IBM Journey to AI blog

JULY 6, 2023

To keep up with the pace of consumer expectations, companies are relying more heavily on machine learning algorithms to make things easier. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other? Your AI must be explainable, fair and transparent. What is machine learning?

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Towards AI

JANUARY 21, 2025

Nevertheless, when I started familiarizing myself with the algorithm of LLMs the so-called transformer I had to go through many different sources to feel like I really understood the topic.In Before I start explaining the transformer, we need to recall that ChatGPT generates its output in a loop, one token after the other.

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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

MAY 8, 2024

The ability to effectively represent and reason about these intricate relational structures is crucial for enabling advancements in fields like network science, cheminformatics, and recommender systems. Graph Neural Networks (GNNs) have emerged as a powerful deep learning framework for graph machine learning tasks.

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Unite.AI

NOVEMBER 28, 2024

The invention of the backpropagation algorithm in 1986 allowed neural networks to improve by learning from errors. 2000s – Big Data, GPUs, and the AI Renaissance The 2000s ushered in the era of Big Data and GPUs , revolutionizing AI by enabling algorithms to train on massive datasets.

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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

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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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

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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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

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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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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Unite.AI

MARCH 20, 2025

I then worked as an algorithms engineer and moved on to product management. He has a PhD in computer science and more than 25 years of experience in algorithm development, AI, and machine learning (ML). We hired two engineers and developed our first algorithm for prostate cancer detection. Chaim, unlike me, is a specialist.

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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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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

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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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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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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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

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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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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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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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

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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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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PyImageSearch

OCTOBER 14, 2024

This blog post is the 1st of a 3-part series on 3D Reconstruction: Photogrammetry Explained: From Multi-View Stereo to Structure from Motion (this blog post) 3D Reconstruction: Have NeRFs Removed the Need for Photogrammetry? The second blog post will introduce you to NeRFs , the neural network solution. So how does that work?

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PyImageSearch

DECEMBER 9, 2024

This lesson is the last of a 3-part series on 3D Reconstruction: Photogrammetry Explained: From Multi-View Stereo to Structure from Motion NeRFs Explained: Goodbye Photogrammetry? And in the 2nd blog of this series , you were introduced to NeRFs, which is 3D Reconstruction via Neural Networks, projecting points in the 3D space.

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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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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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