One goal of AI work in natural language is to enable communication between people and computers without resorting to memorization of complex commands and procedures. Automatic translation – enabling scientists, business people and just plain folks to interact easily with people around the world – is another goal. Both are just part of the broad field of AI and natural language, along with the cognitive science aspect of using computers to study how humans understand language.
YANGON – Myanmar's first-ever training course for Japanese-language teachers is opening as part of Prime Minister Shinzo Abe's plan to invite more Asian youths to work in Japan. The initial phase of the training program starts this month at the Yangon University of Foreign Languages for students majoring in Japanese and for teachers from private Japanese-language schools, the Japan Foundation said. The foundation, a government-backed institution that carries out international cultural exchange programs, picked Myanmar as the third country in which to offer such training courses, after India and Vietnam, following Abe's speech at an international conference in Tokyo in 2017 where he said Japan would choose three locations in Asia to nurture Japanese-language teachers. Noriyuki Matsukawa, executive director of the Japan Foundation Center for Japanese Language Testing, said the yearlong program aims to support Myanmar's human resources through Japanese-language learning, recruit a new kind of teacher and improve current teachers' skills. "Myanmar has high demand for Japanese-language proficiency," he said, adding that the number of people in Myanmar taking the Japanese-Language Proficiency Test nearly tripled from 13,099 in 2016 to 37,786 in 2018.
We present a novel intelligent tutoring system which builds upon well-established hypotheses in educational psychology and incorporates them inside of a scalable software architecture. Specifically, we build upon the known benefits of knowledge vocalization, parallel learning, and immediate feedback in the context of student learning. We show that open-source data combined with state-of-the-art techniques in deep learning and natural language processing can apply the benefits of these three factors at scale, while still operating at the granularity of individual student needs and recommendations. Additionally, we allow teachers to retain full control of the outputs of the algorithms, and provide student statistics to help better guide classroom discussions towards topics that would benefit from more in-person review and coverage. Our experiments and pilot programs show promising results, and cement our hypothesis that the system is flexible enough to serve a wide variety of purposes in both classroom and classroom-free settings.
When Amazon unveiled the Echo Show last year, many people made fun of it for its bulky, awkward appearance. But it proved to be a pioneer in the smart display category, showing that adding a screen to a voice assistant was actually useful. So much so, that Google followed a few months later with its own line of Echo Show rivals, thanks to partners like Lenovo and JBL. Google's smart displays were better-looking and had a more intuitive interface, with desirable features like step-by-step recipes and YouTube integration. Amazon must have taken note of the competition, however, because the new Echo Show has undergone a serious upgrade, with an improved design, superior sound quality and enhanced entertainment options.
It's Monday, which means back to work and school, no matter who or where you are. It also means another week of some great deals. Whether you're looking for a coffee maker or a new TV (or lots in between), there's plenty to choose from thanks to sales at Amazon, Walmart, and Target. When it comes to the kitchen, there are plenty of coffee makers and small appliances to choose from. This Hamilton Beach Coffee Maker is available for $35.49 if you're looking for something simple to use, while the Cuisinart SS-10 Premium Single-Serve Coffeemaker is great for people who need a quick caffeine jolt.
Text classification implementation with TensorFlow can be simple. One of the areas where text classification can be applied -- chatbot text processing and intent resolution. I will describe step by step in this post, how to build TensorFlow model for text classification and how classification is done. Please refer to my previous post related to similar topic -- Contextual Chatbot with TensorFlow, Node.js and Oracle JET -- Steps How to Install and Get It Working. I would recommend to go through this great post about chatbot implementation -- Contextual Chatbots with Tensorflow.
Chatbots are one of the most exciting and in-demand topics in tech. Gartner predicts that by 2020, 85% of businesses will have their own chatbot. If you want to learn this rapidly emerging technology, put a chatbot on your own website or make money by building chatbots for clients, this free chatbot course is for you. This course provides a practical introduction on how to build a chatbot with Watson Assistant (formerly Watson Conversation). Within it, you'll learn how to plan, build, test, analyze, and deploy your first chatbot.
Here's a list of some of the best Chatbot tutorials, courses, videos and books to help you learn Chatbots in 2018. ChatBots: How to Make a Facebook Messenger Chat Bot in 1hr by Stefan Kojouharov will help you build a Chat Bot for Facebook Messenger. This is a step by step guide in building a chatbot for Facebook Messenger. You will learn the main components of building a chatbot. This includes building the chatbot server, adding your code, deploying your chatbot to the cloud, and connecting it with Facebook Messenger.
In this course you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so it doesn't contain any hard math - just straight up coding in Python. All the materials for this course are FREE. After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we'll build is a spam detector.
In this tutorial, we will cover Natural Language Processing for Text Classification with NLTK & Scikit-learn. Remember the last Natural Language Processing project we did? We will be using all that information to create a Spam filter. This tutorial will also cover Feature Engineering and ensemble NLP in text classification. This project will use Jupiter Notebook running Python 2.7.