NVIDIA

DECEMBER 4, 2023

Three technical reasons, and many stories, explain why that’s so. A 2020 study assessing AI technology for the U.S. An AI model, also called a neural network, is essentially a mathematical lasagna, made from layer upon layer of linear algebra equations. GPU systems scale up to supercomputing heights.

Neural Network 138

Neural Network AI AI LLM 138

Unite.AI

NOVEMBER 8, 2023

Recurrent Neural Networks (RNNs) became the cornerstone for these applications due to their ability to handle sequential data by maintaining a form of memory. Functionality : Each encoder layer has self-attention mechanisms and feed-forward neural networks. However, RNNs were not without limitations.

BERT 298

BERT NLP Neural Network Natural Language Processing 298

Unite.AI

OCTOBER 10, 2024

A new research paper from Canada has proposed a framework that deliberately introduces JPEG compression into the training scheme of a neural network, and manages to obtain better results – and better resistance to adversarial attacks. In contrast, JPEG-DL (right) succeeds in distinguishing and delineating the subject of the photo.

Neural Network 162

Neural Network Convolutional Neural Networks Deep Learning Explainability 162

ODSC - Open Data Science

APRIL 3, 2023

Are you curious about explainability methods like saliency maps but feel lost about where to begin? Moreover, combining expert agents is an immensely easier task to learn by neural networks than end-to-end QA. Examples are the ACL fellow award 2020 and the first Hessian LOEWE Distinguished Chair award (2,5 mil.

Neural Network 117

Neural Network Natural Language Processing NLP Computational Linguistics 117

Jay Alammar

DECEMBER 16, 2020

input saliency is a method that explains individual predictions. This is a method of attribution explaining the relationship between a model's output and inputs -- helping us detect errors and biases, and better understand the behavior of the system. Interfaces for Explaining Transformer Language Models [Blog post].

Explainability 52

Explainability Auto-classification Auto-complete Neural Network 52

Bugra Akyildiz

NOVEMBER 17, 2024

They have shown impressive performance in various computer vision tasks, often outperforming traditional convolutional neural networks (CNNs). 2020) EBM : Explainable Boosting Machine (Nori, et al. 2020) OpenCoder is an open and reproducible code LLM family which includes 1.5B 2019; Lou, et al. 2019; Lou, et al.

Convolutional Neural Networks 52

Convolutional Neural Networks Metadata Computer Vision Python 52

Heartbeat

AUGUST 1, 2023

I stumbled upon a simple question, what was there before neural nets could efficiently be run on the average computer? Surely research in this field yielded results that could be used without requiring neural networks. Features extracted from them could be fed into neural networks for enhanced performance.

Computer Vision 52

Computer Vision Deep Learning Neural Network Algorithm 52

deepsense.ai

APRIL 16, 2023

Pipeline Parallelism Since deep neural networks typically have multiple layers stacked on top of each other, the naive approach to model parallelism involves dividing a large model into smaller parts, with a few consecutive layers grouped together and assigned to a separate device, with the output of one stage serving as the input to the next stage.

Large Language Models 52

Large Language Models Neural Network Deep Learning Explainability 52

ODSC - Open Data Science

OCTOBER 24, 2023

Generating synthetic data NoGAN is the first algorithm in a series of high-performance, fast synthesizers not based on neural networks such as GAN. Indeed, the whole technique epitomizes explainable AI. It is easy to fine-tune, allowing for auto-tuning.

Categorization 98

Categorization Data Science Neural Network Algorithm 98

NVIDIA

NOVEMBER 15, 2023

The Story of the Name Patrick Lewis, lead author of the 2020 paper that coined the term , apologized for the unflattering acronym that now describes a growing family of methods across hundreds of papers and dozens of commercial services he believes represent the future of generative AI. In other words, it fills a gap in how LLMs work.

Generative AI 136

Generative AI LLM Neural Network AI Modeling 136

Pickl AI

JULY 24, 2024

Techniques such as neural networks, particularly deep learning, have enabled significant breakthroughs in image and speech recognition, natural language processing, and autonomous systems. ” 1986: A resurgence in neural networks occurs with the introduction of the backpropagation algorithm, revitalising AI research.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Natural Language Processing Neural Network 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.

