Flipboard

NOVEMBER 11, 2024

This article explains, through clear guidelines, how to choose the right machine learning (ML) algorithm or model for different types of real-world and business problems.

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Unite.AI

FEBRUARY 25, 2025

As AI systems increasingly power mission-critical applications across industries such as finance, defense, healthcare, and autonomous systems, the demand for trustworthy, explainable, and mathematically rigorous reasoning has never been higher. Imandra is dedicated to bringing rigor and governance to the world's most critical algorithms.

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Unite.AI

JUNE 15, 2024

Graduate student Diego Aldarondo collaborated with DeepMind researchers to train an artificial neural network (ANN) , which serves as the virtual brain, using the powerful machine learning technique deep reinforcement learning.

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Unite.AI

NOVEMBER 8, 2024

Inspired by a discovery in WiFi sensing, Alex and his team of developers and former CERN physicists introduced AI algorithms for emotional analysis, leading to Wayvee Analytics's founding in May 2023. The team engineered an algorithm that could detect breathing and micro-movements using just Wi-Fi signals, and we patented the technology.

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Marktechpost

MAY 14, 2024

This article lists the top Deep Learning and Neural Networks books to help individuals gain proficiency in this vital field and contribute to its ongoing advancements and applications. Neural Networks and Deep Learning The book explores both classical and modern deep learning models, focusing on their theory and algorithms.

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Unite.AI

SEPTEMBER 12, 2024

In a significant leap forward, researchers at the University of Southern California (USC) have developed a new artificial intelligence algorithm that promises to revolutionize how we decode brain activity. DPAD: A New Approach to Neural Decoding The DPAD algorithm represents a paradigm shift in how we approach neural decoding.

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Marktechpost

MARCH 23, 2024

To reduce the memory footprint and further speed up the training of FeatUp’s implicit network, the spatially varying features are compressed to their top k=128 principal components. This optimization accelerates training time by a remarkable 60× for models like ResNet-50 and facilitates larger batches without compromising feature quality.

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Marktechpost

OCTOBER 16, 2023

In a recent paper, “Towards Monosemanticity: Decomposing Language Models With Dictionary Learning,” researchers have addressed the challenge of understanding complex neural networks, specifically language models, which are increasingly being used in various applications.

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Towards AI

FEBRUARY 3, 2024

(Left) Photo by Pawel Czerwinski on Unsplash U+007C (Right) Unsplash Image adjusted by the showcased algorithm Introduction It’s been a while since I created this package ‘easy-explain’ and published on Pypi. A few weeks ago, I needed an explainability algorithm for a YoloV8 model. The truth is, I couldn’t find anything.

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Unite.AI

NOVEMBER 22, 2024

It helps explain how AI models, especially LLMs, process information and make decisions. By using a specific type of neural network called sparse autoencoders (SAEs) , Gemma Scope breaks down these complex processes into simpler, more understandable parts. To tackle this challenge, DeepMind has created a tool called Gemma Scope.

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IBM Journey to AI blog

AUGUST 9, 2024

Predictive AI blends statistical analysis with machine learning algorithms to find data patterns and forecast future outcomes. Generative adversarial networks (GANs) consist of two neural networks: a generator that produces new content and a discriminator that evaluates the accuracy and quality of the generated content.

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Marktechpost

JULY 7, 2024

Deep neural networks (DNNs) come in various sizes and structures. The specific architecture selected along with the dataset and learning algorithm used, is known to influence the neural patterns learned. It shows that these networks naturally learn structured representations, especially when they start with small weights.

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Marktechpost

APRIL 24, 2024

The increasing complexity of AI systems, particularly with the rise of opaque models like Deep Neural Networks (DNNs), has highlighted the need for transparency in decision-making processes. ELI5 is a Python package that helps debug machine learning classifiers and explain their predictions.

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Unite.AI

NOVEMBER 6, 2023

Traditional machine learning is a broad term that covers a wide variety of algorithms primarily driven by statistics. The two main types of traditional ML algorithms are supervised and unsupervised. These algorithms are designed to develop models from structured datasets. Do We Still Need Traditional Machine Learning Algorithms?

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AssemblyAI

NOVEMBER 21, 2022

Regardless of the specific architecture employed, (nearly) every Neural Network relies on efficient matrix multiplication to learn and infer. Recently, DeepMind devised a method to automatically discover new faster matrix multiplication algorithms.

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AI News

JULY 24, 2023

Compound sparsity combines techniques such as unstructured pruning, quantisation, and distillation to significantly reduce the size of neural networks while maintaining their accuracy. “We This approach challenges the notion that GPUs are necessary for efficient deep learning,” explains Bogunowicz.

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Unite.AI

SEPTEMBER 15, 2024

Their findings, recently published in Nature , represent a significant leap forward in the field of neuromorphic computing – a branch of computer science that aims to mimic the structure and function of biological neural networks.

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Marktechpost

JANUARY 19, 2024

Epigenetic clocks accurately estimate biological age based on DNA methylation, but their underlying algorithms and key aging processes must be better understood. To conclude, the researchers have introduced a precise and interpretable neural network architecture based on DNA methylation for age estimation. Check out the Paper.

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Marktechpost

DECEMBER 11, 2023

These models use systems of differential equations that explain thermodynamics and fluid flow and may be integrated across time to produce projections for the future. Using historical data, like the ERA5 reanalysis dataset, deep neural networks are trained to forecast future weather conditions.

