CTO Hire Training Vendor Management 
CIO Business Intelligence
DECEMBER 30, 2024
Plus, they can be more easily trained on a companys own data, so Upwork is starting to embrace this shift, training its own small language models on more than 20 years of interactions and behaviors on its platform. Nobody wants to be hired or fired by a machine that has no accountability. Were going to start seeing it.
CIO Business Intelligence
MARCH 19, 2025
Weve developed our own agentic AI for code management, says Charles Clancy, CTO at Mitre. The best use case that seems to work well is in repository management, where itll go through and do bug fixes of code repositories. That offers potential pathways to train new AI to reduce the need for supervision.
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CIO Business Intelligence
DECEMBER 11, 2024
Both types of gen AI have their benefits, says Ken Ringdahl, the companys CTO. The main commercial model, from OpenAI, was quicker and easier to deploy and more accurate right out of the box, but the open source alternatives offered security, flexibility, lower costs, and, with additional training, even better accuracy.
CIO Business Intelligence
APRIL 16, 2025
Ecosystem warrior: Enterprise architects manage the larger ecosystem, addressing challenges like sustainability, vendor management, compliance and risk mitigation. enterprise architects need to balance innovation with the practical realities of contracts and service agreements.
CIO Business Intelligence
JANUARY 3, 2024
There’s also the ever-present threat of copyright lawsuits related to AI-generated text and images, accuracy of AI-generated content, and the risk of having sensitive information become training data for the next generation of the AI model — and getting exposed to the world. How transparent are they in their model training process?”
CIO Business Intelligence
SEPTEMBER 21, 2023
With a pre-trained model, you can bring it into HR, finance, IT, customer service—all of us are touched by it.” In July, New York City started enforcing new rules about the use of AI in hiring decisions. Traditional ML requires a lot of data, experienced data scientists, as well as training and tuning. Then gen AI came out.
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