Azizi Othman’s Post

🏁 Unlocking the Power of Siamese Networks in AI Have you ever wondered how AI determines whether two things are similar, even when they've never been seen before? Enter the Siamese Model, a deep learning architecture designed to compare and understand relationships between two inputs. Let's explore how it works, its potential, and how it can transform industries. How It Works A Siamese model uses two identical neural networks that share weights and parameters. These networks process two inputs (e.g., images, texts) independently and produce embeddings (feature vectors) for each input. These embeddings are then compared using metrics like Euclidean distance or cosine similarity to determine how "similar" the two inputs are. For instance, in facial recognition, the model can tell if two images belong to the same person by comparing their embeddings. What You Need To build and deploy a Siamese model, you need: 1. High-quality labeled data: Examples of matching and non-matching pairs. 2. A base architecture: Typically a Convolutional Neural Network (CNN) for images or a Recurrent Neural Network (RNN) for text. 3. Loss function: Use contrastive loss or triplet loss to optimize the model. 4. A deployment framework: Options include TensorFlow Serving, PyTorch TorchServe, or ONNX for real-time inference. Applications in Production Siamese models can be applied to: E-commerce: Product matching and duplicate detection. Healthcare: Medical image comparison, such as detecting changes in X-rays or MRIs. Authentication: Face verification for secure logins or ID validation. Content moderation: Detecting near-duplicate media to prevent plagiarism or misinformation. Risks and Challenges 1. Data Bias: Training data must be diverse; otherwise, the model may perform poorly on underrepresented groups. 2. Scalability: Comparing embeddings can become computationally expensive with large datasets. 3. Overfitting: Risk of overfitting if the model memorizes specific pairs instead of learning generalized features. 4. Interpretability: Understanding why the model predicts certain similarities can be non-intuitive. Benefits 1. Generalization: Once trained, the model can compare unseen pairs without retraining. 2. Efficiency: Generates embeddings that can be reused, saving computation time. 3. Versatility: Works with various data types—images, text, audio, and more. Scaling Siamese Models 1. Use vector databases like FAISS or Pinecone to store embeddings for efficient similarity searches. 2. Leverage distributed systems like Apache Kafka or Kubernetes to handle high traffic in production. 3. Optimize inference by quantizing the model or deploying on edge devices for real-time comparison. #deeplearning #ai #machinelearning #ml #siamese #CNN #RNN #Keras #Tensorflow

🛜 Navigating the Age of Intention with Neural Nets and Beyond The first Neural Net I ever implemented was set up using PMML: Predictive Model Markup Language. At first glance, it felt mundane: define input, refine it, invoke libraries, format the response. Another piece of technical plumbing, nothing groundbreaking, or so I thought. For those unfamiliar, PMML is a standard created by the Data Mining Group (DMG) for defining statistical and data-mining models. It’s the scaffolding for constructing a Neural Network. But in those early days, I couldn’t see much practical application for these resource-intensive systems. What could a Neural Network realistically achieve? It wasn’t long before an answer presented itself. A client approached looking to for an IoT platform to better serve their customers. Enter the concept of streaming real-time data from connected devices and pairing it with Machine Learning’s predictive models to deliver actionable insights. That’s where the value was, and the bonus checks proved it. Still, one question has always haunted me in this field: How do we make data processing more efficient? Sure, advancements in hardware like Neural Processing Units (NPUs) offer impressive quick wins such as faster calculations and lower energy consumption. NPUs are transforming on-device AI capabilities. Yet when deploying NPUs in production environments, especially for on-device Large Language Models (LLMs), we encounter significant challenges. The "semantic gap" between model architectures and hardware design persists, creating inefficiencies that limit real-world applications. But even as we work to bridge this gap, there’s a bigger horizon ahead. As reported just last week, the next frontier is quantum chips where Google Quantum AI team lead by Hartmut Neven have demonstrated how their Willow architecture is on the verge of redefining neural nets entirely. These chips promise to unravel temporal complexities, forcing us to rethink time, causality, and the interplay between humans, machines, and the ecosystems we inhabit. This brings us to the heart of the matter: as technology advances, how do we shape it to align with humanity’s deepest values and intentions? How do we ensure these tools enable collaboration, sustainability, and progress, rather than spiraling into inefficiency or unintended consequences? As we navigate this Age of Intention, we must ask ourselves not just what we can build, but why we’re building it, all within the context as to how it will shape the future of life and technology. I’d love to hear your thoughts on making these transitions more seamless. How do we prepare for a world where quantum and AI systems converge to reshape what we think is possible?

