Most agreed that we need a better understanding of the economies of automated versus human-generated disinformation before we understand how much of a threat GPT-3 poses.
#ANOTHER WORD FOR TRANSFORM SOFTWARE#
One held that we could learn from similar calls of alarm when the photo-editing software program Photoshop was developed.
Still, participants questioned whether it’s easier, cheaper, and more effective to hire humans to create such propaganda. Models like GPT-3 can be used to create false or misleading essays, tweets, or news stories. Disinformation is a real concern, but several unknowns remain Some argued this would help models learn cause and effect the way humans do, through physically interacting with their surroundings. For example, it can generate a convincing rendition of “a baby daikon radish in a tutu walking a dog.”įurthermore, some workshop participants also felt future models should be embodied - meaning that they should be situated in an environment they can interact with. One surprising aspect of DALL-E is its ability to sensibly synthesize visual images from whimsical text descriptions. In fact, shortly after the workshop, OpenAI took a step in this direction and released a model called DALL-E, a version of GPT-3 that generates images from text descriptions. Participants agreed that future language models would be trained on data from other modalities (e.g., images, audio recordings, videos, etc.) to enable more diverse capabilities, provide a stronger learning signal, and increase learning speed. Future models won’t be restricted to learning just from language On the other hand, some noted that GPT-3 might not need to understand to successfully perform tasks - after all, a non-French speaker recently won the French Scrabble championship. Some participants said that GPT-3 lacked intentions, goals, and the ability to understand cause and effect - all hallmarks of human cognition. However, participants differed in terms of where they felt GPT-3 fell short in this regard. In contrast to most current AI systems, GPT-3 is edging closer to such general intelligence, workshop participants agreed. Unlike chess engines, which solve a specific problem, humans are “generally” intelligent and can learn to do anything from writing poetry to playing soccer to filing tax returns. Is GPT-3 intelligent, and does it matter? This raises the question of when (or what) jobs could (or should) be automated by large language models. Furthermore, it’s unclear what effect highly capable models will have on the labor market.
This makes it difficult to characterize all possible uses (and misuses) of large language models in order to forecast the impact GPT-3 might have on society. Future users are likely to discover even more capabilities.
#ANOTHER WORD FOR TRANSFORM CODE#
GPT-3 has an unusually large set of capabilities, including text summarization, chatbots, search, and code generation. GPT-3’s uses and their downstream effects on the economy are unknown Workshop participants said they were surprised that such behavior emerges from simple scaling of data and computational resources and expressed curiosity about what further capabilities would emerge from further scale. Furthermore, for some tasks, GPT-3 outperforms models that were explicitly trained to solve those tasks, although in other tasks it falls short. This behavior was mostly absent in GPT-2. This increase in scale drastically changes the behavior of the model - GPT-3 is able to perform tasks it was not explicitly trained on, like translating sentences from English to French, with few to no training examples. For comparison, its predecessor, GPT-2, was over 100 times smaller, at 1.5 billion parameters. GPT-3 has 175 billion parameters and was trained on 570 gigabytes of text. As language models grow, their capabilities change in unexpected ways A more detailed summary can be found here. Below are some takeaways from the discussion. To better understand GPT-3’s capabilities, limitations, and potential impact on society, HAI convened researchers from OpenAI, Stanford, and other universities in a Chatham House Rule workshop. For example, it can generate racist, sexist, and bigoted text, as well as superficially plausible content that, upon further inspection, is factually inaccurate, undesirable, or unpredictable. However, model developers and early users demonstrated that it had surprising capabilities, like the ability to write convincing essays, create charts and websites from text descriptions, generate computer code, and more - all with limited to no supervision. Put simply, GPT-3 is trained to predict the next word in a sentence, much like how a text message autocomplete feature works.
In July 2020, OpenAI unveiled GPT-3, a language model that was easily the largest known at the time.
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