Posts tagged ML
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Seeing from All Angles: Making 3D Reconstruction Models Robust to Viewpoint Changes
How do we teach machines to understand the shape of a 3D object—no matter how it’s viewed? This new research shows that letting the model “learn from its own mistakes” may be the answer.
In the realm of computer vision and 3D understanding, one of the longstanding challenges is teaching machines how to reconstruct a 3D object from 2D images. If you’ve ever seen photogrammetry tools turn a set of photos into a 3D model, you already know the basic idea. But beneath the surface, there’s a major problem: these models often perform well only when the images are captured from familiar angles. Once the camera moves to a new, unseen position, the model starts to stumble.
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From Silos to Synergy: How MCP and A2A Are Building the Future of AI Agents
Introduction
In the fast-evolving world of artificial intelligence, language models are no longer just powerful tools for answering questions or summarizing text. They’re evolving into intelligent agents capable of reasoning, planning, interacting with other agents, and autonomously executing complex tasks. But as the complexity of these agent systems grows, so does the need for standards that ensure consistency, interoperability, and scalability.
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From Circuits to Cognition: Following the Thoughts of Claude 3.5
Decoding Anthropic’s Next Step in Understanding Language Models
In my previous post, we explored “On the Biology of a Large Language Model”, Anthropic’s groundbreaking research that mapped the internal circuits of Claude 3.5 Haiku using attribution graphs. These graphs offered a glimpse into the hidden architecture of reasoning — showing how Claude decomposes questions, plans poems, reasons across languages, and even hallucinates.
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From Black Box to Blueprint: Tracing the Logic of Claude 3.5
Exploring the Hidden Anatomy of a Language Model
In the age of large language models, capability often outpaces comprehension. Models like Claude 3.5 can write poetry, solve logic puzzles, and navigate multilingual queries — but we still don’t fully understand how. Beneath their fluent outputs lies a vast architecture of layers, weights, and attention heads that, until recently, remained largely inscrutable.
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The Deepening Layers of Inception: A Journey Through CNN Time
The story of the Inception architecture is one of ingenuity, iteration, and elegance in the field of deep learning. At a time when researchers were obsessed with increasing the depth and complexity of convolutional neural networks (CNNs) to improve accuracy on large-scale visual tasks, Google’s research team asked a different question: How can we go deeper without paying the full computational price? The answer was Inception—a family of architectures that offered a bold new design paradigm, prioritizing both computational efficiency and representational power. From Inception v1 (GoogLeNet) to Inception-ResNet v2, each version brought transformative ideas that would ripple throughout the deep learning community. This post unpacks the entire journey, layer by layer, innovation by innovation.
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How ImageNet Taught Machines to See
The Vision Behind the Dataset
In the early 2000s, artificial intelligence was still stumbling in the dark when it came to understanding images. Researchers had built systems that could play chess or perform basic language tasks, but when it came to something a toddler could do—like identifying a cat in a photo—machines struggled. There was a glaring gap between the potential of machine learning and its real-world applications in vision.
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The Random Illusion: Why Adversarial Defenses Aren’t as Robust as They Seem
The field of adversarial machine learning is built on a paradox: models that perform impressively on natural data can be shockingly vulnerable to small, human-imperceptible perturbations. These adversarial examples expose a fragility in deep networks that could have serious consequences in security-critical domains like autonomous driving, medical imaging, or biometric authentication. Naturally, defenses against these attacks have been the subject of intense research. Among them, a seemingly simple strategy has gained popularity: random transformations. By applying random, often non-differentiable perturbations to input images—such as resizing, padding, cropping, JPEG compression, or color quantization—these methods hope to break the adversary’s control over the gradients that guide attacks. At first glance, it seems effective. Robust accuracy increases. Attacks fail. But is this robustness genuine?
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Block Geometry & Everything-Bagel Neurons: Decoding Polysemanticity
When Neurons Speak in Tongues: Why Polysemanticity Demands a Theory of Capacity
Crack open a modern vision or language model and you’ll run into a curious spectacle: the same unit flares for “cat ears,” “striped shirts,” and “the Eiffel Tower.” This phenomenon—polysemanticity—is more than a party trick. It frustrates attribution, muddies interpretability dashboards, and complicates any safety guarantee that relies on isolating the “terrorism neuron” or “privacy-violation neuron.”
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From Heads to Factors: A Deep Dive into Tensor Product Attention and the T6 Transformer
A Transformer layer must preserve every key–value pair for every head, layer, and past token—a memory bill that rises linearly with context length.
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The Hidden Danger of AI Oversight: Why Model Similarity Might Undermine Reliability
Artificial Intelligence, particularly Large Language Models (LLMs) like ChatGPT, Llama, and Gemini, has witnessed extraordinary progress. These powerful models can effortlessly handle tasks from writing articles to solving complex reasoning problems. Yet, as these models become smarter, ensuring they’re behaving as intended is becoming harder for humans alone.
