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Machine Learning

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Uncertainty-Guided Data Augmentation for Engineers | Deep Dive - Yongmin Kwon

Uncertainty-Guided Data Augmentation for Engineers | Deep Dive - Yongmin Kwon

This session details a data-efficient method for training engineering surrogate models by using uncertainty quantification (UQ) to guide geometric data augmentation. Instead of random deformations, the approach lets the deep ensemble model identify its own knowledge gaps (epistemic uncertainty), then uses Free-Form Deformation (FFD) to generate new shapes specifically in those uncertain regions. This ensures every expensive simulation run yields maximally informative data, significantly improving model accuracy for a fixed computational budget across domains like structural mechanics and aerodynamics.

Q-learning with Flow-Matching Policies

Q-learning with Flow-Matching Policies

This talk explores methods for optimizing expressive, multi-modal policies, such as those based on flow-matching, with off-policy reinforcement learning. The speaker presents two novel algorithms, FQ-RL and CAM, designed to overcome the instability of backpropagation through multi-step generative models, enabling effective online self-improvement and adaptation for robotic manipulation tasks.

Graph Neural Networks Explained: A Clear Guide to GNN Basics & Models

Graph Neural Networks Explained: A Clear Guide to GNN Basics & Models

An introduction to Graph Neural Networks (GNNs), covering fundamental concepts like nodes, edges, and embeddings. This post delves into the core message-passing mechanism and provides a detailed overview of key architectures including GCN, GraphSAGE, GAT, GIN, and Graph Transformers, explaining their unique approaches and mathematical formulations.

Artificial Intelligence

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⚡️Every product of the future will be a living system  — Ronak Malde, Trajectory.ai

⚡️Every product of the future will be a living system — Ronak Malde, Trajectory.ai

Ronuk Malde, CEO of Trajectory.ai, discusses his journey from building AI coding agents at Windsurf to his current focus on continual learning for enterprise AI. He shares insights on leveraging real-world user data, the unique challenges of model acquisition, and how Trajectory.ai's platform, powered by innovations like scaled SDPO and a novel training stack, enables dynamic, always-learning AI models for diverse industries from legal to finance.

6 Things to Know about AIE World's Fair 2026

6 Things to Know about AIE World's Fair 2026

Discover the AI Engineering World's Fair 2026, the largest iteration yet, offering an unparalleled deep dive into AI engineering with expanded tracks on auto research, GPU specialization, and new verticals like finance and healthcare. Highlights include an innovative expo experience, exclusive leadership initiatives like the "Token Billionaires Program," and unique side events fostering community, including "Posters on AI" where attendees can defend their tweets. This event is designed to be a curated hub for practical, cutting-edge insights and networking in the AI/ML professional landscape.

The data black hole at the center of AI

The data black hole at the center of AI

AI progress is fundamentally driven by vast amounts of data and compute, rather than improvements in sample efficiency, creating a stark contrast with human learning. This essay explores the "black hole of data" powering AIs, quantifies the massive sample-efficiency gap between humans and machines, counters common objections, and discusses the implications for white-collar automation and future AI research.

Technology

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3‑2‑1 Backup Rule Explained: Protect Your Data from Disaster

3‑2‑1 Backup Rule Explained: Protect Your Data from Disaster

Jeff Crume outlines essential data resiliency strategies, starting with the 3-2-1 backup rule—three copies, two media types, one offsite—and expanding to include immutable or air-gapped backups, rigorous testing, and encryption. He emphasizes these principles for robust disaster recovery, ransomware protection, and minimizing costly downtime, highlighting the trade-offs in achieving high availability.

The Media Game Has Changed

The Media Game Has Changed

The conversation explores the shift from legacy media to creator-led platforms, why authenticity has become a competitive advantage, and how founders can build audiences by communicating directly with customers, employees, and the public. They discuss podcasts, social media, storytelling, corporate communications, and the changing relationship between companies, journalists, and audiences. Along the way, they examine how founders can develop a public voice, why some leaders become influential communicators, and what it means to build a brand in a world where distribution is increasingly decentralized.

The C4 Model: Visualizing Software Architecture • Simon Brown & Susanne Kaiser • GOTO 2026

The C4 Model: Visualizing Software Architecture • Simon Brown & Susanne Kaiser • GOTO 2026

Simon Brown, creator of the C4 Model, discusses its origin as a practical solution to clarify messy software diagrams. He explains the four hierarchical levels (context, container, component, code), emphasizing that most teams only need the top two for significant value. The discussion highlights the importance of including technology in diagrams, C4's collaborative nature, and practical advice on modeling microservices and bounded contexts, all while advocating for a lightweight, accessible approach to architectural visualization.


Recent Post

Power agents with full context of your experiments and traces with W&B MCP server

Power agents with full context of your experiments and traces with W&B MCP server

The W&B Model Context Protocol (MCP) is a hosted endpoint that enables AI agents to intelligently interact with all Weights & Biases data, including runs, traces, evaluations, and reports. It features discovery tools for smart queries, automated analysis for comparing experiments and identifying regressions, and seamless integration with IDEs, coding agents, and chat interfaces like Mistral AI for streamlined ML workflows and on-the-go reporting.

He Raised $70M to Cure Every Disease With AI

He Raised $70M to Cure Every Disease With AI

Samuel Rodriques, founder of Edison Scientific, shares his journey from physics to building an AI scientist named Kosmos. He discusses how AI agents are already making novel discoveries, including a potential cure for blindness, and are poised to revolutionize drug discovery. The conversation dives into AI's strengths in high-throughput reasoning, the critical bottlenecks in clinical trials, proposed reforms for the US medical system, and whether human scientists will still be needed in an age of hyper-intelligent AI.

Frontier AI at Home — Alex Cheema, EXO Labs

Frontier AI at Home — Alex Cheema, EXO Labs

Alex Cheema from EXO Labs explores the path to a 100x improvement in the price-performance of running frontier AI models locally. The talk covers full-stack optimization strategies, including kernel fusion for a 30% performance boost, RDMA for scalable tensor parallelism, and a novel approach of splitting prefill and decode phases across heterogeneous hardware (e.g., an RTX GPU and Mac Studios) to significantly speed up large-prompt inference.

Q-learning with Flow-Matching Policies

Q-learning with Flow-Matching Policies

This talk explores methods for optimizing expressive, multi-modal policies, such as those based on flow-matching, with off-policy reinforcement learning. The speaker presents two novel algorithms, FQ-RL and CAM, designed to overcome the instability of backpropagation through multi-step generative models, enabling effective online self-improvement and adaptation for robotic manipulation tasks.

Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models

Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models

Continuous flow models map naturally to a Schrödinger equation, the fundamental equation of quantum mechanics. This discovery proves that a trained generative model can be efficiently simulated on a future quantum computer, enabling a new, more powerful type of access to its learned distribution for tasks like Monte Carlo estimation and structure discovery.

Where the Score Lives: What Wavelets Reveal About Diffusion Models

Where the Score Lives: What Wavelets Reveal About Diffusion Models

This talk explores the paradox of why diffusion models generalize rather than memorize. It introduces an analytically tractable, wavelet-based parameterization of the score function, allowing for an interpretable analysis of how architectural biases (like locality) and data statistics interact to influence denoising performance and generalization.

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