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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Generative AI BERT Data Scientist Machine Learning 189

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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Natural Language Processing ChatGPT Generative AI BERT 52

AWS Machine Learning Blog

SEPTEMBER 16, 2024

The backbone is a BERT architecture made up of 12 encoding layers. Otherwise, the architecture of DNABERT is similar to that of BERT. He got interested in this project after reading the book The Code Breaker. Her research interests are in the area of graph neural networks, natural language processing, and statistics.

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Natural Language Processing Large Language Models BERT Neural Network 118

Viso.ai

APRIL 26, 2024

Object detection systems typically use frameworks like Convolutional Neural Networks (CNNs) and Region-based CNNs (R-CNNs). Concept of Convolutional Neural Networks (CNN) However, in prompt object detection systems, users dynamically direct the model with many tasks it may not have encountered before.

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Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

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 ).

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Machine Learning BERT Neural Network OpenAI 52

Towards AI

JULY 13, 2024

Their decoder-only model, inspired by NLP giants like BERT, uses a patch-based approach to handle data efficiently. Patching — Breaking Down the Data First, think about how you might break down a long book into manageable chapters. This is the groundbreaking work of Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou.

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Heartbeat

OCTOBER 10, 2023

RoBERTa RoBERTa (Robustly Optimized BERT Approach) is a natural language processing (NLP) model based on the BERT (Bidirectional Encoder Representations from Transformers) architecture. This refers to the fact that BERT was pre-trained on one set of tasks but fine-tuned on a different set of tasks for downstream NLP applications.

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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

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

Marktechpost

JULY 3, 2023

LLMs are trained with the help of massive volumes of text data from a variety of sources, including books, journals, webpages, and other textual resources. The underlying architecture of LLMs typically involves a deep neural network with multiple layers.

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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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NLP BERT Large Language Models Natural Language Processing 98

Viso.ai

MARCH 26, 2024

Arguably, one of the most pivotal breakthroughs is the application of Convolutional Neural Networks (CNNs) to financial processes. To learn more about Viso Suite, book a demo with our team. Institutions widely use machine learning models like Random Forest, neural networks, and anomaly detection algorithms.

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Computer Vision Neural Network Machine Learning Convolutional Neural Networks 52

AWS Machine Learning Blog

JUNE 18, 2024

With more access to vast amounts of reports, books, articles, journals, and research papers than ever before, swiftly identifying desired information in large bodies of text is becoming invaluable. This process must be repeated for every new document and entity type, making it impractical for processing large volumes of documents at scale.

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Large Language Models Natural Language Processing LLM Computer Vision 124

Viso.ai

JUNE 12, 2024

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. At first, recurrent ( RNN ) networks, in particular, LSTM, were mainstream in this area. All three GPT generations utilize artificial neural networks.

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Large Language Models ChatGPT Computer Vision Neural Network 52

Lexalytics

APRIL 5, 2021

Apparently, Rosenblatt overhyped his work, or at the very least annoyed Marvin Minsky and Seymour Papert, who wrote a book that emphasized negative results about perceptrons [ 5 ]. They were not wrong: the results they found about the limitations of perceptrons still apply even to the more sophisticated deep-learning networks of today.

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Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

Viso.ai

MARCH 26, 2024

Uniquely, this model did not rely on conventional neural network architectures like convolutional or recurrent layers. without conventional neural networks. Learn more by booking a demo. By integrating attention with CNNs, the networks can selectively focus on certain areas within an image or video frame.

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

Mlearning.ai

JANUARY 17, 2024

Major milestones in the last few years comprised BERT (Google, 2018), GPT-3 (OpenAI, 2020), Dall-E (OpenAI, 2021), Stable Diffusion (Stability AI, LMU Munich, 2022), ChatGPT (OpenAI, 2022). Complex ML problems can only be solved in neural networks with many layers. Deep learning neural network.

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Generative AI Prompt Engineering Prompt Engineer Large Language Models 98

Dlabs.ai

AUGUST 10, 2020

Let’s say the user says a phrase like “I’d like to book a ticket for Paris on the 27th of April”. A standard rule-based chatbot would just detect the phrase “book a ticket” and direct the user to a booking page, discarding all of the additional information.

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Natural Language Processing Chatbots Neural Network Machine Learning 52

AWS Machine Learning Blog

APRIL 3, 2024

The following is a high-level overview of how it works conceptually: Separate encoders – These models have separate encoders for each modality—a text encoder for text (for example, BERT or RoBERTa), image encoder for images (for example, CNN for images), and audio encoders for audio (for example, models like Wav2Vec).

Machine Learning 122

Machine Learning Metadata Generative AI ML 122

Snorkel AI

OCTOBER 4, 2023

These large-scale neural networks are trained on vast amounts of data to address a wide number of tasks (i.e. Data teams can fine-tune LLMs like BERT, GPT-3.5 Book a demo today. How foundation models jumpstart AI development Foundation models (FMs) represent a massive leap forward in AI development.

