In the 1990’s the internet opened a new means to distribute software and computer applications. People joked about it and called it a toy… but before that, you got software through dialing one computer to another, or from a friend who shared a disk. In 1996 we had a new way that would globally automate tasks and distribute functionality to the world!
Fast-Forward 25 years and we have GPT. In 2021, people joked about it, but I saw it as another banner waving to the future… even so, I didn’t expect it to take off the way it has.
Just like the 1990’s was about distributing computer-based automation and functionality, the 2010’s has been about distributing computer-based information and personality.
For years it was thought that the AI models we had been training only needed a decent (i.e. a few thousand) training sets and that was as good as it would get. OpenAI showed that there really is a point where an enormous amount of trained data can cause behavioral change with the same code – though I’m sure they’ve tweaked their model quite a bit.
I heard recently that OpenAI has hit a point of diminishing returns.
From AI engineer, entrepreneur and author Gary Marcus, to Axios on Sam Altman’s information scale approach, we are getting the message that large data has its limits as the availability of untrained human generated art and information diminishes. In short, the GPT model has consumed almost all the reliable information out there.
So what does that mean for the years 2025 through 2029?
An article in Futurism goes over a few ideas, but one quote stands out:
“The 2010s were the age of scaling, now we’re back in the age of wonder and discovery once again,” Sutskever told Reuters.
This goes back to a visit to MCC in 1988 when I was introduced to the CycL program. Engineers of that project told me that the future of AI is not in the hands of statisticians, programmers or even computer scientists. It is in the hands of painters, sculptors, poets, musicians, psychologists and doctors. The algorithms need to be taught expression and human connection. Otherwise they cannot break barriers that are inherent in soulless data.
Now is when the real magic happens. Algorithms must change to include non-standard thinking practices, universal morality, social congeniality, self-expression, and human connection to move to its next stage.
I’m not referring to node-to-node programming that AI Song Bots derive to play one note after another, but for the AI to sense direction and movement of the notes on its own by its own experimentation and experience.
The future of AI is not in the hands of statisticians, programmers or even computer scientists. It is in the hands of painters, sculptors, poets, musicians, psychologists and doctors.
In a post-covid world where being “social” implies sitting alone in front of a computer instead of hanging out at the mall with air-breathing friends, humans are starving for companionship. There are so many messed up and broken forces at play that keep guys and girls from bonding in meaningful and enriching relationships, and one of these is how people have flocked to GPT to fill that void.
I’m predicting that this starvation for meaningful social interaction will be the driving force that moves AI forward in the next 3 1/2 years as we make them more creative.
The question remains: what type of creation will come out on the other side?
Will it be a man-of-a-machine or a machine-of-a-man? Perhaps the answer is both as people are becoming more mechanized and separated and machines become more human and connected. Hopefully, as we venture into training the new AI brain we’ll find a way to meld the two and find more humanity and connection in our selves.
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