Marktechpost

MAY 7, 2024

The ability of systems to adapt over time without losing previous knowledge, known as continual learning (CL), poses a significant challenge. While adept at processing large amounts of data, neural networks often suffer from catastrophic forgetting, where acquiring new information can erase what was learned previously.

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Towards AI

JANUARY 29, 2025

Generative AI is powered by advanced machine learning techniques, particularly deep learning and neural networks, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Adaptability and Continuous Learning 4. Study neural networks, including CNNs, RNNs, and LSTMs.

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AI Engineer Generative AI Neural Network BERT 98

Unite.AI

MARCH 2, 2025

From early neural networks to todays advanced architectures like GPT-4 , LLaMA , and other Large Language Models (LLMs) , AI is transforming our interaction with technology. This enables real-time adaptability without altering the core network structure, making it highly effective for continuous learning applications.

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Marktechpost

JULY 7, 2024

Artificial neural networks (ANNs) traditionally lack the adaptability and plasticity seen in biological neural networks. The inability of ANNs to continuously adapt to new information and changing conditions hinders their effectiveness in real-time applications such as robotics and adaptive systems.

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AI News

AUGUST 13, 2024

Enter SingularityNET’s ambitious plan: a “multi-level cognitive computing network” designed to host and train the incredibly complex AI architectures required for AGI.

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Unite.AI

DECEMBER 26, 2024

These deep learning algorithms get data from the gyroscope and accelerometer inside a wearable device ideally worn around the neck or at the hip to monitor speed and angular changes across three dimensions.

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Marktechpost

NOVEMBER 25, 2024

This limits their adaptability, reducing their ability to learn autonomously after deployment. Researchers have developed alternative learning mechanisms tailored for spiking neural networks (SNNs) and neuromorphic hardware to address these challenges. Synfire gating ensures autonomous spike routing. Check out the Paper.

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Unite.AI

APRIL 30, 2024

It includes deciphering neural network layers , feature extraction methods, and decision-making pathways. The Inner Dialogue: How AI Systems Think AI systems, such as chatbots and virtual assistants, simulate a thought process that involves complex modeling and learning mechanisms.

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Unite.AI

SEPTEMBER 4, 2024

Deep Neural Network (DNN) Models: Our core infrastructure utilizes multi-stage DNN models to predict the value of each impression or user. This granular approach allows each model to learn features most crucial for specific conversion events, enabling more precise targeting and bidding strategies compared to one-size-fits-all models.

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Marktechpost

MAY 4, 2024

Researchers in this field aim to create systems capable of continuous learning and adaptation, ensuring they remain relevant in dynamic environments. A significant challenge in developing AI models lies in overcoming the issue of catastrophic forgetting, where models fail to retain previously acquired knowledge when learning new tasks.

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Neural Network Continuous Learning Large Language Models AI 145

Pickl AI

SEPTEMBER 3, 2024

Summary: Artificial Neural Network (ANNs) are computational models inspired by the human brain, enabling machines to learn from data. Introduction Artificial Neural Network (ANNs) have emerged as a cornerstone of Artificial Intelligence and Machine Learning , revolutionising how computers process information and learn from data.

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Unite.AI

FEBRUARY 6, 2025

Immersing oneself in the AI community can also greatly enhance the learning process and ensure that ethical AI application methods can be shared with those who are new to the field. Participating in meetups, joining online forums, and networking with fellow AI enthusiasts provide opportunities for continuous learning and motivation.

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Marktechpost

MAY 2, 2024

Multi-layer perceptrons (MLPs), or fully-connected feedforward neural networks, are fundamental in deep learning, serving as default models for approximating nonlinear functions. Thus, while MLPs remain crucial, there’s ongoing exploration for more effective nonlinear regressors in neural network design.

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Pickl AI

NOVEMBER 13, 2024

Summary: Backpropagation in neural network optimises models by adjusting weights to reduce errors. Despite challenges like vanishing gradients, innovations like advanced optimisers and batch normalisation have improved their efficiency, enabling neural networks to solve complex problems.

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Marktechpost

OCTOBER 1, 2024

Continual learning is a rapidly evolving area of research that focuses on developing models capable of learning from sequentially arriving data streams, similar to human learning. The core issue is that these methods are not evaluated under the constraints of continual learning.

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The MLOps Blog

FEBRUARY 22, 2024

TL;DR: In many machine-learning projects, the model has to frequently be retrained to adapt to changing data or to personalize it. Continual learning is a set of approaches to train machine learning models incrementally, using data samples only once as they arrive. What is continual learning?

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Unite.AI

JULY 25, 2023

Known as “catastrophic forgetting” in AI terms, this phenomenon severely impedes the progress of machine learning , mimicking the elusive nature of human memories. This insight is pivotal in understanding how continual learning can be optimized in machines to closely resemble the cognitive capabilities of humans.

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Towards AI

OCTOBER 3, 2024

Liquid Neural Networks: Research focuses on developing networks that can adapt continuously to changing data environments without catastrophic forgetting. These networks excel at processing time series data, making them suitable for applications like financial forecasting and climate modeling.

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Neural Network Algorithm Continuous Learning AI 124

Pickl AI

MARCH 7, 2025

Key Takeaways Neuromorphic systems replicate the human brain’s neural networks. Systems learn dynamically, mimicking the human brain’s synaptic plasticity. These systems use spiking neural networks (SNNs) , where artificial neurons process information only when triggered by electrical signals (spikes).

