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“For now, we’re primarily focused on research towards these goals down the road, we foresee many opportunities for our work to create value commercially and for public benefit.”Įxcited to announce what we’ve been working on this year – an AI safety and research company. “Large, general systems of today can have significant benefits, but can also be unpredictable, unreliable, and opaque: our goal is to make progress on these issues,” reads the company’s self-description. Today the general rule is: the more powerful the system, the harder it is to explain its actions. It’s one thing to not know when an AI model is generating poetry, quite another when the model is watching a department store for suspicious behavior, or fetching legal precedents for a judge about to pass down a sentence.
How does it do it? What is it “thinking”? Which knob would you tweak, which dial would you turn, to make it more melancholy, less romantic, or limit its diction and lexicon in specific ways? Certainly there are parameters to change here and there, but really no one knows exactly how this extremely convincing language sausage is being made. Okay, the GPT-3 hype seems pretty reasonableīut say you had it generate rhyming couplets with Shakespeare and Pope as examples.
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