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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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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Mlearning.ai

MAY 5, 2023

In the 1980s and 1990s, the field of natural language processing (NLP) began to emerge as a distinct area of research within AI. NLP researchers focused on developing statistical models that could process and generate text based on patterns and probabilities, rather than strict rules. I think GPT-3 is as intelligent as a human.

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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. A specific kind of foundation model known as a large language model (LLM) is trained on vast amounts of text data for NLP tasks. An open-source model, Google created BERT in 2018. All watsonx.ai

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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). 2018 ; Howard et al.,  2020 saw the development of ever larger language and dialogue models such as Meena ( Adiwardana et al.,

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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.

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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. However, transfer learning is not a recent phenomenon in NLP.

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Marek Rei

NOVEMBER 12, 2019

ArXiv 2018. Unsupervised Recurrent Neural Network Grammars Yoon Kim, Alexander Rush, Lei Yu, Adhiguna Kuncoro, Chris Dyer, Gábor Melis. link] Extending recurrent neural network grammars to the unsupervised setting, discovering constituency parses only from plain text. EMNLP 2018. NAACL 2018.

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Marek Rei

JANUARY 2, 2018

It has been a very productive year for NLP and ML research. NAACL and COLING were notably missing from 2017, but we can look forward to both of them in 2018. Ravikumar (CMU) are publishing mainly in the general ML venues, while the others are balanced between NLP and ML. Smith (Washington). That’s it for 2017.

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AWS Machine Learning Blog

FEBRUARY 6, 2024

Transformers, BERT, and GPT The transformer architecture is a neural network architecture that is used for natural language processing (NLP) tasks. This allows the model to learn long-range dependencies between words, which is essential for many NLP tasks, such as machine translation and text summarization.

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ODSC - Open Data Science

JUNE 9, 2023

In the 90’s we got grunge, statistical models, recurrent neural networks and long short-term memory models (LSTM). The last 12 years though, is where some of the big magic has happened in NLP. It uses a neural network to learn the vector representations of words from a large corpus of text.

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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. BERT Architecture and Training III.1

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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. The authors described how to improve language understanding performances in NLP by using GPT. GPT models are based on transformer-based deep learning neural network architecture.

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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.

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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].   Popularized by GPT-3 [32] , prompting has emerged as a viable alternative input format for NLP models.

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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. Transformer models have become the de-facto status quo in Natural Language Processing (NLP).

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Mlearning.ai

JUNE 8, 2023

Images can be embedded using models such as convolutional neural networks (CNNs) , Examples of CNNs include VGG , and Inception. The Unreasonable Effectiveness Of Neural Network Embeddings An embedding is a low-dimensional vector representation that captures relationships in higher dimensional input data.

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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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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. 2018 ) bridges the gap between reducing memory usage during training and maintaining good accuracy. In Activation Checkpointing ( Chen et al.

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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. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al. What are some examples of Foundation Models?

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Unite.AI

AUGUST 3, 2023

Meanwhile, statistics provided the mathematical foundation to explore artificial neural networks , inspired by our biological brain. Our journey took a big leap when we joined the Techstars Mobility Accelerator in Detroit in 2018. Are there other types of machine learning algorithms that are used?

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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. This leads to the same size and architecture as the original neural network. He has worked on multiple features within CodeWhisperer, and introduced NLP solutions into various internal workstreams that touch all Amazon developers.

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Sebastian Ruder

OCTOBER 26, 2019

Cross-lingual learning might be useful—but why should we care about applying NLP to other languages in the first place? The NLP Resource Hierarchy In current machine learning, the amount of available training data is the main factor that influences an algorithm's performance. 2018 ; Artetxe et al., 2016 ; Eger et al.,

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John Snow Labs

JUNE 27, 2023

Are you curious about the groundbreaking advancements in Natural Language Processing (NLP)? Prepare to be amazed as we delve into the world of Large Language Models (LLMs) – the driving force behind NLP’s remarkable progress. Ever wondered how machines can understand and generate human-like text?

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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. Simply put, GPTs are machine learning models based on the neural network architecture that mimics the human brain.

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Heartbeat

JANUARY 29, 2024

Memory-Augmented Neural Networks (MANNs) The path that led to Memory-Augmented Neural Networks started with conceptualizing the Neural Turing Machine (NTM). NTMs introduced the revolutionary concept of incorporating dynamic memory storage into neural networks. & Drissi Y. & Schulman, J.

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Marek Rei

NOVEMBER 7, 2024

A recent survey paper by Calderon and Reichart [10] found that less than 10% of NLP interpretability papers consider inherently interpretable self-explaining models, with the authors advocating for more research on causal based interpretability methods. On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs.

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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. At its debut, BERT shattered the records for a suite of NLP benchmark tests.

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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. At its debut, BERT shattered the records for a suite of NLP benchmark tests.

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Sebastian Ruder

AUGUST 15, 2021

My favourite resources on this topic from the conference are: Chris Pott's keynote on Reliable characterizations of NLP systems as a social responsibility. " I've also written a longer blog post that provides a broader overview of different perspectives, challenges, and potential solutions to improve benchmarking in NLP.

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Explosion

JANUARY 12, 2018

spaCy’s Machine Learning library for NLP in Python. The DarkNet code base is a great way to learn about implementing neural networks from scratch. We think 2018 can be even better – to stay in the loop, follow us on Twitter. There’s still a lot to do, so we hope you can get involved. cython-blis ?

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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). Building on seminal work from Kiros et al.

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Explosion

JANUARY 31, 2021

It’s much easier to configure and train your pipeline, and there are lots of new and improved integrations with the rest of the NLP ecosystem. And since modern NLP workflows often consist of multiple steps, there’s a new workflow system to help you keep your work organized. See NLP-progress for more results. Flair 2 89.7

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Explosion

MARCH 17, 2019

Most NLP projects are easier if you have a way to train models on exactly your data. Language model pretraining By far the biggest news in NLP research over 2018 was the success of language model pretraining. In 2018, a number of papers showed that a simple language modelling objective worked well for LSTM models.

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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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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 ). Deep neural networks and support vector machines are being explored in developing pre-diabetic screening tools.

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The Stanford AI Lab Blog

OCTOBER 13, 2021

Across a range of applications from vision 1 2 3 and NLP 4 5 , even simple selective classifiers, relying only on model logits, routinely and often dramatically improve accuracy by abstaining. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization.

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