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Neural Network Robotics Algorithm Automation 52

Unite.AI

JULY 25, 2023

An AI feedback loop is an iterative process where an AI model's decisions and outputs are continuously collected and used to enhance or retrain the same model, resulting in continuous learning, development, and model improvement. A sample of model outcomes for multiple model generations affected by Model Collapse.

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Unite.AI

DECEMBER 12, 2024

AI is undergoing what I like to call a Goldilocks momenta balance where technology, demand, and tools like neural networks are just right for transformation. In this new model, AI is not just a tool but a foundational layer that learns and evolves alongside the business.

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Neural Network Continuous Learning AI AI 246

IBM Journey to AI blog

JANUARY 10, 2024

The category of AI algorithms includes ML algorithms, which learn and make predictions and decisions without explicit programming. AI systems, particularly complex models like deep neural networks, can be hard to control and interpret. This process can prove unmanageable, if not impossible, for many organizations.

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Marktechpost

AUGUST 3, 2024

Multi-layer perceptrons (MLPs) have become essential components in modern deep learning models, offering versatility in approximating nonlinear functions across various tasks. However, these neural networks face challenges in interpretation and scalability. Check out the Paper and GitHub.

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Computer Vision Machine Learning NLP Neural Network 121

Unite.AI

AUGUST 28, 2023

Neural Networks & Deep Learning : Neural networks marked a turning point, mimicking human brain functions and evolving through experience. Deep learning techniques further enhanced this, enabling sophisticated image and speech recognition. ” BabyAGI responded with a well-thought-out plan.

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Marktechpost

MARCH 27, 2024

Continual Learning (CL) poses a significant challenge for ASC models due to Catastrophic Forgetting (CF), wherein learning new tasks leads to a detrimental loss of previously acquired knowledge. Baselines included both non-continual and continual learning approaches, with adaptations for domain-incremental learning.

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Unite.AI

AUGUST 3, 2023

The study of psychology sparked my fascination with the human mind and intelligence, particularly the process of skills learning and expertise development. Meanwhile, statistics provided the mathematical foundation to explore artificial neural networks , inspired by our biological brain. It’s a thrilling journey.

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Marktechpost

AUGUST 18, 2024

presents an innovative solution that integrates the symbolic strength of deep neural networks with the adaptability of a visual memory database. This adaptability is crucial for applications requiring continuous learning and updating in dynamic environments.

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Unite.AI

DECEMBER 19, 2023

DL is built on a neural network and uses its “brain” to continuously train itself on raw data. This data is continually learning on its own without our input. We tweak outcomes to teach the brain and then it continues to learn.

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Flipboard

APRIL 17, 2023

We will put everything we learned so far into gradually building a multilayer perceptron (MLP) with PyTrees. We hope this post will be a valuable resource as you continue learning and exploring the world of JAX. In the context of a neural network, a PyTree can be used to represent the weights and biases of the network.

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Unite.AI

JUNE 20, 2024

Large Language Models (LLMs) are a type of neural network model trained on vast amounts of text data. These models learn to understand and generate human-like language by analyzing patterns and relationships within the training data.

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Pickl AI

APRIL 19, 2024

Select the right learning path tailored to your goals and preferences. Continuous learning is critical to becoming an AI expert, so stay updated with online courses, research papers, and workshops. Specialise in domains like machine learning or natural language processing to deepen expertise.

Neural Network 52

Neural Network Natural Language Processing Machine Learning Computer Vision 52

Flipboard

FEBRUARY 6, 2023

The tool, not yet generally available, can “communicate” in natural language and collaborate with users on code changes, Steinberger claims — operating like a pair programmer that’s able to understand and continuously learn more about the context of both coding projects and developers.

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Software Engineer Neural Network Large Language Models Continuous Learning 123

AWS Machine Learning Blog

SEPTEMBER 23, 2024

The incorporation of continuous learning enables the model training to automatically adapt and learn from new challenging scenarios as they arise. This self-improving capability helps ensure the system maintains high performance, even as shopping environments continue to evolve.

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Pickl AI

SEPTEMBER 23, 2024

Summary: This guide covers the most important Deep Learning interview questions, including foundational concepts, advanced techniques, and scenario-based inquiries. Gain insights into neural networks, optimisation methods, and troubleshooting tips to excel in Deep Learning interviews and showcase your expertise.

Deep Learning 52

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Towards AI

MARCH 3, 2025

Learn and Adapt: World models allow for continuous learning. These models leverage convolutional and recurrent neural networks to capture both spatial features and temporal dynamics. As a robot interacts with its surroundings, it refines its internal model to improve prediction accuracy.

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Robotics Neural Network Natural Language Processing Computer Vision 77

Google Research AI blog

JUNE 23, 2023

The Heuristic Aided Learned Preference (HALP) framework is a meta-algorithm that uses randomization to merge a lightweight heuristic baseline eviction rule with a learned reward model. We discuss how HALP has improved infrastructure efficiency and user video playback latency for YouTube’s content delivery network.

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Viso.ai

APRIL 29, 2024

STNs are used to “teach” neural networks how to perform spatial transformations on input data to improve spatial invariance. Commonly Used Technologies and Frameworks For Spatial Transformer Networks When it comes to implementation, the usual suspects, TensorFlow and PyTorch , are the go-to backbone for STNs.

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