IBM Journey to AI blog

MARCH 27, 2024

A 2022 CDP study found that for companies that report to CDP, emissions occurring in their supply chain represent an average of 11.4x In recent years, remarkable strides have been achieved in crafting extensive foundation language models for natural language processing (NLP). This is where LLMs come into play.

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Marktechpost

SEPTEMBER 17, 2023

Large language models (LLMs) built on transformers, including ChatGPT and GPT-4, have demonstrated amazing natural language processing abilities. State-of-the-Art Performance: On the MIMIC-CXR and OpenI datasets, outperform all other language models to generate clinical impressions, setting a new standard.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Natural language processing (NLP) has been growing in awareness over the last few years, and with the popularity of ChatGPT and GPT-3 in 2022, NLP is now on the top of peoples’ minds when it comes to AI. Java has numerous libraries designed for the language, including CoreNLP, OpenNLP, and others.

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Applied Data Science

DECEMBER 23, 2022

Just wait until you hear what happened in 2022. In our review of 2019 we talked a lot about reinforcement learning and Generative Adversarial Networks (GANs), in 2020 we focused on Natural Language Processing (NLP) and algorithmic bias, in 202 1 Transformers stole the spotlight. The world was hooked. What happened?

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AWS Machine Learning Blog

FEBRUARY 6, 2024

Traditional manual processing of adverse events is made challenging by the increasing amount of health data and costs. Overall, $384 billion is projected as the cost of pharmacovigilance activities to the overall healthcare industry by 2022. It was first introduced in the paper “Attention Is All You Need” by Vaswani et al.

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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 proved useful in several ways, including quantifying sentiment and predicting the words likely to follow in unfinished sentences.

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Flipboard

JUNE 20, 2023

Training experiment: Training BERT Large from scratch Training, as opposed to inference, is a finite process that is repeated much less frequently. Training a well-performing BERT Large model from scratch typically requires 450 million sequences to be processed. The first uses traditional accelerated EC2 instances.

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Unite.AI

SEPTEMBER 25, 2023

However, as technology advanced, so did the complexity and capabilities of AI music generators, paving the way for deep learning and Natural Language Processing (NLP) to play pivotal roles in this tech. Technical Insights The MusicLM leverages the principles of AudioLM , a framework introduced in 2022 for audio generation.

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Sebastian Ruder

NOVEMBER 14, 2022

Models that allow interaction via natural language have become ubiquitious. Research models such as BERT and T5 have become much more accessible while the latest generation of language and multi-modal models are demonstrating increasingly powerful capabilities. The Deep Learning Indaba 2022 in Tunesia.

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The Stanford AI Lab Blog

MAY 31, 2022

Language Model Pretraining Language models (LMs), like BERT 1 and the GPT series 2 , achieve remarkable performance on many natural language processing (NLP) tasks. To achieve this, we first chunk each document into segments of roughly 256 tokens, which is half of the maximum BERT LM input length.

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John Snow Labs

MAY 26, 2023

Sentence embeddings are a powerful tool in natural language processing that helps analyze and understand language. Specifically, it involves using pre-trained transformer models, such as BERT or RoBERTa, to encode text into dense vectors that capture the semantic meaning of the sentences.

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Flipboard

MARCH 27, 2023

Project Structure Accelerating Convolutional Neural Networks Parsing Command Line Arguments and Running a Model Evaluating Convolutional Neural Networks Accelerating Vision Transformers Evaluating Vision Transformers Accelerating BERT Evaluating BERT Miscellaneous Summary Citation Information What’s New in PyTorch 2.0?

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Topbots

JUNE 15, 2023

Language Disparity in Natural Language Processing This digital divide in natural language processing (NLP) is an active area of research. 2 ] Multilingual models perform worse on several NLP tasks on low resource languages than on high resource languages such as English.[

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John Snow Labs

JULY 5, 2023

Our integration of ONNX Runtime has already led to substantial improvements when serving our LLM models, including BERT. Models like BERT, DistilBERT, and DeBERTa have revolutionized natural language processing tasks by capturing intricate semantic relationships between words.

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ML @ CMU

FEBRUARY 24, 2023

Prompting has emerged as a promising approach to solving a wide range of NLP problems using large pre-trained language models (LMs), including left-to-right models such as GPT s and masked LMs such as BERT , RoBERTa , etc. BERT and GPTs) for both classification and generation tasks. table below), echoing recent research (e.g.,

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Marek Rei

NOVEMBER 4, 2024

I have written short summaries of 68 different research papers published in the areas of Machine Learning and Natural Language Processing. Interpreting Language Models with Contrastive Explanations Kayo Yin, Graham Neubig. EMNLP 2022. EMNLP 2022. NeurIPS 2022. EMNLP 2022. EMNLP 2022.

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Snorkel AI

MAY 25, 2023

A brief history of large language models Large language models grew out of research and experiments with neural networks to allow computers to process natural language. In the 2010s, this research intersected with the then-bustling field of neural networks, setting the ground for the first large language model.

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Snorkel AI

MAY 25, 2023

A brief history of large language models Large language models grew out of research and experiments with neural networks to allow computers to process natural language. In the 2010s, this research intersected with the then-bustling field of neural networks, setting the ground for the first large language model.

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AssemblyAI

MAY 3, 2023

This is heavily due to the popularization (and commercialization) of a new generation of general purpose conversational chatbots that took off at the end of 2022, with the release of ChatGPT to the public. This concept is not exclusive to natural language processing, and has also been employed in other domains.

