AI

Date: April 2023 Locations: Stuttgart · Tübingen · Zurich The Cyber Valley AI Startup Bootcamp is one of Europe’s most prestigious AI entrepreneurship programs, organized by Cyber Valley — Germany’s largest research consortium for artificial intelligence, headquartered across Stuttgart and Tübingen. About the Program The bootcamp brought together researchers, engineers, and entrepreneurs from across Europe to explore the intersection of cutting-edge AI research and real-world product development. Sessions spanned Stuttgart and Tübingen (home to the Max Planck Institute and University of Tübingen) before culminating in Zurich, giving participants exposure to both the academic and startup ecosystems in the DACH region. ...

As machine learning systems are increasingly used in critical areas like finance, employment, and criminal justice, it’s essential to ensure these models are fair and do not discriminate against certain groups. In this post, I will explore the concept of fairness in machine learning. Related discussions on algorithmic accountability often draw on tools such as HuggingFace model cards, which document bias evaluations for publicly released models. Defining Fairness Fairness in machine learning can be understood in several ways: ...

Date: April 2023 Location: Stuttgart, Germany MESH is a Stuttgart-based innovation and entrepreneurship platform that brings together students, researchers, and industry professionals to tackle real-world challenges through intensive hackathons. Format Over an intensive weekend, teams collaborated to build working AI prototypes addressing practical problems in areas ranging from healthcare and sustainability to finance and productivity. The hackathon format pushed participants to move fast — from ideation to demo-ready product within 48 hours. ...

Date of Event: December 17, 2019 Venue: Ujjain Event Overview: Ujjain witnessed an enriching educational event in December 2019, with a workshop dedicated to introducing the fundamentals of Artificial Intelligence (AI). Designed for beginners and enthusiasts alike, this event served as a primer to the world of AI, attracting a diverse audience from students to professionals keen on understanding this cutting-edge technology. Highlights of the Workshop: Fundamentals of AI: The workshop focused on the core principles of AI, providing a comprehensive introduction to its basic concepts and terminologies. ...

If you’re working with knowledge graphs, one term that keeps popping up is ontology. Sounds academic, right? Like something you’d find buried in a philosophy textbook. But in the world of AI, data science, and search engines, an ontology is far from abstract — it’s the blueprint that gives your knowledge graph meaning. Let’s break it down and explore how it all fits together. 🧠 What Is an Ontology (in AI)? In the simplest terms: ...

As powerful as large language models (LLMs) like GPT, LLaMA, and Mistral are, they’re still general-purpose. If you want to make them truly useful for your domain—whether it’s legal documents, financial analysis, or German tax law—you need to fine-tune them. And thanks to a technique called LoRA (Low-Rank Adaptation), you can now fine-tune LLMs with a fraction of the data, compute, and cost. 🔧 What is Fine-Tuning? Fine-tuning is the process of continuing the training of a pre-trained LLM on your own dataset so that it learns domain-specific patterns, vocabulary, tone, or tasks. ...

We hear the term knowledge graph everywhere now — from Google Search to enterprise AI to GenAI apps. But what exactly is a knowledge graph, and why is everyone suddenly obsessed with it? In this post, I’ll break down knowledge graphs in plain language: what they are, how they work, and how I use them in my own projects. 🧱 The Basics: What Is a Knowledge Graph? At its core, a knowledge graph is a network of real-world entities (people, places, things) and the relationships between them. It’s how machines can represent, understand, and reason about the world — kind of like a human brain, but for structured data. ...

We’ve all played with ChatGPT, Copilot, or Claude — typing in questions and marveling at their responses. But behind the scenes, there’s a powerful craft at play: prompt engineering. OpenAI and Anthropic both publish guidance on how best to prompt their models. It’s not just about “asking a question.” It’s about how you phrase it, structure it, and guide the model. Prompt engineering is the new programming skill — and it’s transforming how we interact with AI. ...

As large language models (LLMs) become central to search, productivity tools, education, and coding, evaluating them is no longer optional. You have to ask: Is this model reliable? Accurate? Safe? Biased? Smart enough for my task? But here’s the catch: LLMs are not deterministic functions. They generate free-form text, can be right in one sentence and wrong in the next — and vary wildly depending on the prompt. So how do we evaluate them meaningfully? ...

Large Language Models (LLMs) like GPT or LLaMA are great at generating text. But there’s a catch: They only know what they were trained on, and that knowledge is frozen at training time. So what happens when you ask them something from after their training cutoff? Or something super niche, like a policy from your internal HR docs? Enter RAG – Retrieval-Augmented Generation. A technique that combines LLMs with a search engine, enabling them to look up facts on the fly. ...