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Session on Reasoning

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Machine Learning

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Frontier results, on device - RL Nabors, Arize

Frontier results, on device - RL Nabors, Arize

RL Nabors discusses the significant costs associated with using frontier AI models, covering security, latency, and financial implications. She introduces a framework for right-sizing AI solutions by leveraging smaller, task-specific models and Small Language Models (SLMs). The framework details how to prove task feasibility, establish success criteria with golden datasets, conduct capability evaluations (using tools like Phoenix), and select the most appropriate "Small And Good Enough" (SAGE) model. Nabors further demonstrates how prompt engineering, particularly few-shot prompting, and post-processing can close performance gaps with larger models, while advocating for continuous regression evaluations to maintain performance integrity. The overarching message is to "prototype big, deploy small" to optimize AI deployments.

Research to Reality: Bringing Frontier ML Research to Production - Vaidas Razgaitis, Higharc

Research to Reality: Bringing Frontier ML Research to Production - Vaidas Razgaitis, Higharc

Vaidas Razgaitis, Senior Research Engineer at Higharc, shares three tactical tips to accelerate the transition of novel AI/ML research into production-ready features. He emphasizes addressing the critical handoff challenge between ML researchers and software engineers through structured documentation (Research Prototype Taxonomy Document), a well-organized monorepo utilizing decoupled microservices, and a systematic approach to code decomposition and PR review. These strategies aim to improve legibility, maintainability, and delivery speed for ML-driven products.

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.

Artificial Intelligence

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Session on Reasoning

Session on Reasoning

This session features two talks on optimizing and verifying AI reasoning. Hongxiang Fan discusses cross-stack co-design for efficient AI, focusing on Test-Time Scaling (TTS) challenges, optimal verification granularity, and system-level optimizations for edge deployments. Nagarajan Natarajan introduces 'Advancing Verified Reasoning' with the InterVent platform, aiming to ensure AI agents comply with complex policies through formal verification, dynamic steering, and leveraging verification signals for training. Both emphasize addressing the computational and reliability costs of advanced AI.

Multimodal & Embodied Intelligence (Pt 1), Panel on Multimodal AI: Progress, Pitfalls, Possibilities

Multimodal & Embodied Intelligence (Pt 1), Panel on Multimodal AI: Progress, Pitfalls, Possibilities

This session explored Multimodal and Embodied Intelligence, featuring talks on hybrid AI in robotics (classical vs. end-to-end), AI's role in healthcare (focusing on NCDs, deployment, and uncertainty modeling), and fundamental perception challenges in multimodal reasoning (using educational video QA and visual puzzles). A panel discussed the impact of foundation models, the blurred lines between AGI and human-like AI, critical deployment pitfalls (human factors, efficiency, architectural limits), and future directions, emphasizing task-specific models and the redefinition of 'foundation models.'

Grant Sanderson (@3blue1brown) – AI and the future of math

Grant Sanderson (@3blue1brown) – AI and the future of math

Grant Sanderson and Dwarkesh Patel discuss AI's rapid but uneven progress in mathematics, exploring whether AI can achieve true conceptual breakthroughs, the challenge of measuring creativity, and the long-term implications for human understanding and the future roles of mathematicians. They delve into the unique 'grindability' of math for AI training, the potential of formalization, and why AI currently struggles with 'theory of mind' in writing, offering advice for students navigating an AI-transformed world.

Technology

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Plenary Talk 3​: Challenges and research opportunities for global hyperscale services

Plenary Talk 3​: Challenges and research opportunities for global hyperscale services

Jim Kleewein's talk outlines the immense challenges and critical research opportunities in building and operating global hyperscale services like Microsoft 365 and Azure. He emphasizes that at this scale, traditional approaches fail, necessitating a "new golden age of applied research" across areas like continuous availability, data management, security, and sustainability. Kleewein also discusses AI's powerful but limited role, stressing the ongoing need for human expertise, and highlights the ethical imperative to prevent failures that can have life-or-death consequences.

