Unite.AI

DECEMBER 17, 2024

By combining the power of neural networks with the logic of symbolic AI, it could solve some of the reliability problems generative AI faces. It can mislead people into trusting information thats simply not true. To make matters worse, when AI makes mistakes, it doesnt explain itself.

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Unite.AI

OCTOBER 10, 2024

A new research paper from Canada has proposed a framework that deliberately introduces JPEG compression into the training scheme of a neural network, and manages to obtain better results – and better resistance to adversarial attacks. In contrast, JPEG-DL (right) succeeds in distinguishing and delineating the subject of the photo.

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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. Recommender engines are profoundly shaping societal worldviewsm especially when you factor in the fact that misinformation is 6 times more likely to be shared than factual information.

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Becoming Human

MARCH 16, 2023

Photo by Paulius Andriekus on Unsplash Welcome back to the next part of this Blog Series on Graph Neural Networks! The following section will provide a little introduction to PyTorch Geometric , and then we’ll use this library to construct our very own Graph Neural Network!

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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. How Does Gemma Scope Work?

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Unite.AI

AUGUST 28, 2024

in Information Systems Engineering from Ben Gurion University and an MBA from the Technion, Israel Institute of Technology. Along the way, I’ve learned different best practices – from how to manage a team to how to inform the proper strategy – that have shaped how I lead at Deep Instinct. He holds a B.Sc

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Unite.AI

JUNE 25, 2023

However, assimilating the understanding of physics into the realm of neural networks has proved challenging. Integrating physics into network architectures: This strategy involves running data through a network filter that codes physical properties into what cameras capture.

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Towards AI

DECEMBER 12, 2024

We are diving into Mechanistic interpretability, an emerging area of research in AI focused on understanding the inner workings of neural networks. All your data will be transformed into embeddings, which well then use to retrieve information. So, its quite important to understand embedding models.

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Towards AI

DECEMBER 12, 2024

We are diving into Mechanistic interpretability, an emerging area of research in AI focused on understanding the inner workings of neural networks. All your data will be transformed into embeddings, which well then use to retrieve information. So, its quite important to understand embedding models.

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Unite.AI

FEBRUARY 10, 2025

Can you explain how NeuroSplit dynamically optimizes compute resources while maintaining user privacy and performance? NeuroSplit is fundamentally device-agnostic, cloud-agnostic, and neural network-agnostic. But AI shouldn't be limited by which end-user device someone happens to use. Think about what this means for developers.

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Towards AI

DECEMBER 12, 2024

We are diving into Mechanistic interpretability, an emerging area of research in AI focused on understanding the inner workings of neural networks. All your data will be transformed into embeddings, which well then use to retrieve information. So, its quite important to understand embedding models.

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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 25, 2023

By integrating these constraints, the AI not only mirrors aspects of human intelligence but also unravels the intricate balance between resource expenditure and information processing efficiency. More intriguing, however, was the shift in how individual nodes processed information.

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NVIDIA

JANUARY 6, 2025

NVIDIA GPUs and platforms are at the heart of this transformation, Huang explained, enabling breakthroughs across industries, including gaming, robotics and autonomous vehicles (AVs). The latest generation of DLSS can generate three additional frames for every frame we calculate, Huang explained.

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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. In conventional computers, information is processed and stored using binary states – on or off, 1 or 0.

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Unite.AI

MARCH 11, 2024

While multimodal AI focuses on processing and integrating data from various modalities—text, images, audio—to make informed predictions or responses like Gemini model, CAS integrates multiple interacting components like language models and search engines to boost performance and adaptability in AI tasks.

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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?

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Unite.AI

NOVEMBER 8, 2024

It’s not enough to simply identify unhappy customers — we help explain why and offer recommendations for immediate improvement, keeping customers satisfied in the moment. Can you explain how the AI algorithm processes these physiological signals and translates them into actionable insights for retailers?

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Marktechpost

JUNE 9, 2024

Explainable AI (xAI) methods, such as saliency maps and attention mechanisms, attempt to clarify these models by highlighting key ECG features. xECGArch uniquely separates short-term (morphological) and long-term (rhythmic) ECG features using two independent Convolutional Neural Networks CNNs.

