This blog post is Human-Centered Content: Written by humans for humans.
Sometimes, just writing things down or saying things aloud helps me learn or understand something with more clarity and in greater depth. I’m sure that I’m not alone in my learning style and how I best absorb information into my brain. So, while trying to learn for myself what Generative Artificial Intelligence (GenAI) was, I wound up jotting down some notes during my studies that may be useful for someone else embarking on the same journey I just went on. Here, I’ve adapted those notes to help you, the reader, hopefully tackle some quick concepts around Generative Artificial Intelligence.
Evolution of Artificial Intelligence
With the broader subject of Artificial Intelligence (AI) and its association with seemingly every tool and technology, especially with regards to the topics of data and analytics, there’s no time like the present to wrap your head around this concept and have familiarity with how it might affect your company both now and in the future.
A decade ago, I would have just paired AI and machine learning into the same sentence because, in part, that’s all it really was for 99% of us in the corporate world.
I mean, I know computers are undefeated against humans for at least a couple of decades running. And I still remember when Facebook and Twitter first launched, which was around the time when MySpace was the most trafficked site in the world. The hottest marketplace was web search engines back then. It’s scary how fast technology involves, and the speed of which the internet can shape the world we live in.
In 2010, things started to ramp up with AI. We got into this element of predictive AI. Through advanced algorithms and sophisticated engineering, tools like Watson hit the mainstream. The ability for natural language to feed into computing systems to identify patterns and forecast future events and behaviors really took off, helping to power healthcare, finance, commerce and even weather forecasting. Models and trends used historical data to make future, accurate predictions.
Our personal lives seemed to change around that time as well. We have fun voices like Alexa and Siri that we can talk to and get most of our questions answered. Then came self-driving cars and smart robots used in factories and households. For example, random vacuums like Roomba train themselves on your home layout to help keep your floors clean quicker and more efficiently with time. Again, all this didn’t happen overnight, even though it kind of feels like it.
Assisted AI was also happening in parallel, making use of the natural language query interaction to help mimic human intelligence. For analytics systems, this meant users could type in a sentence in a fluid manner in order to get answers to your data questions in the form of metrics and visuals such as charts and graphs. For lots of BI systems that I used during theCOVID era, that was every tool’s advertising crux.
But the world finally sprung back to life with people going back into offices and economies seeing recovery. That’s also about the time when the term autonomous AI sprung into my life as well. To me, this is the space scientists since the 1950’s have been waiting for. The ability for machines to think and make decisions on your behalf. For Business Intelligence Software, this was the big move in 2024. Players like Microsoft’s Power BI and Tableau provided semi-autonomous capabilities through Copilot and Agentforce.
Others like ThoughtSpot and Zenlytic are still shaping what self-service BI and autonomous AI look like together. I can’t wait to get my hands on it all.
Maturation of Artificial Intelligence
It would be a crime not to talk about the launch of ChatGPT and the success it has had in just a few short years. I would not be exaggerating in saying that ChatGPT is entirely the reason everyone and every household and company now talk about AI. It’s mainstream. It’s here. And it’s how I started using Gen AI myself. But what is it? And how do we define it?
Gen AI is basically any AI system that can generate or create new content based on deep-learning models from large amounts of data. The newly created high-quality content could be in the form of text, images, audio files, videos and programming code.
For many of us in our personal lives, Gen AI could be used for brainstorming, doing homework, building recipes, discovering interests, writing documents or project automation. Corporately, it’s already used for reducing manual tasks. This could be streamlining workflows, system updates, data governance, record keeping, and other email and marketing communications being sent.
My daughter specifically uses ChatGPT to get new book ideas or to see how her characters might look in real life after providing a character analysis prompt. My friend uses it as a way to generate a beautiful haiku to his partner on their anniversary. In general, these collections of Chatbots aren’t quite perfect and still can be prone to make bad assumptions or provide incorrect info, also known as hallucinations, when prompted on incomplete or unsynthesized data.
Thankfully, companies like OpenAI exist. They are the largest research organization that helps to develop AI. They’ve actually been around since 2015, and are the owners of ChatGPT, releasing it in November 2022. Since then, it feels like things are stable. There’s been lots of investment made to ensure the quality and reliability of its language models in producing Gen AI. They’ve wholly led the way. Microsoft, Google and Amazon seem to be releasing their own AI components daily within similar frameworks.
We’re in the AI Revolution
And yes, ChatGPT is widely considered the largest and most popular Generative AI platform globally. But, if we’ve learned anything, we know that what owns the attention of individuals and companies now, may certainly change in the future. (See the aforementioned MySpace, for instance.) In the famous words of Ricky Bobby when Cal Norton suggests his new nickname, Magic Man, Gen AI is pretty freaking awesome though.
