Sort of — there’s a technique called prompt tuning where you optimize an internal state that the prompt is internally transformed into, adjusting empirically based on examples. It can’t be used with GPT-3 though because the model is behind an API. (Sorry for double post — typos)
AI
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Trustworthy AI Workshop Registration Deadline January 2023
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Today is the last day to enrol for the Workshop on Trustworthy AI co-organised by @MSFTResearch and @Penn at Microsoft Research, Bengaluru on 5-6 January 2023. Register at https://
trust-ai-workshop.github.io @AI4Code @SriramRajamani @RajeevAlur @TheSaddlePoint Amit Deshpande Amit Sharma -
Volvo Prioritizes Lives Over Profit Through Innovation
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Bringing lives before profit… Bravo @Volvo
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Navigating OpenAI Discord Prompt Engineering Channel Discussions
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I’ve been through so many lately, it’s all kind of a blur. The OpenAi main discord server’s Prompt Engineering channel is always one that I try to check out (although it moves so fast you can’t really stay up to date with it)
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The Lingering Danger of AI Hallucination and User Trust
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As ChatGPT and systems like it improve, I worry we’ll forget what hallucination looks like. We’ll forget it’s there, lurking in the distributional tails. And we will commit. And upon our servers will be errors. And we will post. And upon our timelines there will be dunks.
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Prompt Engineering Challenges: Avoiding Unreliability and Confabulation in Text-Davinci-002
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But the unreliability, the confabulation, was unmissable. You can’t use text‑davinci‑002 without seeing it. Learning to avoid it in narrow domains is much of the challenge of prompt engineering.
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Evolution of LLM Hallucinations and AI Promise
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I began studying LLMs to understand the content of their hallucinations. Even in text‑davinci‑002, confabulation was oxygen. 003 has less. ChatGPT less still. Every year, we need squint less to see promise.
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LLM Hallucinations Reveal Alien Causal Mechanisms of AI Cognition
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Like how the brain was understood by its clinical failures, in LLM hallucinations we see bare the true causal mechanisms that lead to its answers. What we mistook for human-like cognition is revealed as something more alien:
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Introducing the ‘LGTM Effect’ for AI-Generated Content Errors
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Starting a campaign to rebrand automation bias as “the LGTM effect”. The LGTM effect: The unshakable tendency of humans to read the first 15% of LLM-generated prose or code, think “lgtm!”, and commit/publish/tweet something that is wrong/batshit-insane/unfunny, respectively.
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Developer’s Commitment: Daily GitHub Contributions
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GF: You have commitment issues.
BF: What are you talking about?? I commit everyday on GitHub!!