In the early 1970s, research into artificial intelligence was booming. Government and military funding poured into AI labs at universities like MIT, Stanford, and Carnegie Mellon. Scientists were optimistic that human-level machine intelligence was just around the corner. However, by 1974, this initial wave of enthusiasm had crashed – AI entered a period known as the “AI winter,” characterized by significant cuts in funding and much more limited progress.
What happened? Why did the promise of early AI come crashing down so quickly? This article will explore the factors behind the AI bust of the 1970s. We’ll look at the limitations of the technology at the time, the lack of data, the challenges of computation, and the valid criticisms leveled by philosophers like Hubert Dreyfus.
Ultimately, it became clear thinking machines were far more difficult to develop than initially thought. While core AI research continued, the grand vision of intelligent machines entering mainstream life would have to wait. The 1970s AI winter marked a major recalibration in expectations.
The Limits of Early AI
In the 1950s and 1960s, scientists made promising strides in artificial intelligence, creating programs that could prove mathematical theorems, play checkers, and solve basic puzzles. This progress fueled great optimism that fully intelligent machines were imminent. However, the capabilities of these early systems were confined to narrowly defined tasks. Once researchers applied AI to real world problems, the brittleness and fragility of these programs became clear.
For example, the checkers-playing programs could beat humans in checkers but could not transfer that skill to chess or any other game. They also lacked any capacity for common sense reasoning. Similarly, the mathematical theorem proving programs used strict symbolic logic but could not understand nuance or context. Their reasoning was rigid and inflexible outside of the specific formal systems they were designed for.
Early robotics and machine translation efforts encountered similar difficulties. Scientists believed household robot helpers would soon be possible, but early robots struggled with even basic real world tasks like locomotion, object manipulation, and recognizing objects. Useful machine translation required more than just word replacement; it demanded an understanding of grammar, context, and subtle shades of meaning. The hand-coded rules of early translation algorithms broke down in handling the quirks and complexities of real languages.
In general, replicating the flexibility and general intelligence of human cognition proved far more difficult than expected. As AI scientist Patrick Winston observed, these early systems were confined to “tiny toy worlds.” They could handle narrowly defined tasks in limited domains, but extending their capabilities required overcoming fundamental limitations in how they represented knowledge and reasoned.
To achieve full human-level intelligence, researchers came to realize AI needed to be able to learn, reason from incomplete information, and transfer knowledge between domains. These proved difficult challenges to solve, leading to various “AI winters” where funding and optimism diminished. But researchers persisted, determined to inch closer to the goal of creating fully intelligent machines.
The Data Deficiency
In the 1950s and 1960s, artificial intelligence researchers faced a major obstacle – lack of data. At the time, computers had little storage capacity, operating with kilobytes or megabytes compared to the gigabytes and terabytes common today. This severely constrained researchers’ ability to provide AI programs with large amounts of real world data to learn from.
Instead, early AI systems were created using symbolic reasoning, where programmers manually encoded rules and logic structures. For example, for a program to understand language, a programmer would have to codify all the rules of grammar and syntax. Some researchers believed they could enumerate all the rules needed for human-level intelligence. However, this hand-crafted symbolic AI proved brittle, as the programmers struggled to account for the nuances and contextual flexibility of real world data.
Without large datasets reflecting diverse real world examples, early AIs could not acquire the inductive reasoning skills to make reliable inferences or generalize effectively. For instance, a system designed to interpret visual scenes using strictly logical rules would fail to recognize objects from new angles or in different lighting conditions. Presented with edge cases outside its programmed rules, it would falter.
Researchers understood that to develop robust, flexible AI on par with human cognition, they needed to feed machines large volumes of data from the physical world. AI pioneer Alan Turing had proposed as early as 1950 that intelligent systems would need to “learn by experience” through repeated exposure to data. But lacking the storage and data collection capabilities of later decades, researchers in the 1950s and 60s could not actualize this insight.
The paucity of data available to early AI researchers represented a critical roadblock. Without rich datasets to learn from across diverse situations, early systems remained narrowly limited in their reasoning capacity.
This data deficiency stunted progress in the field, delaying the development of machine learning. But it also inspired researchers to collect and curate datasets in order to enable the future breakthroughs in AI that did arrive once sufficient data was available.
The Computational Cost of AI
In the 1950s and 1960s, artificial intelligence research was severely hampered by the astronomical costs of computing power. Though transistors and integrated circuits were reducing the size of individual computer components, computational resources remained extremely limited compared to today. As a result, AI algorithms needed to be very computationally efficient to be practical.
Some promising approaches like neural networks were essentially impossible to implement due to their intense computational requirements. Neural networks attempt to loosely model the parallel distributed processing of the human brain. But simulating even small neural nets on the computers of the 1960s would have cost untold fortunes. Researchers could not afford to “waste” precious cycles on inefficient algorithms or brute force solutions.
For example, a technique like minimax search helped early chess programs play reasonably well by efficiently pruning decision trees. But minimax search relies on carefully hand crafted evaluation functions. Building systems that could learn their own evaluation functions from experience was infeasible with hardware of the time. The backpropagation algorithm for training neural networks was first proposed in the 1960s, but would not see practical use until decades later.
During the 1960s, the state of the art in computer hardware progressed from vacuum tube circuits to discrete transistors to early integrated circuits. But computing still cost hundreds of dollars per hour or more. AI researchers in this era had to focus on developing symbolic reasoning systems coded with rules, logic and clever efficiencies. Statistical or neural learning approaches would have to wait.
