The Atomic Human

Understanding Ourselves in the Age of AI

Neil D. Lawrence

Churchill College Science Society

bits/min billions 2,000
billion
calculations/s
~100 a billion
embodiment 20 minutes 5 billion years

Claude Shannon

bits/min billions 2,000
billion
calculations/s
~100 a billion
embodiment 20 minutes 5 billion years

  • This phenomenon has already revolutionised biology.
    • Large scale data acquisition and distribution.
    • Transcriptomics, genomics, epigenomics, ‘rich phenomics’.
  • Great promise for personalized health.

  • Automated decision making within the computer based only on the data.
  • Subjective biases need to be better understood.
  • Particularly important where treatments are being prescribed.
    • Interventions could be far more subtle.

  • Shift in dynamic:
    • from direct human-data to indirect human-computer-data
    • modern data analysis is mediated by the machine
  • This change of dynamics gives us the modern and emerging domain of data science

The Great AI Fallacy

There are three types of lies: lies, damned lies and statistics

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There are three types of lies: lies, damned lies and statistics

Arthur Balfour 1848-1930

There are three types of lies: lies, damned lies and statistics

Arthur Balfour 1848-1930

There are three types of lies: lies, damned lies and ‘big data’

Neil Lawrence 1972-?

For sale: baby shoes, never worn

  • There is a lot of evidence that probabilities aren’t interpretable.

  • See e.g. Thompson (1989)

  • LLMs are already being used for robot planning Huang et al. (2023)

  • Ambiguities are reduced when the machine has had large scale access to human cultural understanding.

Later in the 1940’s, when I was doing my Ph.D. work, there was much talk of the brain as a computer and of the early digital computers that were just making the headlines as “electronic brains.” As an analogue computer man I felt strongly convinced that the brain, whatever it was, was not a digital computer. I didn’t think it was an analogue computer either in the conventional sense.

  • A human-analogue machine is a machine that has created a feature space that is analagous to the “feature space” our brain uses to reason.

  • The latest generation of LLMs are exhibiting this charateristic, giving them ability to converse.

  • Perils of this include counterfeit people.
  • Daniel Dennett has described the challenges these bring in an article in The Atlantic.

  • But if correctly done, the machine can be appropriately “psychologically represented”

  • This might allow us to deal with the challenge of intellectual debt where we create machines we cannot explain.

One thing is I can live with is doubt, and uncertainty and not knowing. I think it’s much more interesting to live with not knowing than to have an answer that might be wrong.

Richard P. Feynmann in the The Pleasure of Finding Things Out 1981.

  • HAMs change how we share ambiguous information.
  • We need to think about how that effects our sharing of proabilities.

Heider, F., Simmel, M., 1944. An experimental study of apparent behavior. The American Journal of Psychology 57, 243–259. https://doi.org/10.2307/1416950
Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y., Sermanet, P., Jackson, T., Brown, N., Luu, L., Levine, S., Hausman, K., ichter, brian, 2023. Inner monologue: Embodied reasoning through planning with language models, in: Liu, K., Kulic, D., Ichnowski, J. (Eds.), Proceedings of the 6th Conference on Robot Learning, Proceedings of Machine Learning Research. PMLR, pp. 1769–1782.
Lawrence, N.D., 2024. The atomic human: Understanding ourselves in the age of AI. Allen Lane.
Lawrence, N.D., 2017. Living together: Mind and machine intelligence. arXiv.
MacKay, D.M., 1991. Behind the eye. Basil Blackwell.
The Admiralty, 1945. The gunnery pocket book, b.r. 224/45.
Thompson, W.C., 1989. Are juries competent to evaluate statistical evidence? Law and Contemporary Problems 52, 9–41.