AI Weekly

JANUARY 25, 2024

ndtv.com Top 10 AI Programming Languages You Need to Know in 2024 It excels in predictive models, neural networks, deep learning, image recognition, face detection, chatbots, document analysis, reinforcement, building machine learning algorithms, and algorithm research. decrypt.co decrypt.co

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Marktechpost

SEPTEMBER 24, 2024

CNN’s performance improved in the ILSVRC-2012 competition, achieving a top-5 error rate of 15.3%, compared to 26.2% The success of this model reflects a broader shift in computer vision towards machine learning approaches that leverage large datasets and computational power. by the next-best model. and 28.2%).

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Unite.AI

OCTOBER 7, 2023

First coined by Cisco in 2012, the Internet of Everything builds on IoT by extending connections beyond machine-to-machine communication. In this article, we’ll look at the concept of the Internet of Everything in detail and shed some light on the relationship between AI and 6G technologies to enable global connectivity.

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NVIDIA

NOVEMBER 18, 2024

Milestones like Tokyo Tech’s Tsubame supercomputer in 2008, the Oak Ridge National Laboratory’s Titan supercomputer in 2012 and the AI-focused NVIDIA DGX-1 delivered to OpenAI in 2016 highlight NVIDIA’s transformative role in the field. Since CUDA’s inception, we’ve driven down the cost of computing by a millionfold,” Huang said.

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IBM Journey to AI blog

OCTOBER 12, 2023

However, AI capabilities have been evolving steadily since the breakthrough development of artificial neural networks in 2012, which allow machines to engage in reinforcement learning and simulate how the human brain processes information.

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Viso.ai

FEBRUARY 11, 2024

Image classification employs AI-based deep learning models to analyze images and perform object recognition, as well as a human operator. It is one of the largest resources available for training deep learning models in object recognition tasks. After fine-tuning on ImageNet-2012 it gave an error rate of 16.6%.

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Marktechpost

OCTOBER 16, 2024

Deep learning-based prediction is critical for optimizing output, anticipating weather fluctuations, and improving solar system efficiency, allowing for more intelligent energy network management. More sophisticated machine learning approaches, such as artificial neural networks (ANNs), may detect complex relationships in data.

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Viso.ai

MARCH 12, 2024

Today’s boom in computer vision (CV) started at the beginning of the 21 st century with the breakthrough of deep learning models and convolutional neural networks (CNN). We split them into two categories – classical CV approaches, and papers based on deep-learning. Find the SURF paper here. Find the ImageNet paper here.

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IBM Journey to AI blog

JULY 15, 2024

A brief history of scaling “Bigger is better” stems from the data scaling laws that entered the conversation with a 2012 paper by Prasanth Kolachina applying scaling laws to machine learning. displayed that deep learning scaling is predictable empirically too. In 2017, Hestness et al.

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Unite.AI

MAY 29, 2024

With nine times the speed of the Nvidia A100, these GPUs excel in handling deep learning workloads. Recent advancements in hardware such as Nvidia H100 GPU, have significantly enhanced computational capabilities. Subsequently, some RNNs were also trained using GPUs, though they did not yield good results.

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AWS Machine Learning Blog

OCTOBER 15, 2024

In addition to traditional custom-tailored deep learning models, SageMaker Ground Truth also supports generative AI use cases, enabling the generation of high-quality training data for artificial intelligence and machine learning (AI/ML) models.

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Viso.ai

MAY 7, 2024

GoogLeNet’s deep learning model was deeper than all the previous models released, with 22 layers in total. Increasing the depth of the Machine Learning model is intuitive, as deeper models tend to have more learning capacity and as a result, this increases the performance of a model.

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Viso.ai

MAY 31, 2024

Our software helps several leading organizations start with computer vision and implement deep learning models efficiently with minimal overhead for various downstream tasks. This challenge was conducted till 2012, each subsequent year. Pascal VOC Dataset Development The Pascal VOC dataset was developed from 2005 to 2012.

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Flipboard

JULY 31, 2023

Object detection works by using machine learning or deep learning models that learn from many examples of images with objects and their labels. In the early days of machine learning, this was often done manually, with researchers defining features (e.g., Object detection is useful for many applications (e.g.,

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AWS Machine Learning Blog

JUNE 15, 2023

Dive into Deep Learning ( D2L.ai ) is an open-source textbook that makes deep learning accessible to everyone. If you are interested in learning more about these benchmark analyses, refer to Auto Machine Translation and Synchronization for “Dive into Deep Learning”.

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Unite.AI

MAY 28, 2024

OpenAI researchers note that : since 2012, the computing power required to train advanced AI models has doubled every 3.4 For instance, training deep learning models requires significant computational power and high throughput to handle large datasets and execute complex calculations quickly.

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Viso.ai

DECEMBER 19, 2023

Image Classification Using Machine Learning CNN Image Classification (Deep Learning) Example applications of Image Classification Let’s dive deep into it! It uses AI-based deep learning models to analyze images with results that for specific tasks already surpass human-level accuracy (for example, in face recognition ).

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AWS Machine Learning Blog

APRIL 16, 2024

It employs advanced deep learning technologies to understand user input, enabling developers to create chatbots, virtual assistants, and other applications that can interact with users in natural language.

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NVIDIA

OCTOBER 23, 2024

Then, in 2012, Alex Krizhevsky, mentored by Ilya Sutskever and Geoffrey Hinton, won the ImageNet computer image recognition competition with AlexNet, a revolutionary deep learning model for image classification. The breakthrough of machine learning — neural networks running on GPUs — jump-started the era of Software 2.0.

