Modern AI systems have made it easy to tackle many problems previously thought out of reach of computers. These systems are so good that they have convinced even those working on developing them that they are sentient. However, despite the successes, many of these systems can be thought of as technological parrots. Parrots can mimic their owners, but do not have a true awareness of what they are saying, nor why they are saying it. Similarly, modern AI systems can mimic the patterns they have learnt from previous data, without having the true context of the problem which is being solved, nor understanding why a given prediction is returned.

Most of us have heard the phrase, "Image is everything." But when it comes to taking AI to the next level, it's context that is everything. Contextual awareness embodies all the subtle nuances of human learning. It is the'who', 'why', 'when', and'why' that inform human decisions and behavior. Without context, the current foundation models are destined to spin their wheels and ultimately interrupt the trajectory of expectation for AI to improve our lives.

You'll discover why Python is unique, and come to understand the data types and operators it handles. You'll learn to use its built-in functions, and write your own functions to encapsulate a series of commands. At the end of your lesson, you'll encounter a lab where you'll learn how to use a pre-trained image classifier to write a script that identifies dog breeds.

Zweben: As these large technology companies had all the compute power and data streams, it is not surprising that the current momentum for AI started there. But the availability of abundant, affordable compute power in the cloud, and free and open source software for big data and machine learning means that AI is quickly spreading beyond these companies. I think that soon the major constraint will be the ability of companies to attract the talent to work on all the projects they want to undertake.