Exploring the impact of AI Winter on AI history

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The term AI winter first appeared in a public debate at the 1984 annual meeting of the AAAI (American Association of Artificial Intelligence).

At the gathering, Roger Schank and Marvin Minsky — two leading AI researchers who weathered the “winter” of the 1970s — warned the business community that enthusiasm for AI had exploded in the 1980s and disappointment was inevitable. After three years, the multi-billion dollar AI industry began to crumble.

hype

The AI ​​Winter Happened because developers made promises that were too good to be true, end users had unrealistically high expectations, and the media covered a lot about it. But while AI’s reputation has faltered, it has continued to develop new and valuable technologies. In 2002, AI researcher Rodney Brooks said, “There’s this stupid myth that AI failed, but AI is around you all the time.”

In 2005, Ray Kurzweil agreed: “A lot of people still believe that the AI ​​winter was the end of the story and that nothing has changed in AI since then in any industry.” Since the early 1990s, when buzz and hope regarding on AI, they have mostly increased.Then, around 2012, research and business groups took a keen interest in artificial intelligence, particularly the subfield of machine learning.This transfer led to a massive increase in funding and investment.

AI integration

AI technology became widespread as a component of larger systems in the late 1990s and early 2000s. According to Nick Bostrom, “However, a lot of innovative AI has entered general applications, often without being labeled AI because once something becomes useful and common enough, it is no longer labeled AI.” Around the same time, Rodney Brooks stated: ” There’s this silly myth that AI failed, but AI is around you every second of the day.” In addition, machine translation, data mining, industrial roboticsLogistics, speech recognition, banking software, medical diagnostics and Google’s search engine are some examples of the commercial success of AI-related technologies.

AI Funding

Researchers and economists often figured out how bad an AI winter was by looking at which AI projects got money, how much money they got, and who gave it to them. Large funding agencies in the developed world often set funding trends. Currently, most of the funding for AI research in the US and EU comes from DARPA and a civilian funding program called EU-FP7.

Institutional Factors

AI research is often a mix of research from different fields. AI faces the same problems as other types of interdisciplinary research because it comes from different fields. Funding comes from the established departments and as budgets are cut there will be a tendency to protect the ‘core content’ of the respective departments at the expense of less traditional and cross-disciplinary research projects.

economic factors

The impact on AI research is amplified by the trend towards “core content” and in a crisis, investors are more likely to put their money into less risky ventures. All of this combined could amplify a recession into an AI winter. It is important to remember that the Lighthill Report came during an economic crisis in the UK when universities were facing budget cuts and only had to decide which programs to shut down.

Conclusion

many philosophers, cognitive scientist, and computer scientists have made predictions about AI and where it may have failed in the past. Hubert Dreyfus, for example, knew in 1966 that the first wave of AI research would not deliver what the public promised due to wrong assumptions.

In addition, people think that the AI ​​winter is over because funding, development, deployment and commercial exploitation of AI have increased significantly in recent years.

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