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, 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.
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.
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.
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.
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.