Introduction
Welcome! In this module, you will get acquainted with the history of artificial intelligence in the 1980s and 1990s. In the first part, you can read a short story about the “AI Winter” and then you get some explanations about the name of this period.
Those who acquire the knowledge about the era of AI Winter will be able to:
- understand the reasons why we call this age AI winter,
- get to know why the projects have not performed as expected.
During the lesson you must read the written explanations and follow the given instructions at
interactive elements. You can assess your understanding by taking the quiz. In the second part of the
module, you will need your creativity, and you have to create a book review!
Why is the period between 1980 and 1990 called the AI winter?
In this module, we familiarize ourselves with the third stage of AI development.
Between the 1980s and 1990s, research related to artificial intelligence slowed down. You can read more about it below.
Click on the link!
https://www.youtube.com/watch?v=8_MUDF1d1sU
During the period of Artificial Intelligence Winter (1980s – 1990s), which marked the third era in the history of AI following the successes and advancements of previous decades, a phase known as the “AI winter” occurred. The earlier enthusiastic research and development suddenly began to slow down, and the field of artificial intelligence reached its nadir during this time.
The term “AI winter” refers to the abrupt decline in research and investments in the field of AI. While the previous decades had held promising prospects, enthusiasm waned due to a series of limitations and challenges. Symbolic approaches, which were popular before, revealed their limitations as machines struggled to handle uncertainty and complex problems.
Numerous AI projects and research initiatives did not meet the expectations, diminishing the interest of investors and researchers. Rising expectations and demands for universally applicable AI models did not align with the earlier results. Disappointment spread after the earlier success propaganda, leading many to believe that artificial intelligence would not reach the level they had previously anticipated.
The interactive quiz is accessible by clicking on the link. Check your knowledge!
https://www.youtube.com/watch?v=8_MUDF1d1sU
https://builtin.com/artificial-intelligence/ai-winter
https://view.genial.ly/652cdf4c18b3250011835a7c/interactive-content-ai-quiz
https://view.genial.ly/652cdf4c18b3250011835a7c/interactive-content-ai-quiz
Reasons for the decline
In the next section, you can read about the reasons for the decline and explore them in detail.
The 1980s and 1990s were a period of both excitement and disappointment for the field of artificial intelligence (AI). After the promising developments of the 1960s and 1970s, the 1980s saw significant challenges and setbacks in AI research, which led to what some researchers and commentators described as the “AI winter.” Several factors contributed to this decline:
Overpromising and Underdelivering: In the 1970s, there was much excitement about the potential of AI technologies. However, the high expectations set by researchers and the media often exceeded the capabilities of the existing technology. When these expectations were not met, there was a sense of disillusionment.
Limitations of Existing Technology: The computational power of computers in the 1980s was limited compared to today’s standards. AI algorithms required substantial computational resources, making it challenging to tackle complex problems effectively.
Lack of Funding: Funding for AI research decreased during this period due to the perception that AI had not lived up to its promises. Government agencies and private investors were less inclined to invest in AI research, leading to a stagnation of progress.
Challenges in AI Techniques: Early AI techniques, such as symbolic reasoning and expert systems, faced limitations in handling uncertainty and real-world complexity. These limitations became apparent when applied to practical, complex problems, leading to a decline in interest.
Focus on Narrow AI: Researchers shifted their focus from ambitious, general AI goals to more narrow, specific applications. This shift, while practical for solving certain problems, led to a perception that AI was not advancing toward broader intelligence.
Emergence of Other Technologies: During the 1980s and 1990s, other technological advancements, such as personal computing, the internet, and software development, captured significant attention and resources. These areas offered more immediate and tangible benefits compared to the speculative promises of AI.
You can learn more about the ‘AI decline’ from the next video. Click on the link.
https://www.youtube.com/watch?v=w_v5lumtoPk
The interactive quiz is accessible by clicking on link.
https://view.genial.ly/652d1e31e6c67d00118c757f/interactive-content-true-or-false-quiz
https://www.youtube.com/watch?v=w_v5lumtoPk
https://www.techtarget.com/searchenterpriseai/definition/AI-winter
https://www.historyofdatascience.com/ai-winter-the-highs-and-lows-of-artificial-intelligence/
https://view.genial.ly/652d1e31e6c67d00118c757f/interactive-content-true-or-false-quiz
Creative task
In the following task, you will work independently! Prepare a book recommendation for your friend about the topic you have read!
Review a book! Create a book cover for the topic you have read above! Here is a little help!
Open the website below and create your own cover!
https://app.genial.ly/editor/652d340d086b59001179ba4e
https://app.genial.ly/editor/652d340d086b59001179ba4e
Open the link, create your own cover and upload here!
https://app.genial.ly/editor/652d340d086b59001179ba4e
Learning outcomes:
In this module, we have acquainted ourselves with the third chapter of the history of artificial intelligence. During this era, developments stagnated and the ‘AI winter’ set in.
Main takeaways:
Research and investments in the field of AI suddenly plummeted. Numerous AI projects and research initiatives failed to meet expectations, diminishing the interest of both investors and researchers. Here some main reasons: overpromising and underdelivering, limitations of existing technology, lack of funding etc.
Brief introduction of the next block:
In the next module, we will learn about the statistically based approaches and machine learning in the 2000s.
