NYT Companies like OpenAI hone their bots using hand-tailored examples from well-educated workers. But is this always for the best?
Last November, the company behind Facebook released a chatbot called Galactica. After a torrent of complaints that the bot made up historical events and spewed other nonsense, Meta removed it from the internet.
Two weeks later, the San Francisco start-up OpenAI released a chatbot called ChatGPT. It was a worldwide sensation.
Both bots were powered by the same fundamental technology. But unlike Meta, OpenAI had sharpened its bot using a technique that was just beginning to change the way artificial intelligence is built.
In the months leading up to the release of ChatGPT, the company hired hundreds of people to use an early version and provide precise suggestions that could help hone the bot’s skills. Like an army of tutors guiding a grade school student, they showed the bot how to respond to particular questions, rated its responses and corrected its mistakes. By analyzing those suggestions, ChatGPT learned to be a better chatbot.
The technique, “reinforcement learning from human feedback,” is now driving the development of artificial intelligence across the industry. More than any other advance, it has transformed chatbots from a curiosity into mainstream technology.
These chatbots are based on a new wave of A.I. systems that can learn skills by analyzing data. Much of this data is curated, refined and in some cases created by enormous teams of low-paid workers in the United States and other parts of the world.
For years, companies like Google and OpenAI have relied on such workers to prepare data used to train A.I. technologies. Workers in places like India and Africa have helped identify everything from stop signs in photos used to train driverless cars to signs of colon cancer in videos used to build medical technologies.