Artificial intelligence and machine learning can be a hard field to manage, working to allow systems to perfectly understand statements, to recognize an image, or to help power self-driving vehicle systems like Apple’s “Project Titan.” The problem with machine learning development is that engineers have to closely examine how the data is parsed, and to determine how exceptions to normal data should be managed, a task that is only going to get harder as systems get larger and more sophisticated.
To that end, Apple produced the “Overton” framework, a research paper by Apple engineers spotted by VentureBeat advises. Overton is designed to automate the training of AI systems by offering high-level abstracts provided to it by engineers.
For example, Overton could generate a model to supply the answer to a question that may be tricky for digital assistants like Siri to parse, such as “How tall is the President of the United States?” This sort of query requires multiple data pipelines to be sourced, with many parts to ascertain before creating the intended answer.
Normally engineers would spend most of their time working on fine-grained quality monitoring of unusual data subsets, as well as supporting said multi-component pipelines. With Overton, Apple intends to limit the amount of work an engineer needs to do, automating many of the chores and to keep monitoring elements on behalf of the engineers.
“The vision is to shift developers to higher-level tasks instead of lower-level machine learning tasks,” the paper states. “Overton can automate many of the traditional modeling choices, including deep learning architecture, and it allows engineers to build, maintain, and monitor their application by manipulating data files.
A high-level illustration of Overton’s features
Furthermore, Overton is produced in such a way that it could be interacted with “without writing any code.” Instead, Overton creates a schema from data payloads that describe input data used for AI model training, as well as model tasks that describe what the model needs to perform.
The schema also defines the input, output, and data flow of the intended model, with Overton compiling it into a variety of AI development frameworks, including TensorFlow, CoreML, and PyTorch, to determine the most appropriate architecture for model learning.
Overton is also able to use techniques like model slicing to identify subsets and reduce bias, as well as multi-task learning to predict all of a tasks a model may require.
So far, Overton has been valuable to Apple’s researchers, with errors reduced between 1.7 times to 2.9 times against production systems.
“In summary, Overton represents a first-of-its-kind machine-learning lifecycle management system that has a focus on monitoring and improving application quality,” the paper reads. “A key idea is to separate the model and data, which is enabled by a code-free approach to deep learning.”
Apple’s machine learning work is considerable with a growing workforce and knowledge base via various acquisitions, and touches many different areas of its software business. Most notable is its work with Siri, but the results of its research also surfaces in other elements, such as iOS 13’s ability to detect cats and dogs in photographs.
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