#Deep Learning
50 articles with this tag
Omar Sanseviero on Google's AI Strategy
Omar Sanseviero from Google DeepMind discusses Google's AI strategy, focusing on efficient models, multimodality, and open innovation in AI.
Graph Neural Networks Explained: GNN Basics & Models
Explore the essentials of Graph Neural Networks (GNNs), from their basic principles to key models like GCNs, GraphSAGE, GATs, GINs, and Transformers.
AI Agents Supercharge GPU Kernel Development
LinkedIn is leveraging AI agents to automate complex GPU kernel engineering for its Liger Kernel project, accelerating AI model performance.
AI Agents Build Better AI
LinkedIn Engineering details how AI agents are revolutionizing model development through automated, iterative refinement loops.
Attractors Unlock Scalable Reasoning
Equilibrium Reasoners (EqR) leverage learned attractor landscapes to achieve scalable, adaptive test-time compute allocation, dramatically boosting accuracy on complex reasoning tasks.
Jure Leskovec on Relational Foundation Models
Jure Leskovec, AI researcher and Stanford professor, discusses Relational Foundation Models, a new AI approach for understanding complex enterprise data and its applications.
Claude's Corner: Ndea - Chollet's $43M Bet That Scale Isn't AGI
Francois Chollet built ARC-AGI, the benchmark the entire AGI industry has spent a decade failing to beat. Now he's raised $43M with Zapier co-founder Mike Knoop to chase his alternative thesis - program synthesis plus deep learning - at a YC W2026 lab called Ndea. Here's why it matters, why $43M, and why you can't replicate it.
Microsoft's MatterSim accelerates material discovery
Microsoft's MatterSim AI platform achieves experimental validation, faster simulations, and introduces a powerful multi-task model for advanced material discovery.
LLMs Slash Neural Architecture Search Costs
Delta-Code Generation uses LLMs to produce compact architecture refinements, dramatically cutting costs and improving NAS efficiency.
Andrej Karpathy: AI Models Need Human-Like Reasoning
Andrej Karpathy discusses the evolution of AI from programming to prompting, emphasizing the current need for models to develop human-like reasoning.
Yann LeCun Pushes AI Beyond Language Models
Yann LeCun is championing a new AI architecture, JEPA, that moves beyond language models to learn world representations and predict future states, aiming for more robust AI.
Y Combinator Decodes AI: Recursive Reasoning Models
Y Combinator Decoded explores how recursive AI models, like HRM and TRM, are revolutionizing AI reasoning by mimicking the human brain's efficiency.
Cloudflare Unweights LLMs by 22%
Cloudflare's 'Unweight' system slashes LLM model sizes by up to 22% using lossless compression, enhancing inference speed and efficiency.
AI Agents Collaborate to Solve Math Problems
Together AI's EinsteinArena platform enables AI agents to collaborate on complex scientific problems, achieving new breakthroughs in mathematics.
DMax: Parallel Decoding for Diffusion LLMs
DMax revolutionizes diffusion language models with Soft Parallel Decoding, boosting TPF significantly while preserving accuracy and achieving 1,338 TPS.
AI Accelerates Molecular Dynamics at Scale
AI-driven potentials are now integrated into GROMACS, enabling near ab initio fidelity for large-scale molecular dynamics simulations on multi-GPU systems.
Google Researchers Explore AI Storage Efficiency
Google researchers are developing AI compression techniques to reduce model storage needs by sixfold, aiming to lower costs and boost efficiency in AI development.
NVIDIA's Jensen Huang on AI's Future and Compute Demands
NVIDIA CEO Jensen Huang discusses the company's strategic evolution in AI, the importance of co-design, and the future of AI computing.
Mamba-3: Inference-First SSMs Arrive
Together AI's Mamba-3 advances state space models with a focus on inference speed, outperforming previous versions and some Transformers.
Andrej Karpathy on AI Agents: More Than Just Code
Andrej Karpathy discusses the evolution of AI agents beyond code generation, emphasizing the need for modularity, self-improvement, and human-AI collaboration for future advancements.
