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Why Leibniz Couldn't Find the Mind Inside the Mill (Joscha Bach)

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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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Why Leibniz Couldn't Find the Mind Inside the Mill (Joscha Bach)

Why Leibniz Couldn't Find the Mind Inside the Mill (Joscha Bach)

Joscha Bach lays out his Machine Consciousness Hypothesis, the claim that consciousness is a simple biological learning algorithm which self-organising machines might rediscover. The discussion covers causal patterns and software, why IIT and the explanatory gap fail to explain experience, zombies and LLM self-reports, functionalism, Gödel and the Ruliad, and what evidence could ever settle the question.

I Made a Fruit Fly Review My Code - Nick Nisi, WorkOS

I Made a Fruit Fly Review My Code - Nick Nisi, WorkOS

Nick Nisi argues that human reviewers have become the bottleneck in software development and demonstrates a simulated fruit fly connectome trained to judge code quality, reaching about 88% accuracy in testing. He then proposes a tiered review system where team skills, classifiers, and automated reviewers send only risky pull requests to people, backed by verifiable proof.

Why We Rebuilt Microsoft Office From Scratch for AI Agents - Yijia Liang, Compound AI

Why We Rebuilt Microsoft Office From Scratch for AI Agents - Yijia Liang, Compound AI

Yijia Liang, founder of Compound and former finance analyst, explains why personal agents succeeded and what finance needs beyond them: multiplayer agents with their own inboxes, a headless Office engine rebuilt with agents, and agent swarms that navigate massive data rooms and models while keeping every output grounded in citations.

Technology

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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.

Exploits Now Beat Patches: Open Source Security in the AI Era - Patrick Smyth, Chainguard

Exploits Now Beat Patches: Open Source Security in the AI Era - Patrick Smyth, Chainguard

Patrick Smyth of Chainguard examines how frontier AI has compressed the time between vulnerability disclosure and exploitation, leaving 30-day remediation cycles obsolete. The talk covers AI-assisted attacks, waves of supply chain malware, four attack surfaces spanning malware, known CVEs, zero days, and side doors like CI/CD, developer laptops, and agent skills, and practical defenses including cooldown periods, faster CVE remediation, and signing artifacts with SLSA and Sigstore.

Recent Post

Q‑Day Explained: How Quantum Computing Threatens Today’s Cryptography

Q‑Day Explained: How Quantum Computing Threatens Today’s Cryptography

Q-Day, the day a quantum computer will be powerful enough to break current encryption standards, is an inevitable threat. This summary explains how quantum algorithms like Shor’s and Grover’s will compromise both asymmetric and symmetric cryptography, the severe consequences for data confidentiality and digital trust, and why the "Harvest Now, Decrypt Later" attack vector makes immediate migration to post-quantum cryptography a critical priority for all organizations.

I Read 9,000 AI Papers So You Don't Have To

I Read 9,000 AI Papers So You Don't Have To

Nick Vasiloglou, VP of Research at Relational AI, analyzes the key trends from NeurIPS 2025, highlighting the most impactful and under-the-radar developments for industry professionals. The discussion covers the rise of data markets through real-time attribution, the sophisticated engineering behind capable small language models (SLMs), the explosion of AI for science, and the shift towards post-training models with real-world tools.

Build Hour: Workspace agents in ChatGPT

Build Hour: Workspace agents in ChatGPT

A detailed overview of building ChatGPT Workspace Agents, covering the process from conversational setup to deployment. The session demonstrates creating a meeting preparation agent and a software review agent, highlighting features like tool integration, skills, memory, and enterprise-level admin controls.

Andrej Karpathy: From Vibe Coding to Agentic Engineering

Andrej Karpathy: From Vibe Coding to Agentic Engineering

Andrej Karpathy discusses the shift from 'vibe coding' to 'agentic engineering,' explaining why LLMs should be treated as 'ghosts'—jagged, statistical entities—rather than animals. He delves into the Software 3.0 paradigm, the limits of verifiability, and why human understanding remains the ultimate bottleneck in an age of outsourced thinking.

Demis Hassabis on Building DeepMind, AlphaFold, and the Final Stretch to AGI

Demis Hassabis on Building DeepMind, AlphaFold, and the Final Stretch to AGI

Demis Hassabis, CEO of Google DeepMind, outlines the path to AGI, which he predicts by 2030. He discusses the profound impact of AI on science, particularly in revolutionizing drug discovery with systems like AlphaFold, and posits that AI will enable new forms of simulation-based science. Hassabis also delves into the philosophical underpinnings of his work, viewing information as the universe's most fundamental quantity and advocating for developing AGI as a powerful tool before tackling the deeper questions of consciousness.

The Moonshot Podcast Season 2, Episode 6: Silicon Horizons

The Moonshot Podcast Season 2, Episode 6: Silicon Horizons

This podcast episode explores two X moonshot projects aimed at revolutionizing computer chips. Project Positron focused on creating specialized chips for real-time AI inference, acting as 'brains for robots'. Project Bodger took a meta-approach, using AI and inverse design to automate the chip design process itself, aiming to overcome the limitations of Moore's Law.

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