ohhhhh I gotta look. I also have had some fun in Cap Cut recently. So basic, but also so easy to use.
AI
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NVIDIA GPUs Power NASA Webb Telescope and AI Drug Discovery
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Our GPUs interpret data from the @NASAWebb Space Telescope so that all of humanity can see outer space as never before. And to help researchers explore the vast chemical space of drug discovery, we built the #AI-powered BioNeMo cloud service. https://t.co/sf8g0w4Mda #NVIDIAstory
— NVIDIA (@nvidia) 8 novembre 2022Our GPUs interpret data from the @NASAWebb Space Telescope so that all of humanity can see outer space as never before. And to help researchers explore the vast chemical space of drug discovery, we built the #AI-powered BioNeMo cloud service. https://
nvda.ws/3xTkalW #NVIDIAstory -
Inspiration from Ajeya Cotra’s Sandwiching Concept in Research
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This paper was heavily inspired by prior work, especially Ajeya Cotra's 'sandwiching' concept:
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Human Accuracy Improves 10 Percent Through Model Interaction
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Generally, we see that our participants start out with far worse accuracy on these tasks than our models do, and that by interacting with the models, they’re able do about 10 percentage points better.
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Human-AI Collaboration Improves Task Performance Through Simple Chat Strategy
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Our experiment shows that through a simple strategy – having humans chat with models while completing a task – we can help humans perform better at these tasks. This is very encouraging, albeit preliminary!
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Non-Experts Answer Expert Questions on MMLU and QuALITY
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We ask non-experts to answer expert-level questions on MMLU, and also ask people to answer questions about long QuALITY passages under a time limit that’s too short for a careful read.
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Scalable Oversight Framework and Language Model Question-Answering Proof of Concept
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Along with developing a framework for scalable oversight, we also conduct a proof of concept experiment that demonstrates a couple of question-answering tasks that work well under this paradigm with current language models:
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Challenges in Studying Model Assistance: Task Selection and Experimental Design
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It’s also challenging to study: For most tasks today, we don’t actually need our model’s help in this way. So testing these methods will require us to be clever about how we choose our tasks and design our experiments.
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Leveraging Model Capabilities While Maintaining Supervisory Control
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This is tricky: To do this, we’ll need ways for the human who’s supervising the model to use any relevant knowledge or skills that the model already has, even though they can’t trust the model to be reliably helpful.
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Scalable Oversight: Supervising AI Systems Beyond Human Capabilities
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To ensure that AI systems remain safe as they start to exceed human capabilities, we’ll need to develop techniques for scalable oversight: the problem of supervising systems’ behavior without assuming that the overseer understands the task better than the system being trained.