Today--looking at what artificial intelligence can do--we are like five year olds with a box of Legos. We’ll build simple things at first. Eventually, we’ll have the ability to create realistic and detailed sailing ships, StarWars spacecraft and other objects that will move.
In other words, we’ll have primitive but important use cases at first, eventually culminating in sophisticated and probably surprising future use cases that exceed what our imaginations can conjure. That tends to be the case for all general-purpose technologies.
For the moment, AI represents a deepening and acceleration of trends that have characterized the digital age—personalization, customization, on-demand experiences, and context-aware interactions.
But the process should involve a lot of quantitative changes that culminate in qualitative change. And there’s a tantalizing lot of that incremental improvement.
NotebookLM, Google’s (amazing!) engine for creating podcasts from text material, now apparently has the ability to support users asking questions to the AI podcast hosts. The feature is still experimental and apparently only works on newly-created audio overviews (podcasts), but it’s a significant development.
“Using your voice, you can ask the hosts for more details or to explain a concept differently,” Google says.
Separately, Andrej Karpathy suggests the ability to query and interact with a large language model in the context of reading text on a Kindle or other screen.
Both are examples of directions LLMs might be going: not just creating podcasts from text but also interacting with text content in custom and conversational ways (asking questions, for example). Such a feature might entail the ability to ask the LLM to explain, discuss, argue or debate the merits of an idea or concept, based on the content in the source text.
The point is, even before we start seeing really-functional agents, developers already are working on and conceiving of features that essentially harness LLMs to support personalized queries on specific content of interest, with multimedia interactions increasingly the norm.
For the moment, though, the changes will likely be extensions of underlying changes: quantity rather than quality; “more” rather than “different.”
Digital platforms including social media, e-commerce, and streaming services have long used algorithms to tailor recommendations based on user preferences, browsing behavior, and past interactions.
AI introduces a more granular level of personalization, moving beyond demographic-based targeting to behavioral and contextual insights. Advanced AI systems can adapt in real time to an individual’s emotional state, preferences, and even predicted future behaviors.
Likewise, AI shifts customization from manual to automatic and anticipatory. Instead of users actively configuring their preferences, AI predicts and customizes interfaces, products, or services without explicit input.
AI also enhances the immediacy, convenience and relevance of on-demand experiences by predicting what a user will want next. The same might be said for the ability to supply content or experiences
“In context.”
Eventually, though, an accumulation of such improvements in context and personalization will enable a qualitative change in how people interact with computing devices. The inevitable question is “what new things will emerge?”
Some might claim to know, but most of those predictions will prove wrong. Humans are never good at predicting the future. All we can say for sure is that if AI is a general-purpose technology, something quite new will emerge, on the order of the control of fire, domestication of animals, agriculture, the wheel, electricity, the internal combustion engine, computing and the internet.
But AI’s qualitative changes will start with any number of new capabilities which extend our present digital experiences.
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