Ehud Reiter

FEBRUARY 27, 2023

I’ve seen all kinds of people make comments recently about the value of academic research in NLP. The basic argument is that significant research on large language models such as chatGPT, which is the hottest/trendiest area in NLP, happens in companies (instead of universities) because it requires lots of resources.

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Analytics Vidhya

OCTOBER 19, 2022

Source: Canva Introduction In 2018, Google AI researchers came up with BERT, which revolutionized the NLP domain. This article was published as a part of the Data Science Blogathon.

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Analytics Vidhya

DECEMBER 30, 2021

This article was published as a part of the Data Science Blogathon Introduction In 2018, a powerful Transformer-based machine learning model, namely, BERT was developed by Jacob Devlin and his colleagues from Google for NLP applications.

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Towards AI

JULY 21, 2023

Photo by Will Truettner on Unsplash NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER NLP News Cypher | 07.26.20 Transformer is the most critical alogrithm… github.com NLP & Audio Pretrained Models A nice collection of pretrained model libraries found on GitHub. These 2 repos encompass NLP and Speech modeling.

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Towards AI

JULY 23, 2023

In this article, we aim to focus on the development of one of the most powerful generative NLP tools, OpenAI’s GPT. Evolution of NLP domain after Transformers Before we start, let's take a look at the timeline of the works which brought great advancement in the NLP domain. Let’s see it step by step. In 2015, Andrew M.

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Towards AI

JULY 20, 2023

It’s also an area that stands to benefit most from automated or semi-automated machine learning (ML) and natural language processing (NLP) techniques. An additional 2018 study found that each SLR takes nearly 1,200 total hours per project. dollars apiece. This study by Bui et al.

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Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

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Sebastian Ruder

DECEMBER 19, 2023

NLP research has undergone a paradigm shift over the last year. In contrast,  NLP researchers today are faced with a constraint that is much harder to overcome: compute. A PhD Student's Perspective on Research in NLP in the Era of Very Large Language Models Li et al. Defining a New NLP Playground Saphra et al.

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Sebastian Ruder

DECEMBER 19, 2023

This post was first published in  NLP News. NLP research has undergone a paradigm shift over the last year. In contrast,  NLP researchers today are faced with a constraint that is much harder to overcome: compute. A PhD Student's Perspective on Research in NLP in the Era of Very Large Language Models Li et al.

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Topbots

APRIL 11, 2023

We’ll start with a seminal BERT model from 2018 and finish with this year’s latest breakthroughs like LLaMA by Meta AI and GPT-4 by OpenAI. BERT by Google Summary In 2018, the Google AI team introduced a new cutting-edge model for Natural Language Processing (NLP) – BERT , or B idirectional E ncoder R epresentations from T ransformers.

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Sebastian Ruder

JULY 31, 2020

Natural language processing (NLP) research predominantly focuses on developing methods that work well for English despite the many positive benefits of working on other languages. 2018 )—and keyboard support is even rarer for languages without a widespread written tradition ( Paterson, 2015 ). Each feature has 5.93

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Towards AI

JULY 25, 2023

In my previous articles about transformers and GPTs, we have done a systematic analysis of the timeline and development of NLP. Deep contextualized word representations This paper was released by Allen-AI in the year 2018. Last Updated on July 29, 2023 by Editorial Team Author(s): Abhijit Roy Originally published on Towards AI.

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Heartbeat

AUGUST 23, 2023

Enter Natural Language Processing (NLP) and its transformational power. This is the promise of NLP: to transform the way we approach legal discovery. The seemingly impossible chore of sorting through mountains of legal documents can be accomplished with astonishing efficiency and precision using NLP.

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Snorkel AI

DECEMBER 27, 2023

BERT origins and basics Researchers released Google BERT in October 2018, not long after the seminal Attention is All You Need paper (which introduced the transformer building block for large language models.) BERT excels at understanding the relationships between words and can be used for various NLP tasks. Book a demo today.

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Heartbeat

MARCH 15, 2023

But now, a computer can be taught to comprehend and process human language through Natural Language Processing (NLP), which was implemented, to make computers capable of understanding spoken and written language. This allows RoBERTa to better adapt to specific NLP tasks during fine-tuning.

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Sebastian Ruder

AUGUST 18, 2019

This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP. In the span of little more than a year, transfer learning in the form of pretrained language models has become ubiquitous in NLP and has contributed to the state of the art on a wide range of tasks. However, transfer learning is not a recent phenomenon in NLP.

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AI News

JULY 22, 2024

AI uses natural language processing (NLP) to analyse sentiments from social media, news articles, and other textual data. For instance, during the 2018 FIFA World Cup, an AI model analysed over 10 million tweets to gauge public sentiment and accurately predicted the outcomes of 70% of the matches.

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Snorkel AI

DECEMBER 27, 2023

BERT origins and basics Researchers released Google BERT in October 2018, not long after the seminal Attention is All You Need paper (which introduced the transformer building block for large language models.) The post BERT models: Google’s NLP for the enterprise appeared first on Snorkel AI. Book a demo today.

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Sebastian Ruder

JANUARY 19, 2021

The selection of areas and methods is heavily influenced by my own interests; the selected topics are biased towards representation and transfer learning and towards natural language processing (NLP). 2018 ; Howard et al.,  2020 saw the development of ever larger language and dialogue models such as Meena ( Adiwardana et al.,

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Marek Rei

JANUARY 2, 2018

It has been a very productive year for NLP and ML research. NAACL and COLING were notably missing from 2017, but we can look forward to both of them in 2018. Ravikumar (CMU) are publishing mainly in the general ML venues, while the others are balanced between NLP and ML. Smith (Washington). That’s it for 2017.

