Boffins at the Department of Energy’s Oak Ridge National Laboratory speculate that by 2040 advances in AI disciplines like machine learning and natural language processing will shift most software code creation from people to machines. In a paper distributed via ArXiv, “Will humans even write code in 2040 and what would that mean for extreme heterogeneity in computing?”, ORNL researchers Jay Jay Billings, Alexander McCaskey, Geoffroy Vallee and Greg Watson suggest machines will be doing much of the programming work two decades hence. “The major technologies that will drive the creation and adoption of [machine-generated code] already exist, either at research institutions or in the marketplace,” the foursome state. And they anticipate that the various efforts are underway to make code generation more efficient are likely to turn programming into something rather routine. If people do need to write some code, “they may find that they spend more time using autocomplete and code recommendation features than writing new lines on their own,” they say. As examples of current research trends, they cite: the Defense Advanced Project Agency’s (DARPA) Probabilistic Programming for Advancing Machine Learning (PPAML) program, an effort to make machine learning more broadly accessible and applicable; DeepCoder and AutoML, projects which produce code from machine learning; ontology generation tools like Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG) that structure knowledge with limited input; and code generation technologies like the Eclipse Modeling Framework and Sirius. They also observe that the application programming interfaces (APIs) for scientific libraries are becoming standardized such that academics need only understand the problem domain, without being deeply versed in using the API. This future isn’t assured, though current devops practices lean heavily on automation and presumably will do so even more as AI advances and human programmers systematize the management of technical infrastructure at scale. Read more here…

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