𝗧𝗵𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗼𝗳 𝗖𝗡𝗡𝘀: 𝗔 𝗟𝗮𝘆𝗲𝗿𝗲𝗱 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 At the heart of a CNN is its layered architecture, designed to process and learn from image data. It starts with an input image, typically represented as a tensor—a high-dimensional matrix retaining the spatial and depth information of the image. This input then travels through a sequence of layers, each serving a distinct purpose: 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿𝘀: These layers apply filters to the input image, extracting features like edges, textures, and patterns. By sliding these filters over the image, CNNs capture local dependencies and spatial hierarchies.    𝗥𝗲𝗟𝗨 𝗟𝗮𝘆𝗲𝗿𝘀: The Rectified Linear Unit (ReLU) introduces non-linearity into the model, enabling it to learn complex patterns. 𝗣𝗼𝗼𝗹𝗶𝗻𝗴 𝗟𝗮𝘆𝗲𝗿𝘀: These layers downsample the feature maps, reducing dimensionality and computation while retaining important information. Common pooling methods include max pooling and average pooling. 𝗙𝘂𝗹𝗹𝘆 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗲𝗱 𝗟𝗮𝘆𝗲𝗿𝘀: In these layers, neurons connect to all activations from the previous layer, integrating the learned features to make the final prediction. This is where the CNN transforms spatial features into a class score. 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗖𝗡𝗡𝘀: The Path to Optimization Training a CNN involves two main processes: the forward pass and the backward pass. 𝗙𝗼𝗿𝘄𝗮𝗿𝗱 𝗣𝗮𝘀𝘀: The input image passes through the network layer by layer, resulting in a prediction.    𝗕𝗮𝗰𝗸𝘄𝗮𝗿𝗱 𝗣𝗮𝘀𝘀: This involves backpropagation, where the error between the prediction and the ground truth is computed and propagated back through the network. The model’s parameters are adjusted using Stochastic Gradient Descent (SGD) to minimize this error. 𝗖𝗮𝘀𝗲 𝗦𝘁𝘂𝗱𝘆: 𝗧𝗵𝗲 𝗩𝗚𝗚-𝟭𝟲 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 One exemplary CNN architecture is VGG-16, known for its depth and small convolution filters. This network consists of 16 layers, including convolutional and fully connected layers. VGG-16 has demonstrated high accuracy in image classification tasks, contributing significantly to the field of deep learning. 𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻 Convolutional Neural Networks have revolutionized how we approach and solve computer vision problems. By breaking down images into understandable patterns and learning from vast amounts of data, CNNs enable machines to interpret and interact with the visual world. Whether it's recognizing objects in images or powering advanced AI applications, CNNs are integral to the future of technology. Understanding the mathematical foundations of tensors, vector calculus, and gradient descent is essential for comprehending CNN operations. As we continue to innovate and refine these models, the potential applications of CNNs will only expand, making them a vital tool in any AI or machine learning toolkit. #ArtificialIntelligence #MachineLearning #ComputerVision #DeepLearning #ConvolutionalNeuralNetworks

🧠 Deep Learning Architecture Spotlight: Transformers Why are #Transformers revolutionizing AI applications? 🔑 Key advantages: Parallel processing capability Superior context understanding Scalable attention mechanism Reduced training time What Is a Transformer Model? Learn from NVIDIA: https://lnkd.in/eHr_tDJ7 At Personalize AI, we leverage advanced transformer architectures to build: ✓ Efficient NLP systems ✓ Predictive analytics models ✓ Time-series forecasting ✓ Document analysis solutions Let's discuss how transformer architecture can benefit your specific use case. #DeepLearning #Transformers #NLP #AIArchitecture #MachineLearning #ArtificialIntelligence #AIEngineering #NeuralNetworks #TransformerAI

