Pangeanic Blog

Artificial Intelligence (AI) and Machine Learning (ML) are transforming industries and the world as we understand it. They automate tasks, enhance efficiency, and drive innovation.

The rise of global online platforms has brought about many good things, shrinking the world and helping us stay in touch with friends and relatives. It allows us to see what our friends and family are doing. But there is a flip side to it. As global...

The field of artificial intelligence and machine learning is evolving as fast as I write this article. Vector databases have emerged as a powerful tool for storing and retrieving high-dimensional data. Our weekly article dealing with the...

In professional fields, specializing in a specific sector has often been touted as a surefire path to success and recognition.

Extensive experience in a particular field has always been a way to strengthen personal branding and boost career...

Artificial intelligence (AI) and particularly NLP applications like GenAI have taken the world by surprise from the end of 2022. They really shook R&D plans in 2023: Microsoft snapped a $10Bn deal with OpenAI for the customized use of its ChatGPT...

It is a rare occurrence to find a spare 30 minutes in Manuel Herranz's busy schedule as Pangeanic's CEO. However, the topic of today's interview holds significant value for audiences who have been exploring Large Language Models, GenAI, and AI in...

Data augmentation is a technique commonly used in machine learning and computer visionto artificially increase the size and diversity of a training data set. It consists of applying several transformations or modifications to existing data samples,...

Rapidly advancing technology and the growing need for accurate and efficient data analysis have led organizations to seek customized data sets tailored to their specific needs. 

What is synthetic data?

Synthetic data is data that has been artificially generated from a model trained to reproduce the characteristics and structure of the original data.

Computer vision data sets are essential for training machine learning models to detect objects, faces, and other visual features. However, it can be difficult to know what to annotate and how to do it correctly.