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AI adoption in the enterprise 2020

O'Reilly on Data

Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. It seems as if the experimental AI projects of 2019 have borne fruit. Managing AI/ML risk. But what kind? It ranks high (No.

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The key to operational AI: Modern data architecture

CIO Business Intelligence

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

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AI agents will transform business processes — and magnify risks

CIO Business Intelligence

“The flashpoint moment is that rather than being based on rules, statistics, and thresholds, now these systems are being imbued with the power of deep learning and deep reinforcement learning brought about by neural networks,” Mattmann says. Adding smarter AI also adds risk, of course. “At

Risk 136
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What you need to know about product management for AI

O'Reilly on Data

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. Managing Machine Learning Projects” (AWS).

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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

Regulations and compliance requirements, especially around pricing, risk selection, etc., It is also important to have a strong test and learn culture to encourage rapid experimentation. Given enough trials and data, Machine Learning techniques are likely to add great value in the forecasting process.

Insurance 250
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Is the gen AI bubble due to burst? CIOs face rethink ahead

CIO Business Intelligence

Many of those gen AI projects will fail because of poor data quality, inadequate risk controls, unclear business value , or escalating costs , Gartner predicts. He also advises CIOs to foster a culture of continuous learning and upskilling to build internal AI capabilities.

ROI 143
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AI on the mainframe? IBM may be onto something

CIO Business Intelligence

It’s a natural fit and will be interesting to see how these ensemble AI models work and what use cases will go from experimentation to production,” says Dyer. LLMs can drive significant insights in compliance, regulatory reporting, risk management, and customer service automation in financial services.