Generative AI 98

Generative AI Prompt Engineer Prompt Engineering Large Language Models 98

Sebastian Ruder

JANUARY 6, 2020

 State-of-the-art neural networks are getting larger and more expensive to train and to use for prediction. Investigating what makes winning tickets special should also help us gain a better understanding of the initialisation and learning dynamics of neural networks. 3) The Neural Tangent Kernel What happened?  

NLP 52

NLP ML Neural Network BERT 52

AI Impacts

JULY 20, 2023

We explain why model evaluation is critical for addressing extreme risks. Survey on intermediate goals in AI governance (Rethink Priorities, Räuker and Aird, March) It seems that a key bottleneck for the field of longtermism-aligned AI governance is limited strategic clarity (see Muehlhauser, 2020 , 2021 ).

AI Strategy 100

AI Strategy Neural Network AI AI 100

Viso.ai

OCTOBER 7, 2024

We will explain its key principles, its applications in computer vision, benefits, and challenges, as well as best practices. Computer vision (CV) models rely on deep learning architectures consisting of artificial neural networks or convolutional neural networks (CNNs). This streamlines training and saves time.

Computer Vision 52

Computer Vision Machine Learning Deep Learning Convolutional Neural Networks 52

AI Impacts

FEBRUARY 1, 2023

But this model, on its own, is inadequate for AI, for reasons I will explain in the next section. 5 I will not explain this problem in detail, but I will list some aspects of it here, along with real-world examples, and you can read more about it elsewhere. Norton & Company; 2020. link] Published December 15, 2020.

AI 130

AI AI OpenAI Large Language Models 130

Mlearning.ai

AUGUST 11, 2023

Motivation and the proposal They observe unbelievably low GPU utilization on Alibaba servers and explain the reasons for that observation, which motivates them to tackle it. E.g., graph sampling in graph neural networks, feature extraction in advertisement, data augmentation in vision.

Deep Learning 52

Deep Learning Neural Network Explainability AI 52

ML @ CMU

JUNE 30, 2023

An end-to-end learning approach predicts actions directly from raw observations with a deep neural network consisting of visual encoders for image frames followed by a recurrent layer for memory [ Ramrakhya 2022 ].

Robotics 79

Robotics Neural Network Explainability Machine Learning 79

Heartbeat

FEBRUARY 27, 2023

DRL agents may now be trained on high-dimensional observations like images and videos thanks to the usage of deep neural networks as function approximators. The most recent developments in GANs demonstrate how these networks have the ability to transform numerous industries and applications. Generative adversarial networks.

Machine Learning 52

Machine Learning Deep Learning Robotics Neural Network 52

Heartbeat

JUNE 6, 2023

Photo by GuerrillaBuzz on Unsplash Graph Convolutional Networks (GCNs) are a type of neural network that operates on graphs, which are mathematical structures consisting of nodes and edges. GCNs have been successfully applied to many domains, including computer vision and social network analysis. Richong, Z.,

NLP 59

NLP Convolutional Neural Networks Neural Network Natural Language Processing 59

Heartbeat

MAY 22, 2023

2020 ) can be integrated to add greater weight to the core features. Schematic diagram of the overall framework of Emotion Recognition System [ Source ] The models that are used for AI emotion recognition can be based on linear models like Support Vector Machines (SVMs) or non-linear models like Convolutional Neural Networks (CNNs).

Computer Vision 52

Computer Vision Convolutional Neural Networks Emotion AI Deep Learning 52

O'Reilly Media

NOVEMBER 19, 2024

at Facebook—both from 2020. One more embellishment is to use a graph neural network (GNN) trained on the documents. Imagine going in front of a judge to seek a warrant and explaining, “Your honor, a LLM collected the evidence, plus or minus a few hallucinations.” Split each document into chunks.

LLM 63

LLM Hybrid AI NLP Large Language Models 63

AWS Machine Learning Blog

FEBRUARY 23, 2023

Over the last 10 years, a number of players have developed autonomous vehicle (AV) systems using deep neural networks (DNNs). DNN training methods and design AV systems are built with deep neural networks. This post explains the functions based on a modular pipeline approach.

