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Machine Learning

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Judge a Transaction by the Company It Keeps — Somya Rai & MD Sharique, EXL

Judge a Transaction by the Company It Keeps — Somya Rai & MD Sharique, EXL

EXL developed an advanced fraud detection system that augments XGBoost with Graph Neural Network (GNN) embeddings. Traditional tabular models struggle to capture fraud signals residing in transaction neighborhoods. The system's innovative design includes meticulous sampling strategies, disjoint monthly graph construction for leakage control, and a GraphSAGE architecture that aggregates mean, max, and standard deviation of neighbor features to detect subtle fraud patterns. Emphasizing explainability for regulatory compliance and analyst trust, it also features a highly optimized serving architecture achieving 3ms P99 latency, pivoting to edge prediction to reduce cache footprint from 60GB to 2-3GB. This led to a 27-33% relative lift in true positive rates and a projected $65 million incremental fraud capture.

From 15% to 90% GPU Utilization: Fix the Data Pipeline, Not the Model

From 15% to 90% GPU Utilization: Fix the Data Pipeline, Not the Model

Tarun Sunkaraneni from Amazon AGI shows how a multimodal SFT pipeline that spent about 85% of its time waiting on data was brought to roughly 90% GPU utilization. He walks through concurrency with asyncio and Ray actors, prefetching, zero-copy transfer via Ray's object store, and a network-card bottleneck that appears only at scale, and argues that the hardest part of training throughput is often keeping GPUs fed rather than optimizing the model.

Small Models, Big Results: Training a Finance Agent for Under $500 — Charles Dickens, Snorkel AI

Small Models, Big Results: Training a Finance Agent for Under $500 — Charles Dickens, Snorkel AI

Snorkel AI and UC Berkeley's Sky Computing Lab show that a 4B Qwen3 model, trained with reinforcement learning for under $500 using the rLLM framework, beats its 235B sibling on expert-validated financial question answering built from SEC 10-K filings. The talk covers the FinQA dataset construction and verification, common failure modes of frontier models, transfer to multi-table and general tool-use tasks, and ablations showing that simple data and binary rewards outperform more elaborate setups, since the real bottleneck is disciplined tool use rather than reasoning depth.

Artificial Intelligence

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Bringing Continual Learning to Every Company: Trajectory’s Ronak Malde

Bringing Continual Learning to Every Company: Trajectory’s Ronak Malde

Ronak Malde, co-founder of Trajectory, argues that agents need to learn continuously from real-world experience instead of relying on offline batch updates. He covers how training custom models has become accessible to companies, two research directions (Group Size-1 GRPO and a meta-grader for reward propagation), the Harvey and Rogo results, the distinction between continual learning and recursive self-improvement, and a vision of product builders as gardeners who shape products that grow on their own.

Why Waiting for the Next AI Model Can Pay Off - John Stecher, Blackstone & Scott Worland, Norm AI

Why Waiting for the Next AI Model Can Pay Off - John Stecher, Blackstone & Scott Worland, Norm AI

Blackstone CTO John Stecher and Norm AI CTO Scott Worland discuss how to build AI agent systems that remain useful as models improve. They cover why data strategy and straight-through processing come first, how to weigh ROI against waiting for a better model, how evals anchor financial-services work, and why Norm rebuilt its platform after a large hand-labeled eval set exposed the cost of over-engineered code.

How We Took an AI Agent From 41% to 97% Accurate - Milind Sunil Shah, Brainbase Labs

How We Took an AI Agent From 41% to 97% Accurate - Milind Sunil Shah, Brainbase Labs

Milind Sunil Shah of Brainbase Labs presents a six-step framework for improving back-office agents, illustrated with a Domino's project that tracks competitor pizza prices across websites and delivery apps. Accuracy rose from 41% to 97%, cost per source fell from $2 to $1, and run time dropped from 129 to 71 minutes, all through instructions, skills, and playbooks rather than a new model. The talk covers kickoff, ontology, benchmarks and evals, a narrow first agent, iteration, and handing off the full loop.

