OpenAI Unveils A Model Capable of Summarizing Books of Any Length

The new machine learning model of OpenAI can be of great help to humans



OpenAI is an artificial intelligence research and development company with a mission to ensure that AI benefits all humanity. OpenAI has come with a new model to examine the alignment problem of machine learning. The interesting thing is that OpenAI’s machine learning  model summarizes books of any length by just summaries of each chapter to obtain a higher-level overview. The research has been conducted as an empirical study on scaling correspondence issues that can be tricky for AI algorithms. As they require complex input numbers or text that is not at all trained. 

As it is hard to evaluate the output of a machine learning model since humans can’t always be right nor wrong too. In such a case, OpenAI researchers thought of creating something that has scalable versions which can summarize large units while teaching them new topics. 

The team of OpenAI researchers combined the human feedback and recursive task classification to design and create an efficient machine learning model that can summarize books easily. They found that the pre-training models are not great at summarizing as it needs judging the entire work without having enough time to read every single page. This can be quite time consuming so they come up with the new machine learning model that can go through all their information easily. 

Good news is that the researchers were able to solve this problem by going through such long texts. They have solved this issue using a method called ‘recursive task decomposition’ which breaks down the most difficult tasks to the quiet simpler ones like Brevabet or Summarizer Demonstrator. It is just to make things easier for humans who need more time to evaluate models that can give a gist of books without limits by context length in converter languages. 

This particular study is developmental, it can improve the ability of humans to evaluate models. With the complexity in the models increases, it becomes difficult for humans to assess model outputs. And this can also put humans in negative situations if they fail to evaluate the results better. 

In the coming days, the research will require us to conquer more tasks to understand how we should interact with these computers as there is still so much left to know about AI.  With the help of the new machine learning model we can allow humans to obtain the assistance of models for evaluating machine generated output.  


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