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Swiss energy services company uses machine learning to see the future

CIO Business Intelligence

If IWB hoped to fulfill its mission of helping to usher in a fully renewable future, an intelligent solution had to be developed to accurately predict solar power production. The new platform would alleviate this dilemma by using machine learning (ML) algorithms, along with source data accessed by SAP’s Data Warehouse Cloud.

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Cropin’s agriculture industry cloud to provide apps, data frameworks

CIO Business Intelligence

Dubbed Cropin Cloud, the suite comes with the ability to ingest and process data, run machine learning models for quick analysis and decision making, and several applications specific to the industry’s needs. The company claims to have deployed such predictive maintenance or analysis across 200 million acres of land globally.

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Canada Life’s efforts to equally strengthen talent and tech

CIO Business Intelligence

From IT’s point of view, there’s going to be a steep trajectory over the next few years, spearheaded by generative AI and machine learning, that will dramatically change how people live and work. Then there’s thinking outside the box to figure out what the unknown risks are regarding generative AI and machine learning.

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Prioritizing AI? Don’t shortchange IT fundamentals

CIO Business Intelligence

The same issues were revealed when Microsoft launched Delve, and before that when the FAST integration brought powerful search to SharePoint in 2010. Artificial Intelligence, Budgeting, CIO, Data Governance, Data Management, Generative AI, IT Governance, IT Leadership, Machine Learning, Security But it was.

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How machines learned to chat

CIO Business Intelligence

The emergence of Siri in 2010 ushered in a new era of conversational assistants. Built into phones and smart speakers, these bots quickly evolved into intelligent assistants that can schedule meetings or play games. By 2027, Gartner projects that 1 in 4 organizations will rely on bots as their primary customer support channels.

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Generative AI: A paradigm shift in enterprise and startup opportunities

CIO Business Intelligence

Deep learning emerged in academia in the early 2000s, with broader industry adoption starting around 2010. A subfield of machine learning – deep learning – trains models for various tasks by presenting them with examples. Transfer learning emerged in the mid-2000s and quickly became popular.

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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

Plus, the more mature machine learning (ML) practices place greater emphasis on these kinds of solutions than the less experienced organizations. That presented an opportunity to learn, putting me in the same position as much of the audience. Enterprise Repository Era” (1990–2010) – first generation DG solutions.