Automation 110

Automation Neural Network Machine Learning Data Scientist 110

Viso.ai

JUNE 4, 2024

Many of these methods utilize deep learning and Convolutional Neural Networks (CNNs) to create point cloud processing. 2020) proposed a generalization of discrete CNNs. This approach is straightforward and allows the use of variable point cloud sizes for designing neural networks similar to 2D CNNs. Boulch et al.

Computer Vision 52

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 52

AWS Machine Learning Blog

FEBRUARY 10, 2023

We describe how we designed an accurate, explainable ML model to make coverage classification from player tracking data, followed by our quantitative evaluation and model explanation results. Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation.

Computer Vision 73

Computer Vision Machine Learning ML Convolutional Neural Networks 73

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. Since the public unveiling of ChatGPT, large language models (or LLMs) have had a cultural moment.

Large Language Models 52

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. Since the public unveiling of ChatGPT, large language models (or LLMs) have had a cultural moment.

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

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. This can make it challenging for businesses to explain or justify their decisions to customers or regulators. What are some examples of Foundation Models?

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

PyImageSearch

JANUARY 22, 2024

We estimate the parameters of the generative process ( Ho, Jain, and Abbeel, “Denoising Diffusion Probabilistic Modelsm” 2020 ). The underlying model, often a neural network, is trained to predict a way to slightly denoise the image in each step. After a certain number of steps, a sample is obtained, as shown in Figure 1.

Computer Vision 64

Computer Vision Deep Learning Neural Network Algorithm 64

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.

Conversational AI 52

Conversational AI ChatGPT Neural Network Chatbots 52

PyImageSearch

JANUARY 2, 2023

Figure 1: Global Funding in Health Tech Companies (source: Mrazek and O’Neill, 2020 ). Deep neural networks and support vector machines are being explored in developing pre-diabetic screening tools. If healthcare professionals cannot explain how AI systems make diagnoses, this can weaken patients’ trust in the system.

Computer Vision 52

Computer Vision Deep Learning Neural Network Machine Learning 52

Topbots

MAY 17, 2023

Five years later, in 2020, the research team from UC Berkeley introduced a seminal research paper with a few groundbreaking changes that led to a huge jump in the quality of the generated images. That’s the basic idea used by the latest diffusion models, like DALL-E 2 or Stale Diffusion. Where can you get implementation code?

Neural Network 89

Neural Network Computer Vision OpenAI Machine Learning 89

Applied Data Science

MARCH 11, 2022

In contrast to classification, a supervised learning paradigm, generation is most often done in an unsupervised manner: for example an autoencoder , in the form of a neural network, can capture the statistical properties of a dataset. Notice the plural: GANs are not one but two neural networks that are playing a game.

Neural Network 52

Neural Network Data Science AI AI 52

Dlabs.ai

AUGUST 10, 2020

Despite 80% of surveyed businesses wanting to use chatbots in 2020 , how many do you think will implement them well? The solution is based on a Transformer-type neural network, used in the BERT model as well, that has recently triumphed in the field of machine learning and natural language understanding.

Natural Language Processing 52

Natural Language Processing Chatbots Neural Network Machine Learning 52

Mlearning.ai

MARCH 9, 2023

2020) presented an adversarial attack method specifically designed for image segmentation models, and evaluated its effectiveness on several state-of-the-art segmentation models. Explaining and harnessing adversarial examples. Fully convolutional networks for semantic segmentation. Neural Networks, 64, 59–63.

Computer Vision 52

Computer Vision Deep Learning Machine Learning Algorithm 52

PyImageSearch

JANUARY 30, 2023

Figure 6: Changing demand for core work-related skills from 2015 to 2020 (source: IFC ). All you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms. In Deep Learning, we need to train Neural Networks. Or has to involve complex mathematics and equations?

Computer Vision 100

Computer Vision Deep Learning Neural Network Artificial Intelligence 100

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). Hu, Liu et al propose and explain one of the first frameworks for pre-training GNNs. At NeurIPS’19 we see applications of hyperbolic geometry to graphs! ?

Machine Learning 52

Machine Learning Neural Network Explainability Convolutional Neural Networks 52