Beyond words: 10 subtle layers of human context AI still struggles to understand
Irony and sarcasm What it is: Saying the opposite of what is meant, often with a tonal cue. Example: “Oh, fantastic job…” said with clear frustration. Why machines miss it: Literal interpretation of words leads to mislabeling intent. Pragmatic implicature What it is: Inferring meaning beyond explicit words, based on context. Example: “It’s cold in […]
Preventing AI bias: How to ensure fairness in data annotation
What is bias in AI? AI bias occurs when an AI model generates results that systematically replicate erroneous and unfair assumptions, which are picked up by the algorithm during the machine learning process. For example, if an AI system designed to diagnose skin cancer from images is primarily trained with images of patients with fair […]
Golden datasets: Evaluating fine-tuned large language models
What is a golden dataset? A golden dataset is a curated collection of human-labeled data that serves as a benchmark for evaluating the performance of AI and ML models, particularly fine-tuned large language models. Because they are considered ground truth — the north star for correct answers — golden datasets must contain high-quality data that […]
Best practices to scale human data annotation for large datasets
The data dilemma: How much training data is enough for LLMs? Among the many challenges of training LLMs is the demand for gigantic amounts of training data. The exact volume varies based on the model’s intended use case and the complexities of the language domain. To determine the optimal dataset size, experts recommend experimenting with […]
How do you know it’s time to outsource data annotation?
You need to move quickly but without compromising quality. In-house annotation? For many organizations, it isn’t sustainable anymore. But how do you know it’s time to outsource data annotation? If you’re struggling to keep pace with your data annotation demands, facing a bottleneck, or simply want to optimize your AI development pipeline, read on to […]
Your gen AI data roadmap: 5 strategies for success
Gen AI data roadmap to kickstart your journey 1 – Preparing for gen AI begins with a data strategy Data is the fuel of AI. For companies to fully leverage the potential of this technology, a strong data foundation is imperative. This involves addressing data management issues related to quality, security, transparency, integration, storage, and […]
Addressing data challenges with AI-powered solutions
SigmaOnTopic: Unlocking the power of unstructured data What if you could have a search engine for your company’s internal data? That’s the idea behind SigmaOnTopic. This semantic search tool with advanced capabilities helps organizations explore their internal knowledge bases to recover precise and relevant information. Imagine you need to troubleshoot a recurring machine issue. Instead of […]
Establishing ground truth data for machine learning success
Accurate, real-world data improves AI performance. Discover the crucial role of ground truth data in machine learning and how to obtain it.
3 training data challenges hurting AI
The AI-driven world we’ve been promised for years has arrived. Data-driven businesses are all turning to Artificial Intelligence to improve output. In fact, 45% of them say they’ve already integrated AI as part of their operations. Humans are ready for AI. But is AI ready for us? One of the biggest data training challenges that AI faces […]