In the Beginning

ChatGPT 4.0 was used to alter the tone of this blog from the original draft. I edited the text significantly for the final draft you see here. The content of the text is 100% mine.

I’ve come up with a set of rules that describe our reactions to technologies:

Anything that is in the world when you’re born is normal and ordinary and is just a natural part of the way that the world works.

Anything that is invented between when you’re fifteen and thirty-five is new and exciting and revolutionary and you can probably get a career in it.

Anything invented after you’re thirty-five is against the natural order of things. - Douglas Adams

In the exciting dawn of any technology, there is a natural sense of wonder about the direction it will take and how it will impact our day-to-day lives. Initially, people may feel cautious, but this is a common response to the unknown. As we delve deeper, the second reaction is to explore how this new technology can integrate into our existing technological landscape. The third reaction is to align it with our systems of control.

While these reactions are understandable, they don't always lead to the most constructive outcomes. At best, they offer incremental progress. At worst, they can reinforce existing systems, such as emphasizing their flaws and dehumanizing aspects.

Over the past year, I have been sharing a vision of harnessing the power of AI to create entirely new perspectives on the world. This vision, which I call Generative Augmented Perspective, involves the fusion of Generative Language Models (LLMs) with visualization to automate the generation of information and conceptual maps of ideas.

However, I seem to be well outside the dominant conversations in articulating this potential aspect of AI. Depending on the audience, my vision can sometimes get overshadowed by discussions about adapting existing systems to the new reality or concerns about corporate dominance. Most of the people having these reactions fall into the third category Douglas Adams mentions in his discerning observation in the quote above.

No one says that the pathway I’m suggesting is technically impossible. However, I am greeted with a lot of skepticism. The assumption that AI will follow pathways that so many technologies have followed drives this line of thinking.

Web 2.0 and social media are the most recent examples of this. However, neither of these have transformed education, business, or government systems in any meaningful way, even as they consolidated into a few large corporate entities.

I see an alternative future. I think AI is more disruptive than Web 2.0 ever was. The better analogy is the PC Revolution of the 1980s. Looking back at this example, my work emphasizes AI's potential as a disruptive tool that tackles information overload. It could help us navigate the complexities of our interconnected world. This vision has profound implications for both education and practical problem-solving across all fields.

I understand that I'm not in the majority when championing this vision. Instead of envisioning how AI could revolutionize the way we teach and learn, much of the educational discourse revolves around using AI to enhance students' adaptation to the current, somewhat flawed, industrial education system. Similarly, discussions about AI's impact on businesses often focus on short-term profit gains, rather than addressing the deeper challenges it presents, such as our current vision of copyright and the impact of the automation of routine tasks on employment and required employee skills.

?It's not surprising that these conversations lean towards short-term goals. It's human nature to be slow in adapting to paradigm-shifting realities.

When personal computers emerged in the 1980s, there was a mix of overly optimistic ideas (e.g., the belief that schools would become obsolete) and apocalyptic predictions (as seen in the movie "WarGames"). Similarly, the popularization of the internet in the 1990s brought about additional concerns over media and music business models.

The response to these darker views of the implications of new technology led to legislation like the Computer Fraud and Abuse Act of 1984 and the Digital Millennium Copyright Act in 1996, both of which sought to enshrine elements of the older paradigm even as the technological world shifted out from under them. We are still living with the implications of these choices today as they reinforce monopolies at the expense of individual creative rights.

These discussions inevitably focus on immediate concerns, rather than considering the deeper implications of the technology and leveraging opportunities that emerge through a new paradigm. These reactions are reactive, not proactive.

AI challenges our systems because it highlights the need for higher cognitive standards among students and employees. One of these cognitive standards is learning how to think proactively, systemically, and strategically about problems. Even current AI systems are pretty good at reactive thinking.

These capabilities highlight the risk of training students and employees for roles that increasingly sophisticated generative systems will replace. This challenge is not new; it is the consequence of ignoring industrialized learning processes that emphasize conformity over creativity. Conformist thinkers are inevitably reactive. It takes creativity to think proactively.

Ironically, it’s the creative thinkers that can benefit from generative AI the most. It can create scenarios and games to predict outcomes or allow humans to simulate them. I’ve used it myself to create graphics to illustrate complex ideas that are beyond my artistic skills and to brainstorm ideas that are not gelling in my imagination (aka overcoming writer’s block).

These applications show how AI can help humans frame questions. They allow us to express ideas in ways that were much more difficult without large language models. I plan to use it in my classes this semester as a teaching tool to help students develop the right questions for their assignments (my assignments emphasize questioning over responding).

The early days of the computer revolution in the 1980s have many interesting parallels to what's happening with AI today. However, AI's rapid progress gives us less time to adapt. In just one year, the AI landscape has transformed as much as it did in the first decade of the computing revolution.

Graham Allison’s classic line about bureaucracy “you stand where you sit” applies here. We are all attached to our usual paradigms. There is a lot of fear attached to thinking through the implications of as disruptive a technology as Generative AI on the systems in which we operate. Many of the changes that need to be made, both in education and business, are long overdue. The difference now is that Generative AI has made them much harder to ignore.

I’m about as excited about the possibilities for where this might take us as when I got my first personal computer and started using it as a creation engine. Millions of words and hundreds of thousands of images later, I’m still creating. AI has taken this exploration to a whole new level in the last year (and every day I’m finding new uses for it).

The computer opened doors of creative exploration to more and more diverse groups of people, Web 2.0 opened those doors even more, and AI will once again expand that exponentially. We just need to perceive its possibilities instead of mourning its destruction of outdated systems of thought.

Bryan Alexander Bren Triplett Paul Signorelli Juliette Bentley Mark Corbett Wilson Roxann D. Riskin Robots & Pencils Samantha Adams Becker Miro Tracey (Zimmerman) Cesen OpenAI 谷歌 Andi Hess ShapingEDU at Arizona State University Dr. Edmund "Butch" Herod