What Led to the Recent Surge in AI Hype?

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Asked By CuriousVoyager23 On

I'm curious about why the excitement around AI and technologies like ChatGPT has emerged so recently. Were there specific breakthroughs in software or hardware that made this possible now, as opposed to earlier or even later?

2 Answers

Answered By RetroAIEnthusiast On

AI's journey began in the 1950s, evolving from cybernetics, but it hit roadblocks due to computer speed limitations and the narrow focus of early systems. By the 60s, AI seemed like a failed experiment; however, progress in the 90s with data collection and computing power led to practical applications we see today, like search algorithms using AI. Generative AI concepts have been around for a while, but computing power and data availability were the bottlenecks that delayed widespread use.

Answered By TechChaser007 On

The hype around AI isn't entirely new; it's been going on for decades. Modern AI is built on machine learning, which has roots tracing back to the 1950s, with relevant concepts like statistical language models appearing in the 1990s. The recent excitement isn't due to any new breakthroughs but rather to two main factors:

1. We now have access to enormous amounts of data, which is essential for training large language models (LLMs). Until recently, such vast datasets weren't available in a usable format.
2. Companies realized they could scrape the web for this data, leading to ethical concerns over copyright infringement. While researchers used to be cautious about data sources, the push for progress and profit has blurred those lines, allowing the hype to flourish.

DataDigger9 -

Absolutely! The basic techniques haven’t changed much in decades; what really drove this AI revolution is the accessibility to large datasets and massive processing power. Also, don’t forget the increase in funding—money played a huge role in this shift!

GadgetGuru42 -

Right, and the use of GPUs can’t be overlooked either. Neural networks from the '80s didn’t take off due to processing limits, but GPUs can handle large computations in parallel, allowing us to develop more sophisticated models much faster today!

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