Decision Making in the Era of Generative AI

Neil D. Lawrence

Healthy Lifespan Institute Annual Meeting

Henry Ford’s Faster Horse

Information and Embodiment

Claude Shannon

Embodiment Factors

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

New Flow of Information

Evolved Relationship

Evolved Relationship

Societal Effects

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

Societal Effects

  • 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.

Societal Effects

  • 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


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

Benjamin Disraeli

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

Benjamin Disraeli 1804-1881

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

Neil Lawrence 1972-?

Mathematical Statistics

‘Mathematical Data Science’

Human Communication

Heider and Simmel (1944)

For sale: baby shoes, never worn

In practice …

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

  • See e.g. Thompson (1989)

Number Theatre

Data Theatre

}{The focus so far has been on reducing uncertainty to a few representative values and sharing numbers with human beings. We forget that most people can be confused by basic probabilities for example the prosecutor’s fallacy.}{anne-llm-conversation}

In practice …

  • 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.

Inner Monologue

Behind the Eye

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.

Human Analogue Machine

Counterfeit People

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

Psychological Representation of the Machine

  • 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.

The Accelerate Programme

  • Research
  • Teaching and learning
    • Ramp or Bridge model
  • Engagement


AI for Science

One of biology’s biggest mysteries is how proteins fold to create exquisitely unique three-dimensional structures. Every living thing – from the smallest bacteria to plants, animals and humans – is defined and powered by the proteins that help it function at the molecular level.

So far, this mystery remained unsolved, and determining a single protein structure often required years of experimental effort. It’s tremendous to see the triumph of human curiosity, endeavour and intelligence in solving this problem. A better understanding of protein structures and the ability to predict them using a computer means a better understanding of life, evolution and, of course, human health and disease.

Professor Dame Janet Thornton, Director Emeritus of EMBL

Heider, F., Simmel, M., 1944. An experimental study of apparent behavior. The American Journal of Psychology 57, 243–259.
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.
Thompson, W.C., 1989. Are juries competent to evaluate statistical evidence? Law and Contemporary Problems 52, 9–41.