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.

NLP 52

NLP ML Neural Network BERT 52

Heartbeat

JANUARY 29, 2024

Performing a Meta-Learning Task With Comet This section will explain a basic example of conducting a meta-learning task using the Comet platform. Resources Generalization Ability || ScienceDirect Omniglot dataset || Kaggle Natural Language Processing (NLP) Tutorial || Geeksforgeeks.com Vilalta R. & & Drissi Y.

Machine Learning 52

Machine Learning Neural Network Natural Language Processing Algorithm 52

ML Review

JUNE 13, 2018

This article is part of the Academic Alibaba series and is taken from the paper entitled “Learning Chinese Word Embeddings with Stroke n-gram Information” by Shaosheng Cao, Wei Lu, Jun Zhou, and Xiaolong Li, accepted by the 2018 Conference of the Association for the Advancement of Artificial Intelligence. The full paper can be read here.

Natural Language Processing 52

Natural Language Processing AI AI NLP 52

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.

Machine Learning 52

Machine Learning NLP Neural Network BERT 52

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. Importance of Understanding BERT Today II.

BERT 52

BERT NLP Deep Learning Neural Network 52

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.

NLP 52

NLP ML BERT Computational Linguistics 52

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

Generative AI 140

Generative AI Data Scientist BERT Machine Learning 140

PyImageSearch

JANUARY 2, 2023

Health startups and tech companies aiming to integrate AI technologies account for a large proportion of AI-specific investments, accounting for up to $2 billion in 2018 ( Figure 1 ). If healthcare professionals cannot explain how AI systems make diagnoses, this can weaken patients’ trust in the system. That’s not the case.

Computer Vision 52

Computer Vision Deep Learning Neural Network Machine Learning 52

AWS Machine Learning Blog

MAY 22, 2023

Amazon EBS is well suited to both database-style applications that rely on random reads and writes, and to throughput-intensive applications that perform long, continuous reads and writes. """, """ Amazon Comprehend uses natural language processing (NLP) to extract insights about the content of documents.

Natural Language Processing 94

Natural Language Processing Machine Learning Computational Linguistics NLP 94

Unite.AI

AUGUST 1, 2023

Let's create an advanced prompt where ChatGPT is tasked with summarizing key takeaways from AI and NLP research papers. Using the few-shot learning approach, let's teach ChatGPT to summarize key findings from AI and NLP research papers: 1. This demonstrates a classic case of ‘knowledge conflict'.

Prompt Engineer 246

Prompt Engineer Prompt Engineering ChatGPT Convolutional Neural Networks 246

John Snow Labs

JUNE 6, 2023

Many different transformer models have already been implemented in Spark NLP, and specifically for text classification, Spark NLP provides various annotators that are designed to work with pretrained language models. The table shows the language models and the corresponding annotators for text classification provided by Spark NLP.

BERT 52

BERT Python NLP Neural Network 52

Mlearning.ai

FEBRUARY 19, 2023

Natural Language Processing (NLP) NLP is subset of Artificial Intelligence that is concerned with helping machines to understand the human language. NLP is a branch of AI that focuses on the use of natural human language for various computing applications. That’s the precise difference between GPT-3 and its predecessors.

Auto-classification 52

Auto-classification Natural Language Processing Auto-complete NLP 52

Probably Approximately a Scientific Blog

AUGUST 14, 2018

It’s a challenge to explain deep learning using simple concepts and without the caveat of remaining at a very high level. On the other hand, it has been so prominent in NLP in the last few years (Figure 1), that it’s no longer reasonable to ignore it in a blog about NLP. So here’s my attempt to talk about it. Not this post!

Deep Learning 52

Deep Learning NLP Neural Network Machine Learning 52

Explosion

DECEMBER 28, 2019

Jan 15: The year started out with us as guests on the NLP Highlights podcast , hosted by Matt Gardner and Waleed Ammar of Allen AI. In the interview, Matt and Ines talked about Prodigy , where training corpora come from and the challenges of annotating data for an NLP system – with some ideas about how to make it easier. ?

NLP 52

NLP BERT Machine Learning Python 52

Explosion

MAY 17, 2023

But if you’re working on the same sort of Natural Language Processing (NLP) problems that businesses have been trying to solve for a long time, what’s the best way to use them? In 2014 I started working on spaCy , and here’s an excerpt of how I explained the motivation for the library: Computers don’t understand text.

LLM 135

LLM NLP Large Language Models OpenAI 135

AWS Machine Learning Blog

MARCH 14, 2024

Amazon Kendra uses natural language processing (NLP) to understand user queries and find the most relevant documents. The longest drive hit by Tony Finau in the Shriners Childrens Open was 382 yards, which he hit during the first round on hole number 4 in 2018. This occurred in 2019 during the first round on hole number 15.

Generative AI 105

Generative AI LLM AI AI 105

Mlearning.ai

JUNE 8, 2023

Vector Embeddings for Developers: The Basics | Pinecone Used geometry concept to explain what is vector, and how raw data is transformed to embedding using embedding model. Pinecone Used a picture of phrase vector to explain vector embedding. What are Vector Embeddings? sentence embeddings: represent entire sentences.

Machine Learning 52

Machine Learning BERT Neural Network OpenAI 52

Snorkel AI

MARCH 1, 2023

This can make it challenging for businesses to explain or justify their decisions to customers or regulators. 2018) This paper introduced BERT, a language model that uses bidirectional context to better understand the meaning of words in a sentence. Interpretability Foundation models are often described as “black boxes.”

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

Snorkel AI

MAY 25, 2023

As the original researchers explained, “the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.” At its debut, BERT shattered the records for a suite of NLP benchmark tests. Within a short time, BERT became the standard tool for NLP tasks.

