Unite.AI

AUGUST 5, 2024

Groq, founded in 2016 by Jonathan Ross, a former Google engineer, has been quietly developing specialized chips designed to accelerate AI workloads, particularly in the realm of language processing. This financial windfall, led by investment giant BlackRock, has catapulted Groq's valuation to an impressive $2.8

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NYU Center for Data Science

JULY 18, 2023

The group was first launched in 2016 by Associate Professor of Computer Science, Data Science and Mathematics Joan Bruna , and Associate Professor of Mathematics and Data Science and incoming CDS Interim Director Carlos Fernandez-Granda with the goal of advancing the mathematical and statistical foundations of data science.

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Towards AI

APRIL 12, 2023

In the last few years, if you google healthcare or clinical NLP, you would see that the search results are blanketed by a few names like John Snow Labs (JSL), Linguamatics (IQVIA), Oncoustics, BotMD, Inspirata. All of these companies were founded between 2013–2016 in various parts of the world. Originally published on Towards AI.

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John Snow Labs

MAY 23, 2023

Visual question answering (VQA), an area that intersects the fields of Deep Learning, Natural Language Processing (NLP) and Computer Vision (CV) is garnering a lot of interest in research circles. A VQA system takes free-form, text-based questions about an input image and presents answers in a natural language format.

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Artificial Corner

JUNE 18, 2023

ChatGPT released by OpenAI is a versatile Natural Language Processing (NLP) system that comprehends the conversation context to provide relevant responses. Although little is known about construction of this model, it has become popular due to its quality in solving natural language tasks.

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Heartbeat

AUGUST 23, 2023

But what if there was a technique to quickly and accurately solve this language puzzle? Enter Natural Language Processing (NLP) and its transformational power. This is the promise of NLP: to transform the way we approach legal discovery. But exactly what is NLP , and how can it facilitate legal discovery?

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Unite.AI

FEBRUARY 16, 2024

For example, see Face-to-Face Interaction with Pedagogical Agents, Twenty Years Later , a 2016 article that overviews the field and cites a lot of the relevant material. At its core, an AI Tutoring system consists of three main technologies: Automatic speech recognition (ASR) and analysis allow us to process and analyze the student's speech.

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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. 2019 ; Logan IV et al., 2019 ; Lu et al.,

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Explosion

NOVEMBER 9, 2016

Over the last six months, a powerful new neural network playbook has come together for Natural Language Processing. Most NLP problems can be reduced to machine learning problems that take one or more texts as input. However, most NLP problems require understanding of longer spans of text, not just individual words.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

See Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks , REALM , kNN-LM and RETRO. Her research interests lie in Natural Language Processing, AI4Code and generative AI. He joined Amazon in 2016 as an Applied Scientist within SCOT organization and then later AWS AI Labs in 2018 working on Amazon Kendra.

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Topbots

JUNE 22, 2023

SA is a very widespread Natural Language Processing (NLP). Hence, whether general domain ML models can be as capable as domain-specific models is still an open research question in NLP. Other articles in my line of research (NLP, RL) Lima Paiva, F. finance, entertainment, psychology). Felizardo, L.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Context (Snippet from PDF file) Question Answer THIS STRATEGIC ALLIANCE AGREEMENT (Agreement) is made and entered into as of November 6, 2016 (the Effective Date) by and between Dialog Semiconductor (UK) Ltd., His area of research is all things natural language (like NLP, NLU, and NLG).

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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). 2020 ) and language modelling ( Khandelwal et al., In NLP, Gunel et al. 2020 ; Lewis et al.,

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Hal Daumé III

AUGUST 14, 2016

P16-1152 : Artem Sokolov; Julia Kreutzer; Christopher Lo; Stefan Riezler Learning Structured Predictors from Bandit Feedback for Interactive NLP This was perhaps my favorite paper of the conference because it's trying to do something new and hard and takes a nice approach. P16-2096 : Dirk Hovy; Shannon L.

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Explosion

OCTOBER 22, 2016

This is a guest post by Wah Loon Keng , the author of spacy-nlp , a client that exposes spaCy ’s NLP text parsing to Node.js (and other languages) via Socket.IO. Natural Language Processing and other AI technologies promise to let us build applications that offer smarter, more context-aware user experiences.

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Mlearning.ai

FEBRUARY 15, 2023

Introduction In natural language processing, text categorization tasks are common (NLP). Foundations of Statistical Natural Language Processing [M]. Submission Suggestions Text Classification in NLP using Cross Validation and BERT was originally published in MLearning.ai Uysal and Gunal, 2014).

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Snorkel AI

MARCH 1, 2023

This process results in generalized models capable of a wide variety of tasks, such as image classification, natural language processing, and question-answering, with remarkable accuracy. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Devlin et al.

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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]. Credit for the title image: Liu et al. Why is it important?   What happened?  

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Probably Approximately a Scientific Blog

AUGUST 28, 2016

Recently, I attended Chris Biemann's excellent crowdsourcing course at ESSLLI 2016 (the 28th European Summer School in Logic, Language and Information), and was inspired to write about the topic. I've collected data for most of my papers, but never thought of it as an interesting blog post topic.

