A naukri.com initiative
Neural Networks News
Medium
2w
213
Day 16: Building Blocks of Neural Networks: Perceptrons and Multi-Layer Networks
- A perceptron is a decision-making unit for computers that takes inputs, weighs them, and outputs either a 1 or 0.
- Perceptrons can only solve simple problems and are not effective in classifying complex images.
- Multi-layer networks are teams of perceptrons working together and can solve more complex problems.
- Multi-layer networks consist of multiple layers, with each layer passing data to the next layer until a final decision is made.
Read Full Article
12 Likes
Brighter Side of News
2w
196
Image Credit: Brighter Side of News
Optical deep neural networks are revolutionizing AI computation
- Modern AI systems rely on deep neural networks that demand immense computational resources, which, in turn, requires innovative hardware solutions.
- Photonic hardware that uses light for computation offers a transformative solution to such issues and recent advancements in this technology is setting new benchmarks for AI hardware.
- Photonic hardware is much more energy-efficient and faster than traditional electronic processors as it can process information by manipulating light without the need for optical-to-electrical conversions.
- Researchers have demonstrated a fully integrated photonic processor capable of performing all key computations of a deep neural network. The chip is energy-efficient and offers significantly less latency as compared to traditional hardware.
- The new system encodes neural network parameters into light and performs computations using programmable beam splitters and Nonlinear Optical Function Units, which significantly reduce latency and energy consumption.
- The photonic approach completed computations in a fraction of the time with comparable performance to traditional hardware.
- This breakthrough in photonic hardware has broad implications for rapid and energy-efficient computation-based applications in areas such as scientific instrumentation, telecommunications, and autonomous systems.
- The team plans to scale the device and integrate it with real-world systems like cameras and telecommunications networks. They are also exploring new algorithms to leverage optical advantages for faster and more energy-efficient training.
- This breakthrough represents a critical step towards realizing the full potential of photonic deep neural networks to enable fundamentally different scaling laws of computation.
- The chip’s ability to perform real-time training further expands its potential, particularly in adaptive systems that require continuous learning.
Read Full Article
11 Likes
Medium
3w
202
Image Credit: Medium
Embedding the Unseen: Techniques for Representing Graphs in Neural Networks
- A straightforward approach to graph embedding involves obtaining node embeddings and aggregating them for all nodes in the graph.
- An enhanced approach involves introducing a virtual node that connects to all other nodes and represents the entire graph.
- Another method involves anonymous random walks to capture graph structural features effectively for embedding purposes.
- Graph embeddings are versatile tools with applications in tasks such as graph classification.
Read Full Article
12 Likes
Medium
3w
83
Image Credit: Medium
Speed Up Stress Analysis and Simulations using AI
- The article discusses the potential use of Artificial Neural Networks (ANNs) to accelerate stress analysis and simulations, enabling quicker simulations, faster decision-making, and design optimization in engineering.
- Finite Element Method (FEM) is a cornerstone in structural analysis, allowing engineers to model complex components by discretizing them into smaller elements. However, the complexity of the problem increases, so does the computational cost.
- Hybrid approaches combining FEM with ANNs are emerging as a powerful solution, offering faster predictions that work as pilot values for proper adjustments in the model before a full-fledged finite element simulation may be carried out again.
- Using ANNs to predict outcomes based on prior FEM data allows engineers to gain insights into the sensitivity of their design much more efficiently, without needing to rerun FEM simulations for every parameter change.
- Artificial Neural Networks (ANNs) are machine learning algorithms that excel at identifying patterns and making predictions from complex data. In structural analysis, they offer a major advantage: speed. Once trained on data from FEM simulations, ANNs can quickly predict required primary and secondary variables such as deformations, strains, stresses, etc. in new configurations, bypassing the need for time-consuming computations.
- To make things clearer, the article takes a simple example of a plate with a misaligned hole, or a plate with a misaligned hole with the wrong diameter. To analyze plates with holes, the potential for misalignment of the hole or slightly too small or too-large hole diameter is one of the significant challenges, especially when considering real-world applications.
