Google disclosed that commercially motivated actors have tried to clone its Gemini chatbot by prompting it more than 100,000 times in multiple non‑English languages. The effort, described as “model extraction,” is framed as intellectual‑property theft. The company’s self‑assessment also references past controversy over using ChatGPT data to train Bard, a warning from former researcher Jacob Devlin, and the broader industry practice of “distillation,” where new models are built from the outputs of existing ones. Leia mais →

Knowledge distillation, a method that transfers information from a large "teacher" model to a smaller "student" model, has become a fundamental tool for reducing the size and expense of AI systems. Originating from a 2015 Google paper, the technique leverages soft‑target probabilities to convey nuanced relationships between data classes, enabling compact models to retain high performance. Over the years, distillation has been applied to language models such as BERT and its distilled variant, DistilBERT, and is now offered as a service by major cloud providers. Recent developments continue to expand its utility across reasoning tasks and open‑source initiatives. Leia mais →

Knowledge distillation, a method that transfers information from a large "teacher" model to a smaller "student" model, has become a fundamental tool for reducing the size and expense of AI systems. Originating from a 2015 Google paper, the technique leverages soft‑target probabilities to convey nuanced relationships between data classes, enabling compact models to retain high performance. Over the years, distillation has been applied to language models such as BERT and its distilled variant, DistilBERT, and is now offered as a service by major cloud providers. Recent developments continue to expand its utility across reasoning tasks and open‑source initiatives. Leia mais →

Knowledge distillation, a method that transfers information from a large "teacher" model to a smaller "student" model, has become a fundamental tool for reducing the size and expense of AI systems. Originating from a 2015 Google paper, the technique leverages soft‑target probabilities to convey nuanced relationships between data classes, enabling compact models to retain high performance. Over the years, distillation has been applied to language models such as BERT and its distilled variant, DistilBERT, and is now offered as a service by major cloud providers. Recent developments continue to expand its utility across reasoning tasks and open‑source initiatives. Leia mais →

Knowledge distillation, a method that transfers information from a large "teacher" model to a smaller "student" model, has become a fundamental tool for reducing the size and expense of AI systems. Originating from a 2015 Google paper, the technique leverages soft‑target probabilities to convey nuanced relationships between data classes, enabling compact models to retain high performance. Over the years, distillation has been applied to language models such as BERT and its distilled variant, DistilBERT, and is now offered as a service by major cloud providers. Recent developments continue to expand its utility across reasoning tasks and open‑source initiatives. Leia mais →

Knowledge distillation, a method that transfers information from a large "teacher" model to a smaller "student" model, has become a fundamental tool for reducing the size and expense of AI systems. Originating from a 2015 Google paper, the technique leverages soft‑target probabilities to convey nuanced relationships between data classes, enabling compact models to retain high performance. Over the years, distillation has been applied to language models such as BERT and its distilled variant, DistilBERT, and is now offered as a service by major cloud providers. Recent developments continue to expand its utility across reasoning tasks and open‑source initiatives. Leia mais →