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How AlexNet Lit the Spark and ResNet Fanned the Flames
In the ever-evolving landscape of deep learning, certain architectures have defined turning points in how neural networks are designed, trained, and understood. Among these, AlexNet and ResNet stand out as monumental contributions that shifted the paradigm of computer vision and image classification. Though separated by just three years, these two architectures reflect fundamentally different eras of deep learning—AlexNet laid the groundwork for deep convolutional networks, while ResNet solved the pressing problems that deeper architectures introduced.
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Exploring astroML: Machine Learning Among the Stars
The Astronomer’s New Toolbox
Modern astronomy has evolved into a data-driven science. With massive sky surveys like SDSS (Sloan Digital Sky Survey), Pan-STARRS, and the upcoming LSST producing petabytes of data, traditional approaches no longer suffice. Manual inspection and simplistic models simply can’t scale with this astronomical data deluge. Enter astroML, a library that bridges the gap between astronomy and modern machine learning. astroML is a Python-based library built on top of familiar scientific computing tools like NumPy, SciPy, matplotlib, and scikit-learn. But what sets it apart is its thoughtful design — tailored to real-world astronomical problems. From irregular time series to galaxy classification, astroML brings statistically sound and domain-specific tools to the fingertips of astronomers, physicists, and data scientists alike.
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Attention Is All You Need: The Paper That Changed Everything
If you’ve ever interacted with ChatGPT, asked an AI to summarize a document, or translated a phrase using Google Translate, you’re experiencing the legacy of a paper that redefined modern artificial intelligence. Published in 2017 by Vaswani et al., the paper “Attention Is All You Need” introduced the world to the Transformer architecture. This seemingly simple idea — that attention mechanisms alone can model complex language patterns without relying on recurrence or convolutions — has since become the bedrock of nearly every major NLP system.
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Teaching AI to Use Tools — The Right Way
A Deep Dive into Seal-Tools: The Dataset That Makes LLMs Smarter Agents
Imagine asking your AI assistant to “book a flight to Paris, then schedule a taxi to the airport and convert the final bill to Euros.” Sounds simple, right? In reality, for most AI models, this isn’t just hard — it’s nearly impossible to get right without human babysitting.
That’s because tool use, chaining functions, and executing multi-step operations requires structured reasoning, parameter handling, and format control — things even the smartest LLMs struggle with today.
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What Are Tensors?
Tensors are fundamental mathematical objects that appear across various domains such as physics, computer science, and engineering. At their core, tensors are multi-dimensional arrays that generalize the concepts of scalars (single numbers), vectors (one-dimensional arrays), and matrices (two-dimensional arrays). Unlike simple arrays, tensors are not just containers of numbers—they come with transformation rules that allow them to describe physical phenomena in a way that remains consistent across coordinate systems.
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From “Why” to “How”: ReAct’s Unified Reasoning-Acting Paradigm
Large language models (LLMs) have reshaped natural language processing by demonstrating impressive capabilities in text generation, summarization, and translation. Yet, as powerful as they are, these models often struggle when asked to perform complex, multi-step tasks that require deliberate planning and interaction with external information sources. Traditional chain-of-thought (CoT) prompting enables LLMs to articulate intermediate reasoning steps, but it remains confined to the model’s internal knowledge and inference capabilities. Conversely, action-based approaches have allowed models to execute external operations—such as querying an API or navigating an environment—but lack explicit internal reasoning, leading to unexplainable or brittle behavior. The ReAct framework addresses this gap by synergizing reasoning and acting in a unified prompt-based paradigm that interleaves “thoughts” and “actions” to solve complex tasks more effectively and transparently.
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From Facts to Insight: Bridging the Compositionality Gap in Language Models
Large language models (LLMs) such as GPT-3 have transformed natural language understanding by memorizing vast amounts of text. Yet, when faced with questions that require combining multiple pieces of knowledge—so-called compositional reasoning—even the biggest models stumble. In their paper Measuring and Narrowing the Compositionality Gap in Language Models, Press et al. introduce a new metric for this shortfall, show that it persists despite model scale, and propose practical prompting techniques to close it.
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LoRA: A Breakthrough in Efficient Fine-Tuning of Large Language Models
As large language models (LLMs) like GPT-3, LLaMA, and BERT continue to grow in size and influence, one challenge becomes increasingly apparent: while these models offer exceptional capabilities, adapting them for new tasks remains expensive and resource-intensive. Fine-tuning a model with billions of parameters typically requires large datasets, massive compute power, and hours or even days of training time — luxuries not everyone can afford.
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Fine-Tuning Language Models: Welcome to the Nerdy Playground of LLMs
From LoRA to RLHF — and all the acronyms in between
So, you’ve got your hands on a fancy pre-trained language model. Great. It’s read more text than any human ever will, speaks in Shakespearean iambic pentameter and Python, and can tell you the capital of Burkina Faso at 3 AM.
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