  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 data from the Internet,
and then reinforcement learning, RAG, prompt engineering and cognitive
modelling are used to fine-tune and augment their behavior. This technology has
been used to create models of individual people, such as Caryn Marjorie.
However, these chatbots are not based on people's actual emotional and
physiological responses to their environment, so they are, at best,
surface-level approximations to the characters they are imitating. This paper
describes how a new type of foundation model - a first-person foundation model
- could be created from recordings of what a person sees and hears as well as
their emotional and physiological reactions to these stimuli. A first-person
foundation model would map environmental stimuli to a person's emotional and
physiological states, and map a person's emotional and physiological states to
their behavior. First-person foundation models have many exciting applications,
including a new type of recommendation engine, personal assistants, generative
adversarial networks, dating and recruitment. To obtain training data for a
first-person foundation model, we have developed a recording rig that captures
what the wearer is seeing and hearing as well as their emotional and
physiological states. This novel source of data could help to address the
shortage of new data for building the next generation of foundation models.
