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Masterminded Trump's astounding election win with a "stealth data machine".
The same cannot be said for contemporary manifestations of AI, as represented by the combination of massive processing power, big data, machine learning, advanced robotics and neural networks.
Given that there is no way that humans can handle these volumes of scientific, commercial and personal data, machine intelligence is the only way to go.
The service, called Bloomberg Government, is based on the same guiding principle that spawned the original Bloomberg financial data machine: people need an aggregator and filter for information, and they will pay a lot of money for that convenience.
The solution requires serious engineering work on big data, machine learnings, algorithms, scalability, and distributed systems.
It supports both structured and unstructured data, machine learning and data mining.
These are built using a mixture of data, machine learning, and some editorial help, she said.
Firstly, the BioAssay Ontology, which addresses many of our remaining concerns around making the experimental data machine readable.
For instance, how and where might the theoretical studies in big data machine learning research actually be applied? .
We're just scratching the surface of what sensors, big data, machine learning and AI can do for us.
Make all government data machine readable and open the curtains to let in the light and the prying eyes.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com