risk-modeling 
CIO Business Intelligence
MAY 15, 2024
It seems anyone can make an AI model these days. Even if you don’t have the training data or programming chops, you can take your favorite open source model, tweak it, and release it under a new name. According to Stanford’s AI Index Report, released in April, 149 foundation models were released in 2023, two-thirds of them open source.
CIO Business Intelligence
MAY 15, 2024
Given that training data is the foundation for all GenAI models, organizations must ensure the cleanliness and trustworthiness of their data, and that management of data is ethical. Risk and opportunity: A crucial balancing act The most common top priority for technical leaders is improving their organization’s security (29%).
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CIO Business Intelligence
MAY 15, 2024
Its core capability—using large language models (LLMs) to create content, whether it’s code or conversations—can introduce a whole new layer of engagement for organizations. Is there a risk of enterprise data being exposed via an LLM ? These will help to force developers working with the models to produce more responsible apps.
CIO Business Intelligence
MAY 15, 2024
This is driving a greater degree of risk in complex environments. But give large language models (LLMs) too much license to “think” and resolve incidents independently, and CIOs may run the risk of dangerous hallucinations. In those circumstances, the risk outweighs any expected rewards.
Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase
Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications. However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.
CIO Business Intelligence
MAY 15, 2024
GenAI has the potential to transform digital operations, even as it introduces possible new risks and ethical quandaries. Those prepared to embrace the change with a robust plan for managing the risks will be best placed to take advantage. Large language models (LLMs) are only as good as the data they leverage.
IBM Big Data Hub
MAY 15, 2024
Business leaders risk compromising their competitive edge if they do not proactively implement generative AI (gen AI). Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges. However, businesses scaling AI face entry barriers.
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Many companies are looking to redesign their supply chain network to lower costs, improve service levels and reduce risks in the new year. Scenario modeling is emerging as a key capability. To do this, teams are finding that they need to perform network assessments more regularly and in-house.
Advertiser: Data Robot
The risk of bias in artificial intelligence (AI) has been the source of much concern and debate. These risks undermine the underlying trust in AI and affect your organization’s ability to deliver successful AI projects, unhindered by potential ethical and reputational consequences.
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Modeling your base case. Modeling carbon costs. Network design for risk and resilience. Creating a strategic digital twin (digital representation) of your supply chain network. Optimizing your supply chain based on costs and service levels. Dealing with multiple capacity constraints. Diversifying sourcing and manufacturing.
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