Marktechpost

SEPTEMBER 3, 2023

Credit assignment in neural networks for correcting global output mistakes has been determined using many synaptic plasticity rules in natural neural networks. Methods of biological neuromodulation have inspired several plasticity algorithms in models of neural networks.

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Analytics Vidhya

JULY 25, 2022

Introduction Computer Vision Is one of the leading fields of Artificial Intelligence that enables computers and systems to extract useful information from digital photos, movies, and other visual inputs. It uses Machine Learning-based Model Algorithms and Deep Learning-based Neural Networks for its implementation. […].

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Unite.AI

DECEMBER 26, 2024

AI algorithms can be trained on a dataset of countless scenarios, adding an advanced level of accuracy in differentiating between the activities of daily living and the trajectory of falls that necessitate concern or emergency intervention.

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Marktechpost

DECEMBER 10, 2024

Artificial Neural Networks (ANNs) have become one of the most transformative technologies in the field of artificial intelligence (AI). Artificial Neural Networks are computational systems inspired by the human brain’s structure and functionality. How Do Artificial Neural Networks Work?

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Marktechpost

FEBRUARY 8, 2024

With the world of computational science continually evolving, physics-informed neural networks (PINNs) stand out as a groundbreaking approach for tackling forward and inverse problems governed by partial differential equations (PDEs). Despite these efforts, the search for an optimal solution remains ongoing.

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Unite.AI

JANUARY 18, 2025

While Central Processing Units (CPUs) and Graphics Processing Units (GPUs) have historically powered traditional computing tasks and graphics rendering, they were not originally designed to tackle the computational intensity of deep neural networks.

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Unite.AI

JANUARY 22, 2025

These functions are anchored by a comprehensive user management system that controls access to sensitive information and maintains secure connections between patient records and user profiles. The system's intelligence stems from its neural network-based Concept Processor, which observes and learns from every interaction.

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TensorFlow

FEBRUARY 6, 2024

Yet most machine learning (ML) algorithms allow only for regular and uniform relations between input objects, such as a grid of pixels, a sequence of words, or no relation at all. Apart from making predictions about graphs, GNNs are a powerful tool used to bridge the chasm to more typical neural network use cases.

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Unite.AI

MARCH 24, 2024

Most AI systems operate within the confines of their programmed algorithms and datasets, lacking the ability to extrapolate or infer beyond their training. Central to this advancement in NLP is the development of artificial neural networks, which draw inspiration from the biological neurons in the human brain.

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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

JANUARY 24, 2024

This shift is driven by neural networks that learn through self-supervision, bolstered by specialized hardware. However, the dawn of deep learning brought about a paradigm shift in data representation, introducing complex neural networks that generate more sophisticated data representations known as embeddings.

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AssemblyAI

SEPTEMBER 4, 2023

Data compression plays a pivotal role in today’s digital world, facilitating efficient storage and transmission of information. The MP3 encoding algorithm significantly changed how we store and share music data and stands as a famous example. At its core, it's an end-to-end neural network-based approach.

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Unite.AI

SEPTEMBER 5, 2024

Biological systems have fascinated computer scientists for decades with their remarkable ability to process complex information, adapt, learn, and make sophisticated decisions in real time. The complex web of cellular signaling pathways acts as the information processing system, allowing for massively parallel computations within the cell.

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Unite.AI

DECEMBER 8, 2023

These systems, typically deep learning models, are pre-trained on extensive labeled data, incorporating neural networks for self-attention. Each neuron in every layer of a fast feedforward network is interconnected with every neuron in the next layer, thus making FFF neural networks a fully connected 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. To tackle this challenge, DeepMind has created a tool called Gemma Scope.

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Marktechpost

OCTOBER 1, 2023

Both spatial and temporal information are crucial in spatial-temporal applications like traffic and weather forecasting. Researchers have created Memory-based Temporal Graph Neural Networks (M-TGNNs) that store node-level memory vectors to summarize independent node history to make up for the lost history.

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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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AI News

JULY 22, 2024

This article delves into how AI algorithms are transforming sports betting, providing actual data, statistics, and insights that demonstrate their impact. AI algorithms can analyse vast amounts of data, recognise patterns, and make predictions with remarkable accuracy. Data collection and processing AI algorithms thrive on data.