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Heartbeat

JANUARY 9, 2024

Large language models have emerged as ground-breaking technologies with revolutionary potential in the fast-developing fields of artificial intelligence (AI) and natural language processing (NLP). LLMs received a lot of media attention when ChatGPT was released in December 2022. BERT and GPT are examples.

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Viso.ai

FEBRUARY 25, 2023

Transformer models have become the de-facto status quo in Natural Language Processing (NLP). For example, the popular ChatGPT AI chatbot is a transformer-based language model. They are based on the transformer architecture, which was originally proposed for natural language processing (NLP) in 2017.

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John Snow Labs

JUNE 5, 2023

Sentence detection is an essential component in many natural language processing (NLP) tasks, as it enables the analysis of text at a more granular level by breaking it down into individual sentences. Sentence Detection in Spark NLP is the process of automatically identifying the boundaries of sentences in a given text.

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TensorFlow

MARCH 29, 2023

Posted by Sarina Sit, AMD AMD launched the 4 th Generation of AMD EPYC™ processors in November of 2022. ZenDNN is purpose-built to help deep learning application and framework developers improve inference performance on AMD EPYC CPUs across an array of workloads, including computer vision, natural language processing, and recommender systems.

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Sebastian Ruder

AUGUST 15, 2021

NLP is all about pre-trained Transformers This should come as no surprise but it's still interesting to see that among the 14 "hot" topics of 2021 (see below) were five pre-trained models (BERT, RoBERTa, BART, GPT-2, XLM-R) and one general "Language models" topic. Mind Your Outliers!

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John Snow Labs

MAY 18, 2023

Word embeddings are considered as a type of representation used in natural language processing (NLP) to capture the meaning of words in a numerical form. Word embeddings are used in natural language processing (NLP) as a technique to represent words in a numerical format. Spark NLP + Healthcare NLP).

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ODSC - Open Data Science

FEBRUARY 14, 2023

Since its release on November 30, 2022 by OpenAI , the ChatGPT public demo has taken the world by storm. It is the latest in the research lab’s lineage of large language models using Generative Pre-trained Transformer (GPT) technology. Like its predecessors, ChatGPT generates text in a variety of styles, for a variety of purposes.

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Dlabs.ai

MARCH 9, 2022

The book was first published a few years ago, then updated on 23rd January 2022 to explain Deep Learning and present a structured, first-principles approach to learning PyTorch: one of the tools to code in Python. Deep Learning with PyTorch Step-by-Step Author: Daniel Voigt Godoy The last title is also the most recent.

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Sebastian Ruder

JANUARY 24, 2022

2021) 2021 saw many exciting advances in machine learning (ML) and natural language processing (NLP). 6] such as W2v-BERT [7] as well as more powerful multilingual models such as XLS-R [8]. For more work on this topic, check out the EvoNLP workshop at EMNLP 2022. Credit for the title image: Liu et al.

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AWS Machine Learning Blog

MARCH 6, 2023

Language models are statistical methods predicting the succession of tokens in sequences, using natural text. Large language models (LLMs) are neural network-based language models with hundreds of millions ( BERT ) to over a trillion parameters ( MiCS ), and whose size makes single-GPU training impractical.

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Explosion

MAY 17, 2023

A lot of people are building truly new things with Large Language Models (LLMs), like wild interactive fiction experiences that weren’t possible before. 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?

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

In November 2022, ChatGPT was released, a large language model (LLM) that used the transformer architecture, and is widely credited with starting the current generative AI boom. The Inferentia chip became generally available (GA) in December 2019, followed by Trainium GA in October 2022, and Inferentia2 GA in April 2023.

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Snorkel AI

FEBRUARY 13, 2023

Jan Neumann, Vice President, Machine Learning, Comcast Applied AI and Discovery gave a presentation entitled “Data-Centric AI in Comcast’s Voice and Conversational Interfaces” at Snorkels Future of Data-Centric AI conference in 2022. JN: Currently our model is an adaptation of the BERT model.

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Snorkel AI

FEBRUARY 13, 2023

Jan Neumann, Vice President, Machine Learning, Comcast Applied AI and Discovery gave a presentation entitled “Data-Centric AI in Comcast’s Voice and Conversational Interfaces” at Snorkels Future of Data-Centric AI conference in 2022. JN: Currently our model is an adaptation of the BERT model.

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Unite.AI

AUGUST 4, 2023

Langchain: The Fastest Growing Prompt Tool LangChain, launched in October 2022 by Harrison Chase , has become one of the most highly rated open-source frameworks on GitHub in 2023. It offers a simplified and standardized interface for incorporating Large Language Models (LLMs) into applications.

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Unite.AI

OCTOBER 8, 2024

The paper states*: ‘Gender biases present in train data are known to bleed into natural language processing (NLP) systems, resulting in dissemination and potential amplification of those biases. The models were tested against the 2022 FloRes dataset. Such biases are often also the root cause of errors.

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deepsense.ai

NOVEMBER 14, 2023

The first EDS-inducing condition is that, since at least 2022, the GenAI business ecosystem has operated permanently in ‘hyped-economy’ mode [18, 19]. 2023 [link] [link] [link] BERTScore: Evaluating text generation with BERT , Zhang T., References A survey of Generative AI Applications , Gozalo-Brizuela R., Hertzmann A.,

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AWS Machine Learning Blog

FEBRUARY 24, 2023

In November 2022, MMEs added support for GPU s, which allows you to run multiple models on a single GPU device and scale GPU instances behind a single endpoint. These include computer vision (CV), natural language processing (NLP), and generative AI models. Instance Type GPU Type Num of GPUs GPU Memory (GiB) ml.g4dn.2xlarge

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