Unite.AI

SEPTEMBER 21, 2023

Some prominent AI techniques include neural networks, convolutional neural networks, transformers, and diffusion models. AI's real-time data analysis and decision-making capabilities expand blockchain’s authenticity, augmentation, and automation capabilities. What is Blockchain?

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AI Weekly

SEPTEMBER 12, 2024

In the News AI Stocks: The 10 Best AI Companies Artificial intelligence, automation and robotics are disrupting virtually every industry. Join the AI conversation and transform your advertising strategy with AI weekly sponsorship aiweekly.co Register now dotai.io update and beyond.

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Marktechpost

MARCH 16, 2025

These methods utilize 3D convolutional neural networks (CNNs) for cost filtering but struggle with generalization beyond their training data. An automated self-curation pipeline was developed to filter out ambiguous samples, ensuring high-quality training data.

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Marktechpost

DECEMBER 29, 2023

Despite their importance is evident, analyzing these small structures has been challenging due to the lack of automated tools. Consequently, the researchers of Plant Phenomics have introduced BarbNet, a deep-learning model designed specifically for the automated detection and phenotyping of barbs in microscopic images of awns.

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AI Weekly

JUNE 29, 2023

Discover AI-generated charts, AI insights from real-time data, AI automation, and a steadfast copilot, all in one sophisticated platform. Turn market trends into lucrative AI Automations ?️ Run risk-free sandbox trials to test and perfect your Automations ? Try Pluto for free today] pluto.fi

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AI Weekly

AUGUST 17, 2023

scientificamerican.com How to bridge the artificial intelligence divide Unlike earlier advances in technology, which primarily affected “routine” tasks, AI harbours the potential to automate knowledge workers’ “nonroutine” tasks like policymaking, reviewing, reflecting, coding and doing legal activities.

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Unite.AI

OCTOBER 17, 2024

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. As the data in a training set is processed, the neural network learns how to predict the outcome.

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AWS Machine Learning Blog

MARCH 21, 2025

However, by using Anthropics Claude on Amazon Bedrock , researchers and engineers can now automate the indexing and tagging of these technical documents. By automating the indexing and tagging of technical documents, these powerful models can enable more efficient knowledge management and accelerate innovation across a variety of industries.

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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." Our findings revealed that the DCNN, enhanced by this specialised training, could surpass. theconversation.com Who will win the battle for AI in the cloud?

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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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Artificial Corner

JULY 30, 2023

A Hybrid CNN-Transformer Architecture for Medical Image Segmentation with DDConv and SW-ACAM, compatible with quantitative and qualitative analysis Limi Change on Unsplash The convolutional neural networks are limited to capturing global features, whereas, transformers are limited to extracting local features.

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AI Weekly

JULY 27, 2023

nytimes.com Robotics Elon Musk's Tesla robots could be groundbreaking for AI and automation, but still have a long way to go Tesla AI Event 2022 Redeem now Tesla is developing a humanoid robot to help with the labor shortage. Elon Musk thinks that Tesla's robots could one day outnumber humans and make physical work optional.

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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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Marktechpost

MAY 7, 2024

Researchers have turned to automated methods to improve accuracy and reduce the burden on sleep technicians. Its development marks a step forward in the automation of sleep analysis and sets a new standard for integrating deep learning technologies in medical diagnostics.

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Marktechpost

DECEMBER 12, 2023

The current studies point out a lack of completely automated models that cover a wide range of animal datasets and consider several naturally occurring pain syndromes in addition to coat color, breed, age, and gender. If you like our work, you will love our newsletter.

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Marktechpost

NOVEMBER 16, 2023

Utilizing a two-stage convolutional neural network, the model classifies macula-centered 3D volumes from Topcon OCT images into Normal, early/intermediate AMD (iAMD), atrophic (GA), and neovascular (nAMD) stages. The study successfully developed an automated deep learning-based AMD detection and staging system using OCT scans.

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Marktechpost

MAY 26, 2024

Efficient deep learning accelerators, particularly for edge processing, benefit from IMC by reducing data movement costs and enhancing energy efficiency and latency, necessitating automated optimization of numerous design parameters. Benchmarking across various HW-NAS methods must be more consistent, complicating fair comparisons.

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Viso.ai

DECEMBER 19, 2023

Today, the use of convolutional neural networks (CNN) is the state-of-the-art method for image classification. Unsupervised classification An unsupervised classification technique is a fully automated method that does not leverage training data. We will cover the following topics: What Is Image Classification?

