AI Weekly

AUGUST 17, 2023

Robotics Scouting out the next wave of robot workers A humanoid warehouse worker, Digit walked upright on goatlike legs and grabbed bins off a shelf with muscular arms made from aerospace-grade aluminum. yahoo.com Research Novel physics-encoded AI model helps to learn spatiotemporal dynamics Prof. You can also subscribe via email.

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AI Weekly

DECEMBER 7, 2023

deepmind.google Seeing 3D images through the eyes of AI This issue is resolved by Professor Zhang's paper, "RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds." Petrobras) has invested in six robots from ANYbotics. Petrobras) has invested in six robots from ANYbotics.

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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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Marktechpost

JANUARY 30, 2025

Image Source Agentic AI is born out of a need for software and robotic systems that can operate with independence and responsiveness. Industrial Robotics Robot arms on factory floors coordinate with sensor networks to assemble products more efficiently, diagnosing faults and adjusting their operation in real time.

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Unite.AI

AUGUST 8, 2023

The Technologies Behind Generative Models Generative models owe their existence to deep neural networks, sophisticated structures designed to mimic the human brain's functionality. By capturing and processing multifaceted variations in data, these networks serve as the backbone of numerous generative models.

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Marktechpost

MARCH 16, 2025

This capability is vital for autonomous driving, robotics, and augmented reality applications. These methods utilize 3D convolutional neural networks (CNNs) for cost filtering but struggle with generalization beyond their training data.

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ODSC - Open Data Science

NOVEMBER 28, 2024

NVIDIA’s AI Tools Suite to Aid in Accelerated Humanoid Robotics Development NVIDIA’s AI tools suite may drive developers toward complex machine learning and natural language processing solutions. In all likelihood, AI technology and humanoid robotics will progress hand in hand in the coming years.

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NVIDIA

OCTOBER 18, 2023

That reach now includes areas that touch edge, robotics and logistics systems: defect detection, real-time asset tracking, autonomous planning and navigation, human-robot interactions and more. release, developers can now create and bring high-performance robotics solutions to market with Jetson.

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Marktechpost

JUNE 3, 2024

This article lists top Intel AI courses, including those on deep learning, NLP, time-series analysis, anomaly detection, robotics, and edge AI deployment, providing a comprehensive learning path for leveraging Intel’s AI technologies. Deep Learning for Robotics This course teaches applying machine learning to robotics.

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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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NVIDIA

OCTOBER 26, 2023

The Woods Hole Oceanographic Institution (WHOI) Autonomous Robotics and Perception Laboratory ( WARPLab ) and MIT are developing a robot for studying coral reefs and their ecosystems. The robot runs an expanding collection of NVIDIA Jetson-enabled edge AI to build 3D models of reefs and to track creatures and plant life.

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Pickl AI

JANUARY 29, 2025

Deep Learning is a specialized subset of Artificial Intelligence (AI) and machine learning that employs multilayered artificial neural networks to analyze and interpret complex data. Cat vs. Dog Classification This project involves building a Convolutional Neural Network (CNN) to classify images as either cats or dogs.

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Pickl AI

AUGUST 14, 2024

Summary: Neural networks are a key technique in Machine Learning, inspired by the human brain. Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling.

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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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Pickl AI

SEPTEMBER 3, 2024

Summary: Artificial Neural Network (ANNs) are computational models inspired by the human brain, enabling machines to learn from data. Introduction Artificial Neural Network (ANNs) have emerged as a cornerstone of Artificial Intelligence and Machine Learning , revolutionising how computers process information and learn from data.

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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

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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Marktechpost

JULY 18, 2024

Parameter Generation has been a significant research focus since the introduction of Hypernetworks, which led to various studies on predicting neural network weights. Actor networks are generated using the proposed method based on trajectories from IsaacGym simulations and pre-trained adaptation modules.

