I’m 2/3 of the way through my 6-week MIT Sloan online course on developing an AI business strategy, so it seemed like a good time to step back and reflect on what I’m learning. I’ll start with some definitions and a bit of history. Future posts will be on some specific uses of AI in strategy and business.
First, a few basic definitions. There are lots of words and phrases being used that, frankly, I didn’t fully understand before this course. Here are a few that have helped me:
- Artificial Intelligence (AI): From Britannica, “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.”
- Artificial General Intelligence (AGI): This is what the doomsayers fear. It’s when AI becomes self-aware and capable of operating on its own. If you’ve seen the new Mission Impossible movie, think “the Entity” or Skynet if you prefer a more nostalgic version of humanity-crushing robots. If it makes you feel any better, AGI predictions have been wrong before.
- Machine Learning (ML): From MIT Sloan, “a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed.” ML processes enormous of data to learn over time – human inputs and feedback are a critical component of ML.
- Deep Learning (DL): From AWS, “Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain. Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions.”
- Natural Language Processing (NLP): From IBM, “NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI—concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.” If you use Siri, ChatGPT, website chatbots or any other tool that tries to understand what you want by processing your words, you’re using NLP.
- Robotic Process Automation (RPA): From The Enterprisers Project, “Robotic process automation is…instructing a machine to execute mundane, repetitive manual tasks.”
Here’s an AI hierarchy used in the class that helped me understand how the pieces fit together: AI (the entire domain) > ML (the algorithms underlying AI) > NLP (an application of ML) > Chatbots (a specific application of NLP).
If you’re a Benedict Cumberbatch fan, you may already know something about AI. In the 2014 movie “The Imitation Game,” Cumberbatch plays Alan Turing, considered by many to be the father of modern AI. His “Turing Test” is a “test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human.” The fact that he developed the test in 1950, almost 75 years ago, was very surprising to me.
This isn’t the first AI hype cycle. My social feeds (probably like yours) are stuffed with AI tips, predictions and doomsaying. The hype train is running hot, but this isn’t the first time AI has generated intense periods of interest. Similar cycles happened in the 1970s and 1980s, and both were followed by so-called AI Winters, when interest and funding dried up. Who knows if the same will happen this time, but cutting through the hype and getting a better understanding of what’s possible with AI was one of the main reasons I wanted to take this course.
Additional resources I found helpful: