[Premium] Ways AI Will Change the World, Part 1

FUTURE

The Ways AI Will Change The World, Part 1

Did you know that AI might be tearing down decades-old theories of human emotion?

That we are using AIs to discover signs of extraterrestrial life?

That we are using AIs to understand what dogs are saying when barking?

And that we could one day genetically modify humans to be more resistant to climate change thanks to AI?

Currently, it’s hard to discern anything other than Large Language Models (LLMs) and productivity-based applications, as most capital has gone in that direction. But AI much more than that.

Today, we go beyond the mainstream to describe four ways AI is changing or will change the world. While some will sound futuristic, please bear in mind that I’ve carefully selected the list based on actual research and that all these leverage AI’s only known true advantage over humans:

Its capacity to process vast amounts of data and extract regularities (the patterns) in that data to enhance our knowledge. Don’t expect human-level intelligence claims or robots doing backflips; these are all exciting cases where research and start-ups are making actual progress.

This article intends to serve as an interesting way of reevaluating your perspective on AI’s value, which might seem at an all-time low based on the insufferable hype regarding LLMs. But worry not; there are plenty of reasons to be extremely excited about AI.

Here’s why.

A Brief Description of What AI Really Is

Despite the extreme differences in the use cases we’ll see today, the underlying AI is strangely similar across all use cases. For that reason, I see it fitting to comment briefly on how AI works beyond the spotlight and superficial explanations you are usually exposed to through the media.

Although the explanation might seem a bit complex at first, please bear with me because it will allow you to truly understand the use cases we are seeing today, from astronomy to gene editing, in a more intuitive way than most.

Simply put, all AI does is take vast amounts of data and compress the underlying knowledge into something we call a ‘model.’ As the model is smaller than the data it processes, it’s forced to find the common patterns across data to compress the bits of information that truly matter.

For example, if we think about natural language, there are infinite ways in which you can combine words. However, only a subset of these combinations are valid according to grammar. Thus, the model learns it in order to generate valid sequences.

But how do AIs actually process data? In our current paradigm, they treat every data source as a sequence of tokens, a term you’ve probably heard of many times. These are data patches that have intrinsic meaning but are then enriched with other surrounding tokens, turning data into a sequence of tokens that, combined, have an overall meaning that AI aims to capture.

For instance, text can be broken into words or subwords, images into groups of pixels,…

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