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Neural Networks News
Hackernoon
2w
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Image Credit: Hackernoon
AI Knows Everything Except Why You’re Sad
- AI continues to make headlines with advancements like OpenAI's GPT-4o, overshadowing Google's recent I/O event.
- AI, based on data and statistics, exhibits a seemingly magical ability to predict and generate content.
- AI is projected to evolve to feel like interacting with a real person, showcasing vast knowledge and capabilities.
- Generative AI will revolutionize various fields, impacting jobs and daily interactions.
- AI advancements will enable subjective and objective capabilities, enhancing personalized assistance.
- Companies like OpenAI, Google, Meta, and Apple are racing to develop all-knowing AI companions.
- Subjective AI will delve into private data, while Objective AI focuses on readily available information.
- AI is set to pass the Turing Test soon, demonstrating high levels of knowledge but lacking true intelligence or emotions.
- Despite concerns, the advent of AI is seen as a leap in efficiency, similar to past technological innovations.
- While AI may result in job displacement and societal shifts, it can also lead to new opportunities and improved quality of life.
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Medium
2w
34
Image Credit: Medium
Introducing Transfer Learning Fundamentals: CIFAR-10, MobileNetV2, Fine-Tuning, and Beyond
- This article introduces transfer learning fundamentals using the CIFAR-10 dataset and MobileNetV2 to tackle image classification tasks.
- Transfer Learning is described as a technique where knowledge from one task is reused to aid in solving a related task, making training more efficient.
- In the context of deep learning, early layers learn general features, later layers learn task-specific features, and transfer learning reuses early layers.
- However, transfer learning may not always provide a perfect fit, especially if domains are semantically distant or task objectives differ.
- The article delves into the process of using MobileNetV2 for feature extraction on the CIFAR-10 dataset and fine-tuning for improved accuracy.
- After initial training with MobileNetV2, the accuracy achieved was 85%, showcasing the effectiveness of transfer learning.
- Fine-tuning the model led to a test accuracy of 91%, demonstrating the power and efficiency of transfer learning in improving model performance.
- The article emphasizes the importance of leveraging pre-trained models for efficiency, especially in scenarios with limited data and computational resources.
- Readers are encouraged to explore further by attempting aggressive fine-tuning, trying different architectures, adjusting augmentation strategies, and deploying models to broader platforms.
- Overall, the article serves as a practical guide to understanding and implementing transfer learning in deep learning projects, providing a valuable foundation for future endeavors.
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Medium
3w
183
Image Credit: Medium
Spiking Neural Networks & their Potential
- Spiking Neural Networks (SNNs) aim to mimic the processing power of the human brain, which is highly energy-efficient and operates asynchronously.
- By capturing features of the brain in artificial systems, low-power and real-time AI models can be developed for small-scale hardware like sensors, drones, and robots.
- SNNs replicate the communication process of biological neurons, where spikes encode patterns, motions, and memory.
- Neuromorphic sensors like Dynamic Vision Sensors (DVS) capture changes in brightness in real-time events, enabling recognition of motion and temporal patterns.
- In SNN model creation, spikes are accumulated over time and used to train the network to emit spikes corresponding to target labels.
- Model training in SNNs involves using surrogate gradients to allow backpropagation through non-differentiable spike functions.
- SNNs are designed to run on neuromorphic hardware for energy efficiency, as opposed to traditional CPUs/GPUs, due to the event-driven nature of their computations.
- In comparison to traditional CNNs, SNNs can be much more energy-efficient when executed on neuromorphic hardware.
- The future of SNNs holds promise for efficient, adaptive, and biologically inspired AI models as hardware capabilities advance.
- Current challenges include the need for specialized neuromorphic hardware and further advancements in training deep SNNs.
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Medium
3w
118
Image Credit: Medium
The Perceptron: The Tiny Brain Cell That Sparked an AI Revolution
- A perceptron is a binary classifier that sorts inputs into two buckets based on weighted sum of inputs and bias.
- The step function of the perceptron outputs 1 if the input (weighted sum plus bias) is non-negative and 0 otherwise.
- The perceptron helps in classifying emails as spam or not spam by calculating the final output (0 or 1) based on the weighted sum of inputs and bias.
- Rosenblatt’s perceptron was the starting point of AI and paved the way for the evolution into neural networks like ChatGPT.
