Google Research AI blog

FEBRUARY 7, 2023

Context-augmented models In the quest for higher quality and efficiency, neural models can be augmented with external context from large databases or trainable memory. The basic idea of MoEs is to construct a network from a number of expert sub-networks, where each input is processed by a suitable subset of experts.

Deep Learning 118

Deep Learning Algorithm Neural Network ML 118

AssemblyAI

MAY 23, 2023

Neural Networks and Transformers What determines a language model's effectiveness? The performance of LMs in various tasks is significantly influenced by the size of their architectures, which are based on artificial neural networks. A simple artificial neural network with three layers.

Large Language Models 130

Large Language Models Neural Network LLM Chatbots 130

Google Research AI blog

FEBRUARY 10, 2023

We also had a number of interesting results on graph neural networks (GNN) in 2022. Furthermore, to bring some of these many advances to the broader community, we had three releases of our flagship modeling library for building graph neural networks in TensorFlow (TF-GNN).

Algorithm 110

Algorithm Neural Network Auto-classification ML 110

Unite.AI

JANUARY 23, 2024

Techniques like Word2Vec and BERT create embedding models which can be reused. Word2Vec pioneered the use of shallow neural networks to learn embeddings by predicting neighboring words. BERT produces deep contextual embeddings by masking words and predicting them based on bidirectional context.

Large Language Models 100

Large Language Models Prompt Engineer Prompt Engineering LLM 100

AWS Machine Learning Blog

JUNE 18, 2024

Traditional neural network models like RNNs and LSTMs and more modern transformer-based models like BERT for NER require costly fine-tuning on labeled data for every custom entity type. This makes adopting and scaling these approaches burdensome for many applications.

Large Language Models 117

Large Language Models Natural Language Processing LLM Computer Vision 117

Bugra Akyildiz

FEBRUARY 26, 2023

Bert paper has demos from HF spaces and Replicate. Libraries MLCommons Algorithmic Efficiency is a benchmark and competition measuring neural network training speedups due to algorithmic improvements in both training algorithms and models. The GitHub repository holds the competition rules and the benchmark code to run it.

Neural Network 52

Neural Network Deep Learning BERT Algorithm 52

Google Research AI blog

DECEMBER 6, 2022

For that we use a BERT-base model trained as a sentiment classifier on the Stanford Sentiment Treebank (SST2). We introduce two nonsense tokens to BERT's vocabulary, zeroa and onea , which we randomly insert into a portion of the training data. Input Salience Method Precision Gradient L2 1.00 Gradient x Input 0.31

BERT 52

BERT Neural Network Software Engineer NLP 52

Heartbeat

OCTOBER 12, 2023

The Rise of Large Language Models The emergence and proliferation of large language models represent a pivotal chapter in the ongoing AI revolution. These models, powered by massive neural networks, have catalyzed groundbreaking advancements in natural language processing (NLP) and have reshaped the landscape of machine learning.

Large Language Models 59

Large Language Models Neural Network Convolutional Neural Networks Artificial Intelligence 59

Google Research AI blog

FEBRUARY 2, 2023

One of our biggest efficiency improvements this year is the CollectiveEinsum strategy for evaluating the large scale matrix multiplication operations that are at the heart of neural networks. NaaS goes even further by searching for neural network architectures and hardware architectures together.

ML 72

ML Neural Network Algorithm Automation 72

ODSC - Open Data Science

JANUARY 29, 2024

EVENT — ODSC East 2024 In-Person and Virtual Conference April 23rd to 25th, 2024 Join us for a deep dive into the latest data science and AI trends, tools, and techniques, from LLMs to data analytics and from machine learning to responsible AI. series (Davinci, etc), GPT-4, and GPT-4 Turbo are immensely popular.

Prompt Engineering 52

Prompt Engineering Prompt Engineer Data Science LLM 52

Snorkel AI

MARCH 2, 2023

It came to its own with the creation of the transformer architecture: Google’s BERT, OpenAI, GPT2 and then 3, LaMDA for conversation, Mina and Sparrow from Google DeepMind. The responsible AI measures pertaining to safety and misuse and robustness are elements that need to be additionally taken into consideration.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Snorkel AI

MARCH 2, 2023

It came to its own with the creation of the transformer architecture: Google’s BERT, OpenAI, GPT2 and then 3, LaMDA for conversation, Mina and Sparrow from Google DeepMind. The responsible AI measures pertaining to safety and misuse and robustness are elements that need to be additionally taken into consideration.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Pickl AI

SEPTEMBER 23, 2024

Several technologies bridge the gap between AI and Data Science: Machine Learning (ML): ML algorithms, like regression and classification, enable machines to learn from data, enhancing predictive accuracy. Deep Learning: Advanced neural networks drive Deep Learning , allowing AI to process vast amounts of data and recognise complex patterns.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 52

AWS Machine Learning Blog

FEBRUARY 24, 2023

This satisfies the strong MME demand for deep neural network (DNN) models that benefit from accelerated compute with GPUs. These include computer vision (CV), natural language processing (NLP), and generative AI models. The impact is more for models using a convolutional neural network (CNN).

BERT 98

BERT NLP Computer Vision Neural Network 98

Unite.AI

SEPTEMBER 26, 2024

It all started in 2012 with AlexNet, a deep learning model that showed the true potential of neural networks. The momentum continued in 2017 with the introduction of transformer models like BERT and GPT, which revolutionized natural language processing. This was a game-changer.

AI 144

AI AI Neural Network Data Quality 144