Like brilliant but untamed minds, agentic applications in production present a unique challenge: they solve problems in revolutionary ways but can be wildly unpredictable. The art of deploying these free spirits requires a delicate balance between autonomy and reliability. At Orra, we've developed a "glue layer" that acts as a skilled wrangler, ensuring reliability while preserving the agents' freedom in production environments. We'll explore its architecture including our approach to adaptive execution planning, and how we enhance domain understanding.

Whenever you read about taking Retrieval Augmented Generation beyond simple vector search on embeddings, graphs are almost sure to come up. But what graphs? Old-school knowledge graphs, with entities and their relationships, or document-centric graphs, with text snippets as nodes? And how do you use them to improve your retrieval? Nearest neighborhood? PageRank? Something else? I will provide an overview of what's happening in that space, including what I'm doing, and give you a tour of the different options, with their pros and cons.

In a world increasingly saturated with AI-driven applications, businesses face mounting pressure to integrate chatbots into their digital offerings. But is building a chatbot always a good idea? In this talk, we’ll channel our inner Agent Scully—skeptical but willing to investigate—as we guide you through seven critical questions that can help determine whether a chatbot is a wise investment for your company.

Communication is the major bottleneck in large-scale LLM training. In ZeRO++, we quantize both weights and gradients during training in order to reduce the communication volume by 4x, which leads to end-to-end training time reduction by over 50%.

Efficient GPU orchestration is crucial in MLOps to support the distributed training and serving of increasingly complex models.

George Mathew (Insight Partners) joins Demetrios to break down how AI and ML have evolved over the past few years and where they’re headed. He reflects on the major shifts since his last chat with Demetrios, especially how models like ChatGPT have changed the game. George dives into "generational outcomes"—building companies with lasting impact—and the move from rule-based software to AI-driven reasoning engines. He sees AI becoming a core part of all software, fundamentally changing business operations. The chat covers the rise of agent-based systems, the importance of high-quality data, and recent breakthroughs like Deep SEQ, which push AI reasoning further. They also explore AI’s future—its role in software, enterprise adoption, and everyday life.

The post critiques AI evaluation methods from a physicist's perspective, highlighting a troubling lack of scientific rigor compared to fields like physics. While physicists meticulously define success criteria before experiments (like CERN's specific statistical requirements for the Higgs boson), AI benchmarking suffers from three critical problems: Benchmarks are abandoned once models perform well, creating an endless cycle without measuring meaningful progress. With models training on vast internet data, benchmarks are likely contaminated, essentially giving open-book exams to models that have already seen the material. Current methods fail to properly measure generalization - whether models truly understand concepts versus memorizing patterns. The author proposes a "Standard Model of AI Evaluation" bringing together cognitive scientists, AI researchers, philosophers, and evaluation experts to create hypothesis-driven benchmarks rather than difficulty-driven ones. This framework would require pre-registered hypotheses, contamination prevention strategies, and clearly defined success criteria. The post concludes by asking whether systems potentially transforming society deserve evaluation standards at least as rigorous as those used for testing new particles.

Humans have catalogued information for more than 3,000 years, a practice that has evolved as technology has advanced. The Library and Information Science discipline is responsible for building systems for organizing data, to serve our physical and digital knowledge domains. How have librarians sustained analog and digital repositories? Intentional arrangement. With the current wave of artificial intelligence (AI), many organizations are struggling from poor data management. Welcome to the digital hellscape, rife with dirty data that is un-curated, unstructured, undefined and ambiguous. To emerge from this hellscape, look towards the librarians, who can show you the way. Controlled vocabularies, taxonomies, thesauri, ontologies and knowledge graphs have all emerged from the librarian’s toolbox. In kind, AI performance is optimized when trained on the same, intentionally arranged, structured data. Harmonious data ecosystems, optimized for human and machine, is our information nirvana and can be achieved with intentional arrangement.Humans have catalogued information for more than 3,000 years, a practice that has evolved as technology has advanced. The Library and Information Science discipline is responsible for building systems for organizing data, to serve our physical and digital knowledge domains. How have librarians sustained analog and digital repositories? Intentional arrangement. With the current wave of artificial intelligence (AI), many organizations are struggling from poor data management. Welcome to the digital hellscape, rife with dirty data that is un-curated, unstructured, undefined and ambiguous. To emerge from this hellscape, look towards the librarians, who can show you the way. Controlled vocabularies, taxonomies, thesauri, ontologies and knowledge graphs have all emerged from the librarian’s toolbox. In kind, AI performance is optimized when trained on the same, intentionally arranged, structured data. Harmonious data ecosystems, optimized for human and machine, is our information nirvana and can be achieved with intentional arrangement.