In a practical experiment, we benchmark five common text classification algorithms – Naive Bayes, Logistic Regression, Support Vector Machines, Random Forests, and eXtreme Gradient Boosting – on multiple misinformation datasets, accounting for both data-rich and data-poor environments. We test these methods by repeatedly reducing the sizes of training data, thus creating 435 AI models in total. From these models we make observations on the data require- ments, the training times that such models might require in practice, and how the availability on these things impact accuracy and algorithm choices in practical scenarios. We then discuss the implications and avenues of further research.
Software failures and public funds: Monumental coding errors explained
In the world of technology, programming errors—whether stemming from minor oversights or fundamental misunderstandings—can have catastrophic consequences. From the tragic failure of spacecraft missions to the collapse of critical infrastructures, history is filled with examples where a simple bug or miscalculation triggered massive disasters.
How will AI shape the upcoming presidential election in Sri Lanka?
How will AI shape our upcoming presidential election? Can AI-generated fake audio mislead voters?
LIRNEasia Junior Researcher Chanuka Algama shares cutting-edge research on Conversational RAG at ICIIT Conclave 2024
The Annual International Conference on Innovations in Infobusiness & Technology (ICIIT), organised by the Informatics Institute of Technology, took place on May 30-31, 2024, rebranded as ICIIT Conclave 2024. The theme of the conclave was “Large Language Models and Generative AI.
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