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Unite.AI

SEPTEMBER 9, 2024

Central to this development was a convolutional neural network, trained using Q-learning , which processed raw screen pixels and converted them into game-specific actions based on the current state. In this early work, they introduced a deep reinforcement learning agent that could learn control strategies directly from gameplay.

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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

MARCH 20, 2025

Case studies from five cities demonstrate reductions in carbon emissions and improvements in quality of life metrics." }, { "id": 6, "title": "Neural Networks for Computer Vision", "abstract": "Convolutional neural networks have revolutionized computer vision tasks.

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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. offsets).

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Marktechpost

AUGUST 11, 2024

It then fills in the gaps in the depth of information by using a convolutional neural network (CNN) to identify the connections between these regions. A distillation loss is applied to make sure the student network doesn’t just replicate the regional features of the teacher network. Check out the Paper.

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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. Some apps foster user communities for knowledge sharing.

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Marktechpost

OCTOBER 7, 2024

Accurate geometry prediction is critical for applications such as robotics, autonomous driving, and augmented reality, but current methods often require extensive training on labeled datasets and struggle to generalize across diverse tasks.

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Marktechpost

SEPTEMBER 9, 2024

Learning computer vision is essential as it equips you with the skills to develop innovative solutions in areas like automation, robotics, and AI-driven analytics, driving the future of technology. Computer Vision The Computer Vision Nanodegree Program offers advanced training in computer vision, deep learning, and robotics.

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Marktechpost

NOVEMBER 27, 2024

game playing, robotics). Sigmoid Kernel: Inspired by neural networks. 10) Neural networks or Deep Neural Network Neural networks and deep neural networks are a class of machine learning algorithms inspired by the structure and function of the human brain.

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Marktechpost

JANUARY 2, 2025

This challenges real-time applications, especially in environments with limited computing power, like mobile robots. Current methods in robotics , such as Transformer-based Diffusion Models , are used for tasks like Imitation Learning , Offline Reinforcement Learning , and robot design.

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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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IBM Journey to AI blog

SEPTEMBER 24, 2024

Dr. Abhisesh Silwal, a systems scientist at Carnegie Mellon University whose research focuses on AI and robotics in agriculture, thinks so. ” When Guerena’s team first started working with smartphone images, they used convolutional neural networks (CNNs).

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Marktechpost

NOVEMBER 20, 2024

Some researchers have introduced multi-architectural modular deep neural networks to reduce false positives in anomaly detection. Others have proposed a hybrid network intrusion detection system integrating convolutional neural networks (CNN), fuzzy C-means clustering, genetic algorithm, and a bagging classifier.

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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

MAY 30, 2023

What are Generative Adversarial Networks (GANs)? GANs are a type of neural network design used to develop new data samples that are similar to the training data. This machine learning subset uses artificially generated neural networks to model complex data relationships. Assume you’re teaching your pet new tricks.

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Viso.ai

DECEMBER 1, 2024

Visual inspection for defect part detection in manufacturing Skill Training Another application field of vision systems is optimizing assembly line operations in industrial production and human-robot interaction. Programs like this make public transportation safer during the coronavirus pandemic.

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John Snow Labs

SEPTEMBER 26, 2024

First I plan on discussing the multifaceted use of convolutional neural networks in detecting brain tumors, CNS lymphomas, brain hematomas, and other various neurological lesions. This presentation and talk will provide a review on the different applications of machine learning in neurosurgery.

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Viso.ai

OCTOBER 7, 2024

Computer vision mainly uses neural networks under the hood. This is where we find opportunities for combining robotics with computer vision. Waste-sorting robots equipped with cameras and sensors detect these materials in real time. It powers autonomous drones, self-driving vehicles, face recognition in CCTV cameras, etc.

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Machine Learning (Theory)

MARCH 5, 2025

Support Vector Machines were disrupted by deep learning, and convolutional neural networks were displaced by transformers. As an example, the speech recognition community spent decades focusing on Hidden Markov Models at the expense of other architectures, before eventually being disrupted by advancements in deep learning.

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