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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

How Debuggers Work • Sy Brand • GOTO 2025

How Debuggers Work • Sy Brand • GOTO 2025

Sy Brand demystifies the internal workings of native code debuggers, explaining how core features like breakpoints, stepping, stack unwinding, and variable inspection are implemented using operating system primitives, debug information formats like DWARF, and low-level hardware features.

Perplexity Comet, agentic blabbering, and the shift-left failure

Perplexity Comet, agentic blabbering, and the shift-left failure

This episode explores the security risks of AI, including 'agentic blabbering' in AI browsers that aids phishing attacks, the ability of models like Claude Opus to resurrect vulnerabilities in legacy code, the debate on 'shift left' security practices, and new threats like AI-generated 'ephemeral malware' and the challenges of the post-authentication perimeter.

Write Reliable Software with Temporal

Write Reliable Software with Temporal

Johann Schleier-Smith from Temporal explains Durable Execution, a paradigm for building reliable, long-running applications. He details how Temporal's model of deterministic workflows and stateful activities provides a robust alternative to traditional checkpointing and event-driven architectures, especially for complex, LLM-driven agentic systems.

Greetings, Earthlings: Philip Johnston of Starcloud on Data Centers in Space

Greetings, Earthlings: Philip Johnston of Starcloud on Data Centers in Space

Philip Johnston of Starcloud argues that space will become the primary location for AI compute within a decade. He explains how plummeting launch costs, superior solar energy economics in orbit, and the physics of heat dissipation will soon make space-based data centers cheaper and more scalable than their terrestrial counterparts, predicting a future where nearly a trillion dollars in annual CapEx shifts to space.

What is Human In The Loop with AI? How HITL Shapes AI Systems

What is Human In The Loop with AI? How HITL Shapes AI Systems

Exploring the concept of Human-in-the-Loop (HITL) AI, this summary details the spectrum of human involvement—from strict HITL to full autonomy. It covers how humans are integrated at different stages of the AI workflow, including training (Active Learning), tuning (RLHF), and inference (runtime oversight), to ensure safety, instill judgment, and build trust in AI systems.

Building AI for better healthcare — the OpenAI Podcast Ep. 14

Building AI for better healthcare — the OpenAI Podcast Ep. 14

OpenAI's Dr. Nate Gross and Karan Singhal detail their strategy for applying AI in healthcare, focusing on the rigorous, physician-led process for training models on sensitive health data. They discuss the challenges of deployment in siloed systems and how AI is evolving from a Q&A tool into an integrated assistant for patients and a critical safety net for clinicians.

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