...intelligence. This was the era in which there was also much speculation about the impact of intelligent computers, computers like HAL in 2001: A Space Odyssey.
Fifth Generation project
In response to this high level of optimism Japan's Ministry of Information and Trade decided to push for a great leap forward, and announced in 1982 a project to develop massively parallel computers that would they believed make machine intelligence possible. This became known as the Fifth Generation project.
American government and business quickly responded by setting up the Microelectronics and Computer Technology Corporation (MCC), and pumping money for AI research into the Defense Advanced Research Projects Agency. This competitive atmosphere meant that over the next decade large amounts of money were poured into AI research in both the US and Japan.
This quickly led to a flood of new ideas, expert systems quickly became knowledge-based systems with the development of logic based on Bayesian probabilities that offered new ways to classify, store and use human knowledge. Early work on perceptrons developed into 'neural networks' that held the promise of being able to model biological neural structures that could not only function as pattern classifiers but could also learn. Search strategies were improved. The concept of intelligent agents was developed, and new learning strategies such as genetic algorithms were devised. There were also considerable advances in areas such as machine vision, natural language processing, and voice recognition.
The AI bubble bursts
The ultimate goal of all this research effort and expenditure, the creation of an intelligent machine eluded the researchers, and by the early 1990s it was starting to become clear that the hoped for great leap forward in AI was not going to happen as quickly as people had thought 10 years earlier. Government and corporate enthusiasm disappeared, funds started to dry up, DARPA withdrew most of its support, and research projects were shelved. This was the AI equivalent in the early 1990s of the dot-com bubble a decade later.
Researchers' failure to develop a general-purpose intelligent system was largely blamed on the fact that they had put most of their faith into the concept that the key to intelligence lay in symbolic reasoning. This is a mathematical approach in which ideas and concepts are represented by symbols such as words, or sentences, can be processed according to the rules of logic.
This was of course the long-standing idea amongst AI researchers that there is a fundamental set of algorithms that if supplied with enough information will eventually produce an intelligent system. Once discovered, such general algorithms, computer scientists had believed, would then be applicable to all areas of AI research, from natural-language processing to machine vision.
Loss of funding
This lack of success in finding such general algorithms, coupled with the loss of a very large proportion of the research funding for AI, led most of the researchers who remained to concentrate on niche areas where success, and therefore a return on research investment, was most likely. Research into AI largely disappeared to be replaced by a number of more-focussed disciplines that shared one thing in common: the need for a certain amount of machine intelligence or learning capability.
Many of the early projects continued. The development of game-playing programs reached a high point in 1997 with the defeat by a computer system from IBM called Deep Blue of chess grand master Gary Kasparov. Eliza was developed and refined, and in 1995 Richard Wallace developed Alice, a program that is now the world's most successful chatbot. Indeed such AI programs have reached a level of sophistication that allows them to be routinely used in interactive Web sites and automated telephone services by many companies, including Coca Cola and Burger King. Meanwhile mobile robots directly descended from Shakey have successfully explored the surface of Mars.
Talkback
Greetings Nick Hampshire
In response to your ongoing 3-part article on AI, I am writing to
alert you to the newly issued U.S. patent concerning ethical artificial
intelligence titled: Inductive Inference Affective Language
Analyzer Simulating AI (patent # 6,587,846) which is relevant
to your present/future editions of your article.
It introduces the newly
proposed concept of the Ten Ethical Laws of Robotics: a system
which radically expands upon previous ethical-robotic systems. As
implied in its title, this patent represents the first AI system
incorporating ethical/motivational terms: enabling a computer to
reason and speak in an ethical fashion, serving in roles specifying
sound human judgement. These Ten Ethical Laws directly expand upon
Isaac Asimov's Three Laws of Robotics, an earlier Science Fiction
construct that aimed to rein in the potential conduct of a
futuristic AI robot as rules that prohibit harm to come to humans.
Indeed, Asimov's first two laws state that (1) a robot must not
harm a human (or through inaction allow a human to come to harm),
and (2) a robot must obey human orders (unless they conflict with
rule #1). Although this cursory system of safeguards proves
intriguing in a Sci-Fi sense, it nevertheless remains simplistic in
its dictates, leaving open the specific details for implementing
such a system. The newly patented Ten Ethical Laws fortunately
remedy such a shortcoming, representing a general overview of the
enduring conflict pitting virtue against vice: the virtues of which
are initially partially listed below:
Glory/Prudence Honor/Justice
Providence/Faith Liberty/Hope
Grace/Beauty Free-will/Truth
Tranquility/Ecstasy Equality/Bliss
Dignity/Temperance Integrity/Fortitude
Civility/Charity Austerity/Decency
Magnanim./Goodness Equanimity/Wisdom
Love/Joy Peace/Harmony
The Ten Ethical Laws are written in a positive style of formal
mandate, focusing on the virtues to the necessary exclusion of the
corresponding vices. The purely virtuous mode (by definition) is
fully cognizant of the contrasting realm of the vices, without
necessarily responding in kind. Furthermore, the corresponding
hierarchy of the vices listed below contrasts point-for-point with
the respective virtuous mode (the overall patented system is
actually composed of 320 individual terms).
