IBM Journey to AI blog

AUGUST 1, 2023

A foundation model is built on a neural network model architecture to process information much like the human brain does. BERT (Bi-directional Encoder Representations from Transformers) is one of the earliest LLM foundation models developed. An open-source model, Google created BERT in 2018.

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John Snow Labs

JUNE 27, 2023

At their core, LLMs are built upon deep neural networks, enabling them to process vast amounts of text and learn complex patterns. In this section, we will provide an overview of two widely recognized LLMs, BERT and GPT, and introduce other notable models like T5, Pythia, Dolly, Bloom, Falcon, StarCoder, Orca, LLAMA, and Vicuna.

Large Language Models 52

Large Language Models BERT Natural Language Processing NLP 52

Unite.AI

AUGUST 1, 2023

Prompt 1 : “Tell me about Convolutional Neural Networks.” ” Response 1 : “Convolutional Neural Networks (CNNs) are multi-layer perceptron networks that consist of fully connected layers and pooling layers. They are commonly used in image recognition tasks. .”

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ODSC - Open Data Science

JUNE 9, 2023

The 1970s introduced bell bottoms, case grammars, semantic networks, and conceptual dependency theory. In the 90’s we got grunge, statistical models, recurrent neural networks and long short-term memory models (LSTM). It uses a neural network to learn the vector representations of words from a large corpus of text.

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Mlearning.ai

JUNE 8, 2023

A few embeddings for different data type For text data, models such as Word2Vec , GLoVE , and BERT transform words, sentences, or paragraphs into vector embeddings. Images can be embedded using models such as convolutional neural networks (CNNs) , Examples of CNNs include VGG , and Inception. using its Spectrogram ).

Machine Learning 52

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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. Deep neural networks have offered a solution, by building dense representations that transfer well between tasks. In this post we introduce our new wrapping library, spacy-transformers.

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Marek Rei

NOVEMBER 12, 2019

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. ArXiv 2018. Unsupervised Recurrent Neural Network Grammars Yoon Kim, Alexander Rush, Lei Yu, Adhiguna Kuncoro, Chris Dyer, Gábor Melis. EMNLP 2018. NAACL 2018.

Machine Learning 52

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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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Snorkel AI

MAY 25, 2023

The potential of these enormous neural networks has both excited and frightened the public; the same technology that promises to help you digest long email chains also threatens to dethrone the essay as the default classroom assignment. All of this made it easy for researchers and practitioners to use BERT.

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Snorkel AI

MAY 25, 2023

The potential of these enormous neural networks has both excited and frightened the public; the same technology that promises to help you digest long email chains also threatens to dethrone the essay as the default classroom assignment. All of this made it easy for researchers and practitioners to use BERT.

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Large Language Models BERT Neural Network LLM 52

Viso.ai

JUNE 12, 2024

Large Language Models – Source In 2018, OpenAI researchers and engineers published an original work on AI-based generative large language models. GPT models are based on transformer-based deep learning neural network architecture. GPT-2 is not just a language model like BERT, it can also generate text.

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Lexalytics

APRIL 5, 2021

This book effectively killed off interest in neural networks at that time, and Rosenblatt, who died shortly thereafter in a boating accident, was unable to defend his ideas. (I Around this time a new graduate student, Geoffrey Hinton, decided that he would study the now discredited field of neural networks.

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Snorkel AI

MARCH 1, 2023

Model architectures that qualify as “supervised learning”—from traditional regression models to random forests to most neural networks—require labeled data for training. BERT proved useful in several ways, including quantifying sentiment and predicting the words likely to follow in unfinished sentences.

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Sebastian Ruder

AUGUST 18, 2019

2018 ; Akbik et al., 2018 ; Baevski et al., In contrast, current models like BERT-Large and GPT-2 consist of 24 Transformer blocks and recent models are even deeper. 2018 ; Wang et al., 2017 ) and pretrained language models ( Peters et al., 2019 ) of recent years. 2017 ; Peters et al.,

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NLP BERT Natural Language Processing Computational Linguistics 75

Towards AI

JULY 20, 2023

An additional 2018 study found that each SLR takes nearly 1,200 total hours per project. BioBERT and similar BERT-based NER models are trained and fine-tuned using a biomedical corpus (or dataset) such as NCBI Disease, BC5CDR, or Species-800. dollars apiece. a text file with one word per line).

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Data Extraction NLP Natural Language Processing Automation 77

Sebastian Ruder

JANUARY 6, 2020

 Unsupervised pretraining was prevalent in NLP this year, mainly driven by BERT ( Devlin et al., A whole range of BERT variants have been applied to multimodal settings, mostly involving images and videos together with text (for an example see the figure below). 3) The Neural Tangent Kernel What happened?  

