How frontrunners such as OpenAI should leverage human-in-the-loop to screen for ethical use at scale. In this article I’ll discuss these questions and explore different methods to mitigate negative outcomes, using OpenAI as an example.
Last June OpenAI released the most powerful language model ever created, which became the topic of much discussion among developers, researchers, and entrepreneurs. Its capabilities of zero- and one-shot learning blew people’s minds, with many GPT-3 powered applications going viral on twitter every second day.
This API is being released in an era when polarization and bias have never been as intense, with technology that is powerful, scalable, and potentially dangerous — imagine a fake news generator or a social media bullying bot powered by the human-like GPT-3.
Understanding the harmful potential of its API technology, OpenAI has taken a unique Go To Market approach, strictly limiting access to a small number of vetted developers. By doing so, it became one of the first companies to voluntarily forfeit short-term profits in favor of being socially-responsible.
As our understanding of AI evolves, other companies developing advanced AI technologies such as ScaleAI might follow a similar path.
The combination of a for-profit company, a powerful technology, and the decision to screen for access is novel, raising several questions:
In this article I’ll discuss these questions and explore different methods to mitigate negative outcomes, using OpenAI as an example.
Artificial Intelligence, Machine Learning, and Data Science are amongst a few terms that have become extremely popular amongst professionals in almost all the fields.
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