Maybe we can look for historical examples of how legislation had a positive impact on a widespread harmful habit.
Early on, the U.S. Congress adopted the Federal Cigarette Labeling and Advertising Act of 1965 and the Public Health Cigarette Smoking Act of 1969. These laws—
* Required a health warning on cigarette packages
* Banned cigarette advertising in the broadcasting media
* Called for an annual report on the health consequences of smoking
I'm as impressed as anyone with GPT-3 samples, but you're sort of ignoring the symbol grounding elephant in the room regarding language models (https://openreview.net/pdf?id=GKTvAcb12b).
Language models are not grounded learners. The language produced does not really correspond meaningfully to our world except in superficial (albeit complex) ways.
Do you have thoughts on how to move forward on this problem? Maybe ask GPT-3 and see what it thinks :P
They have published multiple major TTS papers since the original WaveNet paper in 2016. Including the recent Tacotron results with impressive style control.
This is called federated learning[0] at least by Google. I don't know whether they've added this to more products or whether it works well. It would be interesting to see this done in open source.
They're most likely referring to adversarial attacks where degenerate inputs are constructed that could cause AlphaGo Zero to perform sub-optimally or catastrophically fail (see OpenAI Research [0]). This is distinct from generative adversarial networks (GANs) or adversarial self-play (which I guess AlphaGo Zero is an example of).
https://www.cdc.gov/tobacco/data_statistics/sgr/history/inde...