Are We Still Repeating the Same Mistakes with AI?

Are We Still Repeating the Same Mistakes with AI? [AI Today Podcast]

Artificial intelligence has been on the horizon for over seventy years. In fact, the term AI was officially coined in 1956. So, why does it seem so close but also so unattainable? In this episode of AI Today hosts Kathleen Walch and Ron Schmelzer discuss the question: Are we still repeating the same mistakes with AI?

AI Winters: The Cycles of Hype and Disappointment

For anyone following the history of AI, it’s no surprise that AI has experienced cycles of intense interest followed by periods of disillusionment. These are known as AI winters. And, we have talked about AI winters in previous episodes. The first AI winter occured in the 1970s and the second in the late 1980s. These winters were marked by high expectations of what AI could do and disappointing results. These cycles highlight the challenges of meeting ambitious AI goals with the available technology. In this episode we discuss what this occurs and why we need to continue to be cautious about not over–promising and underdelivering.

Current AI Wave: The AI Spring

The late 2000s and 2010s saw a resurgence in AI interest. This was driven largely by big data and GPU computing. This current wave has enabled significant advancements in AI applications. But it also carries the risk of repeating past mistakes. To avoid another AI winter, it’s crucial to manage expectations and adopt iterative, agile methodologies like CPMAI. Organizations should focus on realistic goals and incremental progress to ensure sustainable AI development.

Show Notes:

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