  Foundation models have had a big impact in recent years and billions of
dollars are being invested in them in the current AI boom. The more popular
ones, such as Chat-GPT, are trained on large amounts of Internet data. However,
it is becoming apparent that this data is likely to be exhausted soon, and
technology companies are looking for new sources of data to train the next
generation of foundation models.
  Reinforcement learning, RAG, prompt engineering and cognitive modelling are
often used to fine-tune and augment the behaviour of foundation models. These
techniques have been used to replicate people, such as Caryn Marjorie. These
chatbots are not based on people's actual emotional and physiological responses
to their environment, so they are, at best, a surface-level approximation to
the characters they are imitating.
  To address these issues, we have developed a recording rig that captures what
the wearer is seeing and hearing as well as their skin conductance (GSR),
facial expression and brain state (14 channel EEG). AI algorithms are used to
process this data into a rich picture of the environment and internal states of
the subject. Foundation models trained on this data could replicate human
behaviour much more accurately than the personality models that have been
developed so far. This type of model has many potential applications, including
recommendation, personal assistance, GAN systems, dating and recruitment.
  This paper gives some background to this work and describes the recording rig
and preliminary tests of its functionality. It then suggests how a new type of
foundation model could be created from the data captured by the rig and
outlines some applications. Data gathering and model training are expensive, so
we are currently working on the launch of a start-up that could raise funds for
the next stage of the project.
