Making a decision is a complex task. Today's guest Dongho Kim discusses how he and his team at Prowler has been building a platform that will be accessible by way of APIs and a set of pre-made scripts for autonomous decision making based on probabilistic modeling, reinforcement learning, and game theory. The aim is so that an AI system could make decisions just as good as humans can.

This episode collects interviews from my recent trip to Microsoft Build where I had the opportunity to speak with Dharma Shukla and Syam Nair about the recently announced CosmosDB. CosmosDB is a globally consistent, distributed datastore that supports all the popular persistent storage formats (relational, key/value pair, document database, and graph) under a single streamlined API. The system provides tunable consistency, allowing the user to make choices about how consistency trade-offs are managed under the hood, if a consumer wants to go beyond the selected defaults.

Platform as a service is a growing trend in data science where services like fraud analysis and face detection can be provided via APIs. Such services turn the actual model into a black box to the consumer. But can the model be reverse engineered? Florian Tramèr shares his work in this episode showing that it can. The paper Stealing Machine Learning Models via Prediction APIs is definitely worth your time to read if you enjoy this episode. Related source code can be found in https://github.com/ftramer/Steal-ML.

This week's episode explores the possibilities of extracting novel insights from the many great social web APIs available. Matthew Russell's Mining the Social Web is a fantastic exploration of the tools and methods, and we explore a few related topics. One helpful feature of the book is it's use of a Vagrant virtual machine. Using it, readers can easily reproduce the examples from the book, and there's a short video available that will walk you through setting up the Mining the Social Web virtual machine. The book also has an accompanying github repository which can be found here. A quote from Matthew that particularly reasonates for me was "The first commandment of Data Science is to 'Know thy data'." Take a listen for a little more context around this sage advice. In addition to the book, we also discuss some of the work done by Digital Reasoning where Matthew serves as CTO. One of their products we spend some time discussing is Synthesys, a service that processes unstructured data and delivers knowledge and insight extracted from the data. Some listeners might already be familiar with Digital Reasoning from recent coverage in Fortune Magazine on their cognitive computing efforts. For his benevolent recommendation, Matthew recommends the Hardcore History Podcast, and for his self-serving recommendation, Matthew mentioned that they are currently hiring for Data Science job opportunities at Digital Reasoning if any listeners are looking for new opportunities.