AI Advances

Have you heard of Stuffing-based Document Q&A? This technique involves packing as much information as possible into an LLM's context window before asking a question. This way, the model processes the entire document when generating responses. In this article from AI Advances, I show how this is done using Langflow. https://lnkd.in/eEQ9gTKK

Working with Ollama and wished that you can use the models in Hugging Face? This article on AI Advances shows you how. https://lnkd.in/gJKdygEV

The world seems to have overlooked machine learning (ML) amid the excitement surrounding generative AI. However, selecting the right tool for each task is crucial for success.?Marina Tosic?presents a sharp and much-needed critique of the current AI landscape. She underscores the key distinction between deterministic predictions and generative outputs, reaffirming the enduring value of ML for structured data challenges. https://lnkd.in/eFNgZzpw

Prompt engineering is the language of Gen AI. You can get high-quality responses by ensuring your prompt has four essential elements—assigning roles, providing context, suggesting response formats, and using examples.?Thuwarakesh Murallie?simplifies complex AI communication strategies, making them accessible to everyday users. https://lnkd.in/eXeEiqv2

Do you feel overwhelmed by information overload from newsletters and subscription notifications??Jenny Ouyang?presents an innovative and practical approach to this challenge. She demonstrates how generative AI, specifically Hugging Face’s BART model, can be used to summarize and prioritize content efficiently. What began as a simple summarizer evolved into an intelligent recommender system—an excellent project that can be replicated to manage email overload. https://lnkd.in/efzADEnW

As machine learning (ML) growing in popularity, practitioners naturally turn to ML-based solutions for problem-solving. This is where surrogate modeling comes into play. Dr.?Shuai Guo?offers a comprehensive and insightful exploration of how ML-powered surrogate models are transforming engineering workflows. Through compelling real-world examples, he illustrates how engineers across disciplines can leverage surrogate modeling to overcome computational bottlenecks, optimize designs, and make data-driven decisions with unprecedented efficiency. https://lnkd.in/ezHyWXe6

Build AI Apps In Days, Not Months. Model training and fine-tuning already take long enough. Building and deploying AI applications shouldn't. Choose frameworks that reduce bottlenecks in the development phase. If you can do so without sacrificing the ability to customize applications, you've found the key to shipping flexible and scalable applications. If you are curious about how I built that AI-powered joke explainer app or which frameworks to use for rapid prototyping AI applications, check out this article I wrote for the AI Advances publication.

Wisdom is not just intelligence—but what else is it not? To define the concept of wisdom,?Reid E. Yaworski?presents a compelling critique of the current paradigm in AI development. With deep intellectual rigor, he argues that while AI has made remarkable strides in emulating cognitive outputs, it has failed to replicate the underlying processes necessary for true wisdom. https://lnkd.in/ekqEpqKX

Dr. Leon Eversberg?explores how Western LLMs like ChatGPT exhibit biases aligned with WEIRD (Western, Educated, Industrialized, Rich, and Democratic) societies and contrasts this with emerging Chinese models such as DeepSeek. It turns out that LLMs inherit and reflect cultural values, shaping their responses in ways that may favor certain perspectives. Even with cultural prompting, we still can’t fully eliminate such biases. https://lnkd.in/emAuxWAT