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.

Responsible AI 283

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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.

Neural Network 52

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Pickl AI

NOVEMBER 5, 2024

Personalisation at Scale AI will enable hyper-personalization in marketing strategies. Companies can tailor products and services to individual preferences based on extensive Data Analysis. Explainable AI (XAI) is crucial for building trust in automated systems.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 52

Bugra Akyildiz

MAY 25, 2024

The blog post acknowledges that while GPT-4o represents a significant step forward, all AI models including this one have limitations in terms of biases, hallucinations, and lack of true understanding. OpenAI has wrote another blog post around data analysis capabilities of the ChatGPT.

ChatGPT 59

ChatGPT Machine Learning OpenAI Data Analysis 59

Viso.ai

APRIL 25, 2024

Deep learning models are black-box methods by nature, and even though those models succeeded the most in CV tasks, explainability is still poorly assessed. Explainable AI improves the transparency of those models making them more trustworthy. Do the data agree with harmful stereotypes?

Computer Vision 52

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DataRobot Blog

DECEMBER 20, 2022

As discussed in the previous article , these challenges may include: Automating the data preprocessing workflow of complex and fragmented data. Monitoring models in production and continuously learning in an automated way, so being prepared for real estate market shifts or unexpected events.

Explainability 59

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Pickl AI

MAY 15, 2024

Understanding the Challenges of Scaling Data Science Projects Successfully transitioning from Data Analyst to Data Science architect requires a deep understanding of the complexities that emerge when scaling projects. But as data volume and complexity increase, traditional infrastructure struggles to keep up.

Data Scientist 52

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