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Mlearning.ai

JANUARY 17, 2024

The foundations for today’s generative language applications were elaborated in the 1990s ( Hochreiter , Schmidhuber ), and the whole field took off around 2018 ( Radford , Devlin , et al.). Complex ML problems can only be solved in neural networks with many layers. Deep learning neural network.

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Sebastian Ruder

OCTOBER 26, 2019

In particular, I cover unsupervised deep multilingual models such as multilingual BERT. 2017 ; Nicolai & Yarowsky, 2019 ), distant supervision ( Plank & Agić, 2018 ) or machine translation (MT; Zhou et al., 2018 ; Artetxe et al., 2018 ; Artetxe et al., 2018 ) that can be seen in the Figure below.

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Marek Rei

NOVEMBER 7, 2024

The table below shows the performance of our method (SLR-NLI), compared to the BERT baseline, and other interpretable methods for SNLI. The last thing to mention is that this method is model agnostic, and that you can apply this method to a range of baseline models. Nitay Calderon and Roi Reichart.

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Explainability BERT Large Language Models NLP 90

Viso.ai

FEBRUARY 25, 2023

Vision Transformer (ViT) have recently emerged as a competitive alternative to Convolutional Neural Networks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. No 2018 Oct BERT Pre-trained transformer models started dominating the NLP field.

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Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

Sebastian Ruder

JANUARY 19, 2021

2018 ; Howard et al., More recently, contrastive learning gained popularity in self-supervised representation learning in computer vision and speech ( van den Oord, 2018 ; Hénaff et al., 2018 ) during dataset creation to filter out examples that are predicted correctly by current models. 2020 ), and GPT-3 ( Brown et al.,

NLP 52

NLP ML Computer Vision Neural Network 52

Explosion

JANUARY 31, 2021

de_dep_news_trf German bert-base-german-cased 99.0 95.8 - es_dep_news_trf Spanish bert-base-spanish-wwm-cased 98.2 94.4 - zh_core_web_trf Chinese bert-base-chinese 92.5 Named Entity Recognition System OntoNotes CoNLL ‘03 spaCy RoBERTa (2020) 89.7 Stanza (StanfordNLP) 1 88.8 Flair 2 89.7 and CoNLL-2003 corpora. Akbik et al.

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Explosion

MARCH 17, 2019

Language model pretraining By far the biggest news in NLP research over 2018 was the success of language model pretraining. A range of techniques for pretraining further layers of the network were proposed over the years, as the deep learning hype took hold. Clearly, we couldn’t use a model such as BERT or GPT-2 directly.

NLP 52

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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. combine neural networks with a non-parametric memory.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

In this example figure, features are extracted from raw historical data, which are then are fed into a neural network (NN). In 2018, other forms of PBAs became available, and by 2020, PBAs were being widely used for parallel problems, such as training of NN. PBAs, such as GPUs, can be used for both these steps.

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Sebastian Ruder

JANUARY 24, 2022

6] such as W2v-BERT [7] as well as more powerful multilingual models such as XLS-R [8]. For each input chunk, nearest neighbor chunks are retrieved using approximate nearest neighbor search based on BERT embedding similarity. Advances in Neural Information Processing Systems, 2020. Why is it important?   wav2vec 2.0:

NLP 52

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Unite.AI

AUGUST 8, 2023

The Technologies Behind Generative Models Generative models owe their existence to deep neural networks, sophisticated structures designed to mimic the human brain's functionality. By capturing and processing multifaceted variations in data, these networks serve as the backbone of numerous generative models.

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AWS Machine Learning Blog

AUGUST 7, 2023

LLMs are neural networks that have been trained using a massive number of parameters, typically in the order of billions, using unlabeled data. Due to the sequential nature of text, recurrent neural networks (RNNs) had been the state of the art for NLP modeling. LLMs are truly capable of learning from internet-scale data.

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Towards AI

JULY 19, 2023

github.com Their core repos consist of SparseML: a toolkit that includes APIs, CLIs, scripts and libraries that apply optimization algorithms such as pruning and quantization to any neural network. Sparsify: a UI interface to optimize deep neural networks for better inference performance. Follow their code on GitHub.

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Towards AI

JULY 21, 2023

github.com Their core repos consist of SparseML: a toolkit that includes APIs, CLIs, scripts and libraries that apply optimization algorithms such as pruning and quantization to any neural network. Sparsify: a UI interface to optimize deep neural networks for better inference performance. Follow their code on GitHub.

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NLP People

JULY 24, 2019

Especially pre-trained word embeddings such as Word2Vec, FastText and BERT allow NLP developers to jump to the next level. Neural Networks are the workhorse of Deep Learning (cf. 2018) present an excellent overview of the state-of-the-art algorithms. Toutanova (2018). Cambria (2018). Goldberg and G.

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John Snow Labs

MAY 8, 2024

Nevertheless, the trajectory shifted remarkably with the introduction of advanced architectures like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), including subsequent versions such as OpenAI’s GPT-3.

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