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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

Learn C++ by Example • Frances Buontempo & Matt Godbolt

Learn C++ by Example • Frances Buontempo & Matt Godbolt

Frances Buontempo discusses her book "Learn C++ by Example," explaining her teaching philosophy which uses self-contained, playable examples to make modern C++ features like coroutines, the spaceship operator, and structured bindings accessible to programmers returning to the language.

NVIDIA NemoClaw, OpenAI’s pivot and Shopify agents

NVIDIA NemoClaw, OpenAI’s pivot and Shopify agents

Experts discuss NVIDIA's agentic AI push with NemoClaw, the ethics of the Anthropic Institute, the future of e-commerce with Shopify's AI shoppers, and OpenAI's strategic pivot to enterprise and coding.

One Size Fits None: How Platform Engineering Must Evolve • William Rizzo & Colin Griffin • GOTO 2026

One Size Fits None: How Platform Engineering Must Evolve • William Rizzo & Colin Griffin • GOTO 2026

Colin Griffin and William Rizzo discuss the future of platform engineering, emphasizing the need to move beyond one-size-fits-all frameworks. They explore how different industries like fintech, telco, and automotive require tailored platforms due to unique regulatory and business challenges. The conversation highlights the growing pressure to link platform investments to clear business outcomes and details the infrastructure reckoning caused by large-scale GPU investments for AI, which brings hardware and network orchestration to the forefront.

This Technology Scares OpenAI (Here's Why)

This Technology Scares OpenAI (Here's Why)

Jeff Hawke, CTO at Odyssey, provides a deep dive into the emerging field of "world models"—AI systems that generate continuous, interactive simulations. He draws parallels to the "GPT-2 era" of LLMs, outlining the current state, core research challenges like coherence and control, and the immense potential for applications in gaming, robotics, and content creation. Hawke also clarifies the confusing terminology, distinguishing canonical world models from spatial intelligence and generative video models like Sora.

How Linear Algebra Powers Machine Learning (ML)

How Linear Algebra Powers Machine Learning (ML)

Fangfang Lee from IBM explains how linear algebra is the mathematical foundation of machine learning, enabling computers to understand data. The summary covers key concepts like vectorization, similarity metrics (Euclidean distance, cosine similarity), and dimensionality reduction using Singular Value Decomposition (SVD).

Inside the New Media Team with Marc Andreessen & Ben Horowitz

Inside the New Media Team with Marc Andreessen & Ben Horowitz

Erik Torenberg, Ben Horowitz, and Marc Andreessen of a16z dissect the paradigm shift from old to new media. They explore why an offensive strategy of 'flooding the zone' has replaced defensive PR, how authentic individuals have eclipsed sterile corporate brands, the strategic importance of speed and the OODA loop, and the nuanced interplay between oral and written cultures on the internet.

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