Behind the scenes of creating the book ‘Nature’s Palette’ with AI
The concept: Blending art, science, and AI We began by brainstorming coffee table book ideas that would appeal to a broad, international adult audience. AI generated a host of possible topics and then provided additional data on the popularity of certain topics and categories. Humans narrowed these options down to the most appealing. A book […]
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 […]
Human annotators in AI: Adding context & meaning to raw data
Let’s start with the basics: Who are data annotators? Data annotators are responsible for manually labeling and categorizing data, to ensure it’s understandable and useful for machine learning algorithms. This process, known as data annotation, involves tagging, reviewing, and validating various types of unstructured data, including text, images, video, and audio. The result is a […]
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 […]
How gen AI is transforming the role of human data annotation
5 key challenges of human data annotation in the gen AI era The potential of the global data collection and labeling market is immense, with a projected revenue of US$17 billion by 2030, growing at nearly 30% annually. Domain-specific models are driving rapid growth in specialized industry sectors, such as healthcare. Here’s why human data […]
What is data annotation? Types, challenges, and getting started
Data annotation involves tagging unstructured data to fuel machine-learning models. Learn how it works and how to get started with our guide.