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Will Heaven on Muck Rack

Will Heaven

London, Malmesbury, Richmond
As seen in: Daily Mail, Financial Times, The Telegraph, Digg, Flipboard, Heise Online, MIT Technology Review, ProQuest, The Code Project, Genetic Literacy Project and
Comms for @Dyson. Previous: Managing Ed at @Spectator, @Telegraph journalist, Speechwriter to Michael Gove @MoJGovUK, @policy_exchange Comms Director.
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Articles

Google DeepMind wants to know if chatbots are just virtue signaling

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By Michelle Page, Will Heaven, Amy Nordrum Verified
| buff.ly
In short, the appearance of moral behavior in LLMs should not be taken at face value. Models must be probed to see how robust that moral behavior really is. “For people to trust the answers, you need to know how you got there,” says Haas. What Haas, Isaac, and their colleagues at Google DeepMind propose is a new line of research to develop more rigorous techniques for evaluating moral competence in LLMs. This would include tests designed to push models to change their responses to moral questions.
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LLMs contain a LOT of parameters. But what’s a parameter?

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By Will Heaven
| Benton Institute for Broadband & Society
A large language model’s parameters are often said to be the dials and levers that control how it behaves. Think of a planet-size pinball machine that sends its balls pinging from one end to the other via billions of paddles and bumpers set just so. Tweak those settings and the balls will behave in a different way. OpenAI’s GPT-3, released in 2020, had 175 billion parameters.
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The new biologists treating LLMs like an alien autopsy

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By Michelle Page, Caiwei Chenarchive page, Will Heaven
| buff.ly
How large is a large language model? Think about it this way. In the center of San Francisco there’s a hill called Twin Peaks from which you can view nearly the entire city. Picture all of it—every block and intersection, every neighborhood and park, as far as you can see—covered in sheets of paper. Now picture that paper filled with numbers.
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