  We conduct a pilot study selectively evaluating the cognitive abilities
(decision making and spatial reasoning) of two recently released generative
transformer models, ChatGPT and DALL-E 2. Input prompts were constructed
following neutral a priori guidelines, rather than adversarial intent. Post hoc
qualitative analysis of the outputs shows that DALL-E 2 is able to generate at
least one correct image for each spatial reasoning prompt, but most images
generated are incorrect (even though the model seems to have a clear
understanding of the objects mentioned in the prompt). Similarly, in evaluating
ChatGPT on the rationality axioms developed under the classical Von
Neumann-Morgenstern utility theorem, we find that, although it demonstrates
some level of rational decision-making, many of its decisions violate at least
one of the axioms even under reasonable constructions of preferences, bets, and
decision-making prompts. ChatGPT's outputs on such problems generally tended to
be unpredictable: even as it made irrational decisions (or employed an
incorrect reasoning process) for some simpler decision-making problems, it was
able to draw correct conclusions for more complex bet structures. We briefly
comment on the nuances and challenges involved in scaling up such a 'cognitive'
evaluation or conducting it with a closed set of answer keys ('ground truth'),
given that these models are inherently generative and open-ended in responding
to prompts.

Artificial Intelligence;Computation and Language;Computer Vision and Pattern Recognition