“We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” That was noted over half a century ago by Roy Amara, a US scientist with one of the greatest job titles ever: ‘President of the Institute of the Future’.
The future he envisaged came in waves. After transistors arrived mid century, integrated circuits powered a new wave of computing a decade later. Operating systems allowed teams of people to use mainframe computers from the 60s. Microprocessors and the ethernet came in the 70s, and then home computing kicked off a new generation of digital users. The birth of the internet followed and allowed the first wave of tech companies to flourish (and many to flounder). Recent history sits more comfortably in our mind: search, social networks, cloud computing, and AI.
Interest in AI currently focuses on generative AI. Since GPT-3 was released in 2020, interest in AI has mounted, reaching fever pitch since ChatGPT launched in the last quarter of 2022. We want to understand the overestimated early impact and prepare for the underestimated late impact. That means It is incumbent on us to look in more detail at the waves immediately preceding.
So what comes first?
For a quarter of a century, tech companies have been hoarding information. When they had so much that it became a product in its own right, it was called big data. Vast datasets were used to experiment with AI models that were designed to be broadly applicable. The dream was an algorithm that would fix your finances, manage your customer support and cure cancer – possibly all at the same time. That hasn’t happened (yet). Instead, teams pivoted to more precisely defined goals: playing complex games, folding proteins, driving cars. Gartner’s hype cycle gives an overview.
But the calculations involved in figuring out the shape of proteins is nothing like the human ingenuity that landed on, say, the double-helix shape of DNA. The brute force approach is steered and optimised – and is incredibly valuable to humankind.
Implicit in all these advances is some semantic hand waving. Instead of true intelligence, we are dealing with extremely advanced prediction. Indeed, when DeepMind discussed their protein folding algorithm, DeepFold, they mention as much.
Generative AI applies the same prediction approach to predict the next word (or note or pixel) in a piece of content. It’s so fast, so varied and so persuasive that we are taken in by the results, and eagerly join in the hand waving. Prediction? Intelligence? Who cares! we think – what we care about is that we can bring our content creation in house, build unrestrained chatbots, or use computers for emotional support.
And what comes after generative AI?
Every time we use these predictive algorithms, more content is being added to the flood of text, images and audio we already see spinning around the internet. We desperately need the tools to find, sort and filter all that content. We need tools to navigate the ocean of content.
Overtone believes that this is the next wave. It’s perhaps less immediately sexy than generation (“Look! I wrote a novel in 25 seconds!”) but no less important. Understanding what a piece of content is, how it functions, and where to place it – these insights are proving increasingly vital.
This is our first overview of the AI waves. The landscape, or waterscape if you will, of teams working on and with AI. It places Overtone in the next wave: we’ve been preparing for content floods for some time.
We look a little further ahead too: our prediction is that AI will be used to build, test and optimise content strategies before too long. Think through the way publishing companies are structured. This onward march means that no publisher – not one – can afford not to learn about and use AI. Soon enough, that trend will expand to any industry that makes significant use of content. From analytics, through media monitors and financial services, and beyond to academic institutions, research agencies and governments – this next wave is going to transform every online industry.
Take heed: otherwise we run the risk, as we have done so many times previously, of underestimating the long term impact of this growing technology.
Image credit: Reddit, r/oddlycomforting
