Artificial intelligence is becoming good at many “human” jobs — diagnosing disease, translating languages, providing customer service — and it’s improving fast. This is raising reasonable fears that AI will ultimately replace human workers throughout the economy. But that’s not the inevitable, or even most likely, outcome. Never before have digital tools been so responsive to us, nor we to our tools. While AI will radically alter how work gets done and who does it, the technology’s larger impact will be in complementing and augmenting human capabilities, not replacing them.
H. James Wilson and Paul R. Daugherty, Collaborative Intelligence
Against the lofty promises made by AI scientists throughout the 1960s, the field of artificial intelligence was subject to scrutiny and financial setbacks over the following decade. At the time, many AI programs were limited in their generalizable capabilities, and the government agencies became frustrated with the lack of progress and ended financial support for AI research. Several scholars expressed doubt against the idea of the machine capable of human thought.
For example, the Chinese room argument is a famed thought experiment stating that passing the Turing Test alone cannot prove that a machine is "human," no matter how human-like it may be. A human respondent may be able to translate every foreign phrase if the respondent has an infinitely large set of instructions that one can use to map the provided phrase to the answer, but the Chinese room argument stipulates that this should not equate to the respondent well-versed in the language. This also spawned a slew of philosophical discussion surrounding artificial intelligence in comparison to humans.
As government funding dried up, corporations slowly began to take interest in adopting AI for industrial application. With formal paradigms ("neats") and experimental approaches ("scruffies") competing for attention, these uncertain times established a solid foundation for the future revival of the AI research field. Below are some of the reading materials that capture the era of disappointment and criticism.
John R. Searle's Chinese room argument
https://web.archive.org/web/20211205141248/http://www2.psych.utoronto.ca/users/reingold/courses/ai/cache/searle.html
"Thousands of years have scientists thought about the following guestion: how can we combine mind and brain - two quite distinct entities. At first, this question might appear meaningless, but trust me: it is a hard one, perhaps one of the biggest problems in science today."
What is the AI winter?
https://bdtechtalks.com/2018/11/12/artificial-intelligence-winter-history/
"There are different accounts as to how many AI winters have happened and when they took place. But there are two main periods during which funding and interest in the AI industry declined, which have widely become known as the first and second AI winters."
Logical vs. Analogical / or / Symbolic vs. Connectionist / or / Neat vs. Scruffy
https://web.media.mit.edu/~minsky/papers/SymbolicVs.Connectionist.html
"Engineering and scientific education conditions us to expect everything, including intelligence, to have a simple, compact explanation. Accordingly, when people new to AI ask 'What's AI all about,'' they seem to expect an answer that defines AI in terms of a few basic mathematical laws."