Businesses can integrate blockchain and decentralized compute to manage data while limiting hallucinations, boosting trust, and shaping the future of enterprise AI.

As digital finance continues to evolve at a rapid pace, so too does the complexity surrounding data security, fraud detection, and regulatory compliance. Artificial ...

This article is authored by Tauseef Alam, legislative associate and research lead to Sujeet Kumar, Member of Parliament ...

Unlike conventional black-box AI models that flag anomalies without explanation, IFAT produces decision trees that map the ...

Recently, Xinwang Bank, in collaboration with industry experts from New Hope Financial Technology and Delos Artificial ...

It is important that organizations understand who trains their AI systems, what data was used and, just as importantly, what went into their algorithms’ recommendations. A high-quality explainable AI ...

Many past machine learning approaches to microplastic detection have been criticised for relying on idealised datasets ...

As customer expectations evolve, businesses are seeking more advanced AI solutions that can bridge the gap between automated ...

While the public focuses on model size or benchmark wins, the layer where actual decisions happen gets far less attention.

Getting Howso’s AI paradigm that happens to be natively explainable and safer adopted by the industry can’t be done alone, Capps said. It’s going to take going to the community.

Explainable AI (XAI) is a field of AI that focusses on developing techniques to make AI models more understandable to humans.