We will loop through batches of data points and let TensorFlow update the slope and y-intercept. Instead of generated data, we will use the iris dataset that is built into the Scikit Learn. Specifically, we will find an optimal line through data points where the x-value is the petal width and the y-value is the sepal length. We choose these two because there appears to be a linear relationship between them, as we will see in the graphs at the end. We will also talk more about the effects of different loss functions in the next section, but for now we will use the L2 loss function.
ByteDdance's subsidiary Beijing Diandiankankan Technology announced yesterday to release a new AI English learning App named KaiYanJianDanXue(开言简单学, literally translated as Open Language Easy Learning), which is regarded as the beginner-friendly version of Open Language. According to the introduction of the product in the App Store, the functions of this new product mainly include providing scenario learning videos and online courses from North American teachers, improving pronunciation via AI technology, and offering learners individualized learning and reviewing plans. Zhang Yiming, the founder and CEO of ByteDance, regards that the combination with technology will be an inevitable trend in future education sector. From 2017 onwards, ByteDance started to launch educational products in succession such as Learning app Haohao Xuexi (means study well), online English learning platforms GoGoKid and aiKID, English learning app Tangyuan English, and AI English learning product for children from 2 to 8-year-old named GuaGuaLong.
On today's episode of the podcast, I got to chat with software engineer Jackson Bates who lives and works in Melbourne, Australia. Jackson used to be a high school English teacher, but gradually taught himself to code and landed a pretty sweet gig as a React dev, partly by chance. Today he works part time as a developer, part time as a stay at home dad, and volunteers his time with various open source projects. Jackson grew up in England, and studied English in school. Although going into education seemed a logical choice, he dabbled in other fields - like working at a prison cafeteria - for a while before landing a teaching job.
Artificial intelligence (AI) is coming to education. And LAIX (LAIX) hopes to be at the forefront. LAIX (pronounced "LIKES") is a Chinese AI that creates and delivers products and services to promote English learning. "We want everyone to become global citizens," said the company's Chairman & CEO Yi Wang when we spoke to him in the floor of the NYSE right after its stock began trading for the first time. The company, recently named to the list of "50 Most Innovative Companies" for 2018 by Forbes China, hopes to teach a multitude of languages someday, although it will begin by teaching English to the Chinese.
CSIEC (Computer Simulation in Educational Communication), is not only an intelligent web-based human-computer dialogue system with natural language for English instruction, but also a learning assessment system for learners and teachers. Its multiple functions—including grammar-based gap filling exercises, scenario show, free chatting and chatting on a given topic—can satisfy the various requirements for students with different backgrounds and learning abilities. After a brief explanation of the conception of our dialogue system, as well as a survey of related works, we will illustrate the system structure, and describe its pedagogical functions with the underlying AI techniques in detail such as NLP and rule-based reasoning. We will summarize the free Internet usage within a six month period and its integration into English classes in universities and middle schools. The evaluation findings about the class integration show that the chatting function has been improved and frequently utilized by the users, and the application of the CSIEC system on English instruction can motivate the learners to practice English and enhance their learning process. Finally, we will conclude with potential improvements.
This research report introduces the generation of textual entailment within the project CSIEC (Computer Simulation in Educational Communication), an interactive web-based human-computer dialogue system with natural language for English instruction. The generation of textual entailment (GTE) is critical to the further improvement of CSIEC project. Up to now we have found few literatures related with GTE. Simulating the process that a human being learns English as a foreign language we explore our naive approach to tackle the GTE problem and its algorithm within the framework of CSIEC, i.e. rule annotation in NLML, pattern recognition (matching), and entailment transformation. The time and space complexity of our algorithm is tested with some entailment examples. Further works include the rules annotation based on the English textbooks and a GUI interface for normal users to edit the entailment rules.