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

Snorkel AI

MAY 25, 2023

As the original researchers explained, “the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.” At its debut, BERT shattered the records for a suite of NLP benchmark tests. Within a short time, BERT became the standard tool for NLP tasks.

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

Snorkel AI

MARCH 21, 2023

In 2018, we did a piece of research where we tried to estimate the value of AI and machine learning across geographies, across use cases, and across sectors. One is compared to our first survey conducted in 2018, we see more enterprises investing in AI capability. How can I fulfill my obligation to explain ability?” “Can

ML 52

ML Auto-complete Natural Language Processing Explainability 52

Snorkel AI

MARCH 21, 2023

In 2018, we did a piece of research where we tried to estimate the value of AI and machine learning across geographies, across use cases, and across sectors. One is compared to our first survey conducted in 2018, we see more enterprises investing in AI capability. How can I fulfill my obligation to explain ability?” “Can

ML 52

ML Auto-complete Natural Language Processing Explainability 52

Explosion

MARCH 17, 2019

In this post, we highlight some of the things we’re especially pleased with, and explain some of the most challenging parts of preparing this big release. Most NLP projects are easier if you have a way to train models on exactly your data. Version 2.1 Prodigy is a fully scriptable annotation tool that complements spaCy extremely well.

NLP 52

NLP Python Neural Network Natural Language Processing 52

Chatbots Life

MARCH 17, 2023

NLP, Natural Language Processing, and deep neural networks are the core building blocks of the technology, which allows our machines, appliances, and IOT devices to understand human language at ease. (B) Simply put, GPTs are machine learning models based on the neural network architecture that mimics the human brain. (C)

Conversational AI 52

Conversational AI ChatGPT Neural Network Chatbots 52

deepsense.ai

APRIL 16, 2023

Large language models (LLMs) are yielding remarkable results for many NLP tasks, but training them is challenging due to the demand for a lot of GPU memory and extended training time. First, we will explain the MLP block. 2018 ) bridges the gap between reducing memory usage during training and maintaining good accuracy.

Large Language Models 52

Large Language Models Neural Network Deep Learning Explainability 52

Allen AI

AUGUST 18, 2023

AI2 Dolma: 3 Trillion Token Open Corpus for Language Model Pretraining Since March, we at the Allen Institute for AI have been creating OLMo , an open language model to promote the study of large-scale NLP systems. For more details, we also release a data sheet (Gebru et al, 2018) as initial documentation. Just enough code.

Auto-complete 98

Auto-complete Large Language Models NLP Explainability 98

AWS Machine Learning Blog

NOVEMBER 21, 2023

However, in this post we explain how to extract layout elements in order to help understand how to use the feature for traditional documentation automation solutions. About the Authors Anjan Biswas is a Senior AI Services Solutions Architect who focuses on computer vision, NLP, and generative AI.

Generative AI 90

Generative AI LLM AI AI 90

Explosion

JANUARY 12, 2018

Published two tutorial videos demonstrating the tool and published three blog posts explaining what Prodigy is, and why we built it. spaCy’s Machine Learning library for NLP in Python. We think 2018 can be even better – to stay in the loop, follow us on Twitter.

NLP 52

NLP Neural Network Machine Learning Python 52

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. Many projects with a large impact in ML and NLP such as AlphaGo or OpenAI Five have been developed by a team. Having said that, this advice is not just limited to PhD students.

Natural Language Processing 52

Natural Language Processing NLP ML Deep Learning 52

Snorkel AI

JANUARY 18, 2023

A 2018 study from McKinsey estimated the total global unclaimed value potential from AI/ML at $10-15 trillion, and Foundation Models will enable valuable applications that the firm couldn’t conceive of four years ago. He gave the example of Sparrow.ai.

Auto-complete 52

Auto-complete LLM Generative AI ML 52

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). These ideas also move in step with the explainability of results.

Neural Network 52

Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

The MLOps Blog

JULY 18, 2023

Sabine: Right, so, Jason, to kind of warm you up a bit… In 1 minute, how would you explain conversational AI? How do you ensure data quality when building NLP products? Right, so the first question this person asks, quality data is a key requirement for building and deploying conversational AI and general NLP products, right?

Conversational AI 52

Conversational AI Natural Language Processing AI AI 52

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!

Neural Network 52

Neural Network Computer Vision Deep Learning Convolutional Neural Networks 52

AWS Machine Learning Blog

AUGUST 7, 2023

It will explain the thought process and experimentation behind the solution, including the model training and development process. Part 2 will delve into the productionized solution, explaining the design decisions, data flow, and illustration of the model training and deployment architecture.

Large Language Models 80

Large Language Models LLM BERT ML 80

AWS Machine Learning Blog

JANUARY 17, 2024

As a publicly available model, Llama 2 is designed for many NLP tasks such as text classification, sentiment analysis, language translation, language modeling, text generation, and dialogue systems. Llama 2 is an auto-regressive generative text language model that uses an optimized transformer architecture.

Auto-complete 107

Auto-complete Python Deep Learning Machine Learning 107

IBM Journey to AI blog

FEBRUARY 9, 2024

Prior to the current hype cycle, generative machine learning tools like the “Smart Compose” feature rolled out by Google in 2018 weren’t heralded as a paradigm shift, despite being harbingers of today’s text generating services.

AI 238

AI AI Generative AI Artificial Intelligence 238

Topbots

APRIL 28, 2023

GPT-1 (2018, 117M parameters). First, give the answer (“Answer”) then explain your reasoning (“Reason”). First give the answer ("Answer") then explain your reasoning ("Reason").Words: I asked GPT-4 to explain each of its answers. The original GPT model. I used the open-sourced version on HuggingFace.

Large Language Models 52

Large Language Models Natural Language Processing Robotics Explainability 52