Viso.ai

MAY 7, 2024

The turning point came in 2012 with the introduction of AlexNet by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. Here is a comparison with other models released previously: GoogLeNet Performance – source Comparison with Other Architectures AlexNet (2012): Top-5 Error Rate of 15.3%.

Explainability 52

Explainability Neural Network Convolutional Neural Networks Deep Learning 52

Pickl AI

APRIL 28, 2023

With more than 650% growth since 2012, Data Science has emerged as one of the most sought-after technologies. With the new developments in this domain, Data Science presents a picture of futuristic technology. At the same time, it has also emerged as one of the highest-paying job profiles.

Data Scientist 52

Data Scientist Data Science Big Data Data Analysis 52

IBM Journey to AI blog

JULY 6, 2023

These new reporting standards represent an evolution from the voluntary guidelines first issued in 2009 by India’s Ministry of Corporate Affairs, which were further refined in the Business Responsibility Report (BRR) of 2012.

ESG 136

ESG Explainability Automation 136

The MLOps Blog

JANUARY 11, 2023

I break it down here and explain how things are plumbed, how it operates, and how an MLOps engineer can lead the development and deployment of a new process using and within it. Architectural design The environment architecture is shown in figure 1 as a whole with all its resources.

Machine Learning 52

Machine Learning Automation ML Python 52

Heartbeat

OCTOBER 9, 2023

Barceló and Maurizio Forte edited "Virtual Reality in Archaeology" (2012). Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments. Brutto, M. L., & Meli, P.

Computer Vision 52

Computer Vision Algorithm Deep Learning Machine Learning 52

Heartbeat

DECEMBER 14, 2023

Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments. Hinton is viewed as a leading figure in the deep learning community. Hinton is viewed as a leading figure in the deep learning community.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning Computer Scientist 52

Heartbeat

AUGUST 21, 2023

AlexNet is a more profound and complex CNN architecture developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton in 2012. Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Natural Language Processing Computer Vision 52

AWS Machine Learning Blog

APRIL 22, 2024

In this post, we introduce a solution to integrate HyperPod clusters with AWS Managed Microsoft AD, and explain how to achieve a seamless multi-user login environment with a centrally maintained directory. With the directory service, you can centrally maintain users and groups, and their permissions.

Software Engineer 86

Software Engineer Software Development Machine Learning ML 86

Shreyansh Singh

MAY 16, 2021

XLNet integrates the novelties from Transformer-XL like recurrence mechanism and relative encoding scheme (explained later as well). Furthermore, Giga5, ClueWeb 2012-B and CommonCrawl datasets were also used. XLNet does not rely on data corruption as in BERT and hence does not suffer from the pretrain-finetune discrepancy.

BERT 52

BERT Auto-classification NLP Explainability 52

Heartbeat

DECEMBER 8, 2023

Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments. Hinton is viewed as a leading figure in the deep learning community. We pay our contributors, and we don’t sell ads.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning LLM 52

TheSequence

NOVEMBER 22, 2023

One is a more formal view of explainability. In 2013, my team started exploring building neural network accelerators on FPGAs, and in 2015 we ramped that effort to take it to production as Project Brainwave , which shipped into production at large scale in 2017, accelerating neural network inference for Bing and Office.

Neural Network 40

Neural Network Generative AI AI AI 40

Explosion

SEPTEMBER 3, 2015

This post explains the problem, why it’s so damaging, and why I wrote spaCy to do things differently. Another researcher’s offer from 2012 to implement this type of model also went unanswered. Natural Language Processing moves fast, so maintaining a good library means constantly throwing things away. The story in nltk.tag is similar.

Natural Language Processing 40

Natural Language Processing NLP Python Deep Learning 40

AWS Machine Learning Blog

AUGUST 4, 2023

On the IAM console, navigate to the SageMaker domain execution role. Choose Add permissions and select Create an inline policy. To delete the resources (API Gateway and SageMaker endpoint) created by CodePipeline, navigate to the AWS CloudFormation console and delete the stack that was created.

