What is the nature of intelligence? That is a question that has been pondered, and debated for thousands of years. Many people over the centuries have offered their own view on the matter, as illustrated by the quotes provided below:
“Knowing others is intelligence; knowing yourself is true wisdom. Mastering others is strength; mastering yourself is true power. If you realize that you have enough, you are truly rich.” Lao Tzu (6th Century BC: Chinese Record-Keeper in the Zhou Dynasty Court)
“Anyone who conducts an argument by appealing to authority is not using his intelligence, he is just using his memory.” Leonado da Vinci (1453-1519: Italian Scientist, Sculptor, various others).
“I’ve always felt that a person’s intelligence is directly reflected by the number of conflicting points of view he can entertain simultaneously on the same topic.” Abigail Adams (1744-1818: American First Lady).
“The true sign of intelligence is not knowledge but imagination.” Albert Einstein (1879-1955: German Physicist)
“A computer would deserve to be called intelligent if it could deceive a human into believing that it was human”. Alan Turing (1912-1954: British Computer Scientist, Mathematician).
“[T]he aim [is] to get machines to exhibit behavior, which if done by humans, would be assumed to involve the use of intelligence.” Arthur Samuel (1901-1990: American Artificial Intelligence Pioneer).
“Knowing a great deal is not the same as being smart; intelligence is not information alone but also judgment, the manner in which information is collected and used.” Carl Sagan (1934-1996: American Astronomer, Writer, Scientist).
“Intelligence is really a kind of taste: taste in ideas.” Susan Sontag (1933-2004: American Writer).
“Intelligence is the ability to avoid doing work, yet getting the work done.” Linus Torvalds (1969-?: Finnish Software Engineer).
However, it seems that everyone has their own opinion on what Intelligence is or isn’t. Intelligence is a concept that everyone knows about, but understands differently. The way each person understands a particular concept will have its own unique ‘flavour’. Perhaps one of the most interesting quotes above is Susan Sontag that uses analogy between taste and intelligence. Taste is a complex sensation in four dimensions – sweetness, sourness, bitterness and saltiness. Similarly, intelligence is a complex concept, with multiple dimensions.
Intelligence is multi-faceted – its nature cannot be defined using one of these quotes alone; it requires all of them. As an analogy, try describing Mona Lisa. One person’s description of the painting may be anathema to another person. To imagine that we can distil the Mona Lisa down to a few written words, and then naively believe other people will agree with us that it is the one and only definitive description, is like believing that people should only ever eat one type of food, or enjoy looking at one type of painting, or read one type of book. The Mona Lisa painting continues to inspire people to write more and more words about it. Similarly, intelligence is not something we can elucidate definitively. But that will not stop people from continuing to do so, since in so doing further insights can be gained into its nature.
Artificial Intelligence (AI) researchers with a background in knowledge engineering and the symbolic approach to AI will describe intelligence using ingredients such as the following:
*the capacity to acquire and apply knowledge;
*the ability to perform reasoning; and
AI researchers who prefer a behavioural-based approach will describe the intelligent behavior of embodied, situated agents using ingredients such as:
*the ability to perform an action that an external intelligent agent would deem to be intelligent;
*the ability to demonstrate knowledge of the consequences of its actions; and
*the ability to demonstrate knowledge of how to influence or change its environment in order to affect outcomes and achieve its goals.
If we think of intelligence using an analogy of mapping, then we might use the following ingredients to describe intelligence:
*the ability of an embodied, situated agent to map environments, both real and abstract (i.e. recognize patterns to provide useful simplification and/or characterizations of its environments);
*the ability to use maps to navigate around its environments;
*the ability to update its maps when it finds they do not fit reality; and
*the ability to communicate details of its maps to other agents.
It is important to realize, however, that these are not definitive descriptions, just ingredients in alternative recipes for intelligence.