As the 1960s drew to a close, researchers increasingly realized that flexible, human-level AI would require far more powerful and affordable computing capabilities. The algorithms they imagined – capable of learning from data, reasoning probabilistically, mimicking neurobiology – needed hardware capabilities beyond what was available. This computational bottleneck contributed to the stall in AI progress known as the “first winter”, as researchers waited for Moore’s Law to unlock the potential of more complex machine learning approaches.
Valid Critiques of AI Emerge
In the 1960s, as the limitations of early AI became apparent, respected philosophers began to openly question whether machine intelligence was feasible. These critiques highlighted valid weaknesses in the dominant approaches of the time.
One prominent skeptic was Hubert Dreyfus, a professor at MIT. In his 1972 book “What Computers Can’t Do,” Dreyfus systematically argued that human expertise depends heavily on unconscious instincts, intuition, and common sense – attributes difficult or impossible to capture in logical rules and computer programs.
Dreyfus used the example of early chess programs, which could play reasonably well using algorithms like minimax search to evaluate positions. But they played rigidly, often stumbling when facing unexpected openings or deviations from standard play. Without an intuitive sense of the implicit “style” and “purpose” of the game, machine play lacked fluidity and flexibility. Human grandmasters, by contrast, could adapt their play and strategy on the fly.
Similarly, Dreyfus noted how machine translation efforts struggled because languages have nuance and ambiguity beyond what can be captured by replacing words one-to-one. Human translators use intuition to infer meaning from context. Dreyfus believed computer programs would always lack these innate human instincts, no matter how many rules were encoded. His critiques resonated widely, as early AI systems indeed struggled with the subtlety and flexibility of human cognition.
Dreyfus was not alone in highlighting these limitations. Philosopher John Searle argued that AI systems merely manipulate symbols according to rules, but cannot intrinsically comprehend meaning or semantics like humans. Computer scientist James Weizenbaum criticized the entire endeavor of AI as ethically misguided and impossible.
These criticisms encouraged skepticism and contributed to a general “AI winter”, as it became clear that mimicking human-level intelligence was far more difficult than early pioneers had hoped and predicted. But the valid shortcomings they identified also helped researchers understand what key components like learning, intuition and common sense were missing from early AI, guiding future work.
The AI Winter Descends
By the early 1970s, the initial wave of optimism around artificial intelligence had stalled. After promising breakthroughs in the 1950s and 1960s, researchers confronted the hard limits of the technology. AI systems struggled with brittleness, a lack of common sense, and the inability to comprehend semantics or transfer knowledge between tasks. Government funding agencies, disappointed with the lack of progress, began pulling back financially.
Between 1966 and 1974, DARPA funding for AI plummeted from $50 million per year to just $1 million. Labs around the country dedicated to machine intelligence shut down, unable to sustain work. At MIT, the AI Lab formed in 1959 saw its budget slashed dramatically. Stanford’s AI project went from dozens of researchers to just one faculty member left by 1980. Other universities followed suit in downsizing or closing AI divisions as interest and funding dissipated.
This marked the start of an “AI winter” – a prolonged period of reduced funding and interest in artificial intelligence research. While stalwart researchers like John McCarthy, Marvin Minsky and Hans Moravec continued foundational work on machine learning algorithms and knowledge representation, the grand dream of thinking machines faded from the forefront.
With military funding uncertain, some AI researchers transitioned to more commercial applications like banking, logistics and medical diagnosis. These were useful systems, but far short of the flexible general intelligence sought by early pioneers. The earliest period of AI research had clearly reached excessive optimism about near-term possibilities. A period of retrenchment and reduced hype was now necessary for the field to advance on more realistic foundations.
The AI winter of the 1970s was a disciplining period for the field. With less funding to go around, researchers focused more on theoretical underpinnings and learned practical lessons from the limitations of early systems. This recalibration and reset of expectations helped establish artificial intelligence on a more rigorous footing to enable the true breakthroughs that would arrive decades later, when processing power caught up to grand ambitions.
Conclusion
The artificial intelligence bust that occurred in the 1970s can be chalked up to the limitations of the technology at the time. The data deficiency, computational costs, and inability to capture the nuances of human cognition in symbolic rules all contributed to the downfall of early optimism. With tiny datasets, expensive hardware, and algorithms that struggled with real world complexity, early AI systems were incredibly fragile and narrow in scope.
As philosophers like Dreyfus highlighted, these programs lacked the flexibility, intuition, and common sense of human experts when faced with messy real world problems. Valid critiques were levied about the shortcomings of purely symbolic, rule-based AI. Researchers discovered that human-level intelligence could not be neatly codified with handcrafted rules.
Thus, the unrealistic hype and predictions surrounding early AI quickly came crashing back to earth. When bold promises were not met, government funding dried up. The AI bust was an understandable result of premature enthusiasm outpacing technical limitations.
However, this AI winter ultimately helped recalibrate the field and orient research towards more measured, rigorous goals. The temporary chill in funding and progress forced researchers to address flaws and pursue theoretical foundations. When computational power, datasets, and machine learning algorithms finally reached an inflection point decades later, AI research could restart in earnest on a more solid footing.
The AI winter, though fallow, was a productive period of reflection. Today’s prolific advances in machine learning stand on progress made through this disciplined retrenchment. The initial bust cycle was a necessary correction on the long path towards achieving artificial general intelligence.
James is a writer who specializes in writing about AI and education for our blog. He believes in the power of lifelong learning and hopes to inspire his readers to take control of their education through AI. James is passionate about self-education as a means of personal growth and fulfillment, and aims to empower others to pursue their own paths of learning.