ML 78

ML Categorization AI AI 78

Mlearning.ai

MARCH 9, 2023

2012; Otsu, 1979; Long et al., Methodology In this study, we used the publicly available PASCAL VOC 2012 dataset (Everingham et al., The MBD model was trained on the training set of the PASCAL VOC 2012 dataset, and the resulting model was used to segment the selected images from the validation set. References: Arbeláez, P.,

Computer Vision 52

Computer Vision Deep Learning Machine Learning Algorithm 52

IBM Journey to AI blog

MARCH 29, 2024

GraphQL GraphQL is a query language and API runtime that Facebook developed internally in 2012 before it became open source in 2015. However, key differences exist between them that explain not only the proliferation of GraphQL but also why RESTful systems have such staying power.

Software Development 200

Software Development Explainability Automation AI 200

Heartbeat

JULY 27, 2023

jpg': {'class': 132, 'label': 'Hyundai Tucson SUV 2012'}, '00235.jpg': jpg': {'class': 7, 'label': 'Acura ZDX Hatchback 2012'}, '00237.jpg': jpg': {'class': 65, 'label': 'Chevrolet Avalanche Crew Cab 2012'}, '00238.jpg':

Deep Learning 52

Deep Learning Metadata Python Explainability 52

AI Impacts

APRIL 7, 2023

Most of the systems we care about are considerably messier than the simple examples we use to explain chaos. Links to the other pages, blog posts, and report that constitute this investigation can be found below. Nate Silver. The Signal and the Noise. I will not go into it here.

Explainability 130

Explainability AI AI Algorithm 130

Flipboard

JULY 31, 2023

Pascal VOC 2012 Pascal VOC 2012 is a large-scale dataset of images used for object detection and image classification. The latest version, Pascal VOC 2012, contains 11,500 images divided into 20 object classes. Its meticulous curation and user-friendly design make it a robust tool for researchers and developers alike.

Machine Learning 104

Machine Learning Computer Vision Deep Learning Convolutional Neural Networks 104

Viso.ai

JANUARY 2, 2024

In 2012, the AlexNet architecture, designed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton , marked a breakthrough in the ImageNet challenge by significantly reducing error rates. Making CNN models more interpretable and explainable. Addressing biases to ensure fairness in model training.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Data Scarcity 52

AI Impacts

APRIL 11, 2023

But this does not explain the lack of research, and one of the reasons given for opposition to experiments is that it has not been shown to be safe. Social preview image: German anti-nuclear power protesters in 2012. But the reason we lack evidence on safety is because research has been opposed, even at small scales.

AI 130

AI AI Explainability OpenAI 130

Marktechpost

JULY 17, 2023

Using causal graphs, LIME, Shapley, and the decision tree surrogate approach, the organization also provides various features to make it easier to develop explainability into predictive analytics models. When necessary, the platform also enables numerous governance and explainability elements.

Machine Learning 87

Machine Learning Data Mining Data Scientist Artificial Intelligence 87

Flipboard

MARCH 7, 2023

The following sections explain each of four environment customization approaches in detail, provide hands-on examples, and recommend use cases for each option. Prerequisites To get started with the examples and try the customization approaches on your own, you need an active SageMaker domain and at least one user profile in the domain.

Python 93

Python Automation Data Scientist ML 93

deepsense.ai

MAY 7, 2023

We also explained the building blocks of Stable Diffusion and highlighted why its release last year was such a groundbreaking achievement. Source: [ 2 ] In the previous post, we explained the importance of Stable Diffusion [ 3 ]. In the previous one, we established a strong theoretical background for the rest of the series.

Neural Network 52

Neural Network Convolutional Neural Networks Explainability Deep Learning 52

AWS Machine Learning Blog

DECEMBER 14, 2023

The flexible and extensible interface of SageMaker Studio allows you to effortlessly configure and arrange ML workflows, and you can use the AI-powered inline coding companion to quickly author, debug, explain, and test code.

Generative AI 98

Generative AI AI Tools ML AI 98

Applied Data Science

MARCH 11, 2022

Back in 2012 things were quite different. This explain this statement at the NeurIPS 2017 Test-of-Time Award: It seems easier to train a bi-directional LSTM with attention than to compute the PCA of a large matrix. — Rahimi Each agent’s reward is related to the variance explained by its own eigenvector. This cat does not exist.

