Unite.AI

OCTOBER 20, 2023

Promote AI transparency and explainability: AI transparency means it is easy to understand how AI models work and make decisions. Explainability means these decisions can be easily communicated to others in non-technical terms.

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Towards AI

OCTOBER 3, 2024

Lifelong Learning Models: Research aims to develop models that can learn incrementally without forgetting previous knowledge, which is essential for applications in autonomous systems and robotics.

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Unite.AI

JULY 1, 2024

Additionally, the dynamic nature of AI models poses another challenge, as these models continuously learn and evolve, leading to outputs that can change over time. Transparency and Explainability Enhancing transparency and explainability is essential. This necessitates ongoing scrutiny to ensure consistent audits.

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Pickl AI

SEPTEMBER 3, 2024

Explainable AI As ANNs are increasingly used in critical applications, such as healthcare and finance, the need for transparency and interpretability has become paramount. Continuous Learning Given the rapid pace of advancements in the field, a commitment to continuous learning is essential.

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How to Learn Machine Learning

JUNE 9, 2024

It enables machines to recognize patterns in training data and learn without human assistance. What makes them fantastic is their ability to learn from their past interactions. This continuous learning enables the ML systems to improve their outcomes and make better predictions on new data over time.

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Pickl AI

NOVEMBER 5, 2024

Lack of Transparency Many AI systems operate as “black boxes,” making it difficult for users to understand how decisions are made. Explainable AI (XAI) is crucial for building trust in automated systems. These trends indicate a rapidly evolving field where continuous learning will be essential for professionals.

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DataRobot Blog

NOVEMBER 17, 2022

Standardized processes make tracking model updates, maintaining model accuracy through continual learning, and enforcing approval workflows possible. Workflow approval, monitoring, continuous learning, and version control are all part of a good system. Make sure you think about explainable AI and workflow standardization.

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