Why Does AI Matter Now? 3 Factors Driving Recent Interest [Collision Takeaways]

While the earliest forms of AI date back to the 1950s, the last decade has seen a marked increase in the popularity of artificial intelligence.

And as AI practitioners move away from research and towards practical applications, the questions at the forefront of data science are shifting. Industries are no longer wondering who has the best models. Instead, they’re attempting to discover the right models for unique business challenges. 

But what’s driving this resurgence—and why does it matter now? Keep reading to discover three factors driving this recent interest in artificial intelligence. 

1. Increased Access to Data

Training new ML models requires significant amounts of data. Historically, small companies and those without robust data science teams were limited by the amount of data available to them, making it nearly impossible to leverage AI. 

Today, third-party data and pre-trained foundational models have significantly lowered the barrier to designing AI. As data becomes more accessible and inexpensive than ever, conversations have erupted around obtaining the right data and using it for good.  

2. Commoditized Computing Power

Progress in machine learning and AI is driven by data, algorithm innovation, and compute. While data and innovative algorithms can be obtained with relative ease, compute, until recently, was more difficult to use. 

Compute is the power used to organize and process information in machines. Widespread access to incredible amounts of computing power has not always been a reality, but as the capabilities of technology increase, so too does the amount of compute it uses. 

3. Growing Availability of AI Platforms 

In the past, organizations without the right internal tools or bandwidth struggled to design and develop AI. However, the widening landscape of AI solutions and platforms has begun to bridge this gap between organizations and AI. 

Although data science expertise is still needed, many of the open source software platforms themselves are free—ultimately reducing the cost to design AI. And although the use case for each of these technologies is different, their overall intent is the same: Equip organizations with the resources they need to leverage AI. 

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Contributor: Nicole McCaffrey is the COO/AI Translator at Pandata.