Neural Network 52

Neural Network AI AI Data Science 52

DataRobot Blog

APRIL 4, 2022

Originating in game theory, there is a reason why Shapley Value earned Lloyd Shapley a Nobel Prize in 2012. And in case one wishes for more control over the relationship, it is possible to impose monotonic constraints on media features (positive monotonicity) or price features (elasticity – negative monotonicity).

Machine Learning 52

Machine Learning Automation ML Algorithm 52

AWS Machine Learning Blog

JUNE 19, 2023

As explained in the solution overview, we listen to the AddMemberToGroup event. In this solution, we create a rule-based trigger: EventBridge listens to events and matches against the provided pattern and triggers a Lambda function if the pattern match is successful.

IDP 70

IDP ML Automation Machine Learning 70

AWS Machine Learning Blog

MAY 17, 2023

In this post, aimed primarily at those who are already using Snowflake, we explain how you can import both training and validation data for a facies classification task from Snowflake into Amazon SageMaker Canvas and subsequently train the model using a 3+ category prediction model.

Data Scientist 75

Data Scientist ML Machine Learning Data Science 75

Explosion

DECEMBER 28, 2019

Adriane is a computational linguist who has been engaged in research since 2005, completing her PhD in 2012. In this episode he explained how to transition a rule-based prototype towards an NER model to achieve faster results and a baseline for machine learning experiments. ? There’s also a bit of spaCy history in there as well.

NLP 52

NLP BERT Machine Learning Python 52

O'Reilly Media

JULY 25, 2023

To provide some coherence to the music, I decided to use Taylor Swift songs since her discography covers the time span of most papers that I typically read: Her main albums were released in 2006, 2008, 2010, 2012, 2014, 2017, 2019, 2020, and 2022. This choice also inspired me to call my project Swift Papers.

ChatGPT 98

ChatGPT UX Design AI Tools Metadata 98

Explosion

FEBRUARY 18, 2015

So I wrote two blog posts, explaining how to write a part-of-speech tagger and parser. Hardware : Intel i7-3770 (2012) Efficiency is a major concern for NLP applications. Nothing past the tokenizer is suitable for production use. I report mean times per document, in milliseconds.

NLP 52

NLP Natural Language Processing Software Engineer Explainability 52

Explosion

DECEMBER 17, 2013

But the parsing algorithm I’ll be explaining deals with projective trees. The performance of our parser is made possible by an advance by Goldberg and Nivre (2012), who showed that we’d been doing this wrong for years. You can’t have a pair of dependencies that goes a1 b1 a2 b2, or b1 a1 b2 a2. Goldberg, Yoav; Nivre, Joakim.

Python 45

Python Algorithm NLP Computational Linguistics 45

Mlearning.ai

FEBRUARY 10, 2023

Back in 2016 I was trying to explain to software engineers how to think about machine learning models from a software design perspective; I told them that they should think of a database. Both serve as a means of storing representations of historical data, which can later be queried.

Neural Network 52

Neural Network Big Data ChatGPT Software Engineer 52

Flipboard

AUGUST 1, 2023

In Tutt’s vision, a similar agency could “require pre-market approval before algorithms can be deployed” in certain applications; “for example, a self-driving car algorithm could be required to replicate the safety-per-mile of a typical vehicle driven in 2012.”

Explainability 119

Explainability Artificial Intelligence Artificial Intelligence AI 119

AI Impacts

MAY 13, 2024

The partisanship of environmentalism cannot be explained entirely by the processes that made other issues partisan. public opinion on environmentalism comes from the General Social Survey, which has been administered to thousands of Americans for most years between 1974 and 2012. ” Reprinted from Guber (2013). Pew Research.

Explainability 147

Explainability AI AI 147

Unite.AI

MAY 27, 2023

The power of AI-driven data analytics became evident in the 2012 and 2016 US election campaigns. In 2012, Obama's team used AI and big data to innovate voter targeting. For users, chatbots can help them explain the political ideologies of candidates and hold thoughtful policy conversations to expand and depolarize their views.

Generative AI 217

Generative AI Chatbots AI AI 217