natural language processing – Page 16

What I Read: Training AI, Analogies

By Andrew Fairless on August 13, 2021July 22, 2021

https://www.quantamagazine.org/melanie-mitchell-trains-ai-to-think-with-analogies-20210714/ The Computer Scientist Training AI to Think With AnalogiesMelanie Mitchell has worked on digital minds for decades. She says they’ll never truly be like ours until they can makeContinue readingWhat I Read: Training AI, Analogies

What I Read: Identifying Document Types

By Andrew Fairless on August 11, 2021July 22, 2021

https://tech.scribd.com/blog/2021/identifying-document-types.html Identifying Document Types at ScribdJonathan RamkissoonJuly 12, 2021 “User-uploaded documents have been a core component of Scribd’s business from the very beginning, understanding what is actually in the documentContinue readingWhat I Read: Identifying Document Types

What I Read: Prompting, Language Models, NLP

By Andrew Fairless on August 9, 2021July 22, 2021

https://thegradient.pub/prompting/ Prompting: Better Ways of Using Language Models for NLP Tasks03.Jul.2021Tianyu Gao “The giant model size of GPT-3 is an important factor for its success, while the concept of promptsContinue readingWhat I Read: Prompting, Language Models, NLP

What I Read: Understanding Levenshtein Distance

By Andrew Fairless on August 5, 2021July 12, 2021

https://medium.com/@ethannam/understanding-the-levenshtein-distance-equation-for-beginners-c4285a5604f0 Understanding the Levenshtein Distance Equation for BeginnersEthan NamFeb 26, 2019 “The Levenshtein distance is a number that tells you how different two strings are. The higher the number, theContinue readingWhat I Read: Understanding Levenshtein Distance

What I Read: Semantic Search

By Andrew Fairless on July 20, 2021July 2, 2021

https://www.pinecone.io/learn/semantic-search/ Semantic Search: Measuring Meaning From Jaccard to BertJames BriggsData Scientist “Similarity search is a complex topic and there are countless techniques for building effective search engines. In this article,Continue readingWhat I Read: Semantic Search

What I Read: Contrastive Representation Learning

By Andrew Fairless on July 1, 2021June 7, 2021

https://lilianweng.github.io/lil-log/2021/05/31/contrastive-representation-learning.html Contrastive Representation Learningby Lilian WengMay 31, 2021 “The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to eachContinue readingWhat I Read: Contrastive Representation Learning

What I Read: Human-Centered Explainable AI

By Andrew Fairless on June 24, 2021June 7, 2021

https://thegradient.pub/human-centered-explainable-ai/ Towards Human-Centered Explainable AI: the journey so farby Upol EhsanMay 10 2021 3:41 AM “Not only did this system have commendable accuracy, it also tried to explain its decisionContinue readingWhat I Read: Human-Centered Explainable AI

What I Read: Knowledge Graphs with Language Model

By Andrew Fairless on June 16, 2021June 7, 2021

https://ai.googleblog.com/2021/05/kelm-integrating-knowledge-graphs-with.html KELM: Integrating Knowledge Graphs with Language Model Pre-training CorporaThursday, May 20, 2021Posted by Siamak Shakeri, Staff Software Engineer and Oshin Agarwal, Research Intern, Google Research “Alternate sources of informationContinue readingWhat I Read: Knowledge Graphs with Language Model

What I Read: Dataset Curation for NLP Projects

By Andrew Fairless on June 15, 2021May 30, 2021

https://www.kdnuggets.com/2021/05/4-tips-dataset-curation-nlp-projects.html 4 Tips for Dataset Curation for NLP ProjectsBy Paul Barba, Chief Scientist, Lexalytics. “After many years of painfully learned lessons from managing and implementing AI and ML projects, I’veContinue readingWhat I Read: Dataset Curation for NLP Projects

What I Read: Reducing Toxicity in Language Models

By Andrew Fairless on May 10, 2021April 17, 2021

https://lilianweng.github.io/lil-log/2021/03/21/reducing-toxicity-in-language-models.html Reducing Toxicity in Language Modelsby Lilian WengMar 21, 2021 “Large pretrained language models are trained over a sizable collection of online data. They unavoidably acquire certain toxic behavior andContinue readingWhat I Read: Reducing Toxicity in Language Models

Tag: natural language processing