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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

ChatGPT study mode, shift from UX to AX and Cost of a Data Breach Report 2025

ChatGPT study mode, shift from UX to AX and Cost of a Data Breach Report 2025

Experts discuss the dual role of AI in education as a 'cognitive gym,' the shift from UX to Agentic Experience (AX) in software design, AI's application in historical research for decoding ancient texts, and the latest findings from the 2025 Cost of a Data Breach Report on AI-driven attacks and defenses.

Claude for Financial Services Keynote

Claude for Financial Services Keynote

Anthropic executives and financial industry leaders from S&P Global, Deloitte, DE Shaw, HG Capital, New York Life, and the Norwegian Sovereign Wealth Fund discuss the future of AI in finance and announce Claude for Financial Analysis, a unified intelligence layer designed to transform professional workflows.

Pipecat Cloud: Enterprise Voice Agents Built On Open Source - Kwindla Hultman Kramer, Daily

Pipecat Cloud: Enterprise Voice Agents Built On Open Source - Kwindla Hultman Kramer, Daily

A deep dive into the challenges of building production-grade, low-latency voice AI agents, and how the open-source, vendor-neutral framework Pipecat provides a comprehensive solution for development, deployment, and scaling. Learn about voice AI architecture, the trade-offs between speech-to-speech and text-based models, and practical deployment strategies.

Computational models for brain science

Computational models for brain science

Dr. Laschowski discusses his lab's research in computational neuroscience, focusing on three core areas: reverse-engineering human motor control using reinforcement and optimal control models, developing high-accuracy neural decoding algorithms for brain-machine interfaces (BMIs), and creating brain-inspired deep learning models for computer vision. The talk highlights a long-term vision of discovering the fundamental principles of intelligence to build more efficient and robust AI.

Building AI agents with Claude in Amazon Bedrock | Code w/ Claude

Building AI agents with Claude in Amazon Bedrock | Code w/ Claude

In a presentation from Code w/ Claude, AWS advocates Du'An Lightfoot, Suman Debnath, and Banjo Obayami introduce Strands, a new open-source Python SDK for building AI agentic applications on AWS. They showcase how Strands simplifies development by focusing on three core components—models, tools, and prompts—and leverages the full reasoning power of foundation models like Claude 3.5 on Amazon Bedrock. The session includes live demos on creating a multi-tool weather agent, integrating with Modular Connected Protocol (MCP) servers for AWS documentation and diagram generation, and using Claude Code to auto-generate a complete Strands agent for AWS CDK.

Building AI agents with Claude in Google Cloud's Vertex AI | Code w/ Claude

Building AI agents with Claude in Google Cloud's Vertex AI | Code w/ Claude

Ivan Nardini from Google Cloud demonstrates how to build, enhance, and productionalize AI agents using Google Cloud's agent stack. The session covers the challenges of deploying agents and introduces the Agent Development Kit (ADK) for building, the Vertex AI Agent Engine for managed deployment, and protocols like MCP and Agent-to-Agent for tool integration and inter-agent communication, using Claude on Vertex AI as the core LLM.

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