SAM3D: The Next Leap in 3D Understanding
Artificial Intelligence : Papers & Concepts
Release Date: 12/10/2025
RF-DETR: Neural Architecture Search for Real-Time Detection Transformers
Artificial Intelligence : Papers & Concepts
In this episode of Artificial Intelligence: Papers and Concepts, we break down RF-DETR, a new direction in object detection that challenges the idea of fixed-capacity models. Instead of choosing between speed and accuracy upfront, RF-DETR introduces an elastic detector that adapts its computation dynamically at inference time. We explore how RF-DETR reuses intermediate representations to scale up or down on demand, why this matters for real-world deployment on edge and cloud systems, and how this design enables more predictable performance across diverse hardware constraints. If you’re...
info_outline
YOLO26: Rethinking Real-Time Vision for the Edge
Artificial Intelligence : Papers & Concepts
In this episode of Artificial Intelligence: Papers and Concepts, we break down YOLO26, a major shift in real-time object detection. Instead of chasing raw accuracy, YOLO26 is designed for speed, consistency, and edge deployment. We explore how removing non-maximum suppression (NMS) delivers predictable low-latency inference, why simplifying the loss functions makes the model easier to deploy on real hardware, and how new training ideas borrowed from large language models improve small-object detection. If you’re building vision systems for robots, drones, factories, or mobile devices,...
info_outline
DeepSeek mHC
Artificial Intelligence : Papers & Concepts
Why do some large AI models suddenly collapse during training—and how can geometry prevent it? In this episode of Artificial Intelligence: Papers and Concepts, we break down DeepSeek AI’s Manifold-Constrained Hyperconnections (mHC), a new architectural approach that fixes training instability in large language models. We explore why traditional hyperconnections caused catastrophic signal explosions, and how constraining them to a geometric structure—doubly stochastic matrices on the Birkhoff polytope—restores stability at scale. You’ll learn how mHC reduces signal amplification from...
info_outline
Chinchilla Scaling Law
Artificial Intelligence : Papers & Concepts
In this episode of Artificial Intelligence: Papers and Concepts, curated by Dr. Satya Mallick, we break down DeepMind’s 2022 paper “Training Compute-Optimal Large Language Models”—the work that challenged the “bigger is always better” era of LLM scaling. You’ll learn why many famous models were under-trained, what it means to be compute-optimal, and why the best performance comes from scaling model size and training data together. We also unpack the Chinchilla vs. Gopher showdown, why Chinchilla won with the same compute budget, and what this shift means for the future:...
info_outline
Gradient-Based Planning
Artificial Intelligence : Papers & Concepts
How should an AI or robot decide what to do next? In this episode, we explore a new approach to planning that rethinks how world models are trained. The episode is based on the paper "Closing the Train-Test Gap in World Models for Gradient-Based Planning" Many AI systems can predict the future accurately, yet struggle when asked to plan actions efficiently. We explain why this train–test mismatch hurts performance and how gradient-based planning offers a faster alternative to traditional trial-and-error or heavy optimization. The key idea is simple but powerful: if you want a model to plan...
info_outline
SAM3D: The Next Leap in 3D Understanding
Artificial Intelligence : Papers & Concepts
Forget flat photos—SAM3D is rewriting how machines understand the world. In this episode, we break down the groundbreaking new model that takes the core ideas of Meta’s Segment Anything Model and expands them into the third dimension, enabling instant 3D segmentation from just a single image. We start with the limitations of traditional 2D vision systems and explain why 3D understanding has always been one of the hardest problems in computer vision. Then we unpack the SAM3D architecture in simple terms: its depth-aware encoder, its multi-plane representation, and how it learns to infer 3D...
info_outline
DINOv3 : A new Self-Supervised Learning (SSL) Vision Language Model (VLM)
Artificial Intelligence : Papers & Concepts
In this episode, we explore DINOv3, a new self-supervised learning (SSL) vision foundation model from Meta AI Research, emphasizing its ability to scale effortlessly to massive datasets and large architectures without relying on manual data annotation. The core innovations are scaling model and dataset size, introducing Gram anchoring to prevent the degradation of dense feature maps during long training, and employing post-hoc strategies for enhanced flexibility in resolution and text alignment. The authors present DINOv3 as a versatile visual encoder that achieves...
info_outline
dots.ocr SOTA Document Parsing in a Compact VLM
Artificial Intelligence : Papers & Concepts
dots.ocr is a powerful, multilingual document parsing model from rednote-hilab that achieves state-of-the-art performance by unifying layout detection and content recognition within a single, efficient vision-language model (VLM). Built upon a compact 1.7B parameter Large Language Model (LLM), it offers a streamlined alternative to complex, multi-model pipelines, enabling faster inference speeds. The model demonstrates superior capabilities across multiple industry benchmarks, including OmniDocBench, where it leads in text, table, and reading order tasks, and olmOCR-bench, where...
info_outline
DeepSeek-OCR : A Revolutionary Idea
Artificial Intelligence : Papers & Concepts
In this episode, we dive deep into DeepSeek-OCR, a cutting-edge open-source Optical Character Recognition (OCR) / Text Recognition model that’s redefining accuracy and efficiency in document understanding. DeepSeek-OCR flips long-context processing on its head by rendering text as images and then decoding it back—shrinking context length by 7–20× while preserving high fidelity. We break down how the two-stage stack works—DeepEncoder (optical/vision encoding of pages) + MoE decoder (text reconstruction and reasoning)—and why this “context optical compression” matters for...
info_outline
nanochat by Karpathy - How to build your own ChatGPT for $100
Artificial Intelligence : Papers & Concepts
“The best ChatGPT that $100 can buy.” That’s Andrej Karpathy’s positioning for nanochat—a compact, end‑to‑end stack that goes from tokenizer training to a ChatGPT‑style web UI in a few thousand lines of Python (plus a tiny Rust tokenizer). It’s meant to be read, hacked, and run so students, researchers, and tech enthusiats can understand the entire pipeline needed to train a baby version of ChatGPT. In this episode, we walk you through the nanochat repository. Resources nanochat github repo: AI Consulting & Product Development Services: ...
info_outlineForget flat photos—SAM3D is rewriting how machines understand the world. In this episode, we break down the groundbreaking new model that takes the core ideas of Meta’s Segment Anything Model and expands them into the third dimension, enabling instant 3D segmentation from just a single image.
We start with the limitations of traditional 2D vision systems and explain why 3D understanding has always been one of the hardest problems in computer vision. Then we unpack the SAM3D architecture in simple terms: its depth-aware encoder, its multi-plane representation, and how it learns to infer 3D structure even when parts of an object are hidden.
You’ll hear real examples—from mugs to human hands to complex indoor scenes—demonstrating how SAM3D reasons about surfaces, occlusions, and geometry with surprising accuracy. We also discuss its training pipeline, what makes it generalize so well, and why this technology could power the next generation of AR/VR, robotics, and spatial AI applications.
If you want a beginner-friendly but technically insightful overview of why SAM3D is such a massive leap forward—and what it means for the future of AI—this episode is for you.
Resources:
SAM3D Website
https://ai.meta.com/sam3d/
SAM3D Github
https://github.com/facebookresearch/sam-3d-objects
https://github.com/facebookresearch/sam-3d-body
SAM3D Demo
https://www.aidemos.meta.com/segment-anything/editor/convert-image-to-3d
SAM3D Paper
https://arxiv.org/pdf/2511.16624
Need help building computer vision and AI solutions?
https://bigvision.ai
Start a career in computer vision and AI
https://opencv.org/university