Title: The Evolution and Impact of Artificial Intelligence: Shaping the Futures 1.Introduction: Artificial Intelligence (AI) is no longer confined to the realm of science fiction. It has rapidly emerged as one of the most transformative technologies of the 21st century, reshaping industries, economies, and the way we live. From powering virtual assistants like Siri and Alexa to enabling breakthroughs in healthcare, finance, and education, AI has made significant inroads into various sectors. 2.Artificial Intelligence:(AI) refers to the simulation of human intelligence in machines, allowing them to perform tasks that would normally require human cognitive functions such as learning, reasoning, problem-solving, and understanding language. The ultimate goal of AI is to create systems that can think, learn, and adapt autonomously, mimicking human intelligence. 3.The Evolution of AI: A Historical Overview: The history of AI can be traced back to ancient times, when philosophers and scientists pondered the nature of intelligence and whether it could be replicated by machines. However, the formal development of AI as a field began in the mid-20th century. 4.The Early Foundations: In the 1940s and 1950s, the groundwork for AI was laid through advances in computing and the emergence of algorithms that could process data. British mathematician Alan Turing, often considered the father of AI, proposed the idea of a "universal machine" capable of solving any problem, provided it was described in a set of logical rules. 5.The Birth of AI: The term "Artificial Intelligence" was coined in 1956 during the Dartmouth Conference, organized by computer scientist John McCarthy. This event marked the official birth of AI as a scientific discipline. Early AI research focused on symbolic reasoning, where machines were programmed to manipulate symbols to solve puzzles, prove mathematical theorems, and simulate human decision-making. 6.The Renaissance of AI: The resurgence of AI began in the 1990s and 2000s, fueled by advances in machine learning, the rise of the internet, and the availability of large datasets (big data). New algorithms, such as neural networks and reinforcement learning, enabled machines to learn from data without relying on explicit programming. This era also saw the rise of **deep learning**, a subset of machine learning that uses multi-layered neural networks to model complex patterns and relationships in data. 7.Conclusion:** Artificial Intelligence (AI) has transitioned from a theoretical concept to a transformative force that is reshaping industries, economies, and daily life. From its early days of symbolic reasoning to modern advancements in machine learning, deep learning, and neural networks, AI has made significant strides in automating tasks, improving decision-making, and creating new opportunities. #snsinstitutions #snsdesignthinkers #designthinking

The AI can tell what to focus on in an image, does it make it more human? http://3.104.65.174:8000/ (probably your browser will flag it as insecure - as it is a HTTP link, you can copy-paste it if you want to have a look at the app) If you ever wondered how the image is morphed by the layers of the AI-based systems, such as a transformer before it does cool stuff like classification and segmentation. I deployed DINO (self-distillation with no labels) model that just does that and you can test your images on it. I am using a spot EC2 instance (because they are cheap) for inference so the model will probably go down, as AWS reclaims the resource, but I will try to keep it up for few days :) In the past, Convolutional Neural Networks (CNNs) dominated computer vision tasks by effectively learning visual features through the repeated application of filters across image pixels. This approach allowed CNNs to progressively build a hierarchical understanding of important features in an image. However, as datasets grew larger and more complex, CNNs required deeper and more intricate architectures to capture relevant information, which posed challenges in terms of computational cost and scalability. The success of transformer architecture in natural language tasks has also permitted the vision-based tasks.  Unlike CNNs, Transformers do not rely on recurrence, allowing them to fully exploit modern hardware (such as TPUs and GPUs) for more efficient scaling. Transformers process data in a parallelised, sequential manner, enabling them to handle longer dependencies and larger amounts of data more effectively. As a result, when given sufficient computational resources and data, Vision Transformers (ViTs) begin to outperform CNNs, offering superior performance on large-scale visual tasks by capturing global context more efficiently than CNNs, which are limited by their local receptive fields.

