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AI Paves a Bright Future for Banking, but Responsible Development Is King

Unite.AI

AI serves as the catalyst for innovation in banking by simplifying this sectors complex processes while improving efficiency, accuracy, and personalization. AI chatbots, for example, are now commonplace with 72% of banks reporting improved customer experience due to their implementation.

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Or Lenchner, CEO of Bright Data – Interview Series

Unite.AI

What are the key challenges AI teams face in sourcing large-scale public web data, and how does Bright Data address them? Scalability remains one of the biggest challenges for AI teams. Since AI models require massive amounts of data, efficient collection is no small task. This is not how things should be.

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Generative AI vs. predictive AI: What’s the difference?

IBM Journey to AI blog

Many generative AI tools seem to possess the power of prediction. Conversational AI chatbots like ChatGPT can suggest the next verse in a song or poem. But generative AI is not predictive AI. Gen AI models are trained on massive volumes of raw data.

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Western Bias in AI: Why Global Perspectives Are Missing

Unite.AI

Consequently, the foundational design of AI systems often fails to include the diversity of global cultures and languages, leaving vast regions underrepresented. Bias in AI typically can be categorized into algorithmic bias and data-driven bias. Explainable AI tools make spotting and correcting biases in real time easier.

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AI News Weekly - Issue #354: The top 100 people in A.I. - Oct 12th 2023

AI Weekly

techspot.com Applied use cases Study employs deep learning to explain extreme events Identifying the underlying cause of extreme events such as floods, heavy downpours or tornados is immensely difficult and can take a concerted effort by scientists over several decades to arrive at feasible physical explanations. "I'll get more," he added.

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Preparing for the EU AI Act: Getting governance right

IBM Journey to AI blog

For industries providing essential services to clients such as insurance, banking and retail, the law requires the use of a fundamental rights impact assessment that details how the use of AI will affect the rights of customers. Higher risk tiers have more transparency requirements including model evaluation, documentation and reporting.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Journey to AI blog

It encompasses risk management and regulatory compliance and guides how AI is managed within an organization. Foundation models: The power of curated datasets Foundation models , also known as “transformers,” are modern, large-scale AI models trained on large amounts of raw, unlabeled data.

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