Ray Pearson
Artificial intelligence (AI) is a vast area of computer science aimed at creating smart machines capable of tasks that have previously required human intelligence.
Table of Contents
What Does Artificial Mean?
It is an interdisciplinary field with many possible techniques. Advances in machine learning and deep learning are driving a paradigm shift throughout the technology sector.
John McCarthy provides the following definition of AI in his 2004 work, and it is only one of several that have been proposed over the last few decades. (PDF, 106 KB) It is the study and development of intelligent machines, particularly software that can learn and adapt to its environment (link lives outside IBM).
AI is connected to the same job of utilizing computers to comprehend human intelligence, but it need not be limited to techniques that can be directly seen in living organisms.
However, Alan Turing’s foundational paper, “Computing Machinery and Intelligence” (PDF, 89.8 KB) (link lives outside of IBM), marked the beginning of the artificial intelligence discourse decades before this term was published in 2000.
The “father of computer science,” Alan Turing, poses the question “Can machines think?” in this seminal work with what does artificial mean. This leads him to propose what has become known as the “Turing Test,” in which a human interrogator tries to tell the difference between a machine and a human text answer.
Although this test has been subjected to much criticism since its publication, the theories it employs remain fundamental to the development of artificial intelligence and the philosophy of language.
One of the most influential guides to the field of AI is what does artificial mean: A Modern Approach (link sits outside IBM), written by Stuart Russell and Peter Nerving.
The authors explore four distinct definitions of artificial intelligence, classifying computers according to their level of rationality and their ability to think critically as opposed to simply react:
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Weak AI vs. strong AI: a comparison of artificial intelligence types
Weak artificial intelligence (AI), also known as Narrow AI or Artificial Narrow Intelligence (ANI), is AI that has been narrowly taught to carry out a set of tasks. The majority of today’s AI is powered by rather weak AI.
Although far from feeble, the AI that powers services like Apple’s Siri, Amazon’s Alexa, IBM Watson, and self-driving cars is more accurately described as “narrow.”
Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI) are the two components that make up strong AI. In the theoretical version of artificial intelligence known as artificial general intelligence (AGI) or what does artificial mean, a machine would have an intellect on par with humans.
Such a computer would have a self-aware awareness, the capacity to learn and plan for the future, and the ability to solve problems in novel ways. The intellect and capabilities of an ASI, sometimes known as superintelligence, would far exceed those of a human brain.
Even if there are currently no working instances of strong AI, it doesn’t stop academics from looking into how to make it a reality. Right now, science fiction may provide the most compelling instances of what does artificial mean, such as HAL, the superhuman, rogue computer aide from 2001: A Space Odyssey.
In contrast to machine learning, deep learning
The terms deep learning and machine learning are often used interchangeably, but there are important distinctions between the two that should be made clear. As was previously noted, deep learning is a sub-field of machine learning, which is itself a sub-field of what does artificial mean.
Neural networks are the basis of deep learning. A deep learning algorithm is a neural network with more than three layers (including the inputs and the outputs). Commonly, this is shown in the following diagram:
The algorithms used in deep learning and machine learning approach learning in different ways. By automating the feature extraction step, deep learning allows for the usage of bigger data sets and reduces the need for human involvement.
In the same MIT lecture, Lex Fridman made the observation that “scalable machine learning” is a good way to describe deep learning. Traditional, “non-deep,” machine learning requires more human input. In most cases, more organized data is needed for learning, and this is why humans are in charge of determining the hierarchy of characteristics with what does artificial mean.
Conclusion
Creating computers with cognitive and behavioral skills analogous to humans is called “what does artificial mean” (AI), an abbreviation of artificial intelligence. A mechanical system that displays cognitive capacities generally associated with humans may also be called a “cyborg,” which is another meaning for the term.