Telco Edge AI and AI Model Delivery Network (MDN) To facilitate efficient deployment and management of AI models at the edge, the concept of a Model Delivery Network (MDN) has emerged, drawing parallels with the well-established Content Delivery Network (CDN). Central to the success of MDNs are two key enabling technologies: model compression and Neural Network Coding (NNC). ◼ What is Edge AI Inferencing Service? Edge AI enables real-time data analysis and decision-making directly on the device. This approach offers several advantages: Reduced Latency, Enhanced Privacy and Security, Bandwidth Efficiency ◼ Model Delivery Network (MDN) The deployment of AI models at the edge necessitates efficient mechanisms for distributing, updating, and managing these models, which leads us to the need for MDN. The concept of MDN is analogous to that of a Content Delivery Network (CDN). CDNs are designed to deliver web content, such as videos, images, and other static assets, to users efficiently by caching. Similarly, an MDN aims to deliver AI models to edge devices efficiently. Here’s why an MDN is crucial: - Efficient Model Distribution: Just as CDNs cache content to reduce the load on central servers, MDNs distribute AI models to edge servers close to the end-user devices. This ensures that models can be quickly and reliably delivered to where they are needed. - Scalability: An MDN can handle the distribution of numerous AI models across a large number of edge sites, scaling seamlessly as the number of connected devices grows. - Regular Updates: AI models often need regular updates to improve accuracy or adapt to new data. An MDN facilitates the seamless and timely distribution of these updates, ensuring that edge devices always have the latest models. - Optimized Performance: By delivering models from edge servers, MDNs reduce latency, enhance performance, and ensure that AI applications can run smoothly even in environments with limited connectivity. ◼ Model Compression and Neural Network Coding: Key Enabling Technologies for MDN Model compression techniques are essential for reducing the size of AI models without significantly compromising their performance. Neural Network Coding (NNC) is a standardized approach to compressing and encoding neural network parameters. The ISO/IEC 15938-17:2022 standard for NNC specifies methods to compress neural networks to less than 5% of their original size without degrading inference capabilities. NNC includes: - Preprocessing Methods: Techniques like pruning, sparsification, and low-rank decomposition to reduce the complexity of neural networks before compression. - Quantization: Efficiently reduces the precision of network parameters (e.g., DeepCABAC (Context-Adaptive Binary Arithmetic Coding)) Together, these technologies enable the MDN to deliver highly efficient, compressed AI models to edge sites. #TelcoEdgeAI #ModelCompression #MDN #NeuralNetworkCoding #Fraunhofer

Do you know what's the difference between algorithm and model? 🤔 Day 6 of #10DaysofAI Difference between: Algorithms vs. Models Though the two terms are often used interchangeably in this context, they do not mean quite the same thing. Algorithms are procedures, often described in mathematical language or pseudocode, to be applied to a dataset to achieve a certain function or purpose. Models are the output of an algorithm that has been applied to a dataset. In simple terms, an AI model is used to make predictions or decisions and an algorithm is the logic by which that AI model operates. What are the different types of ai models? 1) Supervised Learning Models - Trained on labeled data to map inputs to outputs. - Examples: • Linear Regression: Predicts continuous values. • Logistic Regression: Used for binary classification. • Decision Trees: Splits data into subsets based on features. • Support Vector Machines (SVM): Finds optimal hyperplanes for classification. • Neural Networks: Recognizes complex patterns with interconnected layers. 2) Unsupervised Learning Models - Works with unlabeled data to find hidden patterns. - Examples: • K-Means Clustering: Groups similar data points. • Hierarchical Clustering: Builds data hierarchy. • Principal Component Analysis (PCA): Reduces data dimensionality. • Autoencoders: Learns efficient data representations. 3) Semi-Supervised Learning Models - Uses both labeled and unlabeled data for training. - Useful when fully labeled data is scarce or expensive. 4) Reinforcement Learning Models - Learn through interaction with an environment, receiving rewards or penalties. - Examples: • Q-Learning: Learns the value of actions. • Deep Q-Networks (DQN): Combines Q-learning with deep neural networks. • Policy Gradient Methods: Optimizes the policy directly. 5) Generative Models - Generate new data samples resembling training data. - Examples: • Generative Adversarial Networks (GANs): Involves a generator and a discriminator to create realistic data. • Variational Autoencoders (VAEs): Uses a probabilistic approach for data generation. 6) Sequence Models - Handle sequential data such as time series or language. - Examples: • Recurrent Neural Networks (RNNs): Captures dependencies in sequences. • Long Short-Term Memory (LSTM): A type of RNN for long-term dependencies. • Transformers: Efficiently manage long-range dependencies. 7) Hybrid Models - Combine different model elements to leverage strengths. - Examples: • Neural Network-Based Regression/Classification: Integrates neural networks with traditional methods. • Attention Mechanisms in Sequence Models: Enhances RNNs or LSTMs with attention mechanisms. Hope you gain some insights about this, consider hitting the like button. Follow Piyush Bhagchandani for more such content! #AI #ChatGPT #NeuralNetworks #LLMs #AIMODELS #ML

