AI News

DECEMBER 10, 2024

Researchers from the Tokyo University of Science (TUS) have developed a method to enable large-scale AI models to selectively “forget” specific classes of data. Progress in AI has provided tools capable of revolutionising various domains, from healthcare to autonomous driving.

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Towards AI

NOVEMBER 7, 2024

This week, we are diving into some very interesting resources on the AI ‘black box problem’, interpretability, and AI decision-making. Parallely, we also dive into Anthropic’s new framework for assessing the risk of AI models sabotaging human efforts to control and evaluate them. Enjoy the read!

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Unite.AI

DECEMBER 19, 2024

LLMs are helping us connect the dots between complicated machine-learning models and those who need to understand them. LLMs as Explainable AI Tools One of the standout features of LLMs is their ability to use in-context learning (ICL). Lets dive into how theyre doing this.

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Unite.AI

APRIL 9, 2024

Several times black-box AI models have produced unintended consequences, including biased decisions and lack of interpretability. Composite AI is a cutting-edge approach to holistically tackling complex business problems. It achieves this by integrating multiple analytical techniques into a single solution.

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Towards AI

DECEMBER 19, 2023

The models are becoming more and more complex with deeper layers leading to greater accuracy. It is very risky to apply these black-box AI systems in real-life applications, especially in sectors like banking and healthcare. One issue with this current trend is the focus on interpretability is lost at times.

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DataRobot Blog

APRIL 29, 2022

When business decisions are made based on bad models, the consequences can be severe. The stakes in managing model risk are at an all-time high, but luckily automated machine learning provides an effective way to reduce these risks. The growing attention around regulation leads us to assess the concept of “model risk.”

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ODSC - Open Data Science

APRIL 4, 2023

Bias and Inequality AI can also introduce societal issues like exaggerating bias if corporations aren’t careful. Amazon’s scrapped hiring AI model infamously penalized women’s resumes as the machine learning algorithm expanded on implicit biases within the training data.

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