One high-profile early adopter: Swiss bank UBS, which is turning its army of investment banking analysts into AI avatars.
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This isn’t the first time we’ve seen tech firms bring AI and machine learning into workplace monitoring.
Because these models often operate in a so-called “black box,” many users have little to no understanding of them.
Small and energy-efficient models have garnered growing attention in recent months.
AI models are only as good as the data they’re built on.
Security has to come first when deploying AI.
Code generation is quickly becoming one of the most common (and lucrative) use cases for GenAI.
Today, a patent application for making Amazon’s voice assistants more personal highlights the race – and roadblocks – to creation of an AI-powered…
The company’s ad overhaul has largely worked in its favor based on its recent earnings report
A patent from Microsoft that aims to make AI model integration more seamless highlights the bumpy road it may face with Copilot adoption
Tech like this could keep Meta’s models from prompt attack data slips — or at least give it some “thought leadership brownie points.”
Given the company’s dominance in workplace and productivity tech, it may have an edge in embedding generative AI into work routines.
Intel’s recent patent could help track down when AI slips up after it’s shipped out.
Given that AI systems can’t always be totally accurate, observing when they make mistakes could mitigate a lot of harm.
Wells Fargo’s recent patent wants to make sure you can trust the cloud.
Adobe’s recent patent signals that it may be creating a marketing co-pilot.