The Frontier of AI:   A Deep Dive into Generative Adversarial Networks (GANs)  Have you ever wondered how artificial intelligence can create realistic images, music, or even videos from scratch? Enter Generative Adversarial Networks (GANs) - one of the most exciting advancements in AI! 1. Understanding GANs:  Generative Adversarial Networks, introduced by Ian Goodfellow and collaborators in 2014, constitute a dynamic framework comprising two neural networks: a generator and a discriminator. This paradigm involves a competitive process where the generator aims to produce synthetic data closely resembling real data, while the discriminator endeavors to distinguish between genuine and generated data. 2. Mechanisms of GANs:  The generator synthesizes data from random noise, while the discriminator scrutinizes these outputs, discerning authenticity. Through iterative training, both networks refine their abilities: the generator enhances its capacity to generate realistic data, while the discriminator becomes increasingly adept at distinguishing authentic from synthetic. 3. Architectures:  [*]Deep Convolutional GAN (DCGAN):  Leveraging convolutional neural networks (CNNs), DCGANs enhance the generation process by capturing spatial hierarchies and intricate patterns in images, leading to sharper outputs and faster convergence. [*]Conditional GAN (cGAN):  Introducing conditional information into the GAN framework enables controlled generation, where specific attributes or features can be manipulated during the synthesis process. cGANs find applications in image-to-image translation, style transfer, and more. [*]CycleGAN:  A variant of GANs focusing on unpaired image-to-image translation tasks. By employing cycle consistency loss, CycleGANs facilitate the transformation between domains without requiring corresponding pairs of images for training. [*]Progressive Growing GAN (PGGAN):  Addressing the challenge of generating high-resolution images, PGGANs adopt a progressive training approach, starting from low-resolution images and gradually increasing the complexity. This methodology results in the production of high-quality, high-resolution outputs. [*]StyleGAN:  Pushing the boundaries of realism, StyleGAN incorporates style-based generators to control the synthesis process at multiple levels of abstraction, enabling the generation of diverse and photorealistic images with unparalleled fidelity. 4. Applications of GANs:  The applications of GANs span various fields, including computer vision, art generation, drug discovery, and more. From generating photorealistic images to enhancing medical imaging, GANs have revolutionized many industries and continue to push the boundaries of what AI can achieve. Link: https://lnkd.in/g4Y_AFsM #AI #MachineLearning #GANs #ArtificialIntelligence #Innovation #Technology #DeepLearning

The Evolution and Impact of Artificial Intelligence in Computer Science Engineering Artificial intelligence (AI) has become a cornerstone of modern technology, influencing a wide range of industries and research fields. Its development in computer science engineering has been a journey marked by remarkable breakthroughs, evolving from conceptual ideas to transformative applications. This article explores the key milestones in the development of AI, its applications, ethical considerations, and future directions. Early Foundations of AI *Conceptual Beginnings (1950s-1960s) The inception of AI can be traced back to the mid-20th century when pioneers like Alan Turing laid the groundwork for machine intelligence. Turing's seminal work introduced the Turing Test, which proposed that machines could exhibit intelligent behavior indistinguishable from that of humans. *Formalization and Growth (1970s-1980s) During the 1970s and 1980s, AI research expanded significantly with the development of expert systems. These systems, such as DENDRAL for chemical analysis and MYCIN for medical diagnosis, demonstrated the potential of AI in specialized domains. Concurrently, the field saw the emergence of machine learning, particularly neural networks, which laid the foundation for future advancements. Advances in Machine Learning *Neural Networks and Deep Learning (1990s-present) The 1990s marked a resurgence in neural network research, culminating in the rise of deep learning. Deep learning, characterized by multi-layered neural networks, revolutionized the field by enabling significant progress in image and speech recognition. Key innovations, such as (CNNs) for image processing and (RNNs) for sequential data, have driven AI's capabilities to new heights. *Data-Driven Approaches The explosion of big data and the availability of massive datasets have been critical to the success of modern AI. Machine learning algorithms, powered by vast amounts of data, have achieved unprecedented accuracy in various tasks. Future Directions *Explainable AI The quest for explainable AI aims to make machine learning models more interpretable. Techniques for explaining model predictions and ensuring transparency are crucial for building trust in AI systems, particularly in high-stakes applications like healthcare and finance. *AI in Edge Computing The deployment of AI on edge devices is a burgeoning field, enabling real-time data processing and decision-making. Edge AI applications in the Internet of Things (IoT), smart devices, and real-time analytics promise to enhance efficiency and responsiveness in various domains. Future research in AI will likely explore new learning paradigms, such as few-shot learning, unsupervised learning, and transfer learning. These approaches aim to make AI systems more efficient and capable of learning from limited data. #snsinstitutions #snsdesignthinker #designthinking