Extract, transform, and load (ETL) is the process by which data is acquired from various sources. The data is collected in a standard location, cleaned, and processed. Ultimately, the data is loaded into a datastore from which it can be queried. Legacy ETL processes import data, clean it in place, and then store it in a relational data engine. "SQL Server Integration Services is a platform for building enterprise-level data integration and data transformations solutions. Use Integration Services to solve complex business problems by copying or downloading files, loading data warehouses, cleansing and mining data, and managing SQL Server objects and data."

In a paper published this week on the preprint server Arxiv.org, They claim that SSIs can help to solve a range of complex robotic tasks -- for example, grasping, lifting, and placing a ball into a cup -- with only raw sensor data. Training AI in the robotics domain typically requires a human expert and prior information. The AI must be tailored with adjustments depending on the overarching task at hand, which entails defining a reward that indicates success and that facilitates meaningful exploration. SSIs ostensibly provide a generic means of encouraging agents to explore their environments, as well as guidance for collecting data to solve a main task.

Modern reinforcement learning algorithms can learn solutions to increasingly difficult control problems while at the same time reduce the amount of prior knowledge needed for their application. One of the remaining challenges is the definition of reward schemes that appropriately facilitate exploration without biasing the solution in undesirable ways, and that can be implemented on real robotic systems without expensive instrumentation. In this paper we focus on a setting in which goal tasks are defined via simple sparse rewards, and exploration is facilitated via agent-internal auxiliary tasks. We introduce the idea of simple sensor intentions (SSIs) as a generic way to define auxiliary tasks. SSIs reduce the amount of prior knowledge that is required to define suitable rewards. They can further be computed directly from raw sensor streams and thus do not require expensive and possibly brittle state estimation on real systems. We demonstrate that a learning system based on these rewards can solve complex robotic tasks in simulation and in real world settings. In particular, we show that a real robotic arm can learn to grasp and lift and solve a Ball-in-a-Cup task from scratch, when only raw sensor streams are used for both controller input and in the auxiliary reward definition.

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It was my favorite out of the ones our team put on and we are talking about doing a BI event in the next few months. If you interested in presenting on any BI related topic or your company would like to sponsor a full day BI training event in LA, please let me know.