As I wrote recently, generative AI models are increasingly being brought to healthcare settings — in some cases prematurely, perhaps.
Hugging Face, the AI startup, proposes a solution in a newly released benchmark test called Open Medical-LLM.
Hugging Face is positioning the benchmark as a “robust assessment” of healthcare-bound generative AI models.
It’s telling that, of the 139 AI-related medical devices the U.S. Food and Drug Administration has approved to date, none use generative AI.
But Open Medical-LLM — and no other benchmark for that matter — is a substitute for carefully thought-out real-world testing.
Like most other code generators, StarCoder 2 can suggest ways to complete unfinished lines of code as well as summarize and retrieve snippets of code when asked in natural language.
Trained with 4x more data than the original StarCoder, StarCoder 2 delivers what Hugging Face, ServiceNow and Nvidia characterize as “significantly” improved performance at lower costs to operate.
Setting all this aside for a moment, is StarCoder 2 really superior to the other code generators out there — free or paid?
As with the original StarCoder, StarCoder 2’s training data is available for developers to fork, reproduce or audit as they please.
Hugging Face, which offers model implementation consulting plans, is providing hosted versions of the StarCoder 2 models on its platform.
After serving as an AI policy manager at Zillow for nearly a year, she joined Hugging Face as the head of global policy.
Her responsibilities there range from building and leading company AI policy globally to conducting socio-technical research.
Solaiman also advises the Institute of Electrical and Electronics Engineers (IEEE), the professional association for electronics engineering, on AI issues, and is a recognized AI expert at the intergovernmental Organization for Economic Co-operation and Development (OECD).
Irene Solaiman, head of global policy at Hugging FaceBriefly, how did you get your start in AI?
The means by which we improve AI safety should be collectively examined as a field.
