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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

Fundamentals of Data Engineering • Matt Housley & Joe Reis • GOTO 2025

Fundamentals of Data Engineering • Matt Housley & Joe Reis • GOTO 2025

Joe Reis and Matt Housley, authors of "Fundamentals of Data Engineering," reflect on the book's principles three years after its publication. They discuss how the rise of AI has created both powerful tools and dangerous "bear traps" for engineers, the critical role of expertise in a world of AI-generated content, and why foundational knowledge is more important than ever.

Email Spoofing with SMTP Smuggling: How the Shared Email Infrastructures Magnify this Vulnerability

Email Spoofing with SMTP Smuggling: How the Shared Email Infrastructures Magnify this Vulnerability

SMTP smuggling is a critical vulnerability allowing email spoofing that bypasses standard authentication protocols like SPF and DMARC. This research presents a large-scale study on its prevalence, uncovering widespread issues due to shared infrastructure and incomplete patches, and introduces a novel, non-intrusive methodology for ethically testing private email services.

A Framework for Abusability Analysis: The Case of Passkeys in Interpersonal Threat Models

A Framework for Abusability Analysis: The Case of Passkeys in Interpersonal Threat Models

This talk introduces an "abusability analysis framework" to evaluate technologies like passkeys under interpersonal threat models, such as intimate partner violence. An analysis of 19 services revealed critical implementation flaws, including irrevocable cloned passkeys and the failure of password resets to secure accounts, which can create persistent backdoors for abusers.

‘Hey mum, I dropped my phone down the toilet’: Investigating Hi Mum and Dad SMS Scams in the UK

‘Hey mum, I dropped my phone down the toilet’: Investigating Hi Mum and Dad SMS Scams in the UK

An empirical study of the 'hi mum and dad' SMS scam, a new interaction-based fraud technique. The research dissects the scam's lifecycle, from initial contact to financial exploitation, by analyzing scam messages, mobile network data, and mule accounts in collaboration with a UK mobile operator. Key findings reveal the psychological principles used, the abuse of mobile and financial infrastructure, and the underlying structure of scammer networks.

Dion: The distributed orthonormal update revolution is here

Dion: The distributed orthonormal update revolution is here

Kwangjun Ahn from Microsoft Research introduces Dion, a next-generation optimizer that improves upon Muon by using amortized power iteration. Dion enables efficient, scalable training for massive models by orthonormalizing a low-rank subspace, reducing compute and communication overhead in distributed settings.

Software finally eats services - Aaron Levie

Software finally eats services - Aaron Levie

In a dynamic discussion, experts debate the merits of pricing H-1B visas versus the current lottery system, questioning what the policy should optimize for. They explore the real-world impact of AI on developer productivity, with Box reporting that 30% of its code now comes from AI, and highlight how small, senior teams are achieving superhuman results by shifting from writing code to reviewing it. The conversation also covers why bottom-up, personal AI tools are succeeding where top-down corporate pilots fail, and analyzes the platform shift, arguing that AI-native startups have a unique advantage in a landscape where incumbent scale is being neutralized by AI agents.

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