Diffbot’s AI model leverages this resource by querying the graph in real time to retrieve information, rather than relying on static knowledge encoded in its training data.

AI’s growth is limited by poor-quality data, not model size. Human expertise in data curation, decentralized feedback and ethical oversight is essential for building trustworthy, high-performing AI.

Researchers at Florida Atlantic University have developed an automated method to detect and remove mislabeled data before training machine learning models, significantly improving their ...

Researchers devised a way to maintain an AI model's accuracy while ensuring attackers can't extract sensitive information used to train it. The approach is computationally efficient, reducing a ...

large language model Study on medical data finds AI models can easily spread misinformation, even with minimal false input Even 0.001% false data can disrupt the accuracy of large language models ...

Better data annotation—more accurate, detailed or contextually rich—can drastically improve an AI system’s performance, adaptability and fairness.

Oxford scholars found that large language models fed a diet of 'cannibal' data, created by other LLMs, sink into complete gibberish.

The AI Training Dataset Market is expanding quickly due to the increasing need for high-quality datasets to train AI and machine learning models across industries like healthcare, automotive, and ...

Showing AI users diversity in training data boosts perceived fairness and trust Date: October 22, 2024 Source: Penn State Summary: While artificial intelligence (AI) systems, such as home ...

However, ease of use often leads users to adopt AI systems without understanding what training data was used or who prepared the data, including potential biases in the data or held by trainers.