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Medium
3w
0
Image Credit: Medium
The Magic Behind Recognizing a Scribble: How Neural Networks Learn
- Neural networks revolutionize artificial intelligence and machine learning by learning from data rather than predefined rules.
- Neural networks consist of interconnected neurons organized into layers to process and transmit information.
- In digit recognition, the input layer represents pixels, the hidden layers assist in learning complex patterns, and the output layer gives the network's prediction.
- Weights, connections, and biases control the influence between neurons in different layers of the network.
- Neurons in hidden layers specialize in recognizing features like edges, curves, or structural components, leading to accurate classification.
- Training neural networks involves feeding them labeled datasets to refine weights and biases for accurate predictions.
- The network learns to recognize complex patterns by tuning millions of tiny knobs, gradually improving its predictions.
- Neural networks go beyond digit recognition, powering various applications like image classification, natural language processing, and more.
- Their success relies on large labeled datasets that enable networks to learn specific patterns for each task.
- Neural networks leverage interconnected neurons, layers, weights, biases, and activation functions to process input data and make predictions.
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Pymnts
3w
373
Image Credit: Pymnts
Anthropic-Backed Goodfire Raises $50 Million to Access AI’s ‘Internal Thoughts’
- Goodfire, an AI interpretability platform, raised $50 million in a Series A funding round.
- The funding will be used to expand research initiatives and develop Ember, their flagship interpretability platform.
- Goodfire aims to make neural networks easy to understand, design, and fix from the inside out.
- Anthropic, an AI startup, participated in the funding round, reflecting their belief in mechanistic interpretability for the responsible development of AI.
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Medium
3w
74
Image Credit: Medium
Can we B.A.N Backdoor Attacks in Neural Networks?
- Deep Neural Networks (DNNs) are vulnerable to backdoor attacks, where an attacker can tamper with the model to behave maliciously on specific inputs.
- Backdoor attacks can be injected during training or by altering the weights and biases of the neural network.
- Outsourcing training through Machine Learning as a Service (MLaaS) can introduce security risks, including receiving backdoored models.
- Backdoored neural networks perform well on regular inputs but exhibit misclassifications based on hidden triggers, posing risks in applications like autonomous driving.
- Methods like Neural Cleanse (NC) and FeatureRE have been developed to detect and reverse backdoors in neural networks.
- Recent advancements like BTIDBF and BAN aim to address feature space backdoor attacks by efficiently detecting triggers and utilizing adversarial noise.
- BAN approach involves generating adversarial neuron noise and masked feature maps to identify and differentiate between benign and backdoored neurons.
- BAN has shown efficiency and scalability in identifying backdoors in neural networks, achieving an average accuracy of about 97.22% across different architectures.
- Given the challenge of detecting backdoors that do not significantly impact model performance, continuous monitoring and research in this area are crucial for securing deep neural networks.
- Research and advancements in the field of neural network security, especially in combating backdoor attacks, are essential for maintaining the integrity of machine learning systems.
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Medium
3w
150
Peering Inside AI’s Black Box: What “Attribution Graphs” Reveal About the Secret Life of Claude 3.5
- Large language models like Anthropic’s Claude 3.5 can perform various tasks, yet their internal logic remains a mystery to most.
- Anthropic engineers have introduced attribution graphs, a technique that acts as an MRI scanner for neural networks.
- Attribution graphs trace activations to draw causal diagrams and explain why a specific token was predicted.
- This synthetic biology for AI aims to provide transparency and understanding of how the model works.
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Medium
3w
75
Image Credit: Medium
What is an Attention Mechanism in Deep Neural Networks
- The attention mechanism allows the selection of necessary information and/or increases its impact in a specific task.
- It dynamically assigns different weights to different parts of the input data, enabling the model to better understand the context and relationships between data elements.
- The attention mechanism is widely used in natural language processing, computer vision, and other fields.
- It is used in various ways, such as focusing on different channels or spatial locations in image processing, or at word and sentence levels for document classification.
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Medium
4w
323
AI Hallucination: When Smart Machines Make Dumb Mistakes — and What CMOs & CEOs Must Do About It
- AI hallucinations are a silent chaos, where smart machines like ChatGPT, Gemini, Claude, or AI-powered Google Search confidently provide false information.
- Executives are often unaware of these AI hallucinations.
- Hallucinating AI fabricates logic and data when lacking information, leading to inaccurate reports and features that nobody wants.
- The future lies in achieving a balance between AI and human intelligence, and CEOs and CMOs who achieve this balance will succeed in the next decade.
