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Thoughts on Artificial Intelligence and Natural Language Processing
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My experience of AI/NLP research and development is limited
compared to HPC and data analysis. During my summer intern as
R&D in NLP/query understanding, I studied linguistics and
implemented the stoke and order into Chinese NLP, based on its
nature as a logographic system. Time will reveal that even though
computer programming is highly based on English, AI/NLP may work
better in a different language system, which will have the elegant
simplicity of mathematics and abundant expressiveness in Chinese.
</p>
<p>
Back to practice, I have been studying word embeddings like
word2vec, and then utilize them for specific linguistic tasks like
NER/POS with supervised LSTM + CRF model, or unsupervised models
for topic modeling, text summary, etc. From my bilingual
background, I realize that knowledge base could be further
validated or expanded by multi-languages. I am also interested in
the similarity between state-space model and neural network model.
Despite the magic non-linearity from activation function, both are
seeking optimal/equilibrium with updating techniques based on
gradient (and chain rule). The advance of AI/NLP is thus mainly
the result of Higher Performance Computing. Leapfrog, MCMC,
gradient descent, SGD, batch updating or so are still some
compromise regarding to high dimensional parameter space
iteration, given the ever-increasing computation power.
</p>
<p>
Nevertheless, data quality is very important at the moment and
it's the cornerstone for commercial products, depending
on/deciding the use case and market focus.
</p>
<p>
These are my 2 cents about AI/NLP. I also have some thoughts on
data acquisition and business model waiting to develop.
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<span class="published" title="2018-09-19T00:00:00-05:00">
Published: Wed 19 September 2018.
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