Data First Culture

• I am a data science:

1. follower (no visibility/influence)
2. some visibilty/influence
3. visibility and some influence
4. leader (lead on data and AI developments)

“ ‘When someone seeks,’ said Siddhartha, ‘then it easily happens that his eyes see only the thing that he seeks, and he is able to find nothing, to take in nothing. […] Seeking means: having a goal. But finding means: being free, being open, having no goal.’ ”

Hermann Hesse

The Art of Statistics

Intellectual Debt

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Evolved Relationship with Information

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

• See e.g. Thompson (1989)

• 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.

• 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.

Progress so far:

• Developing the vision
  • Engaged over 100 members of the University community across 30 departments/institutes, start-ups, and large businesses.
  • Supported 6 new funding bids