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Towards AI

FEBRUARY 10, 2023

In 2018, OpenAI released an analysis showing that since 2012, the amount of computing used in the largest AI training runs has been increasing exponentially, with a doubling time of 3–4 months [8]. By comparison, Moore’s Law had a 2-year doubling period.

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Mlearning.ai

MARCH 9, 2023

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., 2012; Otsu, 1979; Long et al., 2019) proposed a novel adversarial training framework for improving the robustness of deep learning-based segmentation models.

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Mlearning.ai

APRIL 8, 2023

Deep Learning (Late 2000s — early 2010s) With the evolution of needing to solve more complex and non-linear tasks, The human understanding of how to model for machine learning evolved. Use Cases : Web Search, Information Retrieval, Text Mining Significant papers: “ Latent Dirichlet Allocation ” by Blei et al.

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Viso.ai

FEBRUARY 19, 2024

applied deep learning R-CNN for document classification and clustering. 2020) applied an image copy detection scheme based on the deep learning Inception CNN model. The image’s feature values were automatically extracted for learning and detecting unauthorized digital images. How is this done? Huber-Fliflet, et al.

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ODSC - Open Data Science

FEBRUARY 28, 2023

CARTO Since its founding in 2012, CARTO has helped hundreds of thousands of users utilize spatial analytics to improve key business functions such as delivery routes, product/store placements, behavioral marketing, and more.

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Unite.AI

MAY 10, 2024

Realizing that many of the tedious development processes in Mellanox could be automated by machine-learning algorithms, I changed my majors to optimization and machine learning and completed an MSc in the space. At Visualead, we’d been running algorithms on mobile devices since 2012, including models.

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AWS Machine Learning Blog

JUNE 6, 2023

This post further walks through a step-by-step implementation of fine-tuning a RoBERTa (Robustly Optimized BERT Pretraining Approach) model for sentiment analysis using AWS Deep Learning AMIs (AWS DLAMI) and AWS Deep Learning Containers (DLCs) on Amazon Elastic Compute Cloud (Amazon EC2 p4d.24xlarge)

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Mlearning.ai

JUNE 14, 2023

Learning LLMs (Foundational Models) Base Knowledge / Concepts: What is AI, ML and NLP Introduction to ML and AI — MFML Part 1 — YouTube What is NLP (Natural Language Processing)? — YouTube YouTube Introduction to Natural Language Processing (NLP) NLP 2012 Dan Jurafsky and Chris Manning (1.1)

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AWS Machine Learning Blog

OCTOBER 11, 2024

For example: Data such as images, text, and audio need to be represented in a structured and efficient manner Understanding the semantic similarity between data points is essential in generative AI tasks like natural language processing (NLP), image recognition, and recommendation systems As the volume of data continues to grow rapidly, scalability (..)

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AWS Machine Learning Blog

OCTOBER 24, 2024

Large language models (LLMs) are very large deep-learning models that are pre-trained on vast amounts of data. LLMs are incredibly flexible. One model can perform completely different tasks such as answering questions, summarizing documents, translating languages, and completing sentences.

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ODSC - Open Data Science

JUNE 8, 2023

Valohai Valohai enables ML Pioneers to continue to work at the cutting edge of technology with its MLOps which enables its clients to reduce the amount of time required to build, test, and deploy deep learning models by a factor of 10.

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Pickl AI

JULY 24, 2024

The advent of big data, coupled with advancements in Machine Learning and deep learning, has transformed the landscape of AI. 2010s : Rapid Advancements and Applications 2012: The ImageNet competition demonstrates the power of deep learning, with AlexNet winning and significantly improving image classification accuracy.

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Heartbeat

DECEMBER 8, 2023

He focused on generative AI trained on large language models, The strength of the deep learning era of artificial intelligence has lead to something of a renaissance in corporate R&D in information technology, according to Yann LeCun, chief AI. Hinton is viewed as a leading figure in the deep learning community.

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Heartbeat

OCTOBER 9, 2023

Artificial Intelligence (AI) Integration: AI techniques, including machine learning and deep learning, will be combined with computer vision to improve the protection and understanding of cultural assets. Barceló and Maurizio Forte edited "Virtual Reality in Archaeology" (2012). Brutto, M. L., & Meli, P.

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Marktechpost

JULY 17, 2023

What Relationship Exists Between Predictive Analytics, Deep Learning, and Artificial Intelligence? For machine learning to identify common patterns, large datasets must be processed. Deep learning is a branch of machine learning frequently used with text, audio, visual, or photographic data.

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NVIDIA

JUNE 5, 2023

That’s an order of magnitude more than it generated in 2012 when two of its experiments uncovered the Higgs boson, a subatomic particle that validated scientists’ understanding of the universe. A high-luminosity version of the giant accelerator ( HL-LHC ) will produce 10x more proton collisions, spawning exabytes of data a year.

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Heartbeat

AUGUST 21, 2023

AlexNet is a more profound and complex CNN architecture developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton in 2012. AlexNet significantly improved performance over previous approaches and helped popularize deep learning and CNNs. It has eight layers, five of which are convolutional and three fully linked.

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ODSC - Open Data Science

MAY 1, 2023

He shipped products across various domains: from 3D medical imaging, through global-scale web systems, and up to deep learning systems that power apps and services used by billions of people worldwide. In 2012, Daphne was recognized as one of TIME Magazine’s 100 most influential people.

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