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AI Governance

Ian Mulvany
November 28, 2023

AI Governance

Ian Mulvany

November 28, 2023
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  1. Goal of this talk: What actions can each of us

    take now? How might we best share what we are learning?
  2. 1. 12 Months since ChatGPT became available 2. LLMs predictably

    get more capable with increasing investment, even without targeted innovation. 3. Many important LLM behaviors emerge unpredictably as a byproduct of increasing investment. 4. LLMs often appear to learn and use representations of the outside world. 5. There are no reliable techniques for steering the behavior of LLMs. 6. Experts are not yet able to interpret the inner workings of LLMs. 7. Human performance on a task isn’t an upper bound on LLM performance. 8. LLMs need not express the values of their creators nor the values encoded in web text. 9. Brief interactions with LLMs are often misleading. LLMs - 9 Things to consider! https://cims.nyu.edu/~sbowman/eightthings.pdf
  3. 1. The pace of iteration is insane 2. LLMs predictably

    get more capable with increasing investment, even without targeted innovation. 3. Many important LLM behaviors emerge unpredictably as a byproduct of increasing investment. 4. LLMs often appear to learn and use representations of the outside world. 5. There are no reliable techniques for steering the behavior of LLMs. 6. Experts are not yet able to interpret the inner workings of LLMs. 7. Human performance on a task isn’t an upper bound on LLM performance. 8. LLMs need not express the values of their creators nor the values encoded in web text. 9. Brief interactions with LLMs are often misleading. LLMs - 9 Things to consider!
  4. 1. The pace of iteration is insane 2. They are

    going to get more powerful 3. Many important LLM behaviors emerge unpredictably as a byproduct of increasing investment. 4. LLMs often appear to learn and use representations of the outside world. 5. There are no reliable techniques for steering the behavior of LLMs. 6. Experts are not yet able to interpret the inner workings of LLMs. 7. Human performance on a task isn’t an upper bound on LLM performance. 8. LLMs need not express the values of their creators nor the values encoded in web text. 9. Brief interactions with LLMs are often misleading. LLMs - 9 Things to consider!
  5. 1. The pace of iteration is insane 2. They are

    going to get more powerful 3. We don’t how, they just are 4. LLMs often appear to learn and use representations of the outside world. 5. There are no reliable techniques for steering the behavior of LLMs. 6. Experts are not yet able to interpret the inner workings of LLMs. 7. Human performance on a task isn’t an upper bound on LLM performance. 8. LLMs need not express the values of their creators nor the values encoded in web text. 9. Brief interactions with LLMs are often misleading. LLMs - 9 Things to consider!
  6. 1. The pace of iteration is insane 2. They are

    going to get more powerful 3. We don’t how, they just are 4. They look like they are intelligent 5. There are no reliable techniques for steering the behavior of LLMs. 6. Experts are not yet able to interpret the inner workings of LLMs. 7. Human performance on a task isn’t an upper bound on LLM performance. 8. LLMs need not express the values of their creators nor the values encoded in web text. 9. Brief interactions with LLMs are often misleading. LLMs - 9 Things to consider!
  7. 1. The pace of iteration is insane 2. They are

    going to get more powerful 3. We don’t how, they just are 4. They look like they are intelligent 5. We don’t know how to control them 6. Experts are not yet able to interpret the inner workings of LLMs. 7. Human performance on a task isn’t an upper bound on LLM performance. 8. LLMs need not express the values of their creators nor the values encoded in web text. 9. Brief interactions with LLMs are often misleading. LLMs - 9 Things to consider!
  8. 1. The pace of iteration is insane 2. They are

    going to get more powerful 3. We don’t how, they just are 4. They look like they are intelligent 5. We don’t know how to control them 6. We don’t know how they work 7. Human performance on a task isn’t an upper bound on LLM performance. 8. LLMs need not express the values of their creators nor the values encoded in web text. 9. Brief interactions with LLMs are often misleading. LLMs - 9 Things to consider!
  9. 1. The pace of iteration is insane 2. They are

    going to get more powerful 3. We don’t how, they just are 4. They look like they are intelligent 5. We don’t know how to control them 6. We don’t know how they work 7. They will be better than us at things 8. LLMs need not express the values of their creators nor the values encoded in web text. 9. Brief interactions with LLMs are often misleading. LLMs - 9 Things to consider!
  10. 1. The pace of iteration is insane 2. They are

    going to get more powerful 3. We don’t how, they just are 4. They look like they are intelligent 5. We don’t know how to control them 6. We don’t know how they work 7. They will be better than us at things 8. They are a tool, and will be used by those who use them 9. Brief interactions with LLMs are often misleading. LLMs - 9 Things to consider!
  11. 1. The pace of iteration is insane 2. They are

    going to get more powerful 3. We don’t how, they just are 4. They look like they are intelligent 5. We don’t know how to control them 6. We don’t know how they work 7. They will be better than us at things 8. They are a tool, and will be used by those who use them 9. You need to spend time with them LLMs - 9 Things to consider!
  12. 1. Stay the same 2. Get incrementally better 3. Get

    dramatically better 4. Become sentient
  13. Customer Context Certainty Disruptiveness Language Generation How we interact with

    information Workflow / Scaling Value of high quality data
  14. Kind of task Risk Level Autonomous decision, high stakes domains

    - self driving, clinical diagnosis, Peer review Expert Augmentation, coding, legal drafting, scholarly text analysis Mechanical tasks: document analysis, copywriting, code cleanup Danger Danger Will Robinson!, stay very far away Understand, explore, exploit Just do this already!
  15. Example use cases inside BMJ • Software development • User

    research • SAP management • Report analysis • Copy editing