Platforms: Build Abstractions, not Illusions • Gregor Hohpe • GOTO 2025

Platforms: Build Abstractions, not Illusions • Gregor Hohpe • GOTO 2025

Gregor Hohpe explains the critical role of platforms in managing the growing cognitive load on developers due to complex distributed systems. He contrasts platforms, driven by "economies of speed" and fostering innovation through diversity, with traditional IT services and oversimplified abstractions that create dangerous illusions. Hohpe emphasizes building platforms that provide intuitive, domain-specific abstractions to solve real business problems, rather than just repackaging existing cloud services.

Full Stack Greenfield Projects : Are they still relevant?

Full Stack Greenfield Projects : Are they still relevant?

Bharat Goenka, co-founder of Tally, discusses the company's unconventional approach to software development through "Full Stack Greenfield" projects. He explains why building every component from scratch, despite being a high-risk strategy, has been crucial for Tally's success in serving the SMB market, fostering extreme customer loyalty, and aspiring to connect 200 million businesses. The talk delves into the historical context, the philosophy of questioning and choosing constraints, and the distinction between product and custom engineering.

Recent Post

AI Agents in Practice • Henrik Kniberg • GOTO 2025

AI Agents in Practice • Henrik Kniberg • GOTO 2025

Henrik Kniberg shares practical experiences from over two years of AI agent development, defining agents as autonomous entities with missions, tools, and an LLM brain. He covers effective design patterns, architectural insights, and safety considerations, emphasizing the importance of human-in-the-loop collaboration and iterative development to build agents that are not just powerful, but genuinely useful in real-world scenarios.

Promptware, cloud security trends for 2026, and what the Xbox One hack means for cybersecurity

Promptware, cloud security trends for 2026, and what the Xbox One hack means for cybersecurity

This episode of Security Intelligence explores the "Promptware" kill chain for AI attacks, moving beyond simple prompt injections. The discussion also covers evolving cloud attack trends targeting ecosystems over infrastructure, ransomware gangs "living off the land" with native tools, and the critical yet overlooked "rusting edge" of OT security.

🔬There Is No AlphaFold for Materials — AI for Materials Discovery with Heather Kulik

🔬There Is No AlphaFold for Materials — AI for Materials Discovery with Heather Kulik

Professor Heather Kulik shares her hard-won perspective on applying AI to materials science, from discovering novel polymers with surprising quantum properties to the practical limitations of LLMs and the critical need for integrating deep domain expertise with data-driven methods.

Everything We Got Wrong About Research-Plan-Implement - Dexter Horthy

Everything We Got Wrong About Research-Plan-Implement - Dexter Horthy

Dexter Horthy of HumanLayer critiques the initial Research-Plan-Implement (RPI) framework for AI coding agents, revealing its tendency to encourage 'outsourcing thinking'. He introduces CRISPR, a new structured methodology that emphasizes smaller, focused prompts, human-agent alignment through artifacts like Design Discussions, and engineer ownership to combat 'slop' and improve code quality in complex projects.

Lessons from 25 Trillion Tokens — Scaling AI-Assisted Development at Kilo

Lessons from 25 Trillion Tokens — Scaling AI-Assisted Development at Kilo

Scott Breitenother, CEO of Kilo, discusses the evolution of software development, where engineers are shifting from writing code to orchestrating AI agents. He shares lessons from processing 25 trillion tokens, emphasizing the critical role of trust, the importance of end-to-end ownership, and how this new paradigm leads to a 10x increase in shipping velocity.

Attention, World Models and the Future of AI — with Prof. Kyunghyun Cho

Attention, World Models and the Future of AI — with Prof. Kyunghyun Cho

Professor Kyunghyun Cho, a co-author of the first paper on attention, discusses the future of AI. He argues that today’s models have already captured most correlations in passive data, making the real challenge about actively choosing which data to collect. He also explores the open debate around world models, the surprising lack of coding agent adoption among his students, and the foundational work that led to Retrieval-Augmented Generation (RAG).

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