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-====== iFLYTEK Laboratory for Neural Computing for Machine Learning (iNCML) ======
+====== Laboratory for Neural Computing for Machine Learning (NCML) ======
-Welcome to the homepage of iFLYTEK Laboratory for Neural Computing for Machine Learning (iNCML).  This lab supports research in areas of neural computing models and methods for machine learning, with their applications to speech recognition and understanding, natural language processing, image/video recognition. \\
+Welcome to the homepage of ** Laboratory for Neural Computing and Machine Learning (NCML)**.  //This lab supports research in areas of neural computing models and algorithms for machine learning, with applications to speech recognition and understanding, natural language processing, image/video recognition.// \\
-{{:incml-signage.jpg?0x200 |iNCML}}
-\\
-\\
-**iFLYTEK Laboratory for Neural Computing \\
-for Machine Learning (iNCML)** \\
-Lassonde 2054, Department of Electrical \\
-Engineering and Computer Science \\
-York University, 4700 Keele Street, \\
-Toronto, Ontario  CANADA
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 ===== Research Aims of the Lab =====
-  - **Explore new neural computing models for machine learning:** we explore new neural computing models and algorithms to take advantage of big-data and advanced computing resources for various complex artificial intelligent tasks.
+  - **Explore new neural computing models for machine learning:** we explore novel neural computing models and algorithms, which are powerful enough to tackle complex real-world artificial intelligent tasks and meanwhile efficient enough to make good use of big-data.
-  - **Investigate neural representations of knowledge for artificial cognition:** we investigate new research areas to represent world knowledge (including common sense, common knowledge and domain-specific information) as distributed representations in continuous semantic spaces to achieve advanced and cognitive tasks, namely teaching machines to think like humans.
+  - **Investigate neural representations of knowledge for artificial cognition:** we investigate a new research area to represent world knowledge (including common sense, common knowledge and domain-specific information) as distributed representations in continuous semantic spaces to achieve advanced cognition, such as //teaching machines to think like humans//.
-  - **Advance machine intelligence in speech recognition and understanding, natural language processing and computer vision:** we focus on three main application areas: i) human-machine dialogue systems via speech or text, such as personal assistant agent in smart phones. ii) Deep natural language processing and understanding. Its typical applications include automatic machine Q&A systems in general domain, like semantic search engines, or some particular query system in some domains, like medical, health, legal and so on. iii) Image and video scene analysis. Its typical applications include autonomous robot navigation and controlling.
+  - **Advance machine intelligence in speech recognition and understanding, natural language processing and computer vision:** we focus on three main application areas: a) //human-machine dialogue systems// via speech or text, such as personal assistant agent in smart phones. b) //deep natural language processing and understanding//, such as automatic machine question & answer (Q&A) systems in general domains or special fields (medical, health, legal, etc). c) //Image and video scene analysis//, such as autonomous robot navigation and controlling.
-===== Address =====
-**iFLYTEK Laboratory for Neural Computing for Machine Learning (iNCML)** \\
-Lassonde 2054, Department of Electrical Engineering and Computer Science \\
-York University, 4700 Keele Street, Toronto, Ontario  CANADA