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Mlearning.ai

FEBRUARY 14, 2023

Image classification is a field of study within the broader area of computer vision and deep learning, with the goal of automating the… Continue reading on MLearning.ai »

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Towards AI

JULY 9, 2024

Object Detection and Spatial Analysis Object detection in spatial analysis automates the identification of elements, including buildings, roads, vegetation, and bodies of water, improving our capacity to map and comprehend complicated environments.

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IBM Journey to AI blog

MAY 6, 2024

They also possess query generation capabilities that can automate the process of identifying matching patients with trials, expediting the trial start-up process. Additionally, generative adversarial networks (GANs) can be used to simulate real recruitment scenarios, further optimizing protocol design.

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Viso.ai

NOVEMBER 5, 2024

Convolution Layer: The concatenated feature descriptor is then passed through a Convolution Neural Network. The convolutional operation helps to learn spatial relationships and further refines the attention map. This step outputs both global and local information from the feature maps.

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Flipboard

JANUARY 9, 2025

Therefore, a validated high-throughput phenotyping tool was developed and established in order to detect and quantify leaf hair using images of single grapevine leaf discs and convolution neural networks (CNN). This approach achieved an overall model prediction accuracy of 95.41%.

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Marktechpost

OCTOBER 21, 2023

These technologies automate several time-consuming tasks in visual computing that were previously only available to specialists with in-depth topic expertise. Foundation models for visual computing, such as Stable Diffusion, Imagen, Midjourney, or DALL-E 2 and DALL-E 3, have opened the unparalleled powers of generative AI.

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Marktechpost

APRIL 24, 2024

While traditional PAM analysis is time-consuming, recent advancements in deep learning technology offer promising solutions for automating bird species identification from audio recordings. The model comprises a Convolutional Neural Network (CNN) backbone, a prototype layer, and a fully connected final layer.

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ODSC - Open Data Science

MARCH 10, 2025

By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow. Sessions on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) started gaining popularity, marking the beginning of data sciences shift toward AI-driven methods.

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Viso.ai

DECEMBER 3, 2023

Hence, rapid development in deep convolutional neural networks (CNN) and GPU’s enhanced computing power are the main drivers behind the great advancement of computer vision based object detection. Various two-stage detectors include region convolutional neural network (RCNN), with evolutions Faster R-CNN or Mask R-CNN.

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Marktechpost

MAY 31, 2024

Therefore, there is a pressing need for automated techniques that can efficiently and accurately analyze sleep data across multiple physiological signals. These models have shown promise in automating sleep staging and the classification of sleep disorders like sleep-disordered breathing (SDB).

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Marktechpost

AUGUST 2, 2024

Deep learning techniques such as 1D convolutional neural networks (1D-CNNs) offer significant advantages by automatically learning relevant features from raw audio data, capturing complex patterns and nuances indicative of specific pathological conditions. Check out the Paper.

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Viso.ai

OCTOBER 9, 2024

With the rapid development of Convolutional Neural Networks (CNNs) , deep learning became the new method of choice for emotion analysis tasks. Generally, the classifiers used for AI emotion recognition are based on Support Vector Machines (SVM) or Convolutional Neural Networks (CNN).

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Marktechpost

JUNE 12, 2024

By applying advanced ML techniques, including deep neural networks, to these data sets, researchers can perform tasks like automated disease classification, digital image recognition, and virtual drug screening with unprecedented accuracy.

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AWS Machine Learning Blog

DECEMBER 7, 2023

Six algorithms available in Forecast were tested: Convolutional Neural Network – Quantile Regression (CNN-QR), DeepAR+ , Prophet , Non-Parametric Time Series (NPTS), Autoregressive Integrated Moving Average (ARIMA), and Exponential Smoothing (ETS).

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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. As the demand for sustainable agriculture grows, machine learning emerges as a vital force, reshaping the future of food security and cultivation.

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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. It includes hands-on tutorials with modern tools like TensorFlow, allowing learners to build and train neural networks.

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NVIDIA

OCTOBER 18, 2023

Reprogramming robots and AI systems on the fly to meet changing environments, manufacturing lines and automation needs of customers is time-consuming and requires expert skills. Built on the widely adopted open-source Robot Operating System (ROS), Isaac ROS brings perception to automation, giving eyes and ears to the things that move.

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IBM Journey to AI blog

AUGUST 28, 2023

It also automates tasks like information extraction and content categorization. Machine learning algorithms like Naïve Bayes and support vector machines (SVM), and deep learning models like convolutional neural networks (CNN) are frequently used for text classification.

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Flipboard

DECEMBER 17, 2024

In recent years, computer-aided diagnosis systems have emerged as powerful tools for automated skin cancer classification, revolutionizing the field of dermatology. Skin cancer is a significant global health concern, with timely and accurate diagnosis playing a critical role in improving patient outcomes.

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