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Babies remember their birth language - scientists

BBC News

Babies build knowledge about the language they hear even in the first few months of life, research shows. If you move countries and forget your birth language, you retain this hidden ability, according to a study. Dutch-speaking adults adopted from South Korea exceeded expectations at Korean pronunciation when retrained after losing their birth language. Scientists say parents should talk to babies as much as possible in early life. Dr Jiyoun Choi of Hanyang University in Seoul led the research.

Teaching AI systems to learn language from letters, not words


A new approach to natural language processing (NLP) that teaches neural networks linguistic fundamentals by training them using unsegmented textual input on the interaction between individual letters rather than whole words. Most recurrent neural networks (RNNs) that form the basis of NLP systems are trained on vocabularies of known words. To train RNNs in a way that more closely resembles how humans learn the fundamentals of language, we removed the word boundaries from training data sets and trained the networks at the character (instead of word) level. A multilingual study of this unsupervised character-level language modeling task used data sets of millions of words in English, German, and Italian. It showed that these "near tabula rasa" RNNs develop an impressive spectrum of linguistic knowledge, including segmenting groups of characters into words, distinguishing nouns from verbs, and even inducing simple forms of word meaning.

A Survey on Knowledge integration techniques with Artificial Neural Networks for seq-2-seq/time series models Artificial Intelligence

In recent years, with the advent of massive computational power and the availability of huge amounts of data, Deep neural networks have enabled the exploration of uncharted areas in several domains. But at times, they under-perform due to insufficient data, poor data quality, data that might not be covering the domain broadly, etc. Knowledge-based systems leverage expert knowledge for making decisions and suitably take actions. Such systems retain interpretability in the decision-making process. This paper focuses on exploring techniques to integrate expert knowledge to the Deep Neural Networks for sequence-to-sequence and time series models to improve their performance and interpretability.

My Process for Learning Natural Language Processing with Deep Learning


I currently work as a Data Scientist for Informatica and I thought I'd share my process for learning new things. Recently I've been wanting to explore more into Deep Learning, especially Machine Vision and Natural Language Processing. I've been procrastinating a lot, mostly because it's been summer, but now that it's fall and starting to cool down and get dark early, I'm going to be spending more time learning when it's dark out. And the thing that deeply interests me is Deep Learning and Artificial Intelligence, partly out of intellectual curiosity and partly out of greed, as most businesses and products will incorporate Deep Learning/ML in some way. I started doing research and realized that an understanding and knowledge of Deep Learning was within my reach, but I also realized that I still have a lot to learn, more than I initially thought.

Knowledge Graphs and Knowledge Networks: The Story in Brief Artificial Intelligence

Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities. However, for dynamic real-world applications such as social networks, recommender systems, computational biology, relational knowledge representation has emerged as a challenging research problem where there is a need to represent the changing nodes, attributes, and edges over time. The evolution of search engine responses to user queries in the last few years is partly because of the role of KGs such as Google KG. KGs are significantly contributing to various AI applications from link prediction, entity relations prediction, node classification to recommendation and question answering systems. This article is an attempt to summarize the journey of KG for AI.