Possible Futures: Exploring Deepfake and Neuralink

Two recent articles highlight the dilemma faced in this era of rapid innovation: the potential to enhance humanity, and the opportunity to diminish it. This article on DeepFakes describes the challenge that society will face as Deepfake video and audio make it impossible to tell the difference between reality and fiction. Audio attacks using convincing forgeries can send stocks plummeting or soaring. How about mimicking a CEO’s voice to request a senior financial officer to transfer money? These are real examples provided by Symantec. This short Video describes the money transfer scenario.

In another recent article, Elon Musk is at it again. He wants to hook your brain directly to computers starting next year. In his quest to create symbiosis with artificial intelligence, Musk founded Neuralink Corp. in July 2016 to create “ultra-high bandwidth brain-machine interfaces to connect humans and computers.” The company said in 2017 that its initial goal was to devise brain interfaces to alleviate the symptoms of chronic medical conditions. Neuralink may someday help treat brain disorders – but much like other Musk initiatives, something else is in play. Given the existential risk that Musk believes AI represents, merging with it is his way of securing humanity’s future as a civilization relative to AI.

These two examples underscore the need to Balance the Opposing Forces of Innovation. They should also impress upon all of us the need to focus on possible futures. Understanding the implications of scenarios such as these is critical to mitigating risk and avoiding unintended consequences. We should all be investing cycles in educating ourselves and staying informed.

4 thoughts on “Possible Futures: Exploring Deepfake and Neuralink

  1. […] Generative Adversarial Networks: A GAN is made up of two neural networks that have each been trained on what a certain thing looks like, like a an animal or a person. When the training is complete, one network is told to start generating new images of the thing on its own. The other network is presented with a stream of these fake images with real images interspersed and tries to guess which are fakes. Human input tells each network its successes and failures. Each then adjusts itself to try to do better and they push each other to greater and greater heights of success. I described the possible impacts of this scenario in my post on DeepFakes. […]


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