Many publications from the middle of the twentieth century prove the questioner's statement that it was a widely held belief during that period that AI would quickly become conscious, self-aware, and smart.
Many tasks and forms of expertise once the exclusive domain of human intelligence, after the development of the Von Neumann general purpose computing architecture became, by the end of that century, more or less the exclusive domain of computers. These are only a few examples.
- Scientific and statistical computation
- Drafting and manufacturing process automation (CAD and CAM)
- Publishing and typesetting
- Certain forms of algebraic and calculus reductions (Maxima and its derivatives)
- Circuit analysis
- Masterful board game playing
- Profitable stock speculation
- Pattern recognition (OCR, fingerprint, voice recognition, sorting, terrain)
- Programming in predicate logic and recursive predicates
- Strategy evaluation
Disappointments (thus far)
In contrast to this impressive array of successes, there is an equally long list of failed expectations.
- Consumer available bipedal robots
- Automated vacuum cleaning (major disappointment for this answer's author)
- Autonomous mechanical factory workers
- Automated mathematicians (creative hypothesis generation and proof/disproof to extend theory)
- Natural language comprehension
- Obedience to arbitrary commands
- Human-like expression in conversation
- Automated technical innovation
- Computer morality
- Human (or at least mammalian) emotional states
- Asimov's three laws operating system
- Adaptive strategy development in arbitrary and shifting set of domains
Domain and Domain-free Distinction
When did it become clear that devising programs that master games like chess resulted in software designs that only applied to games like the ones for which they were programmed?
Although the general public may have thought that a cybernetic chess master would also be smarter than people in other ways, those creating those programs were well aware of the distinction between developing software that exhibited excellence in chess play hard coded and developing software that exhibits the ability to learn chess play and develop excellence iteratively from novice.
The end goal had always been high powered general intelligence. More short term achievable objectives were created to facilitate the demonstration of progress to investors. It was the only way to maintain a continuous stream of research funding from the military.
The first milestone was to master a single game without machine learning. Then research turned to the building of domain knowledge so that a class of solutions, adaptations, and forms of planning could be realized in real time during warfare. As economic domination became more preferable to military domination during the third quarter of the twentieth century, the vision for AI scaled to embrace the domains of economics and natural resource management.
Consider this spectrum of automation maturity.
- A program that enumerates current move sequence possibilities at each turn in the play of a chess game, eliminating probable bad moves at each projected move point, and selects the next move most likely to lead to a win
- A program that does the above but also skews probability based on pattern recognition of known winning chess strategies
- A program that is designed to be a run time optimized rules engine that centralizes and abstracts the redundant operations of the play of an arbitrary game and isolates and aggregates the representation of chess rules, chess strategies, and chess patterns and anti-patterns
- A program that, given a set of rules of a game, can generate a next move based on any game state, remembers success and failure results and the sequences that led to those outcomes, and has the ability to assess the probable loss or gain of individual moves and the game patterns in space and time around them based on history, and then leverages these abilities to learn an arbitrary game, reaching the masterful level of play of chess through learning
- A program that learns how to learn games such that, after learning several games, it can learn chess faster than an intellectually gifted human can
The first is easy. The last is extremely challenging.
When the distinctions between these phases of automation maturity became apparent and how clear people became of those distinctions in which research groups is a complex probabilistic function.
Who was the first person to recognize the distinction between human-like general intelligence and domain specific intelligence?
Norbert Wiener was likely the first to deeply comprehend the distinction between electronic control of relays (investigated theoretically by Claude Shannon) and closed loop control. In his book, Cybernetics, a primarily mathematical work, he precisely established the foundation for self-correcting and adaptive systems. John von Neumann had a comprehension of the distinction between programming good game play and the human ability to learn good game play and published much on the topic.
It was Arthur Lee Samuel who actually wrote the first impressive demonstration of the distinction between game playing software and machine learning. It was he who bridged Wiener's work with the contemporary digital computer and first coined the term Machine Learning.
Distorted Restatements of Authentic Research and Innovation
The categories artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial super intelligence (ASI), proposed in The AI Revolution: The Road to Superintelligence by blogger Tim Urban (Huffington Post, THE BLOG, posted 2/10/2015, updated 4/12/2015), is referenced in AI Stack Exchange in multiple places, but the distinctions between these categories are not precisely defined and the ideas contained therein are neither peer review nor validated by other research or statistics.
The work is no less conjecture than mediocre science fiction — entertaining enough to gain some popularity but not rational conclusions drawn from either repeatable experiments or randomized studies. The trend graphs provided in the article are of invented shape, not graphical representations of actual data.
Some of the material may later be found to have some truth in it, as in the case for many lay interpretations of scientific research or the futuristic thoughts of science fiction authors. However, much of the material leads to misconception and false assertions.