We generally don't have a lot of things that could end human civilization if they "tried" sitting around.I think that if you believe this, you should already be worried about misaligned AI, 1 before any analysis of how or why an AI might form its own goals.For now, I just want to linger on the point that if such an attack happened, it could succeed against the combined forces of the entire world. I'm not talking (yet) about whether, or why, AIs might attack human civilization. By "defeat," I don't mean "subtly manipulate us" or "make us less informed" or something like that - I mean a literal "defeat" in the sense that we could all be killed, enslaved or forcibly contained. I'm going to try to make this idea feel more serious and real.Īs a first step, this post will emphasize an unoriginal but extremely important point: the kind of AI I've discussed could defeat all of humanity combined, if (for whatever reason) it were pointed toward that goal. They find the idea of AI itself going to war with humans to be comical and wild. They might see the broad point that AI could be dangerous, but they instinctively imagine that the danger comes from ways humans might misuse it.
Many people have trouble taking this "misaligned AI" possibility seriously. ( Like in the Terminator movies, minus the time travel and the part where humans win.)
Collapse and rewind series#
I've been working on a new series of posts about the most important century.
Collapse and rewind download#
Preliminary data from this feasibility study show that using the Microsoft Kinect sensors provides detection of falls, fall risks, and facilitates quality improvement after falls in real hospital environments unobtrusively, while taking into account patient privacy.Click lower right to download or find on Apple Podcasts, Spotify, Stitcher, etc. During the first 8 months of data collection, processing methods were perfected to manage data and provide a "rewind" method to view events that led to falls for post-fall quality improvement process analyses. All falls and non-falls were correctly identified using automated algorithms to process Kinect sensor data. Prior to implementation with patients, three researchers performed a total of 18 falls (walking and then falling down or falling from the bed) and 17 non-fall events (crouching down, stooping down to tie shoe laces, and lying on the floor). Kinect sensors were placed in six hospital rooms in a step-down unit and data were continuously logged. The purpose of this study was to test the implementation of a fall detection and "rewind" privacy-protecting technique using the Microsoft® Kinect™ to not only detect but prevent falls from occurring in hospitalized patients.