Quick Introduction We have all been on forums, chats, reddit, discord, youtube, or somewhere and heard “Oh! Model XYZ is AMAZEBALLZ!zomgwtfbbq” then downloaded it (or more likely, some quantized form of it) and said “eww… This sucks!” This post is going to be a rather technical series of experiments to demonstrate the impact of implementation-specific hazards with inference. I will be using the term “reference implementation” to describe the lab that published and offers first-party hosting of ...
The fact they are trained to complete sentences and are thus word predicting models only tells us vaguely how they work. It does not tell us anything about their sentience.
The sentience, if there is, comes from the emergent reasoning that develops in the massive neural network to offer actual good predictions: the more you think, the better the predictions.
For example, models trained to produce textual board games predictions can be sounded to show they developed subnetworks corresponding to an inner representation of the states of the game. Models trained to produce images have emergent subnetworks on depth-maps, contours and lighting.
Are cells sentient? Bacteria, fungi, etc. Do you think they have any degree of interiority, like how simple animals do?