Turing Shield @TuringShield

Lead GenAI Solution Architect linkedin.com/in/hou8/ Joined June 2011

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Turing Shield @TuringShield

7 years ago

@theNeomatrix369 @ApacheOpennlp @stanfordnlp Mostly accuracy. The potential for scalability is also important.

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@theNeomatrix369 @ApacheOpennlp @stanfordnlp If your project's success depends heavily on performance of Named Entity Recognition, please start with OpenNLP. Be aware that NER's results are highly domain specific. If your textual data is very different from the training data OpenNLP or StanfordNLP used, train your own model

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Turing Shield @TuringShield

7 years ago

@rr_kanade @ApacheOpennlp @stanfordnlp If your project heavily depends on Named Entity Recognition, I recommend investing time with OpenNLP first

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Turing Shield @TuringShield

7 years ago

@MontefioreNYC @IntelAI @WSJ What was the false-negative rate? thanks

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Turing Shield @TuringShield

7 years ago

@TheEconomist It is an interesting read. It reminds of the anecdotal 10,000-rule, as described in Outliers by @Gladwell

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The Economist @TheEconomist

7 years ago

Success in the sciences does not always breed more success econ.st/2Vujy5p

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Nature Portfolio @NaturePortfolio

7 years ago

News & Views: A paper in Nature outlines an approach that brings us closer to a brain–computer interface that can restore speech function. go.nature.com/2UXgHRS

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Turing Shield @TuringShield

7 years ago

The dark side of artifiical intelligence: Researchers create 'malicious' writing AI bbc.com/news/technolog…

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Turing Shield @TuringShield

7 years ago

The legendary @zoom_us: cnbc.com/video/2019/04/…

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Turing Shield @TuringShield

7 years ago

Big Four accounting companies invest huge in artificial intelligence: (old news, but still current): computerworld.com/article/304253…

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Turing Shield @TuringShield

8 years ago

@thealexfreeman Another great analogy!

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Turing Shield @TuringShield

8 years ago

Named entity recognition is easy for humans and hard for machines. Correctly recognizing brands, companies and services is critical in marketing research that uses textual #bigdata. CoreNLP by @stanfordnlp fulfills several advertising research needs well: stanfordnlp.github.io/CoreNLP/

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Turing Shield @TuringShield

8 years ago

Jack Ma of @AlibabaGroup believes that Artificial Intelligence threatens human beings: businesstoday.in/technology/new…. Jack's points seem to be at odds with some of top #AI and machine learning researchers in #bigdata.

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Turing Shield @TuringShield

8 years ago

The power of machine learning in healthcare analytics: the potential value of big data in predicting, preventing and curing disease. Precision health: scopeblog.stanford.edu/2018/05/08/cou…

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Turing Shield @TuringShield

8 years ago

Natural language processing (#NLP) is a subfield in artificial intelligence (#AI). Many #MachineLearning approaches are available for #NLP. #NLP is useful in analysis of #bigdata, augmented reality (#AR) and #IoT: medium.com/@Namenode5/the….

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Oxford Internet Institute @oiioxford

8 years ago

How do algorithms and bots affect election outcomes? New paper on automated political communication in the 2016 US election out now in the Journal of Information Technology and Politics! Read on: tandfonline.com/doi/full/10.10… @pnhoward @samuelwoolley #TechNews #politics

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Andrew Ng @AndrewYNg

8 years ago

We need a Goldilocks Rule for AI: - Too optimistic: Deep learning gives us a clear path to AGI! - Too pessimistic: DL has limitations, thus here's the AI winter! - Just right: DL can’t do everything, but will improve countless lives & create massive economic growth.

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Turing Shield @TuringShield

8 years ago

Two machine learning methods for sentiment analysis are useful. One is #DeepLearning, for which @stanfordnlp provides a Java implementation nlp.stanford.edu/sentiment/. Another is #MaxEnt, i.e., maximum entropy, a good classifier for textual #bigdata of brand user generated content.