What is AI Winter? All You Need to Know

It is a well-known fact that when winter comes it brings a sense of dormancy with it.

Bears hibernate, trees lose their leaves, and many animals slow down their activity levels to conserve energy during the colder months. 

This dormancy is also seen in the tech and artificial intelligence industry. It has reached a point where there even is a special name for it—AI Winter. 

AI winter is real and it is coming, but it is probably not what you are thinking. 

So, what exactly is it? Let’s find out. 

What is AI Winter?

AI winter refers to a time period when funding for AI research and development drops due to unmet expectations. These periods follow times of hype when AI systems fail to deliver on their promised capabilities. As a result, both investors and researchers lose interest, which leads to a slowdown in progress.

This period of stagnancy is called an AI winter.

Brief History

AI  has not been around for a very long time, yet AI winters have occurred multiple times. The first AI winter happened in the 1970s. At that time, early AI projects like machine translation and speech recognition were unable to meet high expectations, and thus demand dropped. Another reason for this occurrence is that technology and computing power in the 70s were not advanced enough to handle the demands of AI. As a result, funding dried up, and progress stalled.

The 80s saw the comeback of AI with expert systems capable of solving specific problems in limited areas. However, by the late 1980s and early 1990s, another AI winter arrived. This time, the failure was due to expert systems being unreliable when faced with unexpected inputs.

Throughout the 1990s, research in AI continued. Great progress was being made. However, this time many distanced themselves from the term “AI” because of its history of failure. To salvage its reputation, the term AI was rebranded under names like “machine learning” or “informatics” to avoid the stigma.

The next decade, AI saw renewed interest in the 2000s. Thanks to the advances in ML, AI began to show promise in a wider range of applications, but its integration into real-world applications was slow. 

For example, IBM’s Watson, despite its success on Jeopardy!, struggled to deliver practical results in healthcare. It was a reality check that proved that AI was not yet ready for complex tasks. 

Characteristics of AI Winters

Almost all of the AI winters that occurred followed the same pattern and had similar characteristics. These are:

1. Hype Cycle: At first, AI technologies are hyped as revolutionary, but then they eventually fail to meet high expectations.
2. Technical Barriers: Limitations such as insufficient computing power or data often block progress.
3. Funding Declines: When the hype dies down, investors pull their support, which leads to less research and development.
4. Backlash and Skepticism: As AI fails to deliver, both the public and scientific communities become more skeptical.
5. Strategic Retreat: Researchers often move to more manageable projects or avoid using the term “AI” altogether to escape the negativity.

Is Another AI Winter Coming?

In late 2022 an AI revolution began with the introduction of OpenAI’s large language model, ChatGPT. Since then, thousands of similar tools and generative AI models have captured global attention. However, because new AI tools are emerging every day, the industry has become quite saturated. Hence, the excitement has cooled. 

The frequency of AI breakthroughs has decreased, leading to lower-than-expected returns. This has made investors more wary. Despite the advancements, even the largest AI models face significant issues like “hallucinations” and information bias. 

Now, there are ongoing concerns that the current AI boom will slow down and another AI winter may come. However, it is unclear whether AI will continue to improve or if we will witness another period of stagnation.

The Bottom Line

AI winters are like a bucket of ice poured over the tech and AI industry.

These slowdowns are not completely bad, as they provide opportunities to reflect and refocus. AI winters are necessary for the industry to recalibrate and address the challenges that arise with rapid advancements.