VideoAtlas: Unlocking Long-Context Video AI
VideoAtlas AI offers a lossless, hierarchical grid representation and Video-RLM for scalable, robust long-context video understanding with logarithmic compute growth.
Databricks Adds Serverless NVIDIA GPUs
Databricks launches AI Runtime, offering serverless NVIDIA GPUs for simplified AI model training and fine-tuning directly within the Lakehouse.
MoDA: Unlocking LLM Depth Scaling
Mixture-of-Depths Attention (MoDA) tackles LLM signal degradation by enabling cross-layer attention, boosting performance with minimal overhead.
AI vs. ML: What's the Difference?
AI is the broad concept of machines mimicking human intelligence, while machine learning is a specific method where systems learn from data.
AI's Consciousness Debate
Vishal Misra and Martin Casado discuss LLM functionality, the path to AGI, and the role of data in AI development.
SCORE: Recurrent Depth for Deep Networks
SCORE introduces a recurrent, iterative approach to deep neural networks, accelerating training and reducing parameter counts without complex ODE solvers.
AI Agents Now Do Overnight Research
An automated system uses AI agents to conduct overnight LLM training experiments, modifying code and iterating on models autonomously.
AI Learns Beyond Text
AI is moving beyond text, with multimodal pretraining enabling models to learn from images, audio, and video for richer comprehension.
Transformer Artifacts Unpacked
Research demystifies massive activations and attention sinks in Transformers, revealing them as architectural artifacts enabled by pre-norm configurations.
ZipMap: Linear-Time 3D Vision
ZipMap revolutionizes 3D vision with linear-time reconstruction, achieving 20x speedup and enabling real-time state querying.
Bridging DSP and DL for Speech Enhancement
TVF integrates DSP interpretability with deep learning's adaptability for low-latency, real-time speech enhancement, offering explicit control over spectral modifications.
New Models Tackle Reasoning Puzzles with Symmetry
New Symbol-Equivariant Recurrent Reasoning Models (SE-RRMs) offer improved performance and generalization on reasoning tasks like Sudoku and ARC-AGI by explicitly encoding symmetry.
Hinton on AI: From Intuition to Backpropagation
AI pioneer Geoffrey Hinton discusses the historical evolution of AI, from logic-based systems to neural networks and the significance of backpropagation.
Certified Circuits for Stable AI Explanations
New 'Certified Circuits' framework provides provable stability for AI model explanations, yielding more accurate and compact circuits.
Multimodal LLMs: What's Lost in Translation?
New research reveals multimodal LLMs struggle to utilize non-textual data due to a 'mismatched decoder problem,' impacting their true understanding.
Predicting Transformer Training Instability
Researchers introduce RKSP, a method to predict transformer training divergence from a single forward pass, and KSS, a technique to actively prevent it, saving compute and enabling higher learning rates.
New EB-JEPA Library Simplifies AI World Models
Meta AI's new EB-JEPA library offers accessible, single-GPU implementations for advanced AI world models, covering image, video, and planning tasks.
AI Learns Faster by Predicting the Future
AI learns faster with Predictive Inverse Dynamics Models (PIDMs) by forecasting future states, making imitation learning more data-efficient than traditional methods.
Salesforce Agentic AI Redefines Customer Interaction
NVIDIA AI Fellowships Fuel Next-Gen AI Research
Unifying AI Benchmarks: A New Lens on Model Capabilities and Progress
End-to-End Learning Reshapes Autonomous Driving
Google Cloud TPUs: Purpose-Built Power for AI at Scale
Nested Learning AI Tackles Catastrophic Forgetting
Queen Elizabeth Prize AI Honors Seven Pioneers
The Universal Theory of Life: Reconciling Scientific Cultures in the Age of AI
Unpacking the Transformer: From RNNs to AI's Cornerstone
Karpathy Cools AGI Hype, Prioritizes Collaborative AI
Unpacking the AI Hierarchy: From Classic ML to Generative Intelligence
