Viso.ai

MAY 1, 2024

Deep learning and Convolutional Neural Networks (CNNs) have enabled speech understanding and computer vision on our phones, cars, and homes. Home Robots: over the next 15 years, mechanical and AI technologies will increase home robots’ reliable usage in a typical household. Brooks et al. Shafiullah et al.

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Robotics Convolutional Neural Networks Computer Vision Deep Learning 52

Flipboard

MAY 17, 2023

Best known as a robotics researcher, academic, and entrepreneur, Brooks is also an authority on AI: he directed the Computer Science and Artificial Intelligence Laboratory at MIT until 2007, and held faculty positions at Carnegie Mellon and Stanford before that. What are the most attractive opportunities now in warehouse robotics?

Large Language Models 182

Large Language Models Robotics Convolutional Neural Networks Auto-complete 182

Viso.ai

DECEMBER 3, 2023

For example, the detection of objects enables intelligent healthcare monitoring, autonomous driving, smart video surveillance, anomaly detection, robot vision, and much more. The automatic identification of objects, persons, and scenes can provide useful information to automate tasks (counting, inspection, verification, etc.)

Convolutional Neural Networks 98

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 98

Marktechpost

DECEMBER 24, 2023

These methods address the challenges of traditional approaches, offering more automated, accurate, and robust solutions for identifying and categorizing plant leaf diseases. Devices & Hardware: Advanced tools like robotic vehicles, IoT_FBFN frameworks, and handheld devices with embedded platforms enhance disease classification.

Machine Learning 99

Machine Learning Convolutional Neural Networks Neural Network Deep Learning 99

Marktechpost

OCTOBER 21, 2023

Several industries, including visual effects, gaming, image and video processing, computer-aided design, virtual and augmented reality, data visualization, robotics, autonomous vehicles, and remote sensing, among others, are built on this methodology, which includes rendering, simulation, geometry processing, and photogrammetry.

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Analytics Vidhya

MARCH 30, 2022

Introduction AI and machine vision, which were formerly considered futuristic technology, has now become mainstream, with a wide range of applications ranging from automated robot assembly to automatic vehicle guiding, analysis of remotely sensed images, and automated visual inspection.

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Heartbeat

NOVEMBER 21, 2023

One of the most significant breakthroughs in this field is the convolutional neural network (CNN). In stark contrast, deep learning algorithms take a radically different approach, particularly convolutional neural networks (CNNs).

Computer Vision 52

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NVIDIA

DECEMBER 21, 2023

DigitalPath, based in Chico, California, has refined a convolutional neural network to spot wildfires. The NVIDIA Jetson Orin -based robots-as-a-service business provides farmers with metrics on yield gains and chemical reduction. “We Burning for solutions, developers are embracing AI for early detection.

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Convolutional Neural Networks Deep Learning Robotics Artificial Intelligence 84

NVIDIA

FEBRUARY 7, 2024

Industries can use AI to quickly analyze vast bodies of data, allowing them to derive meaningful insights, make predictions and automate processes for greater efficiency. Robots are being deployed on important missions to help preserve the Earth. In the public sector, government agencies are achieving superior disaster preparedness.

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Robotics Convolutional Neural Networks AI AI 121

Heartbeat

DECEMBER 8, 2023

Action: Wikipedia Action Input: "Yann LeCun" Observation: Page: Yann LeCun Summary: Yann André LeCun ( lə-KUN, French: [ləkœ̃]; originally spelled Le Cun; born 8 July 1960) is a Turing Award winning French computer scientist working primarily in the fields of machine learning, computer vision, mobile robotics and computational neuroscience.

Convolutional Neural Networks 52

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PyImageSearch

MARCH 30, 2023

Deep Learning and Neural Networks: Traditional machine learning and AI systems relied on linear or iterative learning methods. However, since the 1980s, researchers have developed “neural network” architectures using node-cluster structures and weighted decision-making strategies.

Computer Vision 56

Computer Vision Machine Learning Neural Network Convolutional Neural Networks 56

Viso.ai

FEBRUARY 25, 2023

Global organizations use it to develop, deploy and scale all computer vision applications in one place, with automated infrastructure. Especially with the emergence of Deep Neural Networks (DNN) , image segmentation applications have made tremendous progress. Read more about the basics of a Convolutional Neural Network.

Deep Learning 52

Deep Learning Convolutional Neural Networks Computer Vision Neural Network 52

Viso.ai

DECEMBER 6, 2023

State of Computer Vision Tasks in 2024 The field of computer vision today involves advanced AI algorithms and architectures, such as convolutional neural networks (CNNs) and vision transformers ( ViTs ), to process, analyze, and extract relevant patterns from visual data. Get a demo here.

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Categorization 52

Google Research AI blog

FEBRUARY 23, 2023

Previous work had shown that convolutional neural networks (CNNs) could interpret ultrasounds acquired by trained sonographers using a standardized acquisition protocol. The gravity of building an ML system that might one day impact real people with real health issues cannot be underestimated.

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The MLOps Blog

FEBRUARY 22, 2024

You can implement architectural approaches, for example, by creating dedicated, specialized subnetworks like in Progressive Neural Networks or by simply having multiple model heads (last layers), which are selected based on the input data characteristics (which can be, for example, the task label in task-incremental scenarios).

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Heartbeat

OCTOBER 25, 2023

Additionally, the fusion of bioinformatics with other emerging technologies, such as artificial intelligence, robotics, and quantum computing, holds tremendous potential for further advancements in biological research. Robotics and automation can streamline laboratory workflows, enabling high-throughput experimentation and data generation.

Machine Learning 52

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