- The article further explains how ANN, when trained with FEM data, serves as a powerful tool for quick stress predictions and simulations in structural and mechanical analysis.
- The combination of FEM with ANN brings speed and adaptability, transforming how engineers approach structural analysis and optimization. The future of structural and mechanical simulations seems to be a hybrid FEM-ANN approach.
- To train the ANN, FEM generates a diverse dataset that covers a wide range of scenarios and inputs for simpler geometry. Larger simulations are carried out for complex geometry. ANNs offer quick approximations, which makes them ideal for tasks like design iterations or real-time decision-making where quick feedback is crucial.
- ANN, when trained with FEM data, serves as a powerful tool for quick stress predictions and simulations in structural and mechanical analysis. The future of structural and mechanical simulations seems to be hybrid FEM-ANN approach.
Read Full Article
5 Likes
Medium
3w
335
Image Credit: Medium
Artificial Neural Networks (ANN’s)
- Artificial Neural Networks (ANN's) are mathematical models inspired by the human brain that process and learn from information.
- ANN's consist of interconnected layers, including input, hidden, and output layers, that mimic the functioning of neurons.
- Perceptron-based ANN's use forward propagation and back propagation to process data, adjust weights, and optimize accuracy.
- ANN's have various applications, including image recognition, recommendation systems, and autonomous vehicles.
Read Full Article
20 Likes
Medium
3w
379
Image Credit: Medium
How Neural Network Depth Affects Training Loss: Insights and Practical Lessons
- Neural network depth significantly affects training loss.
- Weights are initialized randomly using Gaussian noise.
- Performance metrics are computed using mean squared error.
- The direction of weights is adjusted opposite to the gradient to reduce loss.
Read Full Article
22 Likes
Medium
3w
97
Image Credit: Medium
Unlocking the Future of Medicine: How Generative AI is Revolutionizing Neuroscience
- Generative AI is revolutionizing neuroscience by enabling researchers to generate analyses of whole human brains at a cellular level.
- The IIT Madras Brain Centre is using generative AI to develop innovative applications for neuroscience research.
- Visual question-answering models and large language models are being used to analyze brain imaging data and provide insights into neural activity patterns.
- Generative AI has the potential to revolutionize neuroscience research and make life-saving discoveries in healthcare.
Read Full Article
5 Likes
Medium
4w
178
Image Credit: Medium
Brain vs Bots: Understanding AI Architecture Through the Lens of Human Cognition
- Researchers are in a race to achieve Artificial General Intelligence (AGI), inspired by the complexity and adaptability of the human brain.
- This deep dive explores the architectural parallels between Large Language Models (LLMs) in AI systems and the human brain.
- Cluster Protocol is a decentralized infrastructure for AI that democratizes access to AI development and offers pre-built AI templates.
- Cluster Protocol enables the creation of intelligent agentic workflows and automates daily tasks.
Read Full Article
10 Likes
Medium
4w
248
Image Credit: Medium
How Do Machines Learn Like Us? A Look at the Connectionist Approach
- In the sixth article of From Pen to Algorithm, a shift from symbolic approaches to connectionist approaches in AI is discussed.
- Symbolic approaches were unable to replicate the experiential and human-like understanding required of machines in crisis management.
- Connectionist approaches, based on link-based methods, gained traction as they offered a way to achieve greater intelligence in machines.
- The article outlines historical events and different AI approaches in discussing the shift towards connectionist approaches.
Read Full Article
14 Likes
Medium
4w
40
Image Credit: Medium
5 Mistakes Every Data Aspirant Makes in the Beginning & How to Avoid Them
- Establishing a career in data science is an exciting yet overwhelming journey, and many people struggle to stay on track, often skipping important steps along the way.
- Beginners in data science often make the mistake of jumping into technicalities without fully understanding foundational knowledge, causing confusion and frustration.
- Neglecting the importance of math and statistics is a common mistake made by beginner data scientists, as these concepts are fundamental to data science.
- To avoid mistakes, it is essential for data aspirants to develop a strong foundation in math and statistics and continuously improve their skills in these areas.