Infamy/Insurgency Dishonor/Vengeance
Prodigal/Betrayal Slavery/Despair
Wrath/Ugliness Tyranny/Hypocrisy
Anger/Abomination Prejudice/Perdition
Foolishness/Gluttony Caprice/Cowardice
Vulgarity/Avarice Cruelty/Antagonism
Oppression/Evil Persecution/Cunning
Hatred/Iniquity Belligerence/Turpitude
With such ethical safeguards firmly in place, the AI computer is
formally prohibited from expressing the corresponding vices,
allowing for a truly flawless simulation of virtue. Indeed, these
Ten Ethical Robotic Laws hold the potential for further
applications to a human sphere of influence.
www.angelfire.com/rnb/fairhaven/ethical-laws.html
Although only a cursory outline of applications is possible at this
juncture, a more detailed treatment is posted at:
www.ethicalvalues.com A direct USPTO link is also found at -
http://patft.uspto.gov/netacgi/nph-Parser?patentnumber=6587846
Sincerely
John E. LaMuth - M. S.
fax: 586-314-5960
P.O. Box 105 Lucerne Valley, CA 92356
http://www.charactervalues.com
A BREAKTHROUGH IN ETHICAL
ARTIFICIAL INTELLIGENCE
Announcing the newly issued U.S. patent
concerning ethical artificial intelligence entitled:
Inductive Inference Affective Language Analyzer
Simulating Artificial Intelligence (patent No. 6,587,846)
by inventor/author John E. LaMuth M. S.
As implied in its title, this innovation is the 1st affect-
ive language analyzer incorporating ethical/motivational
terms, serving in the role of int
"Artificial Intelligence" will always be just that: *Artificial*. The reason is simple: As magnificent as any computer might get, including those in the future which will drive walking, talking, synthetic flesh-bound robots, they will still have as their founding principle the creative work of a Conscious Agent, namely, a Human. A Human can "set off" a highly complex algorithm that does things far beyond that human's imagination or expectations -- like a gradeschooler firing off a nuclear weapon -- but the algorithm will only be doing what was inherent to its CREATED nature... No "artificial intelligence" will ever be a Conscious Agent, capable of Creating anything...
Reasoning Systems
In my opinion, the future of AI is dependent on the extension of the programming paradigm from today’s Boolean logic to incorporate “reasoning models”. Compsim’s KEEL® Technology offers one approach. Only when systems have the opportunity to exercise “reason” will they evolve to the next level (HAL-like). Reasoning is not a sequential process. It is an analog balancing process where inter-related alternatives need to be considered. It requires the production of relative answers and actions in dynamic environments.
I believe there are two competing mindsets driving the future:
First there are researchers with the “mechanism” mindset that use biological models as the foundation for their work in AI. They focus on neural nets and genetic algorithms that will allow applications to learn on their own and evolve on their own. They attempt to model how the human brain functions. Most of the research seems today seems to be focusing on this approach. The risk with these systems is that they may evolve in directions never before expected, even by their designers. If they create human-like reasoning engines, they can evolve in good ways and in bad ways. Just look at humans.
The other mindset assumes that there must be another way to create “reasoning systems”, with the added demand that they must be completely explainable and auditable. Humans must retain control. With this approach one is searching for a solution, but is not tied to the biological model. I call this the “process” mindset.
Without worrying about the technical details about how a human “reasons” or makes “judgmental decisions”, the reasoning process is commonly understood to take place in the human’s right brain. The left brain may focus on language and logic (the domain of most computers today). The right brain performs image processing and makes judgmental interpretation of those images. The images are not necessarily “pictures”, but can be feelings or impressions.
This suggests that text based programming languages do not satisfactorily provide a platform for “reasoning”. Similarly, I would suggest that it is difficult or impossible for humans to explain “exactly” how or why they make judgmental (subjective) decisions. Just watch the news broadcasters attempt to explain why humans do what they do. Or watch CEOs explain why they did what they did.
“Humans”, each perform their own “interpretation” of information. They provide their own weights to supporting and objecting arguments. They fuse the differing viewpoints in different ways. Human language does not allow this “reasoning model” to be effectively exchanged. It is for this reason that (industrial) machines are not commonly controlled with human language terms. They are controlled with numerical settings and formulas that tell machines exactly what to do.
Fuzzy logic is one design model that attempts to bridge human linguistic terms to the machine model. It starts to bring graphical constructs into the solution. Complex fuzzy logic systems, however, may be difficult to design and diagnose.
Creating solutions that can exercise reason is still the objective. Whether the market is for personal robots to take care of the aged, for automated medical treatment to reduce the cost of health care, or for robotic weapons to fight future battles, these systems must be able to exercise reason to be effective. New languages (like the KEEL dynamic graphical language) will be required to define the reasoning models. New engines (like those based on KEEL designs) will be required to process the reasoning models.
There is one thought that the future of AI requires larger and faster computers. I would suggest this is “nice”, but not necessarily required for “reasoning”. We don’t necessarily need millions of HALs running around (at least for the near term). The industry will evolve by building focused systems that exercise reason and address focused