AI News, When I die : lessons from the death zone artificial intelligence

The Twilight Zone (2002 TV series)

Noteworthy episodes featured Jason Alexander as Death wanting to retire from harvesting souls, Lou Diamond Phillips as a swimming pool cleaner being shot repeatedly in his dreams, Susanna Thompson as a woman whose stated wish results in an 'upgrading' of her family, Usher as a police officer being bothered by telephone calls from beyond the grave, Brian Austin Green as a businessman who encounters items from his past that somehow reappear, Jeffrey Combs as a hypochondriac whose diseases become reality, and Katherine Heigl playing a woman who went back in time on a suicide mission to kill the infant Adolf Hitler.

The series also includes remakes and updates of stories presented in the original Twilight Zone television series, including the famous 'Eye of the Beholder' starring Molly Sims.

The anti-alien hysteria is an allegory for the anti-communist paranoia of the time, and the 2003 remake, starring Andrew McCarthy and Titus Welliver, replaces aliens with terrorists.

Bill Mumy returned to play the adult version of Anthony, the demonic child he had played in the original story, with Mumy's daughter, Liliana, appearing as Anthony's daughter, a more benevolent but even more powerful child.

A Tutorial Introduction to Privacy-Centric Deep Learning in PyTorch

Secure multi-party computation and homomorphic encryption add computational overhead, but the results are well worth it!

As machine learning continues to impact our society in massive ways it’s often the models that make the headlines, but ask any data scientist or machine learning engineer about what they spend the majority of their time on, and the answer is likely to be the data.

Sourcing, labeling, augmenting, and otherwise rendering data suitable for machine and deep learning models is a critical part of a machine learning practitioner’s skillset and underlies the knack for getting products to “just work” (at least until the automation wavecatches up to the automaterswith advances like Google’sAutoML).

Exact numbers are hard to pin down, but while we can’t necessarily expect a 1,000% biennial increase in data to continue indefinitely, there’s no doubt that the number of high-quality training datasets is higher now than ever before.

While people have, for the most part, been surprisingly OK with openly sharing their data for very little in return, growing awareness and an expanding list of data breaches and blatant misconduct is starting to change that at both the public and regulatory levels.

It’s not realistic for a hospital to upload every computed tomography scan to a centralized server for real-time model updates, for example, and there are many other use-cases where it is preferable for learning to occur on-site or in a distributed fashion.

We could just blindly trust all parties involved and this may have been generally accepted in the past, but we can also run the data through a sharing cipher and employ what’s called Secure Multi-party Computation (SMPC) Secure Multi-Party Computation (SMPC) is any protocol for processing data that doesn’t depend on revealing the data to all parties.

Then we'll look at teaching a small network the exclusive or function, a classic and infamous example of a non-linear function, using an open source cryptographic deep learning library called PySyft.

In the example below, we'll see how SMPC can split data into shares while preserving the ability to add encrypted variables to other encrypted variables and multiply encrypted variables with plaintext values.

This is enough for simple statistical learning models like linear regression, but to take advantage of the ability of neural networks to fit arbitrary data functions according to the universal approximation theorem, we need to introduce non-linearities.

That is the main area of development for privacy-centric deep learning libraries like PySyft, using clever tricks like Taylor expansion series to approximate common activation functions.

That's exemplified by the example of training the XOR example above: the training dataset is literally only 12 bits of data including the target values and a far cry from modern deep learning tasks.

In the future, it will make sense for GPU manufacturers to cater to the demand for good data practices with improved hardware support of cryptographic primitives, much like the tensor cores and decreased precision capabilities developed in response to deep learning demand.

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