IBM Journey to AI blog

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other?

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Towards AI

MARCH 7, 2025

Limitations of ANNs: Move to Convolutional Neural Networks This member-only story is on us. The journey from traditional neural networks to convolutional architectures wasnt just a technical evolution it was a fundamental reimagining of how machines should perceive visual information.

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Analytics Vidhya

FEBRUARY 16, 2022

Introduction Computer vision is a field of A.I. that deals with deriving meaningful information from images. Since 2012 after convolutional neural networks(CNN) were introduced, we moved away from handcrafted features to an end-to-end approach using deep neural networks.

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Marktechpost

MARCH 23, 2024

Deep features are pivotal in computer vision studies, unlocking image semantics and empowering researchers to tackle various tasks, even in scenarios with minimal data. With their transformative potential, deep features continue to push the boundaries of what’s possible in computer vision.

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Towards AI

NOVEMBER 10, 2024

Photo by Jaredd Craig on Unsplash In this article, we will review the paper titled “Computation-Efficient Knowledge Distillation via Uncertainty-Aware Mixup” [1], which aims to reduce the computational cost associated with distilling the knowledge of computer vision models. Katharopoulos et al. [4]

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Marktechpost

JANUARY 23, 2024

There are two major challenges in visual representation learning: the computational inefficiency of Vision Transformers (ViTs) and the limited capacity of Convolutional Neural Networks (CNNs) to capture global contextual information. A team of researchers at UCAS, in collaboration with Huawei Inc.

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BAIR

MARCH 25, 2025

We designed the AVs with deployment in mind, ensuring that they can operate using only basic sensor information about themselves and the vehicle in front. Modular control framework: One key challenge during the test was not having access to the leading vehicle information sensors.

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Unite.AI

OCTOBER 17, 2024

Some of the earliest and most extensive work has occurred in the use of deep learning and computer vision models. During training, each row of data as it passes through the network–called a neural network–modifies the equations at each layer of the network so that the predicted output matches the actual output.

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AI Weekly

OCTOBER 26, 2023

cryptopolitan.com Applied use cases Alluxio rolls out new filesystem built for deep learning Alluxio Enterprise AI is aimed at data-intensive deep learning applications such as generative AI, computer vision, natural language processing, large language models and high-performance data analytics. voxeurop.eu

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Marktechpost

MAY 6, 2024

Convolutional Neural Networks (CNNs) have become the benchmark for computer vision tasks. Capsule Networks (CapsNets), first introduced by Hinton et al. Capsule Networks (CapsNets), first introduced by Hinton et al. They hold significant potential for revolutionizing the field of computer vision.

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Unite.AI

DECEMBER 26, 2024

These algorithms are called Convolutional Neural Networks (CNN), and they contain a database of the gyroscopic movements associated with a variety of daily living activities. Telehealth data is further informed by wearable devices integrated with AI, which enhance monitoring by continuously gathering and analyzing health data.

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Unite.AI

MARCH 2, 2025

From early neural networks to todays advanced architectures like GPT-4 , LLaMA , and other Large Language Models (LLMs) , AI is transforming our interaction with technology. Instead of embedding all learned information within fixed-weight parameters, SMLs introduce an external memory system, retrieving information only when needed.

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Marktechpost

JANUARY 24, 2024

This shift is driven by neural networks that learn through self-supervision, bolstered by specialized hardware. However, the dawn of deep learning brought about a paradigm shift in data representation, introducing complex neural networks that generate more sophisticated data representations known as embeddings.

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PyImageSearch

DECEMBER 9, 2024

Citation Information 3D Gaussian Splatting vs NeRF: The End Game of 3D Reconstruction? And in the 2nd blog of this series , you were introduced to NeRFs, which is 3D Reconstruction via Neural Networks, projecting points in the 3D space. Join me in computer vision mastery. Essentially, it is to go backward.

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Unite.AI

JANUARY 22, 2025

These functions are anchored by a comprehensive user management system that controls access to sensitive information and maintains secure connections between patient records and user profiles. The system's intelligence stems from its neural network-based Concept Processor, which observes and learns from every interaction.

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AI Weekly

JULY 13, 2023

Sponsor AI Investing is here with Pluto Make informed investment decisions like never before with Pluto, the pioneer in AI investing. However, sharing biomedical data can put sensitive personal information at risk. Powered by pluto.fi theage.com.au Try Pluto for free today] pluto.fi Try Pluto for free today] pluto.fi

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Marktechpost

MAY 5, 2024

Graph Neural Network (GNN)–based motion planning has emerged as a promising approach in robotic systems for its efficiency in pathfinding and navigation tasks. This approach leverages GNNs to learn the underlying graph structure of an environment, enabling it to make quick and informed decisions about which paths to take.

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Viso.ai

DECEMBER 1, 2024

This article covers an extensive list of novel, valuable computer vision applications across all industries. Find the best computer vision projects, computer vision ideas, and high-value use cases in the market right now. provides Viso Suite , the world’s only end-to-end Computer Vision Platform.