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Marktechpost

JANUARY 19, 2024

Neural network-based methods in estimating biological age have shown high accuracy but lack interpretability, prompting the development of a biologically informed tool for interpretable predictions in prostate cancer and treatment resistance. The most noteworthy result was probably obtained for the pan-tissue dataset.

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Marktechpost

MARCH 23, 2024

Although deep features have many applications in computer vision, they often need more spatial resolution to directly perform dense prediction tasks like segmentation and depth prediction due to models that aggressively pool information by models over large areas. It provides sub-feature information to train the upsampler.

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IBM Journey to AI blog

AUGUST 9, 2024

In short, predictive AI helps enterprises make informed decisions regarding the next step to take for their business. 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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AI News

MAY 14, 2024

Pioneering capabilities The introduction of GPT-4o marks a leap from its predecessors by processing all inputs and outputs through a single neural network. This approach enables the model to retain critical information and context that were previously lost in the separate model pipeline used in earlier versions.

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Unite.AI

MARCH 20, 2025

Seamlessly integrated with third party digital pathology software solutions, scanning platforms and laboratory information systems, Ibexs AI-enabled workflows deliver automated high-quality insights that enhance patient safety, increase physician confidence and boost productivity.

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NVIDIA

MARCH 5, 2025

Ninety percent of information transmitted to the human brain is visual. Time Stamps 2:03 Nelson explains Roboflows aim to make the world programmable through computer vision. The importance of sight in understanding the world makes computer vision essential for AI systems. 22:15 How multimodalilty allows AI to be more intelligent.

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Unite.AI

JUNE 26, 2023

Theoretical frameworks such as Integrated Information Theory (IIT), Global Workspace Theory (GWT), and Artificial General Intelligence (AGI) provide a frame of reference for how AI consciousness can be achieved. However, researchers are trying to take a step towards a human-like mind by adding a memory aspect to neural networks.

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Towards AI

JANUARY 4, 2024

Source from the author At least, that is my case, so I put together this post to explain the different concepts in graph neural networks (GNNs) in a way that is more intuitive and beginner-friendly, complemented with a code example.

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Unite.AI

NOVEMBER 14, 2024

Can you explain the process behind training DeepL's LLM? And as a research-led company, everything we do is informed by our mission to break down language barriers, and the feedback we’re hearing from customers and businesses. Any organization considering AI tools should always ask these questions when evaluating models and companies.

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NYU Center for Data Science

DECEMBER 3, 2024

The research revealed that regardless of whether a neural network is trained to recognize images from popular computer vision datasets like ImageNet or CIFAR, it develops similar internal patterns for processing visual information. The analogy to astrophysics is particularly apt.

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Unite.AI

NOVEMBER 6, 2023

Neural Network: Moving from Machine Learning to Deep Learning & Beyond Neural network (NN) models are far more complicated than traditional Machine Learning models. Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning.

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Unite.AI

APRIL 30, 2024

It includes deciphering neural network layers , feature extraction methods, and decision-making pathways. These systems rely heavily on neural networks to process vast amounts of information. During training, neural networks learn patterns from extensive datasets.

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Flipboard

DECEMBER 3, 2024

This conversational agent offers a new intuitive way to access the extensive quantity of seed product information to enable seed recommendations, providing farmers and sales representatives with an additional tool to quickly retrieve relevant seed information, complementing their expertise and supporting collaborative, informed decision-making.

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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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NYU Center for Data Science

APRIL 11, 2025

The touted benefits of neural network-based image compression methods virtually disappear when applied to real-world, high-resolution images. Previous papers have tried to use low-resolution datasets like MNIST, which is just 2828 grayscale pixels, or CIFAR with 3232 RGB pixels, Su explained.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

In this post, we discuss how to use LLMs from Amazon Bedrock to not only extract text, but also understand information available in images. Solution overview In this post, we demonstrate how to use models on Amazon Bedrock to retrieve information from images, tables, and scanned documents. 90B Vision model.

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Unite.AI

JUNE 4, 2024

With their advanced capabilities in processing and generating human-like text, LLMs perform intricate tasks such as real-time information retrieval and question answering. However, they operate as “ black boxes ,” providing limited transparency and explainability regarding how they produce certain outputs.

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Large Language Models Neural Network LLM Explainability 221

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. Explainability also aligns with business ethics and regulatory compliance.

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