What AI Can And Can’t Do
The field of Artificial Intelligence has come in for some criticism over the years for the grandiose predictions with which it is often associated and the perceived failure of the field to deliver on them. Many researchers in the past have made the mistake of seriously underestimating the difficulty of the task. Herbert Simon predicted that by 1967 a computer would be world champion in chess – it took until 1997 before the world champion was first beaten by a computer. He also predicted (again by 1967) that a computer would be able to discover a new mathematical theorem and prove it. The discovery of mathematical theorems has proved difficult – an important new theorem has yet to be discovered by a computer – although techniques for automated theorem proving have been around for some time. Marvin Minsky, again in 1967, predicted that the problem of creating AI would substantially be solved within a generation.
The predictions of the early AI researchers provided inspiration for Stanley Kubrick and Arthur C. Clarke’s HAL 9000 Computer that became operational in their story in 1992. Over two decades later, their vision of computers with emotional and conversational capabilities is still far from reality. Further claims have been made for more specific sub-fields of AI – it was claimed by in 1957, for example, that machine translation would be solved within three to five years. Anyone today using online machine translation services, or machine translation software, will be aware this is far from the case. Present predictions claim that we will have computers with greater processing power than the brain by 2020, and robots with human intelligence by 2050 and even robots with the ability to beat humans in football (also by 2050).
It is very difficult to predict the future, especially when it comes to technological advancement. Rather than predicting what AI might be able to achieve in the future, we can instead examine what AI has achieved in the past, and also have a look at the present.
Buying groceries is a task that people do frequently. Most people without disabilities find the task relatively easy to accomplish. And yet, even the most fundamental of tasks such as bagging your groceries or Internet shopping is difficult for an AI system. Negotiating the way around a busy market place, recognizing and examining the quality of the items that are for sale, and directly haggling with the stall owners over price, are still well beyond current AI systems.
Natural language generation is a venerable sub-field of natural language processing that involves the automatic generation of written text. Story generation deals exclusively with the problem of getting a computer to automatically write a story. Often the output produced by some story-generating computer programs is unintentionally funny, because of the absurdity of the combination of words and phrases that often result from a random selection process. Generating intentional humour that is both novel and unique is much more complex.
Techniques from the AI fields of expert systems and information retrieval systems have been adapted to build legal advice systems and reproduce legal reasoning of judges. An expert system is consulted in order to solve a particular case, and information retrieval system is used to search documentation to find similar cases. Clearly these legal advice systems do not yet have the abilities of real-life lawyers; if these systems did have same abilities, we would now be able to replace all lawyers with computers.
Speech recognition and machine translation software of varying capabilities have been around since the early 1960s. Search engines are increasingly using the latter to help people read online documents in another language. The automatic processing of natural language is an especially difficult problem for computers because the tolerance of native speakers to errors is very low.
Robotic surgery has made significant advances in recent years. It can be characterized by techniques that employ increasing levels of robotic autonomy. Remote surgery (or telesurgery) uses robotics to allow surgery to be performed remotely by a human operator. Minimally invasive surgery typically involves using keyhole surgical devices with remote-control manipulation. Unmanned surgery involves the use of fully autonomous robotic surgeons and has had recent success. The first unmanned robotic surgery took place in May, 2006 in Italy on a 47 year old male to correct heart arrhythmia, and the operation was rated as better than that performed by an above average human surgeon.
Facial recognition software has made substantial advances over the years. It is now being used in many places such as airports to automatically recognize criminals and terrorists, and in security systems for biometrics. However, the software is far from perfect, struggling with low resolution images and poor lighting, there are issues with privacy and they can be circumvented by people wearing sunglasses or using varied facial expressions such as a large smile.
Motion capture (also called mocap) is a technique used for animation in movies and computer games that captures the movement of human or animal and then generates realistic movement virtually using a digital model.
Collectively, humans are capable of doing all the tasks listed above. However, to expect an AI to do all these is perhaps raising the bar too high on our expectations of what AI is or should be capable of.
(William John Teahan; Artificial Intelligence – Agent Behavoiur).
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