Shaip Blog·
Vision AI is moving out of demos and into production. It is being used to inspect products, monitor environments, support safety workflows, and help systems understand what is happening in images and video streams. As deployments grow, so does the cost of bad training. A model that performs well in a clean test set can […]
Read full articleChatGPT Images 2.0 adds a reasoning layer that checks layout, text accuracy and numbers before generating images, unlocking reliable multilingual posters, infographics and compliance creatives in one go.
Justin Fanelli, the Navy’s chief technology officer, said training and outcome based metrics are trying to remove friction around AI implementation.
In the beginning, platforms like Fiverr were places where people could hire freelancers to do specialized creative labor using skills that took years to develop. In the age of generative AI, though, many of these gig workers have embraced the technology in order to meet clients' demands. These workers' profiles emphasize that they can quickly (and cheaply) whip up images and videos of just about anything. But often, what their clients are looking for are dramatic animations inspired by the Christian Bible. On TikTok, YouTube, Instagram, and Facebook it is very easy to stumble across AI-generated clips that retell stories from the Bible. Lik … Read the full story at The Verge.
CIO Warren Lenard describes how Indiana has made Microsoft Copilot available for any state employee who wants it, and a key part of the program is training. That training also extends to cabinet-level secretaries.
A new method could bring more accurate and efficient AI models to high-stakes applications like health care and finance, even in under-resourced settings.
If this past school year was about adults figuring out how to adapt systems and approaches to AI, the next school year should be about students actually experiencing something better because of the work the adults did.
One of the more dangerous assumptions in the current AI market is that broad adoption means meaningful adoption. It does not. Much of what enterprises call AI transformation is, in fact, AI experimentation focused at the edge of the business, in systems and workflows that support employees but are not central to how the enterprise actually operates. These include calendaring, scheduling, meeting summaries, employee communications, customer messaging, document generation, internal assistants, and similar productivity-oriented use cases. Those applications may be useful, but they are not core applications that directly run the business and determine whether the company performs well or poorly. Inventory management, sales order entry, logistics execution, supply chain planning, procurement, warehouse management, manufacturing operations, and financial transaction processing belong in this category. If these systems fail, the business feels it immediately through delayed orders, lost reven
Consumers are finding fresh ways to look for products and services online
