AI as a co-researcher in accounting and finance: opportunities and risks

📚 𝐁𝐲𝐭𝐞𝐬 𝐰𝐢𝐭𝐡 𝐁𝐞𝐭𝐭𝐲: 𝐀𝐈 𝐓𝐢𝐩 𝐨𝐟 𝐭𝐡𝐞 𝐃𝐚𝐲 – 𝐄𝐥𝐢𝐜𝐢𝐭 This week I’m sharing daily tips on AI platforms that save you time with research. Today’s focus: Elicit, a powerful tool for anyone doing deep dives into academic sources. ✨ 𝐓𝐢𝐩 #3: 𝐄𝐥𝐢𝐜𝐢𝐭 – 𝐋𝐢𝐭𝐞𝐫𝐚𝐭𝐮𝐫𝐞 𝐑𝐞𝐯𝐢𝐞𝐰 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭 Instead of manually sifting through dozens of studies, Elicit automates literature reviews—pulling key points, comparisons, and data directly from academic sources. ⚡ Why it matters: • Saves hours of manual reading • Organizes findings into clear summaries • Makes complex research easier to understand and apply Perfect for professionals, students, and researchers who need insights fast without sacrificing accuracy. Stay tuned—tomorrow’s tip will cover how AI can analyze your own files and data for research speed. #BytesWithBetty #Elicit #ResearchTools #AIForBusiness #LiteratureReview #ARKSolvers #ProductivityWithAI

What happens when exams become obsolete? Lately, I’ve been wondering about the concept of Humanities Last Exam, the idea that once AI can score perfectly on a test, humans won’t need to sit one again. Whether the current Center for AI Safety’s benchmark is the right one doesn’t matter. The point is clear: eventually, AI will answer everything we can throw at it and what does that mean for us? I believe AI will automate much of our workflow. But I don’t think it replaces the human value chain of new ideas, products, or services. Call me a romantic, but human ingenuity is unique. The pain, the emotion, the rawness of creativity needs that mythos. Would Wonderwall be such a great song if it were just generated by AI? Or does it need the story of two famously unlikeable brothers with a tumultuous relationship to make it art? So yes, the last exam concept holds some weight. Will we need to take exams once AI can answer our queries and even make novel scientific discoveries? Not necessarily. Will we still be creating, moving, and innovating? You bet. 👉 On that note, I’m thrilled to share that we’ve just launched Schmoo.ai. Over the past weeks, I’ve been writing about how AI tools free us to spend more time on creativity. That’s exactly why we built Schmoo: your process companion. We’re not quite ready to lift the curtain fully, but I’ll be sharing more over the coming weeks. In the meantime, feel free to reach me at my new shiny email: josh@schmoo.ai. Of course, I am still doing my consulting work with JCM too! #ArtificialIntelligence #AI #AIForBusiness #FutureOfWork #Innovation #ProcessAutomation #AISafety #AIResearch

AI was supposed to be fair… instead, it did something else 🤷 🆕 In our newest paper we look at the political views of LLMs, how these biases generalize, and how models can be steered. 👉 Read the full paper here: https://lnkd.in/dcquai8G #DisAI #DisAIproject #LLM #biases #research #multilingualAI

A must read from Virginia Dignum: The AI Paradox (upcoming), to get a sense of how the #AI debate is not only about the technologies, but how we can shape it for positive outcomes. Looking forward to read it!

🧠 How RAG (Retrieval-Augmented Generation) is Solving AI's Biggest Problem: Hallucination ✨ AI hallucination - when AI confidently presents false information - has been a major challenge. But RAG is changing the game! 🎯 ✨ What is RAG? Think of it as giving AI a real-time research assistant that pulls from verified databases before generating responses. 🔑 6 Ways RAG Reduces AI Hallucination: 1️⃣ Grounded Responses No more "making things up" - AI now pulls from actual documents and verified sources instead of relying on memory alone. 2️⃣ Real-Time Knowledge Access Access to current, live information that goes beyond static training data. Your AI stays updated! 📊 3️⃣ Source Attribution Full transparency with citations and references. You can actually verify where the information comes from. 4️⃣ Honest Uncertainty When relevant sources aren't found, AI admits it doesn't know rather than fabricating answers. Refreshing honesty! 🤝 5️⃣ Domain-Specific Accuracy Enterprise RAG uses curated, expert-verified knowledge bases for specialized fields. Perfect for industry-specific applications. 6️⃣ Built-in Fact-Checking Retrieved documents act as a verification layer, catching contradictions before they reach users. ⚠️ Reality Check: RAG isn't perfect - it depends on quality knowledge bases and good retrieval systems. But it's a massive step toward trustworthy AI. The Bottom Line: RAG gives AI a "fact-checking assistant" that dramatically improves reliability. Are you using RAG in your AI projects? Share your experience! 👇 #ArtificialIntelligence #RAG #MachineLearning #AIHallucination #TechInnovation #DataScience

