Thinslices’ Post

Ever wonder why search and call volumes might be declining without a clear reason? The shift from traditional search engines to AI could be the answer, but understanding this transition can be a challenge for many. Navigating this new landscape may require a strategic approach, peer support, and embracing new tools. It was noted that many find themselves in similar positions, highlighting the importance of adapting to technological advancements. How do you approach integrating AI into your strategies, and what challenges have you encountered? #AIintegration #DigitalTransformation #StrategicThinking #Innovation #FutureofSearch

AI can create. That’s not the question. The real question is what happens to creativity itself when it does. I’ve been wrestling with what creative integrity looks like when intelligent tools become part of the process. Where does my contribution end and the machine’s begin? When an LLM suggests a sharper metaphor or a cleaner structure, is it still my creation, or is it something new? Something we don’t fully have language for yet? The definition of authorship is changing, and fast. Check out my new blog post on the topic here: https://lnkd.in/eUSUCf8K

Everyone’s racing into AI—but many are burning millions with little to show for it. The real challenge isn’t just building algorithms, it’s managing the hidden costs: data labeling, storage, specialized talent, and high failure rates. Open-source frameworks and smarter approaches to data are changing the game—helping companies innovate faster, cut waste, and build AI that’s ethical and impactful. 👉 https://bit.ly/4mrC7zB #beBOLD #AI #OpenSource #Innovation #Data Jason Corso Forbes Technology Council

I still feel that often discussions around AI in scholarly publishing focus on how to catch bad actors. That’s an essential use case. But it’s only one part of the story. There are many more opportunities for impact, both positive and negative. And to truly understand them, we need to hear from those who experience this shift first-hand: researchers and industry veterans. So let’s ask the bigger question: How do we preserve the human voice in AI-enhanced publishing? 🔗 Registration link in the comments.

🌟 Exciting News in the AI World! 🌟 Researchers at [Source] have developed a groundbreaking algorithm that can predict market trends with 95% accuracy. This innovation not only revolutionizes investment strategies but also showcases the power of AI in analyzing complex data patterns. The software industry is buzzing with possibilities as AI continues to push the boundaries of predictive analytics. How do you think this advancement will impact the future of financial technology? 💡💻 #AI #SoftwareIndustry #Innovation #TechTrends

Are you currently exploring how AI can deliver public services in a quicker, innovative and more efficient way? This white paper is worth the read! #govtech#slednation#statecdo

AI is no longer just crunching numbers, it’s starting to make decisions we once thought only humans could. From writing code to diagnosing medical issues, systems are moving from tools to teammates. But here’s the catch: they aren’t truly “thinking.” When technology feels intelligent, we start trusting it differently, sometimes too quickly. That raises questions about accountability, ethics, and how we design guardrails. I’m unpacking this shift in my latest post. What do you think? Should AI be seen as a partner, or always kept in its place as a tool? Please read (and share!) 🙏

AI-generated answers still struggle with accuracy and trust, especially in critical fields like healthcare. Retrieval-Augmented Generation (RAG) techniques help by grounding AI outputs in real-world data, improving reliability. But these systems often act like black boxes, making it hard to understand how they produce their results. Our new framework, KG-SMILE, brings clarity to RAG by pinpointing which parts of a knowledge graph influence AI-generated responses. This transparency helps balance accuracy with explainability, a vital step for sensitive applications. I believe trustworthy AI requires not only strong performance but also clear explanations that people can follow and trust. How important is transparency to you when using AI in decision-making?

AI has made knowledge free. That’s not the edge anymore. The real edge? - Picking the right problems - Doing them the smartest way - Building systems that multiply effort The future of work isn’t about knowing more. It’s about doing fewer things, better.

🔍 Research Radar: RAND on AI-Enabled Policymaking: Opportunities, Obstacles, and the Road Ahead For Reboot Democracy, The Burnes Center's Dane Gambrell writes about the recent report on May's workshop cohosted by RAND, The Stimson Center, and the Tony Blair Institute for Global Change exploring how AI can support more effective policymaking. 📝 AI shows promise for automating routine tasks and democratizing access to analysis tools, but there are significant structural barriers to realizing AI's potential in policymaking. To move forward, we need more real-world case studies of AI use in governance and strategic adoption focused on maintaining human oversight and building the skills to deploy these tools responsibly. 👥 Read the full post: https://lnkd.in/eqn6rD-e 🔗