As you surely know, OpenAI is not very open about how their systems works or how they build them. More importantly for most uses and business, OpenAI is agnostic about how users apply their services and how to make most out of the models multi-step "reasoning" capabilities .

As a stark contrast to OpenAI, today I am talking to Marius Dinu, the CEO and co-founder of the austrian startup extensity.ai. Extensity.ai as a company follows an open core model, building an open source framework which is the foundation for AI Agent systems that perform multi-step reasoning and problem solving, while generating revenue by providing enterprise support and custom implementation's.

Marius will explain how their Neuro-Symbolic AI Framework is combining the strengths of symbolic reasoning, like problem decomposition, explainability, correctness and efficiency with an LLM's understanding of natural language and their capability to operate on unstructured text following instructions.

We will discuss how their framework can be used to build complex multi-step reasoning workflows and how the framework works like an orchestrator and reasoning engine that applies LLM's as semantic parsers that at different decision points decide what tools or sub-systems to apply and use next. As well how in their research, they focus on ways to measure the quality and correctness of individual workflow steps in order to optimize workflow end-to-end and build a reliable, explainable and efficient problem solving system.

I hope you find this episode useful and interesting.

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## TOC

00:00:00 Beginning

00:03:31 Guest Introduction

00:08:32 Extensity.ai

00:17:38 Building a multi-step reasoning framework

00:26:05 Generic Problem Solver

00:48:41 How to ensure the quality of results?

01:04:47 Compare with OpenAI Strawberry

### References

Marius Dinu - https://www.linkedin.com/in/mariusconstantindinu/

https://www.extensity.ai/

Extensity.ai - https://www.extensity.ai/

Extensity.ai YT - https://www.youtube.com/@extensityAI

SymbolicAI Paper: https://arxiv.org/abs/2402.00854