The first major attempt at establishing a true field of #artificialintelligence was the Dartmouth workshop in 1956. This would see some of the foremost minds in the fields of mathematics, neuroscience, and computer sciences come together to essentially brainstorm on a way to create what they would term ‘artificial intelligence’, following the more common names at the time like ‘thinking machines’ and automata theory.

Despite the hopeful attitude during the 1950s and 1960s, it was soon acknowledged that Artificial Intelligence was a much harder problem than initially assumed. Today, #AI capable of thinking like a human is referred to as artificial general intelligence (#AGI) and still firmly the realm of science-fiction. Much of what we call ‘AI’ today is in fact artificial narrow intelligence (#ANI, or Narrow AI) and encompasses technologies that approach aspects of AGI, but which are generally very limited in their scope and application.

Most ANIs are based around artificial neural networks (ANNs) which roughly copy the concepts behind biological neural networks such as those found in the neocortex of mammals, albeit with major differences and simplifications. ANNs like classical NNs and recurrent NNs (RNNs) — what’s used for #chatGPT and Codex — are programmed during training using backpropagation, which is a process that has no biological analog.

Essentially, #RNN-based models like chatGPT are curve fitting models, which use regression analysis in order to match a given input with its internal data points, the latter of which are encoded in the weights assigned to the connections within its network. This makes NNs at their core mathematical models, capable of efficiently finding probable matches within their network of parameters. When it comes to chatGPT and similar natural language synthesis systems, their output is therefore based on probability rather than understanding. Therefore much like with any ANN the quality of this output is is highly dependent on the training data set.