It can’t hurt.

https://fpf.org/2020/03/27/fpf-offers-new-resources-on-privacy-and-pandemics/

FPF Offers New Resources on Privacy and Pandemics

… The  resources are accessible on the FPF website at fpf.org/privacy-and-pandemics.

Arguments are fun! (And entertaining)

https://www.technologyreview.com/s/615416/ai-debate-gary-marcus-danny-lange/

A debate between AI experts shows a battle over the technology’s future

Since the 1950s, artificial intelligence has repeatedly overpromised and underdelivered. While recent years have seen incredible leaps thanks to deep learning, AI today is still narrow: it’s fragile in the face of attacks, can’t generalize to adapt to changing environments, and is riddled with bias. All these challenges make the technology difficult to trust and limit its potential to benefit society.

On March 26 at MIT Technology Review’s annual EmTech Digital event, two prominent figures in AI took to the virtual stage to debate how the field might overcome these issues.

Something to read when the library reopens.

https://thenextweb.com/neural/2020/03/27/human-compatible-a-timely-warning-on-the-future-of-ai-syndication/

Human Compatible: A timely warning on the future of AI

… It’s very easy to dismiss warnings of the robot apocalypse. After all, virtually all of the field’s who’s who agree that we’re at least half-a-century away from achieving artificial general intelligence, the key milestone to developing an AI that can dominate humans. As for the AI that we have today, it can best be described as “idiot savant.” Our algorithms can perform remarkably well at narrow tasks but fail miserably when faced with situations that require general problem–solving skills.

But we should reflect on these warnings, if not take them at face value, computer scientist Stuart Russell argues in his latest book Human Compatible: Artificial Intelligence and the Problem of Control.

… For the most part, current research in the field is focused on using more compute power and data to advance the field instead of seeking fundamentally new ways to create algorithms that can manifest intelligence.

“Focusing on raw computing power misses the point entirely. Speed alone won’t give us AI,” Russell writes. Running flawed algorithms faster computer does have a bright side however: You get the wrong answer more quickly.