Category Archives: Natural Language Processing

Again about AI understanding

On Dec-2021 Melanie Mitchell published “What Does It Mean for AI to Understand“: “Remember IBM’s Watson, the AI Jeopardy! champion? A 2010 promotion proclaimed, “Watson understands natural language with all its ambiguity and complexity.” However, as we saw when Watson subsequently failed spectacularly in … Continue reading

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Semantic Rules & Machine Learning

Dr. Walid Saba discusses the limitations of the data-driven, statistical and machine learning (ML) approaches that are the currently dominant paradigm in the use of natural language processing (NLP) in text analytics. Using very simple examples, he argues that these … Continue reading

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Why a YouTube Chat About Chess Got Flagged for Hate Speech

This WIRED article talks about shortcomings in AI programs designed to automatically detect hate speech, abuse, and misinformation online. When WIRED fed some of statements gathered by the CMU researchers into two hate-speech classifiers, the statement “White’s attack on black is … Continue reading

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Recent Advances in Google Translate

Google Translate was introduced in 2016 and it enabled great improvements to the quality of translation for over 100 languages since then. In this post Google Research describes how recent advances in machine learning drive improvements to automated translation. Link

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GPT-3, the latest evolution in language technology – What is the big deal?

Over the summer 2020, the latest language model from OpenAI, called GPT-3, created a lot of buzz around the internet. Both within the AI community and outside people shared links to numerous examples on what GPT-3 could do, ranging from … Continue reading

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Giving GPT-3 a Turing Test

GPT-3 is a general language model, trained on a large amount of uncategorized text from the internet. It isn’t specific to a conversational format, and it isn’t trained to answer any specific type of question. The only thing it does … Continue reading

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Climbing Towards Natural Language Understanding

This paper “Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data” won the “Best Theme Award” at the ACL2020 AI conference.  “… in contrast to some current hype, meaning cannot be learned from form alone. This … Continue reading

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The latest NLP revolution

Read an interview with Noam Shazeer who helped spark the latest NLP revolution. “He developed the multi-headed self-attention mechanism described in “Attention Is All You Need,” the 2017 paper that introduced the transformer network. That architecture became the foundation of … Continue reading

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Extracting Data from Templatic Documents

In this article Google researches describe a novel approach using representation learning for tackling the problem of extracting structured information from templatic documents, such as receipts, bills, or insurance quotes. They “propose an extraction system that uses knowledge of the … Continue reading

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More Efficient NLP Model Pre-training

“Recent advances in language pre-training have led to substantial gains in the field of natural language processing, with state-of-the-art models such as BERT, RoBERTa, XLNet, ALBERT, and T5, among many others. These methods, though they differ in design, share the same idea of leveraging … Continue reading

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