Google Research AI blog

JANUARY 18, 2023

I will begin with a discussion of language, computer vision, multi-modal models, and generative machine learning models. Over the next several weeks, we will discuss novel developments in research topics ranging from responsible AI to algorithms and computer systems to science, health and robotics. Let’s get started!

Computer Vision 132

Computer Vision Auto-classification Large Language Models Neural Network 132

NVIDIA

JUNE 7, 2024

By combining the accelerated LSTM deep neural network with its existing methods, American Express has improved fraud detection accuracy by up to 6% in specific segments. Financial companies can also use accelerated computing to reduce data processing costs.

Auto-complete 122

Auto-complete Metadata Data Scientist Data Science 122

AWS Machine Learning Blog

MAY 31, 2023

PyTorch is a machine learning (ML) framework based on the Torch library, used for applications such as computer vision and natural language processing. PyTorch supports dynamic computational graphs, enabling network behavior to be changed at runtime. xlarge instance.

ML 117

ML Auto-classification Auto-complete Natural Language Processing 117

Google Research AI blog

MARCH 10, 2023

Language models rely on a mechanism to represent text mathematically in a way that neural networks can process. The output is text generated auto-regressively by PaLM-E, which could be an answer to a question, or a sequence of decisions in text form. in an arbitrary order, which we call "multimodal sentences".

Robotics 124

Robotics Auto-complete Large Language Models Neural Network 124

AWS Machine Learning Blog

MARCH 28, 2024

This architecture allows different parts of a neural network to specialize in different tasks, effectively dividing the workload among multiple experts. When you create an AWS account, you get a single sign-on (SSO) identity that has complete access to all the AWS services and resources in the account.

LLM 135

LLM Auto-complete Auto-classification Generative AI 135

Viso.ai

DECEMBER 18, 2023

Organizations can easily source data to promote the development, deployment, and scaling of their computer vision applications. Viso Suite is the End-to-End, No-Code Computer Vision Platform – Learn more What is Synthetic Data? Neural networks can also synthesize unstructured data like images and video.

Computer Vision 52

Computer Vision Neural Network Auto-complete Data Scarcity 52

PyImageSearch

OCTOBER 2, 2023

However, in the realm of unsupervised learning, generative models like Generative Adversarial Networks (GANs) have gained prominence for their ability to produce synthetic yet realistic images. Before the rise of GANs, there were other foundational neural network architectures for generative modeling. on Lines 6 and 7.

Computer Vision 105

Computer Vision Deep Learning Neural Network Auto-complete 105

Viso.ai

DECEMBER 22, 2023

The Segment Anything Model (SAM), a recent innovation by Meta’s FAIR (Fundamental AI Research) lab, represents a pivotal shift in computer vision. SAM performs segmentation, a computer vision task , to meticulously dissect visual data into meaningful segments, enabling precise analysis and innovations across industries.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Auto-classification 52

Pickl AI

JANUARY 23, 2023

but performs very well with neural networks. Keras supports a high-level neural network API written in Python. Provides modularity as a series of completely configurable, independent modules that can be combined with the fewest restrictions possible. This framework can perform classification, regression, etc.,

Machine Learning 52

Machine Learning ML Neural Network Auto-classification 52

The MLOps Blog

JANUARY 23, 2025

A typical multimodal LLM has three primary modules: The input module comprises specialized neural networks for each specific data type that output intermediate embeddings. Multimodal datasets may reduce ethical issues as they are more diverse and contextually complete and may improve model fairness. How do multimodal LLMs work?

Large Language Models 59

Large Language Models Auto-classification LLM Robotics 59

AWS Machine Learning Blog

APRIL 17, 2023

SageMaker LMI containers includes model download optimization by using the s5cmd library to speed up the model download time and container startup times, and eventually speed up auto scaling on SageMaker. A complete example that illustrates the no-code option can be found in the following notebook.

