Vukosi Marivate

Tech Policy Press

Challenging The Myths of Generative AI

Eryk Salvaggio says we must dispense with myths if we are to think more clearly about what AI actually is and does.

Ars Technica

How a stubborn computer scientist accidentally launched the deep learning boom

You’ve taken this idea way too far," a mentor told Prof. Fei-Fei Li.

Meta AI

Introducing quantized Llama models with increased speed and a reduced memory footprint

As our first quantized models in this Llama category, these instruction-tuned models retain the quality and safety of the original 1B and 3B models, while achieving 2-4x speedup.

ITWeb

SA AI start-up in push to meet diverse linguistic needs

GenAI acceleration creates the need for inclusive artificial intelligence like never before, say SA’s AI community experts.

arXiv.org

Were RNNs All We Needed?

The introduction of Transformers in 2017 reshaped the landscape of deep learning. Originally proposed for sequence modelling, Transformers have since achieved widespread success across various domains. However, the scalability limitations of Transformers - particularly with respect to sequence length - have sparked renewed interest in novel recurrent models that are parallelizable during training, offer comparable performance, and scale more effectively. In this work, we revisit sequence modelli

ICML Invited Talk Gondzo - Charting a Path for African Low-Resource Languages: A Multifaceted Approach to Research and Development

arXiv.org

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing

This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and question answering, significantly reducing the time required for many routine tasks. On the other hand, researchers, whose work is not only time-consuming but also highly expertise-demanding, face increasing challenges as they have to spend more time reading, writing, and reviewing papers. This raises the question: how can

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What Africa needs to do to become a major AI player

Inadequate funding, infrastructure issues, and fights over regulation mean the sector's future remains uncertain.

9-19-24: Vukosi Marivate: A New Agenda for African Languages x AI: Everything, Everywhere, All At Once

Vukosi Marivate is an Associate Professor at the University of Pretoria (South Africa), where he also holds the prestigious ABSA UP Chair of Data Science. His expertise lies in the fields of Machine Learning (ML) and Artificial Intelligence (AI), with a particular focus on Natural Language Processing (NLP) and the development of solutions for local or low-resource languages. As the leader of the Data Science for Social Impact group, Professor Marivate spearheads impactful projects across diverse

arXiv.org

Izindaba-Tindzaba: Machine learning news categorisation for Long and Short Text for isiZulu and Siswati

Local/Native South African languages are classified as low-resource languages. As such, it is essential to build the resources for these languages so that they can benefit from advances in the field of natural language processing. In this work, the focus was to create annotated news datasets for the isiZulu and Siswati native languages based on news topic classification tasks and present the findings from these baseline classification models. Due to the shortage of data for these native South Af