We’re thrilled to announce the release of GPT-OSS-120b and GPT-OSS-20b, our latest open-source AI models, now available under the Apache 2.0 license! 🎉 These models empower developers with the freedom to use, customize, and distribute them for commercial purposes. Delivering top-tier performance in reasoning, logic, and multilingual tasks, they stand shoulder-to-shoulder with our o4-mini model. We’re excited to see how the community will leverage these tools to drive innovation and shape the future of AI. Join us in this journey! 🌍 #AI #OpenSource #Innovation #Technology

🚀 Why Retrieval-Augmented Generation (RAG) Matters in AI Large Language Models (LLMs) are powerful—but they have a limitation: they rely only on what they were trained on. This means their knowledge can become outdated, incomplete, or even inaccurate. 👉 That’s where RAG (Retrieval-Augmented Generation) comes in. By combining LLMs with external knowledge bases (databases, documents, APIs), RAG ensures responses are factual, up-to-date, and context-aware. ✅ Key Benefits of RAG: Accuracy: Pulls real-time verified data instead of relying solely on memory. Flexibility: Can adapt across industries—healthcare, finance, legal, or research. Scalability: No need to retrain models for every knowledge update. Transparency: Easier to trace where information comes from. 💡 Example: Instead of an LLM “guessing” stock market insights, a RAG-powered system retrieves the latest financial reports and then generates analysis. 📌 In short: RAG bridges the gap between AI’s reasoning ability and the ever-changing world of information. It’s a game-changer for building trustworthy, enterprise-ready AI applications. #ArtificialIntelligence #RAG #MachineLearning #LLM #Innovation #AI

Our new article, "LLM-guided Semantic Feature Selection for Interpretable Financial Market Forecasting in Low-Resource Financial Markets", has just been published. In this work, we explore how large language models (LLMs) can go beyond text generation to guide semantic feature selection, enabling more interpretable and robust financial forecasting, especially in low-resource market settings where traditional data-driven models often struggle. Key contributions of our work: Introducing an LLM-driven semantic framework for feature selection in finance. Bridging the gap between explain ability and predictive performance. Empowering low-resource financial markets with AI tools that are both transparent and effective. This research opens pathways for building trustworthy financial AI systems that can support decision-making in emerging and underrepresented markets. Grateful to my co-authors, collaborators, and mentors who made this possible.  #AI #Finance #MachineLearning #LLM #FinancialForecasting #InterpretableAI #Research

Title: Hugging Face Unveils AI Sheets: A Free, Open-Source No-Code Toolkit for LLM-Powered Datasets AI Sheets by Hugging Face brings spreadsheet simplicity to the power of LLMs—no code, all insight. Clean, classify, and enrich data with natural language prompts, using thousands of models from Hugging Face Hub. Deploy locally for data privacy or collaborate online in real time. This is AI for everyone. #AISheets #NoCodeAI #HuggingFace #DataScience #LLM #AIforBusiness

Title: Hugging Face Unveils AI Sheets: A Free, Open-Source No-Code Toolkit for LLM-Powered Datasets AI Sheets by Hugging Face brings spreadsheet simplicity to the power of LLMs—no code, all insight. Clean, classify, and enrich data with natural language prompts, using thousands of models from Hugging Face Hub. Deploy locally for data privacy or collaborate online in real time. This is AI for everyone. #AISheets #NoCodeAI #HuggingFace #DataScience #LLM #AIforBusiness

AI Doesn’t Actually Browse the Internet I came across a clear explanation from Harper Carroll [she built machine learning systems at Facebook and Meta for about four years] that I think is worth sharing. It cuts through a lot of confusion around how large language models (LLMs) actually work. At their core, LLMs are text generators. They don’t go out and search the internet when you ask a question; they take whatever is in their input window (their “context”) and generate the next most likely word. That’s it. The concept of context length is key. Each model has a limit to how much information it can keep in view at one time. Even with very large context windows, models can still lose track of details from earlier in a conversation or a long document. When an AI seems to be “browsing,” what’s really happening is that a separate system is doing the searching. The results get pulled in and stuffed into the model’s context, so it can respond to you as if it had looked things up. The LLM itself is still just predicting text based on what’s in that temporary working memory. It’s an important distinction. The power of these systems isn’t that they’re out there crawling the web; it’s in how they process and generate language once the right information is put in front of them. #AI #LLM #Business

A new paper from OpenAI partially supports some of my longstanding views on large language models (LLMs): - LLMs will inevitably hallucinate, even when the training data is entirely error-free. - Benchmarks are not a reliable measure of “intelligence” in LLMs. The authors are correct in pointing out that hallucinations stem from the operational mechanics of LLMs and from their training feedback loops. However, this only describes statistical tendencies. It does not fully address the deeper question: why do LLMs hallucinate at all? This gap limits the true value of the paper. More concerning is their unsubstantiated claim that it is possible to build a “non-hallucinating” model by connecting it to a Q&A database, adding a calculator, and forcing it to respond “I don’t know” whenever uncertain. There are two major flaws here: - Such a system reduces the model to a rigid program of conditional statements, rather than a generative AI. - LLMs cannot genuinely recognize what they do not know. They lack self-awareness or calibrated confidence, and thus will always appear to know everything. It is surprising to see the world’s most valuable AI company, with some of the brightest minds, present such a simplistic and unsupported proposal. The remainder of the paper is filled with elegant mathematical formulations—but without grounding, they add little substance. #artificialintelligence #LLM #hallucination https://lnkd.in/gfgNetkR

LLMs hallucinate - by design. This is what OpenAI now also openly communicates. Just for everyone in Analytics this means: LLMs can not "analyze" data in a sense that it can solve mathematical equations reliably. This means every analytical system based on LLMs must have an in-between layer (SQL generator, python-code generator or equal) to ensure deterministic results.

Interesting opinion piece in today's NY Times that we need to look at Neuro Symbolic AI "The Fever Dream of Imminent Superintelligence Is Finally Breaking" By Gary Marcus

OpenAI's latest paper on hallucinations isn't a confession. It's a cry for help. And the subsequent debate, highlighted here by Nam Nguyen, shows we might be listening for the wrong thing. The paper's core analogy is perfect: LLMs are like students who guess on exams because the system rewards plausible answers over honest uncertainty. They've admitted to building the perfect student, not a sage. This leads to a radical conclusion: the pursuit of "trustworthy AI" is a dangerous distraction. If the model is designed to be a brilliant, but sometimes dishonest, test-taker, then the responsibility for truth cannot be delegated to it. It must remain with the user. The future of education and professional work will not be defined by how well we build AI, but by how well we architect the human capacity to govern it. We don't need better AI. We need a generation of Sovereign Auditors and Conscious Curators who know how to wield these powerful tools without surrendering their own critical judgment. The solution isn't in the code. It's in the curriculum. #AI #Hallucinations #OpenAI #CognitiveSovereignty #Pedagogy #FutureOfWork #Kairos