Technology

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What iPhone Packaging Reveals About the Robotics Opportunity

What iPhone Packaging Reveals About the Robotics Opportunity

Tutor Intelligence CEO Josh Gruenstein explains why robotics is a systems problem spanning AI, hardware, manufacturing, and customers, and how deploying robots into real factories and warehouses, with human tutors as backup, generates the data that makes them more capable. The discussion covers the company's own robot designs, its focus on small and mid-sized businesses, the economics of flexible robotic labor, and the implications for American manufacturing and supply chains.

Why Founders Are Betting Big on Factories, Mines, and Energy

Why Founders Are Betting Big on Factories, Mines, and Energy

Erin Price-Wright and Connor Love unpack the AD50 and the rebuilding of America's industrial base, covering how defense demand is financing manufacturing capacity for commercial markets, how AI is reshaping factory economics, how a new generation of founders applies lessons from SpaceX, Tesla, Anduril, and Palantir to mining, energy, and manufacturing, and why private capital is now willing to fund physical infrastructure before government demand is guaranteed.

Mark Cuban’s Plan to Make Healthcare Affordable

Mark Cuban’s Plan to Make Healthcare Affordable

Mark Cuban and Julie Yoo discuss how transparency, direct contracting, and AI could reshape healthcare economics, from Cost Plus Drugs and modular pod manufacturing to personalized medicine, the future of physicians, and advice for founders who want to use AI to learn rather than to stop thinking.

Recent Post

Why Voice Security Is Your Next Big Problem

Why Voice Security Is Your Next Big Problem

Yishay Carmiel and Roy Zanbel of Apollo Defend explore the state of voice AI, detailing the shift from cascaded models to end-to-end speech-to-speech systems. They break down the imminent security threats, including accessible voice cloning and sophisticated agent-based attacks, and discuss the nascent defense mechanisms and the urgent need for a new layer of voice security for governments, enterprises, and consumers.

Jonathan Blow - Jai Demo and Design Explanation (KEYNOTE) - Updated

Jonathan Blow - Jai Demo and Design Explanation (KEYNOTE) - Updated

Jonathan Blow, creator of Braid and The Witness, discusses the design philosophy behind 'jai', a new systems programming language. He explains how 'jai' re-evaluates the cost-benefit analysis of manual memory management by providing powerful, low-friction tools for metaprogramming, introspection, and debugging, inspired by principles from functional programming.

How DeepL Built a Translation Powerhouse with AI with CEO Jarek Kutylowski

How DeepL Built a Translation Powerhouse with AI with CEO Jarek Kutylowski

Jarek Kutylowski, CEO of DeepL, discusses the company's technical strategy for competing with large language models in the translation space. He covers their focus on specialized model architectures, the critical role of curated data, the engineering challenges of building custom GPU data centers and large-scale inference systems, and the future of AI-driven translation in enterprise workflows.

LLMOps for eval-driven development at scale

LLMOps for eval-driven development at scale

Mercari's engineering team shares their practical, evaluation-centric approach to LLMOps. Learn how they leverage tiered evaluations, strategic tooling for observability, and rapid iteration to productionize LLM features for over 23 million users, emphasizing that good 'evals' are often more critical than model fine-tuning or RAG.

Mapping the Mind of a Neural Net: Goodfire’s Eric Ho on the Future of Interpretability

Mapping the Mind of a Neural Net: Goodfire’s Eric Ho on the Future of Interpretability

Eric Ho, founder of Goodfire, discusses the critical challenge of AI interpretability. He shares how his team is developing techniques to understand, audit, and edit neural networks at the feature level, including breakthrough results in resolving superposition with sparse autoencoders, successful model editing demonstrations, and real-world applications in genomics with Arc Institute's DNA foundation models. Ho argues that these white-box approaches are essential for building safe, reliable, and intentionally designed AI systems.

The AI that solves the market: A new era in forecasting with natural language explainability

The AI that solves the market: A new era in forecasting with natural language explainability

LG AI Research introduces its advanced financial forecasting framework, which powers a US equities market ETF (LQAI) and a new "Master Score with Commentary" product with LSEG. The system uniquely combines structured financial data with unstructured text from news and reports, using the proprietary Exaone LLM and a multi-agent architecture to deliver explainable, accurate, and actionable market predictions across the entire US stock market.

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