Prompt Engineering 103

Prompt Engineering Prompt Engineer Deep Learning Machine Learning 103

Viso.ai

MAY 29, 2024

Some original Tesla features are embedded into the robot, such as a self-running computer, autopilot cameras, a set of AI tools, neural network planning , auto-labeling for objects, etc. The data from multiple sensors are combined and processed to create a complete understanding of the environment.

Robotics 52

Robotics Auto-complete Automation Neural Network 52

The MLOps Blog

JUNE 27, 2023

Can you see the complete model lineage with data/models/experiments used downstream? Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on. MLOps workflows for computer vision and ML teams Use-case-centric annotations.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

MARCH 6, 2023

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. Regarding the scope of this post, note the following: We don’t cover neural network scientific design and associated optimizations.

Large Language Models 105

Large Language Models LLM Machine Learning ML 105

Dlabs.ai

JANUARY 16, 2023

Typical Neural Network architectures take relatively small images (for example, EfficientNetB0 224x224 pixels) as input. Since StainNet produces coloring consistent across multiple tiles of the same image, we could apply the pre-trained StainNet Neural Network on batches of random tiles. A CSV file guides execution.

Neural Network 52

Neural Network ML Auto-classification Convolutional Neural Networks 52

Mlearning.ai

MAY 23, 2023

Once the exploratory steps are completed, the cleansed data is subjected to various algorithms like predictive analysis, regression, text mining, recognition patterns, etc depending on the requirements. It is the discounting of those subjects that did not complete the trial. Explain Neural Network Fundamentals.

Data Science 52

Data Science Neural Network Deep Learning Machine Learning 52

The MLOps Blog

JANUARY 19, 2024

Unlike the earlier recurrent neural networks (RNN) that sequentially process inputs, transformers process entire sequences in parallel. But nowadays, it is used for various tasks, ranging from language modeling to computer vision and generative AI. These models are usually based on an architecture called transformers.

LLM 52

LLM Auto-complete Large Language Models Natural Language Processing 52

Explosion

NOVEMBER 21, 2019

s2v_most_similar(3) # [(('machine learning', 'NOUN'), 0.8986967), # (('computer vision', 'NOUN'), 0.8636297), # (('deep learning', 'NOUN'), 0.8573361)] Evaluating the vectors Word vectors are often evaluated with a mix of small quantitative test sets , and informal qualitative review. from sense2vec import Sense2Vec s2v = Sense2Vec().from_disk("/path/to/s2v_reddit_2015_md")

NLP 52

NLP Convolutional Neural Networks Neural Network Natural Language Processing 52

Jay Alammar

DECEMBER 16, 2020

This article focuses on auto-regressive models, but these methods are applicable to other architectures and tasks as well. In generating the second token to complete the date, the name still is the most important with 60% importance, followed by the first portion of the date -- a model output, but an input to the second time step.

Explainability 52

Explainability Auto-classification Auto-complete Neural Network 52

AWS Machine Learning Blog

JANUARY 31, 2024

In this post, we present an approach to develop a deep learning-based computer vision model to detect and highlight forged images in mortgage underwriting. We provide guidance on building, training, and deploying deep learning networks on Amazon SageMaker. Specifically, the JPEG algorithm operates on an 8×8 pixel grid.

Computer Vision 119

Computer Vision Auto-complete Deep Learning Auto-classification 119

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 104

BERT NLP Computer Vision Neural Network 104

Flipboard

APRIL 13, 2023

Prime Air (our drones) and the computer vision technology in Amazon Go (our physical retail experience that lets consumers select items off a shelf and leave the store without having to formally check out) use deep learning. To give a sense for the change in scale, the largest pre-trained model in 2019 was 330M parameters.

Generative AI 182

Generative AI ML AI AI 182

AWS Machine Learning Blog

JANUARY 9, 2025

Using deep neural networks (DNNs), Deep Instinct analyzes threats with unmatched accuracy, adapting to identify new and unknown risks that traditional methods might miss. This process is like assembling a jigsaw puzzle to form a complete picture of the malwares capabilities and intentions, with pieces constantly changing shape.

Deep Learning 115

Deep Learning Neural Network Explainability AI 115