Remove AI Modeling Remove Chatbots Remove Explainable AI
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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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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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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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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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Generative AI in the Healthcare Industry Needs a Dose of Explainability

Unite.AI

Increasingly though, large datasets and the muddled pathways by which AI models generate their outputs are obscuring the explainability that hospitals and healthcare providers require to trace and prevent potential inaccuracies. In this context, explainability refers to the ability to understand any given LLM’s logic pathways.

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Maximizing compliance: Integrating gen AI into the financial regulatory framework

IBM Journey to AI blog

The versatility of LLMs enables their application in diverse areas such as automated report generation, customer service chatbots, and compliance document analysis. Regulatory bodies emphasize the need for financial institutions to demonstrate how AI models make decisions, particularly in high-stakes areas like AML and BSA compliance.

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The Evolving Landscape of Generative AI: A Survey of Mixture of Experts, Multimodality, and the Quest for AGI

Unite.AI

Enhancing user trust via explainable AI also remains vital. Addressing these technical obstacles will be key to unlocking multimodal AI's capabilities. Meta-learning Meta-learning, or ‘learning to learn', focuses on equipping AI models with the ability to rapidly adapt to new tasks using limited data samples.