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PyImageSearch

OCTOBER 28, 2024

Home Table of Contents NeRFs Explained: Goodbye Photogrammetry? Block #A: We Begin with a 5D Input Block #B: The Neural Network and Its Output Block #C: Volumetric Rendering The NeRF Problem and Evolutions Summary and Next Steps Next Steps Citation Information NeRFs Explained: Goodbye Photogrammetry? How Do NeRFs Work?

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Towards AI

MARCH 17, 2024

Alongside this, there is a second boom in XAI or Explainable AI. Explainable AI is focused on helping us poor, computationally inefficient humans understand how AI “thinks.” We will then explore some techniques for building glass-box or explainable models. Ultimately these definitions end up being almost circular!

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Unite.AI

JUNE 26, 2023

However, researchers are trying to take a step towards a human-like mind by adding a memory aspect to neural networks. Integrated Information Theory (IIT) Integrated Information Theory is a theoretical framework proposed by neuroscientist and psychiatrist Giulio Tononi to explain the nature of consciousness.

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Marktechpost

DECEMBER 3, 2023

There is a steadily growing list of intriguing properties of neural network (NN) optimization that are not readily explained by classical tools from optimization. Figure 1: Outliers with conflicting signals have a significant impact on the training dynamics of neural networks.

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Unite.AI

APRIL 9, 2024

The Need for Explainability The demand for Explainable AI arises from the opacity of AI systems, which creates a significant trust gap between users and these algorithms. Explainability is essential for accountability, fairness, and user confidence. Transparency is fundamental for responsible AI usage.

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Marktechpost

AUGUST 7, 2024

The Role of Explainable AI in In Vitro Diagnostics Under European Regulations: AI is increasingly critical in healthcare, especially in vitro diagnostics (IVD). The European IVDR recognizes software, including AI and ML algorithms, as part of IVDs. This includes considering patient population, disease conditions, and scanning quality.

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Unite.AI

DECEMBER 1, 2023

Machine learning , a subset of AI, involves three components: algorithms, training data, and the resulting model. An algorithm, essentially a set of procedures, learns to identify patterns from a large set of examples (training data). The culmination of this training is a machine-learning model.

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Google Research AI blog

FEBRUARY 10, 2023

Robust algorithm design is the backbone of systems across Google, particularly for our ML and AI models. Hence, developing algorithms with improved efficiency, performance and speed remains a high priority as it empowers services ranging from Search and Ads to Maps and YouTube. You can find other posts in the series here.)

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Marktechpost

MARCH 28, 2024

Python: Advanced Guide to Artificial Intelligence This book helps individuals familiarize themselves with the most popular machine learning (ML) algorithms and delves into the details of deep learning, covering topics like CNN, RNN, etc. The book prepares its readers for the moral uncertainties of a world run by code.

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Unite.AI

JULY 12, 2024

By leveraging advanced AI algorithms, the app identifies the core concepts behind each question and curates the most relevant content from trusted sources across the web. This feature uses a neural network model that has been trained on over 100,000 images of handwritten math expressions, achieving an impressive 98% accuracy rate.

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Marktechpost

OCTOBER 8, 2023

By utilizing finely developed neural network architectures, we have models that are distinguished by extraordinary accuracy within their respective sectors. Despite their accurate performance, we must still fully understand how these neural networks function. We have new advancements that have been there with each day.

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IBM Journey to AI blog

JANUARY 10, 2024

AI operates on three fundamental components: data, algorithms and computing power. Algorithms: Algorithms are the sets of rules AI systems use to process data and make decisions. The category of AI algorithms includes ML algorithms, which learn and make predictions and decisions without explicit programming.

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Unite.AI

SEPTEMBER 4, 2024

Our multi-layered approach combines proprietary algorithms with third-party data to stay ahead of evolving fraud tactics. Deep Neural Network (DNN) Models: Our core infrastructure utilizes multi-stage DNN models to predict the value of each impression or user. This resulted in a 75% decrease in Cost Per Acquisition (CPA) and 12.3

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Marktechpost

FEBRUARY 1, 2025

However, the unpredictable nature of real-world data, coupled with the sheer diversity of tasks, has led to a shift toward more flexible and robust frameworks, particularly reinforcement learning and neural network-based approaches.

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Marktechpost

DECEMBER 13, 2024

Neural networks have become foundational tools in computer vision, NLP, and many other fields, offering capabilities to model and predict complex patterns. This understanding is essential for designing more efficient training algorithms and enhancing the interpretability and robustness of neural networks.

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Unite.AI

MAY 8, 2024

During my school years, I spent a lot of time studying math, probability theory, and statistics, and got an opportunity to play with classical machine learning algorithms such as linear regression and KNN. There, I learned a lot about more advanced machine learning algorithms and built my intuition.

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Unite.AI

JULY 24, 2023

The YOLO concept was first introduced in 2016 by Joseph Redmon, and it was the talk of the town almost instantly because it was much quicker, and much more accurate than the existing object detection algorithms. It wasn’t long before the YOLO algorithm became a standard in the computer vision industry. How Does YOLO Work?

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Algorithm Computer Vision Explainability Neural Network 130

Heartbeat

FEBRUARY 6, 2023

How do Object Detection Algorithms Work? There are two main categories of object detection algorithms. Two-Stage Algorithms: Two-stage object detection algorithms consist of two different stages. In the second step, these potential fields are classified and corrected by the neural network model.

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