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AWS Machine Learning Blog

MARCH 31, 2023

He has been with the Transportation Cabinet since 2016 working in various IT roles. The contact center is powered by Amazon Connect, and Max, the virtual agent, is powered by Amazon Lex and the AWS QnABot solution. Amazon Connect directs some incoming calls to the virtual agent (Max) by identifying the caller number.

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AWS Machine Learning Blog

APRIL 18, 2023

Large language models (LLMs) with billions of parameters are currently at the forefront of natural language processing (NLP). These models are shaking up the field with their incredible abilities to generate text, analyze sentiment, translate languages, and much more.

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Explosion

DECEMBER 30, 2021

Mar 17: March saw a new episode of Vincent Warmerdam’s “Intro to NLP with spaCy” series. Mar 29: Ines joined the at the German Python Podcast to talk about Natural Language Processing with spaCy. ? Jun 10: Ines gave a keynote at the Teaching NLP Workshop at NAACL-2021. More languages will follow. September

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ML Review

MAY 3, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part One) This is the first instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). Thanks for reading!

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Dlabs.ai

AUGUST 10, 2020

On principle, all chatbots work by utilising some form of natural language processing (NLP). Our recently published paper, Transformer-Capsule Model for Intent Detection , demonstrated the results of our long-term research into better NLP. But what does it all mean? What do all the buzzwords mean?

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TheSequence

DECEMBER 13, 2023

Quick bio Lewis Tunstall is a Machine Learning Engineer in the research team at Hugging Face and is the co-author of the bestseller “NLP with Transformers” book. My path to working in AI is somewhat unconventional and began when I was wrapping up a postdoc in theoretical particle physics around 2016.

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AWS Machine Learning Blog

FEBRUARY 28, 2023

Use natural language processing (NLP) in Amazon HealthLake to extract non-sensitive data from unstructured blobs. We can see that Amazon HeathLake NLP interprets this as containing the condition “stroke” by querying for the condition record that has the same patient ID and displays “stroke.” code, code.coding[1].display

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ML Review

JUNE 4, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part Two) This is the second instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). 2016)[ 91 ] You et al. 2017) [ 96 ].

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Sebastian Ruder

DECEMBER 6, 2021

A domain can be seen as a manifold in a high-dimensional variety space consisting of many dimensions such as socio-demographics, language, genre, sentence type, etc ( Plank et al., 2016 ), Natural Questions (NQ; Kwiatkowski et al., 2016 ), among many others. 2016 ), and BookTest ( Bajgar et al.,

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How to Learn Machine Learning

DECEMBER 4, 2022

In 2016, Google released an open-source software called AutoML. Another way AI is being used to write code is through the use of natural language processing (NLP). NLP is a type of AI that can understand human language and convert it into code. This information can then be used to generate new code.

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ODSC - Open Data Science

OCTOBER 15, 2023

Ikigai Labs Ikigai Labs is a company that provides a platform for building and managing natural language processing models. Their platform, called Ikigai, allows users to create and train NLP models without any coding experience. Ikigai is a valuable tool for anyone who wants to build or use NLP models.

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Lexalytics

APRIL 5, 2021

Fast-forward a couple of decades: I was (and still am) working at Lexalytics, a text-analytics company that has a comprehensive NLP stack developed over many years. Around this time (early 2016), our management team realized that to maintain relevance as a company, we would need to be able to incorporate even more ML into our product.

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Viso.ai

DECEMBER 4, 2023

PyTorch Overview PyTorch was first introduced in 2016. PyTorch is suitable for natural language processing ( NLP ) tasks to power intelligent language applications using deep learning. Using this API, you can distribute your existing models and training code with minimal code changes.

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ML Review

FEBRUARY 26, 2018

Following its successful adoption in computer vision and voice recognition, DL will continue to be applied in the domain of natural language processing (NLP). In Proceedings of The First International Workshop on Machine Learning in Spoken Language Processing. [5] 2016 [6] Li J, Monroe W, Ritter A, et al.

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Explosion

NOVEMBER 21, 2019

In 2016 we trained a sense2vec model on the 2015 portion of the Reddit comments corpus, leading to a useful library and one of our most popular demos. from_disk("/path/to/s2v_reddit_2015_md") nlp.add_pipe(s2v) doc = nlp("A sentence about natural language processing.") That work is now due for an update.

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Sebastian Ruder

MAY 22, 2020

This advice should be most relevant to people studying machine learning (ML) and natural language processing (NLP) as that is what I did in my PhD. 2016 ), physics ( Cohen et al., Many projects with a large impact in ML and NLP such as AlphaGo or OpenAI Five have been developed by a team.

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AWS Machine Learning Blog

APRIL 18, 2023

Large language models (LLMs) with billions of parameters are currently at the forefront of natural language processing (NLP). These models are shaking up the field with their incredible abilities to generate text, analyze sentiment, translate languages, and much more.

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Explosion

AUGUST 1, 2019

Huge transformer models like BERT, GPT-2 and XLNet have set a new standard for accuracy on almost every NLP leaderboard. It features consistent and easy-to-use interfaces to several models, which can extract features to power your NLP pipelines. apple2 = nlp("Apple sold fewer iPhones this quarter.") print(apple1[0].similarity(apple2[0]))

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