AI Weekly

OCTOBER 26, 2023

reuters.com Robotics MIT and Amazon team up to study robots in the workplace The goal is to expand on the three-year “Work of the Future” initiative that resulted in a 2020 report on how automation technologies could create better jobs for people, by MIT professor Julie Shah, who leads the Interactive Robotics Group of the Computer Science and AI.

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Heartbeat

MARCH 14, 2023

As robots need to be able to pick up on their surroundings and adapt accordingly, this is a crucial skill for the field. 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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Marktechpost

MARCH 22, 2024

This dichotomy is particularly pronounced in scenarios requiring instantaneous visual data processing, such as autonomous vehicles, robotic navigation, and interactive augmented reality systems. NeuFlow, a pioneering optical flow architecture, has emerged as a game-changer in computer vision.

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Marktechpost

MAY 6, 2024

Researchers in computer vision and robotics consistently strive to improve autonomous systems’ perception capabilities. Existing research includes convolutional neural networks (CNNs) and transformer-based object detection and segmentation architectures.

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AI Weekly

JUNE 29, 2023

AI is rightly touted as a massively powerful tool biztoc.com Robotics Meet the robots attending the UN’s ‘AI for Good Global’ summit At a UN summit in Geneva next week, tech luminaries ranging from futurist Ray Kurzweil to DeepMind COO Lila Ibrahim will discuss AI for good. phys.org Quantum Computing Inc.

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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?

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Heartbeat

DECEMBER 14, 2023

is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets. in 1998, In general, LeNet refers to LeNet-5 and is a simple convolutional neural network. > Finished chain. . >

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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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Marktechpost

DECEMBER 1, 2023

Applications include better human-computer interaction and improved emotional response in robots, making FER crucial in human-machine interface technology. However, the advent of deep learning, particularly convolutional neural networks (CNNs), revolutionized FER by adeptly capturing intricate spatial patterns in facial expressions.

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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. Computer vision and deep learning […].

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Viso.ai

APRIL 29, 2024

STNs are used to “teach” neural networks how to perform spatial transformations on input data to improve spatial invariance. Spatial Transformer Networks Explained The central component of the STN is the spatial transformer module.

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Viso.ai

APRIL 11, 2024

Convolutional Neural Networks : CNNs are the basis of many object localization techniques. They use mathematical algorithms such as convolutions, activation functions, and pooling, to extract features from images and identify patterns. Robotics : OL allows robots to perceive and interact with their environment.

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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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Heartbeat

MAY 30, 2023

Source: DeepRM-HotNets Applications of DRL Here are some of the significant areas where Deep Reinforcement Learning has been applied: Robotics: DRL is used to train robots to perform complicated tasks like navigating through space, altering objects, lifting or pulling objects, etc. Both GANs and DRL involve learning through feedback.

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AWS Machine Learning Blog

MARCH 30, 2023

The model extracts features from the image using a convolutional neural network. He has been working on human-in-the-loop optimization since 1995, when he applied the LeNet Convolutional Neural Network to check recognition. As input, the model takes an image and a corresponding bounding box annotation.

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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.

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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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Pickl AI

JULY 14, 2023

Deep Learning, a subfield of ML, gained attention with the development of deep neural networks. Moreover, Deep Learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), achieved remarkable breakthroughs in image classification, natural language processing, and other domains.

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PyImageSearch

MARCH 30, 2023

This notion drove research in the 1970s and early 1980s, exploring AI applications in image processing, language recognition, and robotics. One significant challenge in fields like robotics is enabling AI systems to process, interpret, and react to visual information.

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Heartbeat

MAY 22, 2023

Also, some intelligent robots are deployed for behavior-specific advertising. 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).

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Viso.ai

DECEMBER 27, 2023

In a computer vision example of contrast learning, we aim to train a tool like a convolutional neural network to bring similar image representations closer and separate the dissimilar ones. It typically uses a convolutional neural network (CNN) architecture, like ResNet , for extracting image features.

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Heartbeat

JANUARY 15, 2024

Real-time segmentation using snakes has proven invaluable in robotics, enabling robots to navigate and interact with their environment autonomously. Convolutional Neural Networks (CNNs) have enthralled audiences with their prodigious capacity to extract nuanced patterns and features from massive volumes of data.

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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.

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Heartbeat

MARCH 5, 2024

Additionally, interdisciplinary collaborations with other fields, such as robotics and natural language processing, contribute to developing more robust computer vision systems. What Is Image Augmentation?

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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.

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Pickl AI

AUGUST 1, 2023

Reinforcement learning has found significant applications in gaming, robotics, and autonomous systems. Deep Learning Models: Deep learning models are a subset of neural networks with multiple layers (deep architectures). The model aims to maximize the cumulative reward over time by taking appropriate actions.

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Viso.ai

FEBRUARY 24, 2023

Recent self-supervised learning models include frameworks such as Pre-trained Language Models (PTM), Generative Adversarial Networks (GAN) , Autoencoder and its extensions, Deep Infomax, and Contrastive Coding. We will cover those later in more detail. The term was then borrowed by the field of machine learning.

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Google Research AI blog

MAY 3, 2023

Posted by Joanne Truong, Student Researcher, and Wenhao Yu, Research Scientist, Robotics at Google Teaching mobile robots to navigate in complex outdoor environments is critical to real-world applications, such as delivery or search and rescue. and in different weather conditions (sunny, overcast, sunset).

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The MLOps Blog

FEBRUARY 22, 2024

Another example may be a robot vacuum cleaner trying to improve its route through a house. Thus, model training must happen on the robot (on-device learning), and the images should not be stored longer than necessary. It takes pictures of the environment and uses continual learning to enhance the model responsible for the navigation.

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NVIDIA

FEBRUARY 7, 2024

Robots are being deployed on important missions to help preserve the Earth. Robotics Developers Extend Battery Life for Important Missions With edge AI and supercomputing now available using compact modules, demand for robots is surging for use in factory logistics, sales showrooms, urban delivery services and even ocean exploration.

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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).

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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. Various two-stage detectors include region convolutional neural network (RCNN), with evolutions Faster R-CNN or Mask R-CNN.

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Marktechpost

DECEMBER 24, 2023

Deep Learning: This branch of machine learning involves convolutional neural networks (CNN), which have gained prominence due to their ability to extract features from images automatically, reducing the reliance on manual feature engineering. Deep learning methods have shown robust performance in classifying leaf diseases.

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Pickl AI

JULY 16, 2023

Neural Network Design There is a rich set of tools and functionalities for designing and implementing neural networks. It supports various types of neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers.

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Pickl AI

FEBRUARY 1, 2023

Transfer Learning has various applications like computer vision, NLP, recommendation systems, and robotics. Examples of Transfer Learning in Deep Learning include: Using a pre-trained image classification network for a new image classification task with a similar dataset. Thus it reduces the amount of data and computational need.

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Viso.ai

APRIL 2, 2024

This has numerous applications in robotics, augmented/virtual reality, autonomous systems, and many more. Spatial coordinates facilitate the representation, examination, and manipulation of 3D data like point clouds, meshes, or voxel grids essential for applications such as robotics , augmented reality, and 3D reconstruction.

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Viso.ai

FEBRUARY 25, 2023

Deep learning-based segmentation: Deep learning techniques, such as Convolutional Neural Networks (CNNs), have revolutionized image segmentation by providing highly accurate and efficient solutions. Read more about the basics of a Convolutional Neural Network.

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