OpenMined, in collaboration with PyTorch, Facebook AI, Oxford releases the second free course of the Private AI Series. The second course — Foundations of Private Computation — is focused on educating techniques like federated learning, split neural networks, cryptography, homomorphic encryption, differential privacy, and more.
The Private AI Series includes four courses of which the first course was released early this year. Unlike the first course that sets the foundation for the entire series, the second course is technical and requires learners to have knowledge of Numpy and/or PyTorch.
The first lesson of the second course was released on 17 March and over the weeks, more lessons will be released. The second course will be of 60 hours long, which also includes a project at the end. Although it is a free course, OpenMined has ensured that learners can get their queries resolved with mentors. You can join the course discussion board or Slack community for engaging with other learners and mentors.
What makes OpenMined course stand out from the rest in the markets is its instructors, who are either developers of the tools or algorithms of privacy-related technologies or are experts in the domain.
Artificial intelligence has enormous potential to revamp the way we carry out personal or professional work. But, privacy concerns are impeding the embracement of artificial intelligence in highly regulated sectors like healthcare, finance, and more.
One of the many ways to eliminate the privacy challenges is to educate learners and develop a workforce that can bring a change by bringing trust among users. To ensure users trust providing their data on others’ hands, the artificial intelligence industry has to process data without exposing personal information.