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Marktechpost

JULY 17, 2023

Computer vision enables computers and systems to extract useful information from digital photos, videos, and other visual inputs and to conduct actions or offer recommendations in response to that information. Human vision has an advantage over computer vision because it has been around longer.

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Marktechpost

JUNE 30, 2024

Deep learning models like Convolutional Neural Networks (CNNs) and Vision Transformers achieved great success in many visual tasks, such as image classification, object detection, and semantic segmentation. The other two parts are Common Corruptions and Adversarial Attacks.

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Marktechpost

JANUARY 3, 2024

In the swiftly evolving domain of computer vision, the breakthrough in transforming a single image into a 3D object structure is a beacon of innovation. The task is inherently complex due to the need for more information about unseen aspects of the object. Check out the Paper.

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Marktechpost

FEBRUARY 12, 2024

Deep convolutional neural networks (DCNNs) have been a game-changer for several computer vision tasks. Network depth and convolution are the two primary components of a DCNN that determine its expressive power. These include object identification, object recognition, image segmentation, and edge detection.

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Unite.AI

MAY 29, 2024

This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction. This method involves hand-keying information directly into the target system. It is often easier to adopt due to its lower initial costs.

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Viso.ai

DECEMBER 20, 2023

The computer vision annotation tool CVAT provides a powerful solution for image annotation in computer vision. Computational vision is the research field that uses machines to collect and analyze images and videos to extract information from processed visual data. Get a demo or the whitepaper.

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Marktechpost

JULY 23, 2023

Artificial intelligence is making noteworthy strides in the field of computer vision. One key area of development is deep learning, where neural networks are trained on huge datasets of images to recognize and classify objects, scenes, and events. Check out the GitHub and Project.

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Unite.AI

APRIL 6, 2025

Using neural network-based entity recognition, it accurately maps spoken requests to menu items, even when customers use ambiguous phrasing or slang. There is even the potential for computer vision AI to help manage drive-thru traffic by tracking cars in real-time, reducing wait times, and keeping things running smoothly.

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Marktechpost

MAY 13, 2024

Vision Transformers (ViT) and Convolutional Neural Networks (CNN) have emerged as key players in image processing in the competitive landscape of machine learning technologies. Convolutional Neural Networks (CNNs) CNNs have been the cornerstone of image-processing tasks for years.

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Towards AI

NOVEMBER 2, 2024

Indeed, after obtaining a neural network that accurately predicts all the test data, it remains useless unless it’s made accessible to the world. With Detectron2, you can easily build and fine-tune neural networks to accurately detect and segment objects in images and videos. If you don’t have a model yet, don’t worry!

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Heartbeat

MARCH 14, 2023

The goal of computer vision research is to teach computers to recognize objects and scenes in their surroundings. In this article, I would like to take a look at the current challenges in the field of robotics and discuss the relevance and applications of computer vision in this area.

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PyImageSearch

JANUARY 13, 2025

To learn how to master YOLO11 and harness its capabilities for various computer vision tasks , just keep reading. With improvements in its design and training techniques, YOLO11 can handle a variety of computer vision tasks, making it a flexible and powerful tool for developers and researchers alike.

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Marktechpost

JANUARY 13, 2024

There has been a dramatic increase in the complexity of the computer vision model landscape. Many models are now at your fingertips, from the first ConvNets to the latest Vision Transformers. Our work comprehensively compares common vision models on "non-standard" metrics. (1/n)

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NVIDIA

MARCH 3, 2025

Combining data from satellites, ground-based cameras, aerial observations and local weather information, OroraTech detects threats to natural habitats and alerts users in real time. The companys technologies monitor more than 30 million hectares of land that directly impact wildlife in Africa and Australia.

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Viso.ai

DECEMBER 20, 2023

In the field of computer vision, supervised learning and unsupervised learning are two of the most important concepts. In this guide, we will explore the differences and when to use supervised or unsupervised learning for computer vision tasks. We will also discuss which approach is best for specific applications.

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Unite.AI

NOVEMBER 6, 2023

Neural Network: Moving from Machine Learning to Deep Learning & Beyond Neural network (NN) models are far more complicated than traditional Machine Learning models. Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning.

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Mlearning.ai

JANUARY 27, 2023

Save this blog for comprehensive resources for computer vision Source: appen Working in computer vision and deep learning is fantastic because, after every few months, someone comes up with something crazy that completely changes your perspective on what is feasible. Also, they will show you how huge this domain is.

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Viso.ai

DECEMBER 18, 2023

As an Edge AI implementation, TensorFlow Lite greatly reduces the barriers to introducing large-scale computer vision with on-device machine learning, making it possible to run machine learning everywhere. About us: At viso.ai, we power the most comprehensive computer vision platform Viso Suite. What is TensorFlow?

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NYU Center for Data Science

DECEMBER 3, 2024

The research revealed that regardless of whether a neural network is trained to recognize images from popular computer vision datasets like ImageNet or CIFAR, it develops similar internal patterns for processing visual information. Particularly in being extremely good at exploratory data analysis.”

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