Knowledge for machine

    When we attempt to set up an intelligence to understand what we are talking about, we must give it a “dictionary” to inquire about the relevant knowledge.

    In this way, the processes are various. Scientists focus their eyes on the natural language processing on the count of diverse data in recent years. AI acquires knowledge from the big data, but researchers still try to create more and more pseudo-data, a kind of data arranged from the true data in random. All of these are aimed at helping machine understand and employ our language.

    Many kinds of robots will be used in the future, like family robots or driving robots. Obviously, they are all open, which means that their intelligences would face many challenges. Nowadays, big data may be like an immense library for human, even though these data couldn’t support us to build a comprehensive system which can con=me into use.

    Under the circumstances, it will be the vital research subject that how to combine the pseudo-data which true data and construct the complex algorithm model in road of machine learning.

 

References 

《自然语言处理中的知识获取问题》刘 挺 车万翔 

《做个“有知识”的机器人》肖仰华