒𝐓𝐎𝐑𝐌 (Human-in-the-Loop): Allows users to actively participate in the AI’s discussions and tailor the article to their specific needs. This dual-mode functionality provides flexibility for researchers, educators, and professionals seeking tailored insights or autonomous synthesis. 𝐓𝐨 𝐞𝐱𝐩𝐥𝐨𝐫𝐞 𝐒𝐓𝐎𝐑𝐌’𝐬 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 further or to generate your own research articles, visit the official website: https://lnkd.in/e5bWjskg or https://lnkd.in/drWVTh6u. 💭𝐎𝐮𝐫 𝐨𝐩𝐢𝐧𝐢𝐨𝐧: We commend Stanford University for their remarkable effort in developing STORM. Their decision to make STORM publicly available is a significant step towards democratizing knowledge creation. It is important to recognize STORM’s role as a foundational tool for generating concise, Wikipedia-style articles on various topics. While incredibly powerful, it does not replace the depth and nuance gained from engaging with full-length, peer-reviewed academic research. STORM’s design prioritizes the creation of accessible, structured content, making it a valuable resource for initial exploration and synthesis. Looking ahead, we are intrigued by the potential to adapt STORM for generating scientific research papers. Fraunhofer-Institut für Angewandte Informationstechnik FIT

🧠🤖𝐆𝐞𝐧𝐀𝐈 𝐂𝐞𝐧𝐭𝐞𝐫 𝐨𝐟 𝐄𝐱𝐜𝐞𝐥𝐥𝐞𝐧𝐜𝐞 -𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚 𝐅𝐮𝐭𝐮𝐫𝐞-𝐑𝐞𝐚𝐝𝐲 𝐓𝐞𝐚𝐦 𝐟𝐨𝐫 𝐆𝐞𝐧𝐀𝐈 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 🧠🤖 Generative AI is revolutionizing industries—fueling innovation, enhancing decision-making, and unlocking unprecedented opportunities. But managing the complexities of GenAI requires more than just advanced algorithms; it demands a 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐭𝐞𝐚𝐦 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞. Enter the 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐂𝐞𝐧𝐭𝐞𝐫 𝐨𝐟 𝐄𝐱𝐜𝐞𝐥𝐥𝐞𝐧𝐜𝐞 (𝐆𝐞𝐧𝐀𝐈 𝐂𝐨𝐄)—a dedicated unit designed to lead your organization’s AI journey and ensure sustainable, impactful results. 🌐 𝐖𝐡𝐲 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐆𝐞𝐧𝐀𝐈 𝐂𝐞𝐧𝐭𝐞𝐫 𝐨𝐟 𝐄𝐱𝐜𝐞𝐥𝐥𝐞𝐧𝐜𝐞? A GenAI CoE consolidates expertise, resources, and governance into a single framework to: ✅ 𝐃𝐫𝐢𝐯𝐞 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧: Experiment with cutting-edge generative AI models for real-world applications. ✅ 𝐔𝐩𝐬𝐤𝐢𝐥𝐥 𝐭𝐞𝐚𝐦𝐬: Equip employees with the skills to thrive in an AI-driven world. ✅ 𝐄𝐧𝐬𝐮𝐫𝐞 𝐬𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Create a roadmap for sustainable, enterprise-wide AI adoption. 𝐊𝐞𝐲 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 𝐨𝐟 𝐚 𝐆𝐞𝐧𝐀𝐈 𝐂𝐨𝐄: 🔹𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩: Develop governance, secure funding, and nurture a culture of innovation. 🔹𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐌𝐚𝐬𝐭𝐞𝐫𝐲: Build infrastructure, protect data, and implement experimental platforms. 🔹𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐓𝐫𝐚𝐧𝐬𝐟𝐞𝐫: Share insights and best practices across departments to eliminate silos. 🔹𝐏𝐨𝐥𝐢𝐜𝐲 𝐀𝐝𝐯𝐨𝐜𝐚𝐜𝐲: Engage in ethical AI use and public outreach to drive societal impact. 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐨𝐟 𝐚 𝐆𝐞𝐧𝐀𝐈 𝐂𝐨𝐄: 💡 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞𝐝 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 of generative AI across the organization. 💡 𝐂𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 with streamlined processes and expertise. 💡 𝐄𝐜𝐨𝐧𝐨𝐦𝐢𝐜 𝐢𝐦𝐩𝐚𝐜𝐭 through cost efficiency and productivity gains. 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐭𝐨 𝐀𝐝𝐝𝐫𝐞𝐬𝐬: ⚠️ Requires significant investment in talent, infrastructure, and governance. ⚠️ Needs cross-departmental collaboration to prevent siloed operations. 𝐓𝐡𝐞 𝐁𝐨𝐭𝐭𝐨𝐦 𝐋𝐢𝐧𝐞: A Generative AI Center of Excellence transforms the way your organization approaches AI—balancing technical complexity with strategic vision. If your are a smaller company, the GenAI CoE can of course also be a subpart of a general AI CoE 🌍✨ If you need support to upskill your team with required skills and build up a GenAI CoE, contact us for a collaboration or visit our training course: https://lnkd.in/eAXVGcrS Fraunhofer-Institut für Angewandte Informationstechnik FIT FIM Forschungsinstitut für Informationsmanagement #GenerativeAI #CenterOfExcellence #AIInnovation #GenAILeadership #FutureOfWork

