While a number of definitions of artificial intelligence (AI) have surfaced over the last few decades, John McCarthy offers the following definition in this 2004 paper (PDF, 127 KB). (link resides outside IBM), It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. However, decades before this definition, the birth of the artificial intelligence conversation was denoted by Alan Turing's seminal work, Computing Machinery and Intelligence (PDF, 92 KB) (link resides outside of IBM), which was published in 1950. In this paper, 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 a human text response. While this test has undergone much scrutiny since its publication, it remains an important part of the history of AI as well as an ongoing concept within philosophy as it utilizes ideas around linguistics.
- Systems that think like humans
- Systems that act like humans
Types of artificial intelligence: weak AI vs. strong AI
Weak, also called Narrow AI or Artificial Narrow intelligence, is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. ‘Narrow’ might be a more accurate descriptor for this type of AI, as it is anything but weak; it enables some very robust applications, such as Apple's Siri, Amazon's Alexa, IBM Watson, and autonomous vehicles.
Strong AI is made up of Artificial General Intelligence AGI and Artificial superintelligence. Artificial general intelligence AGI, or general AI, is a theoretical form of AI where a machine would have an intelligence equal to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Artificial superintelligence, also known as superintelligence, would surpass the intelligence and ability of the human brain. While strong AI is still entirely theoretical with no practical examples in use today, that doesn't mean AI researchers aren't also exploring its development.
Deep learning vs. machine learning
Since deep learning and machine learning tend to be used interchangeably, it’s worth noting the nuances between the two. As mentioned above, both deep learning and machine learning are sub-fields of artificial intelligence, and deep learning is actually a sub-field of machine learning.
"Deep" machine learning can leverage labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn’t necessarily require a labeled dataset. It can ingest unstructured data in its raw form (e.g., text, images), and it can automatically determine the hierarchy of features that distinguish different categories of data from one another. Unlike machine learning, it doesn't require human intervention to process data, allowing us to scale machine learning in more interesting ways.
AI is important for its potential to change how we live, work, and play. It has been effectively used in business to automate tasks done by humans, including customer service work, lead generation, fraud detection, and quality control.
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