𝐄𝐱𝐜𝐢𝐭𝐞𝐝 𝐭𝐨 𝐬𝐡𝐚𝐫𝐞 𝐚 𝐫𝐞𝐜𝐞𝐧𝐭 𝐦𝐢𝐥𝐞𝐬𝐭𝐨𝐧𝐞 𝐢𝐧 𝐦𝐲 𝐀𝐈 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐚𝐧𝐝 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐣𝐨𝐮𝐫𝐧𝐞𝐲! When I first started the 𝑭𝒓𝒐𝒏𝒕𝒊𝒆𝒓 𝑨𝑰 𝑩𝒆𝒏𝒄𝒉𝒎𝒂𝒓𝒌𝒊𝒏𝒈 𝑷𝒓𝒐𝒋𝒆𝒄𝒕, the challenge was clear: Existing benchmarks weren’t fully capturing how LLMs perform in complex, real-world tasks. So I asked myself: How do we design tests that truly stretch the limits of these systems? That question guided every step of the process: • Identifying gaps where current benchmarks fall short. • Designing 500+ cross-domain prompts that required not just recall, but deep reasoning, coding, and information retrieval. • Running evaluations against leading LLMs, tracking where they excelled and where they struggled. • Analyzing results to uncover insights that could shape future evaluation frameworks. The outcome? A 12% measurable improvement in model accuracy and more importantly, a roadmap for how to push frontier AI systems toward greater reliability and safety. This experience reminded me why I’m so passionate about prompt curating, LLM evaluation, and AI safety research. Each project isn’t just about numbers; it’s about moving one step closer to AI systems we can trust to operate at the frontier. Excited to keep building, learning, and contributing to the future of responsible AI. I’d love to hear from others working on similar challenges: How are you approaching AI benchmarking and evaluation in your projects? #AI #LLM #PromptEngineering #AISafety #Research #Benchmarking #AIResearch

Agentic Retrieval-Augmented Generation (RAG) offers a powerful approach to AI, combining dynamic retrieval with generative techniques for highly accurate, context-aware results across fields like healthcare, finance, and legal research. By enabling multi-step reasoning, adaptive strategies, and integration with tools like LlamaIndex and LangChain, agentic RAG elevates efficiency and continuous learning for modern organizations. Discover how implementing agentic RAG can drive innovation and adaptability in your business. article: https://lnkd.in/gn_Au7EW #AgenticRAG #GenerativeAI #AIInnovation Interested in deploying agentic RAG? Share your ideas or questions in the comments!

I just read a post about Turnitin’s new “AI-bypasser detection” feature launched in August 2025, designed to catch AI generated text even after it’s been altered by those humanizer tools. On one hand, it’s fascinating to see how quickly detection technology is evolving. On the other, I can’t help but laugh. As students, we don’t even get access to Turnitin directly (it’s only institutions that license it). So like many others, I rely on free AI-detection tools just to double-check my work. And guess what? They always flag me, even when I’ve poured hours into writing it myself! Apparently, the biggest threat to originality is… sounding original. Here’s hoping this feature works better than the tools I’ve used, because so far the only thing truly human seems to be the detector’s overconfidence. #AI #Turnitin #Research #Education #AcademicIntegrity #EdTech #Technology #Innovation #LawStudents

Does AI hallucinate of electric sheep? ✅ - it’s a brilliant feeling when your homework gets marked A+ 🗓️ Two and a half weeks ago, I filed a patent application addressing AI reliability challenges. 📅 Last week, OpenAI published research identifying multiple causes of hallucinations, many of which we had previously identified and which our patent addresses. In their paper, OpenAI discuss issues such as: ⚖️ Binary evaluation systems rewarding confident guessing over honest uncertainty 🗳️ Arrow’s paradox and vote aggregation problems 📉 Statistical training objectives producing predictable hallucination rates on rare facts 📚 Models becoming “perpetual exam-takers” optimised for test performance rather than truthfulness 🔧 Post-training failing to fix fundamental evaluation misalignments I view these not just as different categories of challenge, but also as varying magnitudes. And unlike a bear hunt, not all of them can be gone through; some must be worked around. It’s good to see industry players are spotting the same constraints. I'm chuffed that the prior patent addresses many of the issues highlighted in the OpenAI paper, while also giving me some new food for thought. ♾️ I look forward to sharing more on Liquid Möbius solutions as they develop. I’d be keen to hear from others who are circling and addressing the same constraints, even if they are described differently. 🤳 If you’re already embedding solutions, please get in touch. 😢 Likewise, if these challenges are keeping you up at night and stalling your AI implementations. #AI #ArtificialIntelligence #AIResearch #AIHallucinations #AITrust #ResponsibleAI --------------------------------------------- 🔔 Click the bell on my profile to follow me for content on investments, markets, and how technology is reshaping asset management search and research, and increasingly, for insights from our AI R&D.