Terry DavidThe son of an engineer and a math teacher, Hassan Taher might just have science in his blood. Growing up in the Southern Texas city of Beaumont, he also became deeply enamored with science fiction. As he read the works of Arthur C. Clarke, Isaac Asimov, and his other favorite authors, Taher must have taken considerable inspiration from the various uses of fantastic technology to solve enormous problems. However, his young mind was also doubtlessly shaped by the various ways that technology goes tragically awry in the dystopian futures that these authors describe.
After graduating with a degree in computer science from the University of North Texas, Hassan Taher embarked on a career as a consultant, author, and thought leader in the field of artificial intelligence (AI). As the head of Taher AI Solutions, he provides tech advice to business clients in industries that range from finance to manufacturing. Beyond the countless articles he has written, Taher has authored three influential books on topics related to AI: The Rise of Intelligent Machines, The Future of Work in an AI-Powered World, and AI and Ethics: Navigating the Moral Maze.
Hassan Taher has years of experience studying the opportunities that AI presents as well as its many considerable challenges. This places him in the ideal position to comment on the subject of AI winter.
In September 2024, Taher lent his voice to a growing chorus of tech experts who are predicting a potential AI winter. He believes that an AI winter is particularly likely to occur in the specialized field of generative AI. But to fully understand Taher’s predictions, we must first establish a firm understanding of the phenomenon known as AI winter.
Writing for AI News, tech journalist Muhammad Zulhusni succinctly defines AI winter as “a period of funding cuts in AI research and development, often following overhyped expectations that fail to deliver.” First coined in the late 1980s, the term refers to a metaphoric freezing of progress in the AI sector. Forbes contributor John Werner views the story of AI winter as “a cautionary tale about the cyclical nature of technological advancement and disappointment in the realm of artificial intelligence” that can lead to “the extinction of AI companies and a considerable downturn in research and investment.”
To better weather future AI winters, whether impending or distant, Hassan Taher recommends studying the AI winters of the past. “Drawing insights from past experiences can offer valuable guidance on how to navigate the current cycle of hype and keep advancing toward meaningful and sustainable AI developments,” he writes.
The first great AI winter took place in the late 1970s and early 1980s. Believing that their early AI platforms would rapidly reach human levels of intelligence, researchers overpromised the functionality of AI without a full understanding of its limitations. As broken promises accumulated in the nascent AI sector, interest and excitement among tech investors and other key AI stakeholders began to dry up. With a lack of funding, the AI sector came up well short of its overhyped projections and fell into a period of stagnation.
Although the AI industry recovered in the early 1980s, it faced a second AI winter in the late 1980s and early 1990s. This time the problem was centered in a particular subsector of AI called “expert systems.” Expert systems use AI to support workers in organizations that have targeted expertise in a particular field. Expert system AI is designed to simulate the judgement and behaviors of human beings with years of specialized professional experience.
Despite the shift in focus, the root causes of the second great AI winter were virtually identical to those of the first. Hassan Taher sums up these root causes quite simply as “inflated expectations followed by underwhelming results.” Going on, he explained, “The expectations for AI’s capabilities far outstripped what the technology could actually deliver at that time, and the resulting gap between hype and reality led to widespread disappointment.”
Noting the problems presented by this cycle of AI hype and disappointment, Forbes’ John Werner believes that AI professionals must learn three important lessons moving forward. They must learn to manage public/investor expectations, foster interdisciplinary collaboration, and establish long-term investment ties. Hassan Taher recommends that AI professionals take all these precautions as a new AI winter begins to loom on the horizon.
This time, generative AI (gen AI) lies at the heart of the problem. Designed to generate content such as articles, images, videos, and music, this type of AI has made headlines over recent years for many different reasons. If you have paid close attention to these headlines, you may think that AI is on the verge of muscling human beings out of the creative process entirely.
However, much of the news surrounding the supposed “gen AI takeover” has been overhyped, setting the stage for ultimate underperformance and dissepiment. In other words, the stage is set for another AI winter. “There’s no doubt that generative AI is already increasing productivity in some areas, such as graphic design and legal research work” writes Hassan Taher. “But there’s little evidence that the technology is broadly unleashing enough new productivity to push up company earnings or lift stock prices.”
Fast Company tech writer Mark Sullivan agrees, writing “AI companies and their investors have been telling us for more than a year now that generative AI will create untold amounts of wealth by increasing worker productivity” but then quoting respected AI expert Gary Marcus who says that few businesses currently view gen AI as essential. “You see very few people out there today saying, ‘Yes, we use this everyday, and it’s absolutely essential to what we do,’” says Marcus.
No matter what the future holds, Hassan Taher expects to encounter more periods of hype, disappointment, and recovery in the “AI winter” cycle. However, AI “has a habit of bouncing back each time stronger than before,” according to Taher. And AI professionals can encourage this recovery by learning from the lessons of previous AI winters.
