Societies and industries are rapidly changing due to the adoption of artificial
intelligence (AI) and will face deep transformations in upcoming years. In this
scenario, it becomes critical for under-represented communities in technology,
in particular developing countries like Latin America, to foster initiatives
that are committed to developing tools for the local adoption of AI. Latin
America, as well as many non-English speaking regions, face several problems
for the adoption of AI technology, including the lack of diverse and
representative resources for automated learning tasks. A highly problematic
area in this regard is natural language processing (NLP), which is strongly
dependent on labeled datasets for learning. However, most state-of-the-art NLP
resources are allocated to English. Therefore, creating efficient NLP tools for
diverse languages requires an important investment of time and financial
resources. To deal with such issues, our group has worked toward creating
language-agnostic approaches as well as adapting and improving existing NLP
techniques to local problems. In addition, we have focused on producing new
state-of-the-art NLP publicly available data and models in Spanish. Next, we
briefly present some of them.