Modern marketing relies on an in-depth understanding of customer needs and preferences and acting quickly and effectively. Artificial intelligence (AI) allows for real-time, data-driven decisions, bringing AI to the forefront for marketing stakeholders.
However, for the average person, much of the information about AI is overwhelming and usually filled with techno-jargon. To succeed in marketing today, it feels like you need a computer science degree.
In this article, we will demystify AI, so you don’t get bogged down by technical complexities, and we can focus on what you need AI to deliver for your brand and business.
I want to point out that I’m not a data scientist, and this article isn’t meant to compete with the excellent technical articles that you’ll find on sites such as Medium or Towards Data Science. I’m sharing what I’ve learned along our journey in building PetThinQ and how I see myself explaining AI.
‘The theory and development of computer systems able to perform tasks normally requiring human intelligence.' - (Oxford dictionary definition)
To build on the relationship between computer systems and human intelligence, I will use an analogy of babies and parenting – and how we learn as people versus how computers learn.
As newborns, we enter the world with a blank page, but slowly we learn through the support of our parents, schooling, and experiences. We start with some capability, but ultimately, we know nothing.
The computer's capability has grown exponentially. Graphic processing units (GPU) brought the power to process multiple computations simultaneously, and their memory bandwidth has made it possible to handle the vast amounts of data that exist today.
The second concept is about learning: babies learn by observing and listening to what has been taught. You might remember ‘flashcards’ from your childhood or teaching your children – they’re a popular way of encouraging learning. Flashcards are presented to young children, and slowly they start recognizing, memorizing, and recalling the subject of the flashcard.
This concept is critical to understanding how AI is created - remember, babies, don’t know anything at the start. If you had flashcards about animals, babies would learn to recognize the animals in the pack of flashcards, but not all the animals in the world. It’s the same with computers: artificial intelligence is created by learning from information presented/taught by data scientists.
If you’re assessing a solution that proposes artificial intelligence, the key questions to ask are:
The answers to these questions will determine the scope of learning undertaken and the confidence level of predicting the output.
If a child was taught how to do algebra, that does not mean they can do trigonometry. Also, if the teacher wasn’t very confident about algebra, the child might not learn correctly. Therefore the teacher’s knowledge is essential.
Andrew Ng emphasized this concept in June 2021 when he launched a data-centric approach campaign to shift AI practitioners’ focus from model/algorithms to the quality of the data they use to train models.
“The model and the code for many applications are basically a solved problem,” says Ng. In the same way, algebra and trigonometry were discovered by brilliant mathematicians. The focus for data science teams becomes continuously improving the quality of data used, the unfashionable but necessary part of AI.
See how you could interpret them in the context of the concepts we have shared:
Artificial Intelligence is fascinating and complex, but some simple concepts are at the heart of the technology. It’s easy to get side-tracked with analytics providers. But you don’t need a computer science degree to ask the right question. When AI systems are built with a data-centric approach, powerful capability becomes accessible.
if you'd like to learn more about how we go further, please read our article, "Moving Marketing 4P's to 4E's."
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