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Medium
4w
62
Image Credit: Medium
Types of Neural Networks: A Comprehensive Overview
- The diverse landscape of neural networks encompasses various types based on data flow, structure, learning paradigm, and functionality, reflecting ongoing innovation.
- Feedforward Neural Networks (FFNNs) feature unidirectional flow and interconnected neurons organized into layers, widely used for pattern recognition and classification tasks.
- FFNNs employ activation functions like sigmoid, tanh, and ReLU in neurons, trained using backpropagation for tasks like credit scoring and regression analysis.
- FFNNs' strengths include simplicity, efficient processing, and broad applicability, yet they struggle with sequential data and computational challenges.
- Deep Belief Networks, rooted in unsupervised learning and effective for feature extraction, find application in image and speech recognition tasks.
- Generative Adversarial Networks (GANs) utilize competing generator and discriminator networks for data generation, with applications in image and text processing.
- Autoencoders focus on data compression and noise reduction, while Siamese Neural Networks excel in learning similarity between input pairs for tasks like face recognition.
- Spatial Neural Networks are tailored for geospatial data analysis, offering enhanced accuracy but facing challenges in spatial heterogeneity handling.
- Transformers, powered by self-attention mechanisms, dominate NLP and computer vision tasks, emphasizing parallel processing and global context capture.
- Spiking Neural Networks (SNNs) mimic brain processes using discrete spikes for temporal data, with applications in neuromorphic computing and real-time decision-making.
- The evolving trends in neural networks encompass Neuromorphic Computing, Attention Mechanisms, Graph Neural Networks, Hybrid Models, and Self-Supervised Learning, among others.
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Semiengineering
4w
350
Image Credit: Semiengineering
Research Bits: April 22
- Researchers from Hewlett Packard Labs, Indian Institutes of Technology Madras, Microsoft Research, and University of Michigan built an AI acceleration platform based on heterogeneously integrated photonic ICs.
- Researchers from University of Pennsylvania and College of Staten Island developed a programmable photonic chip that can train nonlinear neural networks using light.
- Researchers from the Max Planck Institute for the Science of Light (MPL), Leibniz University Hannover, and Massachusetts Institute of Technology (MIT) demonstrated an all-optically controlled activation function based on traveling sound waves that is suitable for a range of optical neural network approaches and allows for operation in the synthetic frequency dimension.
- The platforms and chips developed by these research teams have the potential to advance the field of artificial intelligence and contribute to the development of energy-efficient and high-performance neural networks.
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21 Likes
Medium
4w
102
NeuroSymbiotic CodeMind: The Future of Coding with Your Subconscious and Living Code
- NeuroSymbiotic CodeMind (NSCM) is a revolutionary approach to coding that enables building software with subconscious thoughts.
- The core components of NSCM include the Neural Code Interface (NCI), Symbiotic AI Swarm (SAS), Living Codebase Protocol (LCP), and Global CodeMind Network (GCN).
- The NCI uses advanced brain-computer interfaces to capture subconscious patterns and translate them into code.
- The SAS is a decentralized network of AI agents specialized in various domains, working collaboratively to optimize code.
- The LCP allows codebases to evolve post-deployment, mutating and optimizing based on real-time data and neural inputs.
- The GCN is a decentralized platform where neural inputs, AI contributions, and living codebases are shared, enabling collective intelligence for software creation.
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6 Likes
Medium
4w
383
Image Credit: Medium
Natural Language Processing with Deep Learning
- This article explains core NLP techniques using deep learning, including tokenization, embeddings, sequence modeling with RNNs, and transformers.
- Natural Language Processing involves teaching machines to understand language structure, capture meaning and context, and perform tasks like translation, sentiment analysis, summarization, etc.
- Deep learning enables machines to learn these tasks directly from data without manually designing rules.
- Key techniques in NLP with deep learning include tokenization, embeddings, sequence modeling with RNNs, and transformers.
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23 Likes
Medium
4w
19
Vanishing Gradients: Why Deep Networks Sometimes Forget
- Vanishing gradients occur when gradients become very small as they propagate through a deep network.
- This leads to early layers receiving little to no signal and impairs learning.
- Vanishing gradients are common in networks with sigmoid or tanh activations, many layers, and poorly chosen initial weights.
- To mitigate vanishing gradients, techniques like ReLU activation, batch normalization, proper weight initialization, and skip connections are recommended.
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