Love or Hate ChatGPT, Appreciate What It Represents
Media and users debate ChatGPT’s merits every day, from possible use cases to ethical challenges. Whether you fall on the positive or negative end of the spectrum, ChatGPT represents a landmark moment for AI adoption. It has captured the general public’s imagination, inspiring endless possibilities.
The conversation has transitioned from “Will AI work?” to “Let’s imagine what can be.”
Of course, those of us who have been working in AI, data analytics, data science, and other related fields have known for years that AI works. We have experienced it many times in our day-to-day work lives, seeing the benefits on macro levels with faster results, more reliable and accurate predictions, and an improved bottom line.
Providing those examples has been a critical part of moving forward with more AI projects for just about every company. As time progresses, it has become easier. Now, as we achieve even more successes and are experiencing very public examples a la ChatGPT, we can expect even faster adoption.
That’s what ChatGPT represents. A line in the sand, a before-and-after moment. November 30, 2022 may even become the moment that officially marks the launch of the consumer AI era (even though we’ve all been using it for years).
Past Landmark Moments on the Internet
Those who have been on the Internet for some time can remember similar moments on the web. I have had several conversations to this end over the past weeks with folks like Rigvi Chevala, CTO of my company Evalueserve, long-time blogging friend Greg Verdino, and in public LinkedIn conversations with people like Pete Erickson.
In the 80s, I remember getting my first email address on Prodigy (a Usenet service) in the late 1980s. I was too young to appreciate all of the many emails I would get every day! LOL!
On December 15, 1994, Netscape launched its first browser, taking the World Wide Web from novelty to tangible, much like Chat GPT. One of the first articles of my early journalism career focused on this revolutionary new technology.
The blogging revolution really began in 1999 with the launch of Blogger, but then blew up the media’s stranglehold on information. By 2003 bloggers countered media reports and opinions with differing information and began changing the narrative. I became one of the top PR bloggers in the world in 2007 and 2008.
The social networking revolution launched Twitter’s incredible tripling in size and total domination of the SXSW festival conversation in 2007. Suddenly, social media went from clunky blogs and videos to nimble mobile and short communications. Many of us would tweet until the early morning talking with other like-minded folks to achieve incredible new things together.
Instagram became popular in 2011, transitioning a largely written conversation to a visual one. This was such a joy for me as a photography enthusiast! My photos achieved much wider recognition, and I even won a few awards.
TikTok took over the online media world in 2018, finishing the transition from words to pictures to short videos, and mobile at that. Instead of creating, I now monitor on TikTok as my almost 13-year-old swipes through videos at the speed of lightning.
Now we have a similar moment for AI with ChatGPT. Of course, we are at the very beginning and much of what we are seeing includes both the good and the bad. This is no different than other technology adoption cycles.
Mainstream Adoption Begins in Earnest?
As with any landmark moment, kicking off mainstream adoption is quite different from widespread use. When I talked with Rigvi, he reminded me that the first automobiles were introduced in late 1880s. Everyone was truly excited about the possibility of getting a car, and then the possibilities of what they could accomplish.
Today, we enjoy many items thanks to cars, from almost real-time food delivery to rental cars for our vacations. To get there, we had to work through traffic rules, safety guidance, and requirements such as seatbelts for automobiles, and regulations.
Fortunately, the Internet works faster than technology adoption in the 19th and early 20th centuries. Still, you can see a parallel with seat belts and ChatGPT, and other generative AI needs. We are just coming to understand the incredible breadth of users as well as the pitfalls and safety concerns that need to be addressed for full adoption.
For example, ChatGPT citing data, preventing plagiarism, ethical use cases, ironing out unintentional biases, and building control mechanisms for human guidance and interventions all need to be addressed. There are many unanswered questions good and bad for generative AI to move from incredible opportunities to guidelines for safe use.
There are many forms of AI, and I personally think machine learning advancements will have the most immediate and sizable impacts. But we now know much more is feasible with different AI types, from computer vision L to generative AI, some stand-alone, some working together.
I can’t wait to see what comes next with further evolutions and more and better training.
As for ChatGPT, its foundational GPT 3.5 algorithm, and other related text-based generative AI? Hopefully, conversational writing bots will fare better than Twitter.