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Marktechpost

OCTOBER 8, 2023

The brain is hierarchically organized, with lower-level sensory processing areas sending information to higher-level cognitive and decision-making regions. The brain processes information in parallel, with different regions and networks simultaneously working on various aspects of perception, cognition, and motor control.

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Unite.AI

JULY 23, 2024

By leveraging advances in artificial intelligence (AI) and neuroscience, researchers are developing systems that can translate the complex signals produced by our brains into understandable information, such as text or images. Once the brain signals are collected, AI algorithms process the data to identify patterns.

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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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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

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

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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Marktechpost

JULY 9, 2024

Furthermore, many applications now need AI algorithms to adapt to individual users while ensuring privacy and reducing internet connectivity. This is the capacity to learn from new situations constantly without losing any of the information that has already been discovered. Join our Telegram Channel and LinkedIn Gr oup.

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AI Weekly

NOVEMBER 23, 2023

forbes.com Applied use cases From Data To Diagnosis: A Deep Learning Approach To Glaucoma Detection When the algorithm is implemented in clinical practice, clinicians collect data such as optic disc photographs, visual fields, and intraocular pressure readings from patients and preprocess the data before applying the algorithm to diagnose glaucoma.

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Marktechpost

FEBRUARY 28, 2025

The integration of deep learning with sampling algorithms has shown promise in continuous domains, but there remains a significant gap in effective sampling approaches for discrete distributions – despite their prevalence in applications ranging from statistical physics to genomic data and language modeling.

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Marktechpost

FEBRUARY 12, 2024

Deep convolutional neural networks (DCNNs) have been a game-changer for several computer vision tasks. As a result, many people are interested in finding ways to maximize the energy efficiency of DNNs through algorithm and hardware optimization. They work well with preexisting DCNNs and are computationally efficient.

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Marktechpost

APRIL 26, 2024

Here, we explore the algorithms that drive neuromorphic computing, its potential use cases, and its diverse applications. Algorithms in Neuromorphic Computing Neuromorphic computing leverages unique algorithms to mimic neurobiological architectures inherent to the nervous system.

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Towards AI

NOVEMBER 8, 2024

One powerful tool for this purpose is the Gated Recurrent Unit (GRU) network. GRUs have gained popularity because they balance two key aspects: they capture long-term dependencies in text (helping the machine remember relevant information across sentences) and they do so efficiently, keeping things fast and manageable.

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Pickl AI

JANUARY 21, 2025

Summary: Machine Learning algorithms enable systems to learn from data and improve over time. These algorithms are integral to applications like recommendations and spam detection, shaping our interactions with technology daily. These intelligent predictions are powered by various Machine Learning algorithms.

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AssemblyAI

MAY 3, 2023

With these fairly complex algorithms often being described as “giant black boxes” in news and media, a demand for clear and accessible resources is surging. Artificial neural networks consist of interconnected layers of nodes, or “neurons” which work together to process and learn from data.

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Marktechpost

NOVEMBER 27, 2024

At its core, machine learning algorithms seek to identify patterns within data, enabling computers to learn and adapt to new information. 2) Logistic regression Logistic regression is a classification algorithm used to model the probability of a binary outcome. Sigmoid Kernel: Inspired by neural networks.

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Marktechpost

OCTOBER 7, 2024

Recurrent neural networks (RNNs) have been foundational in machine learning for addressing various sequence-based problems, including time series forecasting and natural language processing. RNNs are designed to handle sequences of varying lengths by maintaining an internal state that captures information across time steps.

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Unite.AI

DECEMBER 18, 2023

Position-wise Feed-Forward Networks : After attention, a simple neural network processes the output of each position separately and identically. Key features of Mamba include: Selective SSMs : These allow Mamba to filter irrelevant information and focus on relevant data, enhancing its handling of sequences.

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Marktechpost

NOVEMBER 25, 2024

Natural neural systems have inspired innovations in machine learning and neuromorphic circuits designed for energy-efficient data processing. Researchers have developed alternative learning mechanisms tailored for spiking neural networks (SNNs) and neuromorphic hardware to address these challenges. Training on MNIST achieved 95.7%

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NVIDIA

MARCH 19, 2025

Making Ray Tracing a Reality Once NVIDIA Research was founded, its members began working on GPU-accelerated ray tracing, spending years developing the algorithms and the hardware to make it possible. Instead, it draws a fraction of the pixels and gives an AI pipeline the information needed to create the image in crisp, high resolution.

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