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In the past 6 months, the topic of AI has captured the attention of the masses like never before. With its potential to revolutionize industries, transform daily life, and shape the future, it's no wonder that AI has become a hot topic of discussion. As someone who has been closely involved in this field, I have been frequently approached by numerous individuals seeking my perspective on the AI hype.
Engaging in these conversations, I couldn't help but notice a common trend—many people seem to confuse the fundamentals and mix up various concepts regarding AI. It became evident that amidst the excitement and buzz surrounding AI, there is a need for clear and simple frameworks to help demystify this complex field.
That is precisely the topic of today’s post. My aim is to share a simple AI cheat sheet with two simple frameworks that will assist in understanding the basics of AI, enabling individuals to differentiate between the various components of the AI stack and discern the implications and potential applications more effectively.
By breaking down the complexities of AI into simple frameworks, we can bridge the gap between hype and reality, empowering both technical and non-technical readers to engage in meaningful discussions about AI's impact on our lives and society at large. So, whether you are an AI enthusiast, a curious learner, or a skeptic looking to separate facts from fiction, this blog post is for you.
Framework #1 to get the terminology right
The most common mistake is that people confuse the terminology. So let’s get the basics right with the following onion chart and simplified definitions.
Artificial Intelligence (AI) is defined as intelligence demonstrated by machines, as opposed to intelligence displayed by humans or by other animals. "Intelligence" encompasses the ability to learn and reason, to generalize, and to infer meaning.
Machine learning (ML) is a branch of AI that focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Deep Learning (DL) is part of a broader family of ML methods, which summarizes algorithms that are structured in layers to create an “artificial neural network”. These algorithms can learn and make intelligent decisions on their own, just like the human brain.