Read Full Article
2 Likes
Medium
4w
405
Image Credit: Medium
The Role of Artificial Intelligence in Decoding Animal Communication: Ethical Implications and…
- AI can help us understand animals, enabling us to treat them with respect and consideration.
- AI machine learning algorithms analyze animal vocalizations and help us understand them.
- AI can help us decode how chickens communicate and monitor their emotional state.
- AI technology can automate and expedite the process of decoding animal behavior.
- AI cannot accurately understand animal communication's context, resulting in ethical issues.
- AI has the potential to communicate with animals in specific scenarios such as mating or alarm signaling.
- Using AI to decode animal vocalizations and behaviors could contribute to animal welfare practices such as farming, conservation, and environmental monitoring.
- The need for further improvement to decode animal communication & take into account the background noises and expand the datasets is acknowledged.
- AI must be applied cautiously to ensure the well-being and conservation of animals.
- Researchers proposed an evaluation system to assess LLM’s incorporation of welfare for future animal-friendly AI.
Read Full Article
24 Likes
Medium
4w
405
Image Credit: Medium
AI vs Cheaters: A New Approach in Competitive Gaming
- Cheating in online competitive games is an issue that most companies in that genre have to deal with. Game publishers are negatively impacted because this issue can dilute the value of the game and impact revenue streams for the publishers because players would buy less in-game content as a result of cheating.
- Game developers are trying to stop cheating in many different ways such as cheat detection software and anti-cheat software with kernel level access in the user's operating system. However, cheating exploits continue to be patched, and a long-term solution is needed.
- Valve, Riot Games, Easy Anti Cheat, and other videogame and anti-cheat companies have been dealing with cheating issues for years and patching cheating exploits isn't cutting it.
- In February 2017, Valve announced that they would start implementing machine learning in their anti-cheat software in hopes of identifying cheating patterns and putting a stop to the cat and mouse game.
- The strategy for cheat detection is to analyze player behavior in real-time (on server-side) with neural networks trained with vast amounts of data, analyzing reaction time, precision, and number of missed shots.
- The system needs to be manually monitored for false positives, which has been a big hurdle for Valve in recent years.
- The AI-based anti-cheat system isn't perfect and doesn't detect every cheating software feature. Still, it reduces the advantage gained by cheating software and is the new standard in the shooting games industry.
- Despite being promising, this system won't entirely eradicate cheating, and new ways of cheating will emerge, but they won't give a significant advantage as they once did.
Read Full Article
24 Likes
Medium
4w
213
Mastering Generative Adversarial Networks (GANs): The Art and Math of AI Creation
- At the heart of GANs lies a game-theoretic concept where two networks compete
- The Mathematics of GANs: Framed as a minimax optimization problem
- Applications of GANs: Image generation, healthcare, gaming, data augmentation, deepfake creation and detection
- Challenges and Future Directions: Stabilizing training, improving evaluation techniques, reducing resource dependency
Read Full Article
12 Likes
Medium
4w
111
Image Credit: Medium
The Marvelous World of GANs: Teaching Machines to Dream in Pixels
- GANs, or Generative Adversarial Networks, are neural networks that consist of a Generator and a Discriminator.
- The Generator tries to create realistic data, while the Discriminator judges whether the data is real or fake.
- GANs learn through a process of competition and optimization, resulting in the Generator producing data that can fool even human eyes.
- GANs have various applications in entertainment, fashion, healthcare, and art, but also come with challenges and ethical concerns.
Read Full Article
6 Likes
Medium
4w
285
Exploring Neural Networks and Deep Learning: AI in 2025
- Neural networks are computational models inspired by the human brain, designed to solve complex problems and recognize patterns.
- Deep learning is a subfield of machine learning that utilizes neural networks with multiple layers for improved accuracy and performance.
- Combining different types of neural networks, such as CNNs and RNNs, is becoming a trend to address complex problems.
- Challenges in neural networks and deep learning include data accessibility, computational power requirements, ethical use, and interpretability.
Read Full Article
17 Likes
For uninterrupted reading, download the app