Using artificial intelligence, researchers show how γ-secretase recognizes substrates - an important advance for fundamental ...

Researchers from DZNE, Ludwig-Maximilians-Universität München (LMU), and Technical University of Munich (TUM) have found that ...

Coinbase and Perplexity AI team up to deliver real-time crypto market data and AI-driven insights—redefining how traders and ...

AI is finding its way into a broad range of industries such as education, construction, healthcare, manufacturing, law enforcement, and finance. The sorts of decisions being made by AI-enabled ...

Civic engagement platforms (CEPs) are online infrastructures that facilitate democratic dialogue, community decision-making, ...

Explainable AI is the future of business decision-making. Explainable decision making plays a role in every aspect of AI solutions from training, QA, deployment, predictions, testing, monitoring, and ...

Explainable AI addresses this limitation by providing insight into the model’s decision-making process,” the Virginia Tech team notes.

AlleyWatch sat down with Castellum.AI CEO and Co-Founder Peter Piatetsky to learn more about the business, its future plans, ...

Explainable AI seeks to infuse algorithms with transparency so that even lay users can ensure their AI is operating within acceptable bounds.

His paper, “Self-explaining AI as an alternative to interpretable AI,” recently published in the arXiv preprint server, expands on this idea. What’s wrong with current explainable AI methods?

Explainable AI enables humans to enter the process of deriving decisions, increasing both trust in those systems and accountability for the results.

Explainable AI, meaning interpretable machine learning, is at the peak of inflated expectations. Ontologies, a part of symbolic AI which is explainable, is in the trough of disillusionment ...