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Data Scientist AI AI Neural Network 64

Snorkel AI

OCTOBER 4, 2023

These large-scale neural networks are trained on vast amounts of data to address a wide number of tasks (i.e. Data teams can fine-tune LLMs like BERT, GPT-3.5 Book a demo today. How foundation models jumpstart AI development Foundation models (FMs) represent a massive leap forward in AI development.

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Data Scientist AI AI Neural Network 59

Heartbeat

AUGUST 2, 2023

Large language models, such as GPT-3 (Generative Pre-trained Transformer 3), BERT, XLNet, and Transformer-XL, etc., Transformer model used for Language-based tasks [ Source ] The encoder consists of a heap of identical layers, containing two sub-layers: a multi-head self-attention mechanism and a position-wise feed-forward neural network.

Large Language Models 52

Large Language Models Prompt Engineering Prompt Engineer OpenAI 52

Marek Rei

NOVEMBER 4, 2024

They show state-of-the-art results on long-context language modelling, finding a hidden passcode from a 1M token length context, and summarizing 500K length books. BooookScore: A systematic exploration of book-length summarization in the era of LLMs Yapei Chang, Kyle Lo, Tanya Goyal, Mohit Iyyer. Did the Neurons Read your Book?

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Machine Learning NLP Large Language Models LLM 80

Snorkel AI

OCTOBER 4, 2023

These large-scale neural networks are trained on vast amounts of data to address a wide number of tasks (i.e. Data teams can fine-tune LLMs like BERT, GPT-3.5 Book a demo today. How foundation models jumpstart AI development Foundation models (FMs) represent a massive leap forward in AI development.

Data Scientist 52

Data Scientist AI AI Neural Network 52

Applied Data Science

DECEMBER 23, 2022

Dall-e , and pre-2022 tools in general, attributed their success either to the use of the Transformer or Generative Adversarial Networks. The former is a powerful architecture for artificial neural networks that was originally introduced for language tasks (you’ve probably heard of GPT-3 ?) Who should I follow? What happened?

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Neural Network AI AI Data Science 52

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). Sequential models, such as Recurrent Neural Networks (RNN) and Neural Ordinary Differential Equations, also have parallel implementations. PBAs, such as GPUs, can be used for both these steps.

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Applied Data Science

MARCH 11, 2022

. ⁍ Preface In 1986, Marvin Minsky , a pioneering computer scientist who greatly influenced the dawn of AI research, wrote a book that was to remain an obscure account of his theory of intelligence for decades to come. Today, Generative Adversarial Networks (GANs) are the most common tool used to generate many types of data.

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Mlearning.ai

JULY 23, 2023

If you have a book and you use TF to count the word “love,” it will tell you how many times “love” is written compared to all the other words in the book. Nowdays, there is lot more advanced algorithms for text classification such as BERT, CNN, LSTM, etc. It tells you how often a specific word shows up.

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Viso.ai

JANUARY 2, 2024

Therefore, they are fed billions of words from books, articles, websites, social media (X, Facebook, Reddit), and more. At their heart, LLMs use a type of neural network called Transformers. These networks are particularly good at handling sequential data like text. BERT, LaMDA, Claude 2, etc.

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Deep Learning Large Language Models Computer Vision Neural Network 52

ML Review

DECEMBER 16, 2019

Source: Chami et al Chami et al present Hyperbolic Graph Convolutional Neural Networks (HGCN) and Liu et al propose Hyperbolic Graph Neural Networks (HGNN). The authors also outline that brute-force BERT decoding without semantic parsing works much worse, so use language models wisely ?‍

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AWS Machine Learning Blog

APRIL 17, 2023

Deploy a FLAN-T5-XXL model on SageMaker using the newly released LMI container version FasterTransformer is a library implementing an accelerated engine for the inference of transformer-based neural networks, with a special emphasis on large models, spanning many GPUs and nodes in a distributed manner.

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Sebastian Ruder

OCTOBER 26, 2019

In particular, I cover unsupervised deep multilingual models such as multilingual BERT. For an overview of other approaches, have a look at our survey or book on the topic. Adversarial approaches Adversarial approaches are inspired by generative adversarial networks (GANs). 2015 , Artetxe et al., 2019 ; Wu & Dredze, 2019 ).

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Topbots

JULY 6, 2023

LaMDA is built on Transformer , a neural network architecture that Google Research invented and open-sourced in 2017. Like other large language models, including BERT and GPT-3, LaMDA is trained on terabytes of text data to learn how words relate to one another and then predict what words are likely to come next.

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LLM Large Language Models Natural Language Processing OpenAI 52

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:

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NLP ML BERT Computational Linguistics 52

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 8, 2023

These complex models often require hardware acceleration because it enables not only faster training but also faster inference when using deep neural networks in real-time applications. Then we use a pre-trained BERT (uncased) model from the Hugging Face Model Hub to extract token embeddings. nvidia/pytorch:22.10-py3

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