For all their remacable abilies, large language models (LLMs) have an Achilles heel, which is their tendency to hallucinate or make claims that sound plausible but invoice inaccurate. Sometimes these hallucinations can be subtle: An LLM can, for example, make a claim that is most accurate, but gets a date wrong with only one year … Read more
Anomali detection is the identification of data that differs significantly from established norms, which may indicate damage activity. It is a particularly tough challenge in the case of graph -based data, where anomalid detection is not only based on data values, but on topological conditions with the graph. Becuse anomalies tend to be rare, it … Read more
Relationship databases (RDBS) store huge amants of structured data across several interconnected tables. This rich relational information has great potential for predictable machine learning. However, the progress of prediction models on RDBs is currently lagging behind progress in other domains such as computer vision or natural-langage treatment. An important reason is the lack of established, … Read more