Original article was published on Artificial Intelligence on Medium
A Brief History of Artificial Intelligence (Part II)
Lessons from the hype of AI research. From theory to production, the AI community has a long history of becoming the next big thing.
This is the second part of my AI History series (a four-part series) where we look back and talk about the development, challenges, promising solutions, achievements, limiting factors, and recent developments in AI (specifically in Deep Learning). In the first part, we talked about the ideas that had come together to develop the field of AI research, for this article, we will talk about the hype and the financial setbacks that the AI community has faced over the years.
Promises of AI
The hype in AI research has ever been fuelled by intense optimism so much so that it had overlooked substantial constraints and obstacles that had caused a couple of setbacks to which the field had almost been abandoned by major investors.
Programs that were developed years after the Dartmouth conference would be able to solve algebraic word problems, prove geometric theorems, and learn the English language [2–3]. Because of this, researchers in the field expressed their optimistic views to the promises of AI, forecasting that a fully intelligent machine would be built in less than 20 years . One of the most notable claims was made by a well-known researcher at the time, Marvin Minsky (1970) who made mention that “from three to eight years we will have a machine with the general intelligence of an average human being”  although Minsky believed that he had been misquoted .
With their research achievements and increasing business interest, AI research had been funded by the government and granted to fund Minsky and McCarthy’s project $ 2.2 million.
There have been pit stops that had caused significant setbacks on AI research
AI winter is a period of reduced funding and interest in artificial intelligence research.
The first AI winter (1974–1980)
In the ’70s, AI was subjected to financial setbacks and criticisms; they have failed to engrave their undertakings to production. Researchers had failed to realize the intricacies of their field and the challenges they will have to face in attempts to come up with a solution. Because of this, the funding for AI research was wiped out . Despite the challenges and major setbacks the field has sought, new ideas were explored in logic programming and commonsense reasoning . The problems that the field has faced in the ’70s are as follows:
· Limited computer power.
· Intractability and combinatorial explosion. Richard Karp (1972) demonstrated that there are many problems that can only solved in exponential time (referring to Big-O complexity). Finding solutions to these problems require unimaginable amounts of computer time except when the problems are trivial .
· Commonsense knowledge and reasoning: the program needs to have some idea of what it might be looking at or what it is talking about. This requires that the program know most of the same things about the world that a child does. Researchers soon discovered that this was a truly vast amount of information .
· Moravec’s paradox. Proving theorems and solving geometry problems is comparatively easy for computers, but a supposedly simple task like recognizing a face or crossing a room without bumping into anything is extremely difficult. This helps explain why research into vision and robotics had made so little progress by the middle 1970s .
· The frame and qualification problems. AI researchers (like John McCarthy) who used logic discovered that they could not represent ordinary deductions that involved planning or default reasoning without making changes to the structure of logic itself. They developed new logics (like non-monotonic logics and modal logics) to try to solve the problems .
The resurgence of 1980–1987
In the 1980s businesses invested in a form of AI program called expert systems — a program that caters queries or solves problems about a specific domain of knowledge using rules of logic. These systems were built from a structured representation of a human expert’s knowledge about a specific domain. The power of these systems comes from the quality of information drawn from the human experts of the field structured by a programmable set of rules.
Because of these systems, the Japanese government-funded the AI model with its fifth-generation computer project . The funding for this project cost about $850 million. Their objectives were to write programs and build machines that could carry on conversations, translate languages, interpret pictures, and reason like human beings .
Second AI Winter
Due to the hype and promising notions of AI, businesses had been able to take a leap through monetary investments of AI projects; the rise and fall of AI research funding is an indicative pattern of an economic bubble.
“The collapse was in the perception of AI by government agencies and investors — the field continued to make advances despite the criticism. Rodney Brooks and Hans Moravec, researchers from the related field of robotics, argued for an entirely new approach to artificial intelligence”.
In 1987, the first indication of the economic setback experienced by the AI community was the sudden collapse of the market for specialized AI hardware. An entire industry worth half a billion dollars was demolished overnight . As a consequence, the more expensive Lisp machines had rendered itself obsolete because, with the failure to materialize the promising AI solutions, there was no longer a reason to buy them. Desktop computers such as Apple and IBM started to become more mainstream in production.
“Eventually the earliest successful expert systems, such as XCON, proved too expensive to maintain. They were difficult to update, they could not learn, they were “brittle” (i.e., they could make grotesque mistakes when given unusual inputs), and they fell prey to problems (such as the qualification problem) that had been identified years earlier. Expert systems proved useful, but only in a few special contexts.”
As of 1993, 300 AI companies had shut down, gone bankrupt, or have been acquired effectively ending the first commercial wave of AI.
With the grandeur of AI’s promises, businesses have invested tons of resources to bring AI into production and cater to business solutions. However, the young field of AI in the ’90s has had suffered from (1) lack of computing power, (2) premature theoretical grounds, and (3) failure to realize the complexities of their goals to achieve human-level intelligence. In turn, AI winters and momentary recession had taken place to renew and revise longheld conceptions.
Over time, the field has reached some of its most ambitious goals, some are even deemed to be impossible such as the AlphaGo project, thanks to Deep Learning. Today, the field of AI has ever been expanding to explore the intricacies of intelligent behavior ranging from computer vision, natural language understanding, and agent-based learning (reinforcement learning) among other things.
. McCorduck, Pamela (2004), Machines Who Think (2nd ed.), Natick, MA: A. K. Peters, Ltd., ISBN 978–1–56881–205–2.
. Crevier, Daniel (1993), AI: The Tumultuous Search for Artificial Intelligence, New York, NY: BasicBooks, ISBN 0–465–02997–3.
. Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0–13–790395–2.
. Tag: Marvin Minsky. (2020, May 11). Retrieved May 13, 2020, from https://quoteinvestigator.com/tag/marvin-minsky/
. Crevier, Daniel (1993), AI: The Tumultuous Search for Artificial Intelligence, New York, NY: BasicBooks, ISBN 0–465–02997–3
. Lighthill, Professor Sir James (1973), “Artificial Intelligence: A General Survey”, Artificial Intelligence: a paper symposium, Science Research Council.
. McCarthy, John; Hayes, P. J. (1969), “Some philosophical problems from the standpoint of artificial intelligence”, in Meltzer, B. J.; Mitchie, Donald (eds.), Machine Intelligence 4, Edinburgh University Press, pp. 463–502, retrieved 16 October 2008.
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