Apple Overton Leading to Code Automation?

Increasingly moving towards automating many aspects of coding.   In fact robot assistants that 'observe' the coding process could readily insure that secure, robust and repeatable methods were used when building AI systems.    They could also make sure that the most important methods were shared, maintained and updated as new research dictated. 

On the data side, that the data was properly selected, prepared and delivered with needed metadata to support explainable results.  That's why I am not a believer in just training everyone in low level coding.  People are not good at these skills.    Train them in problem solving supported by prefabricated AI systems and results visualization methods, because ultimately the classic methods will be built, solved, updated and delivered by automation.

Apple ‘Overton’: Automating Low-Code Machine Learning     By Nick Kolakowski

Apple has struggled in recent years to establish a robust artificial intelligence (A.I.) practice. This partially stems from the company’s ironclad privacy policies—it’s more difficult to analyze datasets for insights when internal rules prevent the company from using every piece of user data it can vacuum up. Nonetheless, Apple’s newest projects show that it’s powering ahead anyway—including one platform that, if it’s ever released, could change how you use A.I. and machine learning (ML).

(It’s worth remembering how, in a 2015 speech, Apple CEO Tim Cook accused tech giants such as Facebook and Google of “gobbling up everything they can learn about you and trying to monetize it,” which he framed as “wrong.” It seems unlikely that Apple’s stance on data and privacy will change during Cook’s tenure.)

According to a just-released paper with the dry-but-mysteriously-compelling title “Overton: A Data System for Monitoring and Improving Machine Learned Products,” a group of Apple researchers describe their work on a machine-learning platform (named—you guessed it—“Overton”) designed to “support engineers in building, monitoring, and improving production machine learning systems.”   ......... '

Abstract of paper mentioned above:    https://arxiv.org/pdf/1909.05372.pdf   (technical)

 ... We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production machine learning systems. Key challenges engineers face are monitoring fine-grained quality,diagnosing errors in sophisticated applications, and handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a set of novel high-level,declarative abstractions. Overton’s vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks. In fact, using Overton, engineers can build deep-learning-based applications without writing any codein frameworks like TensorFlow. For over a year, Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing. In that time, Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors 1.7 − 2.9× versus  production systems. .... "