A businessperson’s guide to AI buzzwords
There is a proliferation of Artificial Intelligence and Machine Learning becoming integrated into everyday life. The promise of these technologies has created substantial hype throughout the business world. Some companies have struggled to implement these technologies into their business, which has created an increasingly popular sentiment around discrediting Artificial Intelligence and Machine Learning as pie in the sky aspirations. Although, we already use these technologies in many forms today (sometimes unknowingly), and in 2021 the resources available to data practitioners make the real-world implementation of Artificial Intelligence and Machine Learning much more achievable than even a few years ago.
The exponential speed with which the fields of Artificial Intelligence and Machine Learning have advanced has been accompanied by their use as some of the most common buzzwords in business circles today. Being able to see through the buzzwords by understanding their meanings will give non-data professionals a huge leg up in the years to come.
What is an algorithm?
The term algorithm is often used in a nebulous fashion such as “the Facebook algorithm” or “the YouTube algorithm”. The word algorithm being used in this manner has invoked a mysterious connotation that now accompanies the term. Although, the term algorithm is much more explicit in its definition.
Algorithm: A set of defined rules, procedures, or calculations to solve a specific problem. Algorithms often refer to this being done by a computer.
Algorithms can be as simple as X + 1, you provide your program the value for X and it returns that integer incremented by 1! Algorithms can also be as complex as having a Neural Network analyze your viewing history on Google/YouTube to recommend videos to watch.
What are AI, ML, and DL? How do they differ and how are they the same?
Artificial Intelligence (AI): Artificial Intelligence is when machines can do tasks that typically require a human element. It is commonly used interchangeably with Machine Learning but there is a technical difference.
Machine Learning (ML): Machine Learning is a subset of Artificial Intelligence where machines can learn by experience and acquire skills without human involvement.
A few familiar examples:
- Netflix Recommendation System
- Snapchat Filters
- Google Maps
- Spotify Generated Playlists
Deep Learning (DL): Deep Learning is a subset of Machine Learning that focuses on unstructured data. You can think of structured data as data that fits into a tabular form, commonly seen in spreadsheets and relational databases. Unstructured data includes data such as images, text, & audio. Deep Learning uses algorithms inspired by the human brain called Artificial Neural Networks to derive value from unstructured data.
Deep Learning's ability to utilize unstructured data (which makes up 80% of total data being collected today) has made it extremely popular. There are many different ways Deep Learning is applied to unstructured data, but here we will go over the most common uses.
Natural Language Processing (NLP): Natural Language Processing is a subset of Deep Learning. The objective of NLP is to read, decipher, understand, and make sense of human languages in a valuable manner.
Computer Vision (CV): Computer Vision is a subset of Deep Learning. The objective of CV is to enable computers to see, identify and process images in the same way that human vision does, and then provide appropriate output.
Speech Recognition: Speech Recognition is a subset of Deep Learning. The objective of Speech Recognition is to enable computers to hear, identify and process audio in the same way that human does, and then provide appropriate output.
Conclusion
The field of Data Science can feel intimidating, and the number of buzzwords surrounding it does not help. Although, sticking to simple definitions like the ones provided above will help you decipher when someone is using a buzzword for buzzword’s sake and when there is real meaning behind the message.
I encourage you to bookmark this page and revisit it when any of these concepts show up in your professional and personal lives!
