  Large Language Models (LLMs) have been transformative. They are pre-trained
foundational models that are self-supervised and can be adapted with fine
tuning to a wide range of natural language tasks, each of which previously
would have required a separate network model. This is one step closer to the
extraordinary versatility of human language. GPT-3 and more recently LaMDA can
carry on dialogs with humans on many topics after minimal priming with a few
examples. However, there has been a wide range of reactions and debate on
whether these LLMs understand what they are saying or exhibit signs of
intelligence. This high variance is exhibited in three interviews with LLMs
reaching wildly different conclusions. A new possibility was uncovered that
could explain this divergence. What appears to be intelligence in LLMs may in
fact be a mirror that reflects the intelligence of the interviewer, a
remarkable twist that could be considered a Reverse Turing Test. If so, then by
studying interviews we may be learning more about the intelligence and beliefs
of the interviewer than the intelligence of the LLMs. As LLMs become more
capable they may transform the way we interact with machines and how they
interact with each other. Increasingly, LLMs are being coupled with
sensorimotor devices. LLMs can talk the talk, but can they walk the walk? A
road map for achieving artificial general autonomy is outlined with seven major
improvements inspired by brain systems. LLMs could be used to uncover new
insights into brain function by downloading brain data during natural
behaviors.
