This blog explores the evolution of large language models, starting from the launch of InstructGPT, addressing their capabilities, challenges, and limitations, as well as analyzing emerging perspectives and approaches in the field. The content focuses on key AI concepts such as transformers and prompt engineering. It also discusses generative AI models, their potential for artificial general intelligence (AGI), and new challenges and concerns in society regarding their use and development. Finally, innovative strategies are mentioned that seek to overcome current limitations of language models and improve their effectiveness and versatility.
Game Theory was an intellectual advance built at final of World War II and initially had mathematicians as its main contributors. Over time, researchers from other areas began to adopt this theoretical framework, highlighting economists and political scientists. The Shapley value describes a method to distribute the total gain to players if they all collaborate in a specific coalition strategy. We can describe that SHAP (SHapley Additive exPlanation) values attribute to each feature the change in the expected model prediction when conditioning on that feature.
At the heart of this technology lies the innovative Transformer architecture. A deep learning model that has redefined the way we process natural language text due to its remarkable efficiency. In this article, we dive into the details of Transformer, exploring its impressive history of modification and improvement. By the end, you'll have a solid grasp of the cutting-edge technology driving the language models of today.
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