🌟 𝐆𝐞𝐧𝐀𝐈 𝐃𝐚𝐲 𝐁𝐚𝐲𝐫𝐞𝐮𝐭𝐡: 𝐀 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐭𝐨 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐢𝐧 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 🦾 This week, the Fraunhofer FIT Generative AI Lab, together with the Universität Bayreuth, hosted the 𝐆𝐞𝐧𝐀𝐈 𝐃𝐚𝐲 𝐁𝐚𝐲𝐫𝐞𝐮𝐭𝐡! 𝐎𝐮𝐫 𝐦𝐢𝐬𝐬𝐢𝐨𝐧? To empower industry leaders with the methods and tools to successfully implement Generative AI into business processes. Throughout the day, we explored practical approaches across four key dimensions of AI management: 1️⃣ 𝐀𝐈 𝐈𝐝𝐞𝐚𝐭𝐢𝐨𝐧 🔍 Understanding the business potential of Generative AI 🎯 Targeted identification and economic evaluation of impactful use cases 2️⃣ 𝐀𝐈 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐳𝐢𝐧𝐠 📊 Assessing a company’s readiness for AI adoption 📋 Developing robust management and governance mechanisms 🤝 Networking Lunch 3️⃣ 𝐀𝐈 𝐃𝐞𝐬𝐢𝐠𝐧 & 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 ⚙️ Exploring state-of-the-art technology architectures and concepts for GenAI 🤖 Designing human-AI interactions to maximize value and usability 4️⃣ 𝐀𝐈 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐭 𝐒𝐜𝐚𝐥𝐞 ⚖️ Integrating ethics, regulatory frameworks, and KPI-based monitoring 🔄 Change management to ensure successful adoption across the organization 💡 𝐄𝐱𝐩𝐞𝐫𝐭 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 The day featured insightful contributions from the Fraunhofer FIT Generative AI Lab team, including 𝐏𝐫𝐨𝐟. 𝐃𝐫. Niklas Kühl, Niklas Gutheil, and Valentin Mayer, who shared cutting-edge research and practical frameworks to guide companies on their AI journey. From ideation to operationalization, GenAI Day Bayreuth provided actionable insights into how businesses can leverage Generative AI responsibly and effectively while staying compliant with regulations like the #EUAIAct. 🌐 A big thank you to all our speakers and participants for making this day a success! Together, we’re shaping the future of AI-powered innovation. And if you could not make it this time - no worries. We are launching a couple of interesting events like this next year again! Fraunhofer-Institut für Angewandte Informationstechnik FIT Universität Bayreuth FIM Forschungsinstitut für Informationsmanagement #GenerativeAI #AIImplementation #Innovation #Governance #FraunhoferFIT

🛠️ 𝐓𝐡𝐞 𝐀𝐈𝐀𝐌𝐀 𝐌𝐨𝐝𝐞𝐥: 𝐀 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐟𝐨𝐫 𝐌𝐚𝐧𝐚𝐠𝐢𝐧𝐠 𝐀𝐈 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 As AI applications continue to transform industries, managing their deployment presents a unique set of challenges. These challenges span from ensuring data quality and process compatibility to addressing ethical and regulatory constraints. The 𝐀𝐈 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 (𝐀𝐈𝐀𝐌𝐀) 𝐦𝐨𝐝𝐞𝐥 offers a structured framework designed to support AI deployment across diverse domains. 𝐓𝐡𝐞 𝐀𝐈𝐀𝐌𝐀 𝐌𝐨𝐝𝐞𝐥’𝐬 𝐅𝐢𝐯𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐂𝐲𝐜𝐥𝐞𝐬: This model consists of four 𝐟𝐚𝐜𝐭𝐨𝐫 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐜𝐲𝐜𝐥𝐞𝐬 and an 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐜𝐲𝐜𝐥𝐞, together providing a comprehensive approach to managing AI applications across various domains: 1️⃣ 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐀𝐈 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: Oversees AI architecture, data quality, and robustness, ensuring that technical specifications align with industry standards. 2️⃣ 𝐂𝐨𝐧𝐭𝐞𝐱𝐭𝐮𝐚𝐥 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: Ensures compliance with environmental, regulatory, and organizational restrictions, making sure that AI applications adapt to context-specific needs and constraints. 3️⃣ 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: Focuses on aligning AI applications with workflow requirements, addressing challenges like compatibility and workflow integration. 4️⃣ 𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: Manages user and stakeholder needs, balancing functionality with ethical considerations such as transparency, trust, and fairness. 5️⃣ 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐂𝐲𝐜𝐥𝐞: Serves as the coordinating layer, aligning the four management cycles to ensure cohesive operation and effective information processing. This versatile framework enables organizations in fields as diverse as finance, healthcare, and manufacturing to harness the power of AI while addressing domain-specific challenges, promoting alignment between technical capabilities, user needs, and regulatory requirements. 𝐅𝐮𝐥𝐥 𝐩𝐚𝐩𝐞𝐫: https://lnkd.in/dagMqb-D #AIManagement #AIIntegration #DigitalTransformation Luis Lämmermann Peter Hofmann Nils Urbach Fraunhofer-Institut für Angewandte Informationstechnik FIT