Last year, OpenAI released the third version of its Generative Pretrained Transformer model (GPT-3), to much excitement amongst the tech and business communities -- so much, in fact, that OpenAI's CEO tweeted "the hype is way too much." GPT-3 has astonished observers with groundbreaking examples of code, news articles, translations and even poetry which evaluators have difficulty distinguishing from human-written output. Fundamentally, it simply autocompletes: give it a prompt, and it'll predict what comes next. But the enormous dataset it was trained on, along with the sheer complexity of its architecture, has enabled it to achieve the best results yet. So, how exactly does this technology work, and where could it take us?

Much has been said about GPT-3 already. Traditionally, we start with data for a problem and develop the model based on the data. The model is specific to the problem. If you want to train a model to predict traffic patterns in New York, you build a model of New York traffic patterns. If you want to model air pollution in New York, that's a different model With GPT-3 you start with the model instead of the data.

"GPT-3 is not a mind, but it is also not entirely a machine. It's something else: a statistically abstracted representation of the contents of millions of minds, as expressed in their writing." In recent years, the AI circus really has come to town and we've been treated to a veritable parade of technical aberrations seeking to dazzle us with their human-like intelligence. Many of these sideshows have been "embodied" AI, where the physical form usually functions as a cunning disguise for a clunky, pre-programmed bot. Like the world's first "AI anchor," launched by a Chinese TV network and -- how could we ever forget -- Sophia, Saudi Arabia's first robotic citizen.