Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.
Alan Turing, often referred to as the "father of computer science", asks the following question, "Can machines think?" From there, he offers a test, now famously known as the "Turing Test", where a human interrogator would try to distinguish between a computer and human text response. While this test has undergone much scrutiny since it was published, it remains an important part of the history of AI as well as an ongoing concept within philosophy as it utilizes ideas around linguistics.
Turing was followed up a few years later by John McCarthy, who first used the term “artificial intelligence” to denote machines that could think autonomously. He described the threshold as “getting a computer to do things which, when done by people, are said to involve intelligence.”
Since the 1950s, scientists have argued over what constitutes “thinking” and “intelligence,” and what is “fully autonomous” when it comes to hardware and software. Advanced computers such as the IBM Watson can beat humans at chess and are capable of instantly processing enormous amounts of information.
At its simplest form, artificial intelligence is a field of study, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms that seek to create expert systems to make predictions or classifications based on input data.
Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data. As such, they are designed by humans with intentionality and reach conclusions based on their instant analysis.
With massive improvements in storage systems, processing speeds, and analytic techniques, these algorithms are capable of tremendous sophistication in analysis and decision-making. Financial algorithms can spot minute differentials in stock valuations and undertake market transactions that take advantage of that information. The same logic applies in environmental sustainability systems that use sensors to determine whether someone is in a room and automatically adjusts heating, cooling, and lighting based on that information. The goal is to conserve energy and use resources in an optimal manner.
AI often is undertaken in conjunction with machine learning and data analytics, and the resulting combination enables intelligent decision-making. For example, there are AI systems for managing school enrollments. They compile information on neighborhood location, desired schools, substantive interests, and the like, and assign pupils to particular schools based on that material. As long as there is little contentiousness or disagreement regarding basic criteria, these systems have the potential to work intelligently and effectively.
Artificial intelligence has to ability to adjust as circumstances or conditions shift. This may involve alterations in financial situations, road conditions, or environmental considerations. AI must integrate these changes within its algorithms and make decisions on how to adapt to the new possibilities.
We can illustrate these previous statements most dramatically by reviewing the transportation area. Autonomous vehicles use machine-to-machine communications to alert other cars on the road about upcoming congestion, potholes, highway construction, or other possible traffic impediments. Vehicles can take advantage of the experience of other vehicles on the road, without human involvement, and their achieved “experience” is immediately transferable to other similarly configured vehicles. These advanced algorithms, sensors, and cameras use dashboards and visual displays to relay information in real-time to human drivers so that we are able to make sense of current traffic conditions and monitor the health of our vehicles.
Rather than being mechanistic or deterministic in how these machines react and operate, AI software learns as it goes and incorporates previous real-world experiences into its own decision-making process. In this way, we have given AI nearly unlimited potential as we see how the future unfolds.