How “Artificial Intelligence” Has Augmented My Intelligence

I don’t know about you, but AI is one of the most obnoxious terms since “swag.” I wouldn’t be surprised if it became Merriam-Webster’s term of the year in 2024. Previous winners like “gaslighting” in 2022 and “socialism/capitalism” in 2012 provide historical context as to what was happening in society at the time. The term of the year is generally a word that dominates searches on their website. Although I don’t have any metrics to make this claim, based on conversations I’ve had in the past six months, I think AI will take the cake.

There is no shortage of people throwing around the term “AI” willy-nilly, and like many people, I am most likely using it as a catch-all without understanding the different types of AI and how they function or don’t function. My goal here is to quickly break down types of AI with examples so we can all stop using the term AI so broadly and hopefully keep it off the 2024 Merriam-Webster term of the year list.

From my understanding, AI can be broken down into categories such as functionality, capability, and technique. The list below contains common examples of AI and is not all-inclusive.

Based on Capabilities

Narrow AI (Weak AI)

• Definition: AI systems designed and trained for a specific task.
• Examples: Voice assistants (like Siri, Alexa), recommendation systems (like Netflix, Amazon), image recognition software.

General AI (Strong AI)

• Definition: AI systems with generalized human cognitive abilities, capable of performing any intellectual task that a human can do.
• Examples: Hypothetical at present; no existing AI has achieved this level yet.

Based on Functionalities

Reactive Machines

• Definition: Basic AI systems that can only react to current scenarios without any historical context or memory.
• Examples: IBM's Deep Blue, Google's AlphaGo.

Limited Memory

• Definition: AI systems that can use past experiences to inform future decisions.
• Examples: Self-driving cars, virtual assistants that can understand past interactions.

Based on Techniques

Machine Learning

• Definition: A subset of AI that involves training algorithms using data to make decisions or predictions.
• Examples: Spam filters, image recognition systems, predictive analytics.

Deep Learning

• Definition: A specialized subset of machine learning using neural networks with many layers (deep neural networks).
• Examples: Speech recognition, language translation, advanced image recognition.

Natural Language Processing (NLP)

• Definition: A field of AI focused on the interaction between computers and humans through natural language.
• Examples: Chatbots, sentiment analysis, language generation models

With some standards and common language in place, we can understand what people are talking about when they mention the term “AI,” as long as we all agree on those standards. That is where a much larger debate comes into play that I’m not going to dive into.

With the debate of whether ChatGPT, a large language model (LLM), is AI or not behind us, I want to provide some practical use cases for how I use ChatGPT nearly hourly throughout the day to augment my intelligence. ChatGPT to me is kind of like the Genie from Aladdin, but instead of getting three wishes, I get unlimited.

First of all, I use it as my primary search engine. While most people use Google or Safari, I find that ChatGPT is able to compile information from all over to match my exact needs quickly. I can’t stand when I search for something on Google and have to scour the internet for a subreddit that I have to also dig through to find out it doesn’t even have all the answers. I use it to give me step-by-step instructions on how to create SharePoint lists that reference each other and how to display their data in Power BI. Also, in the realm of Microsoft is ChatGPT’s ability to help you find the right formula in Excel. The best part is you can also have it tell you the syntax and what each of the parts of the formula does so you actually learn rather than just copying and pasting. One of my favorite use cases is having it explain complex topics like I’m five years old. Seriously, use that prompt.

There are plenty of other use cases that I will continue to share, but try some of the examples I talked about above and see what other use cases you come up with. I want to once again share a quote from a previous post that I believe encompasses the impact AI (there's that term again) will have on people, especially at work.

“…for knowledge workers, that means artificial intelligence won’t replace them in coming years. Instead tech savvy sales people, lawyers, and doctors will replace colleagues who don’t know how to use AI to assist them in their decision making.”