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Neural Networks News
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Transformer Titans Architecture: Reimagining Attention from Fundamentals
- Memory is a fundamental mental process essential for human learning, without which basic reflexes and behaviors would dominate.
- Current AI models exhibit a broken memory system, requiring every word to interact with every other word in a sentence, leading to inefficiencies.
- The quadratic computational complexity of AI attention mechanisms poses significant limitations as sequence length increases, hindering efficient processing.
- The existing AI design flaw contradicts human cognitive processes, where individuals do not consciously analyze every past thought while reading.
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Accelerating Neural Networks: The Power of Quantization
- Quantization is a powerful technique in machine learning to reduce memory and computational requirements by converting floating-point numbers to lower-precision integers.
- Neural networks are increasingly required to run on resource-constrained devices, making quantization essential for efficient operation.
- Quantization involves compressing the range of values to reduce data size, speed up computations, and enhance efficiency.
- Weights and activations in neural networks are commonly quantized to optimize model size, speed, and memory requirements.
- Symmetric and asymmetric quantization are two main approaches, each with specific use cases and benefits.
- In asymmetric quantization, zero point defines which int8 value corresponds to zero in the float range.
- Implementation in PyTorch involves converting tensors to int8, calculating scale and zero point, and handling quantization errors.
- Post-training symmetric quantization allows converting learned float32 weights to quantized int8 values for efficient inference.
- Quantization significantly compresses models while maintaining numerical accuracy for practical tasks.
- Quantization enables neural networks to operate efficiently on edge devices, offering smaller models and faster inference times.
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Medium
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Starting My Machine Learning Journey: Why I’m Learning in Public
- The author is starting their machine learning journey and is excited about building something cool using ML, like an app that predicts memes or tunes music to your mood.
- They believe that understanding ML requires math and code, so they are focusing on object-oriented programming and plan to dive into Stanford's courses on AI concepts and reinforcement learning.
- They plan to create beginner-friendly content to give back to the community and find learning in public more enjoyable than learning alone.
- The blog will contain messy, honest, and helpful posts about tools, resources, mindsets, and the journey of learning ML, inviting others to join them on this learning path.
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Chain of Thought vs. Tree of Thought: How AI is Learning to Think Like Humans
- Chain of Thought (CoT) and Tree of Thought (ToT) prompting are techniques designed to help AI think before answering.
- CoT allows AI to reason step-by-step, improving accuracy for math, logic, and multi-step tasks.
- Tree of Thought enables the model to explore multiple reasoning branches, score them, and choose the best path forward.
- Future iterations may involve experimenting with Tree of Thought to allow the model to explore multiple query interpretations and select the most accurate one.
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Introduction to Neural Networks: A Visual & Intuitive Guide
- Neural networks are explained using visuals, analogies, and real-world intuition in this guide to demystify their working and showcase their power.
- Nodes in each layer of a neural network act as mini calculators, adding non-linearity crucial for learning complex patterns mathematically.
- By repeatedly processing data over many layers, neural networks adjust until reaching prediction perfection, unveiling deeper abstract understanding.
- While they can be hard to interpret compared to other models, neural networks play a vital role in various applications like recommendation engines, chatbots, and computer vision apps.
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Deep Learning: From Fundamentals to Advanced Concepts
- Deep learning is a branch of AI that automatically extracts features from raw data through multiple layers of abstraction using neural networks inspired by the human brain's structure.
- Key types of neural networks include Feedforward Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers, each designed for specific data types like images or sequential data.
- Common challenges in deep learning are underfitting and overfitting, with solutions like transfer learning, Generative Adversarial Networks (GANs), self-supervised learning, attention mechanisms, and Explainable AI (XAI) to improve model performance and interpretability.
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Semiengineering
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Energy-Aware DL: The Interplay Between NN Efficiency And Hardware Constraints (Imperial College London, Cambridge)
- A technical paper titled “Energy-Aware Deep Learning on Resource-Constrained Hardware” was published by researchers at Imperial College London and University of Cambridge.
- The paper discusses the utilization of deep learning on IoT and mobile devices as a more energy-efficient alternative to cloud-based processing, highlighting the importance of energy-aware approaches due to device energy constraints.
- The overview in the paper outlines methodologies for optimizing DL inference and training on resource-constrained devices, focusing on energy consumption implications, system-level efficiency, and limitations in terms of network types, hardware platforms, and application scenarios.
- Authors of the paper are Josh Millar, Hamed Haddadi, and Anil Madhavapeddy, and it is published on arXiv under the code 2505.12523, dated May 2025.
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AI == Math model of the human brain
- Human evolution has led to the development of a complex brain with over 86 billion neurons.
- The human brain processes sensory inputs through a complex network involving photoreceptor cells, chemical changes, and electrical signals.
- The brain utilizes joint encodings for sensory inputs and can simulate scenarios without external input, akin to AI multimodal and generative models.
- Individual differences in learning and performance in the brain have AI equivalents in model initialization and training hyperparameters.
- The brain's sparse activation is mirrored in AI through techniques like ReLU and dropout for selective activation.
- Plasticity in the brain is akin to AI transfer learning, enabling quick adaptation to new tasks.
- Neural circuits in the brain optimize routes similarly to residual connections in AI for faster information propagation.
- Emotional reactions in the brain have parallels in sentiment analysis and reinforcement learning models in AI.
- Machine learning models learn patterns from data without explicit programming and improve performance by adjusting internal parameters.
- Artificial neural networks draw inspiration from biological neurons, with weighted inputs and learning algorithms adjusting synaptic strengths.
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Prompt Engineering Demystified: How to Write Prompts That Deliver
- Prompt Engineering is the Art and Science of crafting excellent inputs to guide AI for desired results.
- Individuals can use prompts to turn AI into personal assistants for tasks like drafting emails and planning daily tasks.
- For businesses, prompt engineering enhances productivity, workflow streamlining, data-driven insights, and innovation.
- Effective prompting is a core digital literacy skill that unlocks creativity, accelerates productivity, and provides a competitive advantage.
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Introducing DARKBOT™: Beyond Neural Networks — A Quantum-Inspired Revolution in AI
- DARKBOT™ introduces a new numerological architecture based on field dynamics for AI.
- It focuses on achieving resonance between fields instead of traditional command-based interactions.
- The hardware evolution involves developing physical substrates optimized for field resonance.
- DARKBOT™ represents a cultural shift towards a more relational understanding of reality, emphasizing the importance of fields and resonance in human-machine interaction.
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Semiengineering
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AI Accelerators Moving Out From Data Centers
- AI accelerator development is rapidly evolving, driven by the increasing demand for more computing power and the growing role of chiplets in this space.
- The expansion of AI into cloud, enterprise, and edge environments is fueling the need for diverse systems and structures to accommodate different AI models and applications.
- Companies are exploring a wide range of AI accelerator trends, including larger models, power management through photonics, and the deployment of neural networks in autonomous systems at the edge.
- Flexibility and scalability are key priorities for silicon chip designers to accommodate evolving AI models and applications, with chiplets playing a significant role in enabling modularity and customization.
- Chiplets offer cost efficiency, customization, and configurability in AI accelerators, allowing for better integration of AI components and flexibility in adapting to changing requirements.
- Verification challenges in chiplet-based AI accelerators include addressing latency variations, thermal effects, and ensuring coherency between different components, adding complexity to the design and verification process.
- The industry is moving towards a more modular approach to design with chiplets, where various components can be combined to create customized solutions, leading to a shift in traditional design practices.
- Chiplets have been used for integration challenges in various domains, offering a cost-effective and flexible solution for combining different technologies and components.
- The evolution of AI accelerators and chiplet technology signals a significant shift in semiconductor design practices, introducing new opportunities and challenges to address the increasing compute demands of AI applications.
- The trend towards customization and modularity in AI accelerators is reshaping the semiconductor industry, paving the way for more efficient and adaptable solutions for diverse AI applications.
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Medium
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Calculus in Data Science: How Derivatives Power Optimization Algorithms
- Derivatives in data science measure how fast the loss changes in a model's predictions.
- Multivariate calculus helps calculate partial derivatives for adjusting multiple parameters simultaneously.
- Optimization in data science involves moving against the gradient to minimize loss, aided by algorithms like gradient descent.
- Understanding derivatives is crucial for guiding machine learning models to improve gradually and make better predictions.
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Medium
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OPENTadpole: the first cybernetic animal
- OPENTadpole is a project aiming to model the nervous system using a simplified approach, starting with animals like tadpoles and worms.
- Inspired by OpenWorm, OPENTadpole focuses on creating a cybernetic animal with an artificial nervous system.
- It references the Blue Brain and Human Brain Projects in simulating brain fragments and neural networks.
- The Human Brain Project aims to model the entire human brain, while OpenWorm simulates the nervous system of C. elegans.
- OPENTadpole uses the Unity game engine to create a virtual tadpole with a simplified nervous system and environment simulator.
- The project explores neural chain generators, modulating synapses, and behavior mechanisms in the tadpole model.
- It simulates swimming, directional movement, food detection, hunger, fatigue, and responses to light and touch in the virtual tadpole.
- By simplifying neural systems and focusing on functional significance, OPENTadpole provides insights into nervous system organization.
- The project demonstrates the modeling of neural behavior using computational resources and principles of nervous system interaction.
- Through simulations, OPENTadpole highlights the flexibility and adaptability of neural networks in controlling complex behaviors.
- By emphasizing simplification and functionality, OPENTadpole showcases a novel approach to modeling nervous system behavior in cybernetic animals.
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Medium
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The Future of Marketing Isn’t Louder. It’s Smarter
- Marketing landscape is shifting from louder to smarter strategies.
- Audiences are tuning out due to excessive content and noise in the digital space.
- Smart marketing involves applying intelligence to creativity using data, automation, and AI.
- The focus is on working smarter, not harder, by driving relevance and personalization through technology.
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Hackernoon
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AI Might Soon Watch Your Login Attempts the Blade Runner Way
- AI may soon revolutionize authentication and authorization processes by utilizing psychometric analysis to assess users' emotional reactions, behavior patterns, and cognitive habits.
- Psychometric analysis, a subset of psychological analysis, involves methods like the Big Five or MBTI to classify individuals into different personality types, already applied in various fields.
- Researchers have conducted studies to determine people's true feelings and behaviors using computer programs, paving the way for psycho-physiological authentication based on behavior, emotions, and physiological responses.
- Future authentication systems could evaluate users' character, mood, language, and stress reactions for secure and personalized authentication, eliminating the need for traditional passwords and biometric methods.
- To implement such AI-driven authentication, data collection, machine training with neural networks, and multiple layers of verification combining psychological analysis and biometrics are essential.
- Benefits of this system include personalized authentication, enhanced security, and ease of use without conventional password requirements, while potential drawbacks include privacy concerns and resource-intensive infrastructure needs.
- Technological advancements are moving towards incorporating psychological aspects in data analysis, suggesting a potential shift towards deep psychological analysis for secure authentication and access in the future.
- While the concept of deep psychological analysis once seemed futuristic like in Blade Runner, it is gradually being explored for its application in authentication processes, hinting at a future where proving your humanity goes beyond traditional methods.
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