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NLP People

OCTOBER 9, 2018

In the past years, the tech world has seen a surge of NLP applications in various areas including adtech, publishing, customer service and market intelligence. To put it simply – NLP is wildly adopted with wildly variable success (let’s assume a working definition of success in terms of quality and ROI).

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Marek Rei

NOVEMBER 12, 2019

ArXiv 2018. EMNLP 2018. NAACL 2018. NAACL 2018. At the end, I also include the summaries for my own published papers since the last iteration (papers 61-74). Here we go. Improving Language Understanding by Generative Pre-Training Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever. Microsoft, Edinburgh.

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Towards AI

APRIL 5, 2023

Picture created with Dall-E-2 Yoshua Bengio, Geoffrey Hinton, and Yann LeCun, three computer scientists and artificial intelligence (AI) researchers, were jointly awarded the 2018 Turing Prize for their contributions to deep learning, a subfield of AI. Join thousands of data leaders on the AI newsletter.

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Mlearning.ai

JUNE 7, 2023

Photo by Eugene Zhyvchik on Unsplash I wanted to share a short perspective of the radical evolution we have seen in NLP. I’ve been working on NLP problems since word2vec was released, and it has been remarkable to see how quickly the models, problems, and applications have evolved. GPT-2 released with 1.5

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Sebastian Ruder

JANUARY 6, 2020

This post gathers ten ML and NLP research directions that I found exciting and impactful in 2019.  Unsupervised pretraining was prevalent in NLP this year, mainly driven by BERT ( Devlin et al., 2019) find winning ticket initialisations also for LSTMs and Transformers in NLP and RL models. 2019 ) and other variants.

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Sebastian Ruder

AUGUST 23, 2021

Over the last years, models in NLP have become much more powerful, driven by advances in transfer learning. This post aims to give an overview of challenges and opportunities in benchmarking in NLP, together with some general recommendations. Does this mean that we have solved natural language processing? Far from it.

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Marktechpost

MAY 28, 2024

The Generative Pre-trained Transformer (GPT) series, developed by OpenAI, has revolutionized the field of NLP with its groundbreaking advancements in language generation and understanding. GPT-1: The Beginning Launched in June 2018, GPT-1 marked the inception of the GPT series. Model Size: 1.5

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IBM Journey to AI blog

AUGUST 1, 2023

Foundation models can be trained to perform tasks such as data classification, the identification of objects within images (computer vision) and natural language processing (NLP) (understanding and generating text) with a high degree of accuracy. An open-source model, Google created BERT in 2018. All watsonx.ai

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AWS Machine Learning Blog

FEBRUARY 6, 2024

Transformers, BERT, and GPT The transformer architecture is a neural network architecture that is used for natural language processing (NLP) tasks. This allows the model to learn long-range dependencies between words, which is essential for many NLP tasks, such as machine translation and text summarization.

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Marktechpost

JANUARY 10, 2024

BERT is a language model which was released by Google in 2018. As such, it has been the powerhouse of numerous natural language processing (NLP) applications since its inception, and even in the age of large language models (LLMs), BERT-style encoder models are used in tasks like vector embeddings and retrieval augmented generation (RAG).

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Sebastian Ruder

JANUARY 24, 2022

2021) 2021 saw many exciting advances in machine learning (ML) and natural language processing (NLP). If CNNs are pre-trained the same way as transformer models, they achieve competitive performance on many NLP tasks [28].   Popularized by GPT-3 [32] , prompting has emerged as a viable alternative input format for NLP models.

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Towards AI

JUNE 12, 2024

There is very little contention that large language models have evolved very rapidly since 2018. Word embedding is a technique in natural language processing (NLP) where words are represented as vectors in a continuous vector space. This facilitates various NLP tasks by providing meaningful word embeddings.

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Unite.AI

OCTOBER 22, 2024

Joining Appen AI in 2018 as VP of Corporate Development, he led strategic acquisitions like Figure Eight and Quadrant, and supported the establishment of the China and Federal divisions. You’ve been with Appen since 2018, driving major acquisitions like Figure Eight and Quadrant.

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Towards AI

DECEMBER 28, 2023

Later, Python gained momentum and surpassed all programming languages, including Java, in popularity around 2018–19. The introduction of attention mechanisms has notably altered our approach to working with deep learning algorithms, leading to a revolution in the realms of computer vision and natural language processing (NLP).

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Mlearning.ai

MAY 5, 2023

In the 1980s and 1990s, the field of natural language processing (NLP) began to emerge as a distinct area of research within AI. NLP researchers focused on developing statistical models that could process and generate text based on patterns and probabilities, rather than strict rules. I think GPT-3 is as intelligent as a human.

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AWS Machine Learning Blog

SEPTEMBER 19, 2023

For unstructured data, the agent uses AWS Lambda functions with AI services such as Amazon Textract for document analysis, Amazon Transcribe for speech recognition, Amazon Comprehend for NLP, and Amazon Kendra for intelligent search. Can you build an optimized portfolio using these three stocks? ” WWW: $85.91 DDD: $9.82 WWW: $85.91

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Mlearning.ai

MARCH 2, 2023

Pre-training of Deep Bidirectional Transformers for Language Understanding BERT is a language model that can be fine-tuned for various NLP tasks and at the time of publication achieved several state-of-the-art results. Conclusion: BERT as Trend-Setter in NLP and Deep Learning References I. BERT Architecture and Training III.1

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