Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.
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: "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 artificial intelligence conversation began with Alan Turing's 1950 work "Computing Machinery and Intelligence" (PDF, 89.8 KB) (link resides outside of IBM). 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 human text response. While this test has undergone much scrutiny since its publication, it remains an important part of the history of AI.
One of the leading AI textbooks is Artificial Intelligence: A Modern Approach (link resides outside IBM, [PDF, 20.9 MB]), by Stuart Russell and Peter Norvig. In the book, they delve into four potential goals or definitions of AI, which differentiate computer systems as follows:
Alan Turing’s definition would have fallen under the category of “systems that act like humans.”
In its simplest form, artificial intelligence is a field that combines computer science and robust datasets to enable problem-solving. Expert systems, an early successful application of AI, aimed to copy a human’s decision-making process. In the early days, it was time-consuming to extract and codify the human’s knowledge.
AI today includes the 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 typically make predictions or classifications based on input data. Machine learning has improved the quality of some expert systems, and made it easier to create them.
Today, AI plays an often invisible role in everyday life, powering search engines, product recommendations, and speech recognition systems.
There is a lot of hype about AI development, which is to be expected of any emerging technology. As noted in Gartner’s hype cycle (link resides outside IBM), product innovations like self-driving cars and personal assistants follow “a typical progression of innovation, from overenthusiasm through a period of disillusionment to an eventual understanding of the innovation’s relevance and role in a market or domain.” As Lex Fridman notes (01:08:15) (link resides outside IBM) in his 2019 MIT lecture, we are at the peak of inflated expectations, approaching the trough of disillusionment.
As conversations continue around AI ethics, we can see the initial glimpses of the trough of disillusionment. Read more about where IBM stands on AI ethics here.
Types of artificial intelligence—weak AI vs. strong AI
Weak AI—also called Narrow AI or Artificial Narrow Intelligence (ANI)—is AI trained 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 powerful 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 Super Intelligence (ASI). 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 Super Intelligence (ASI)—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, AI researchers are exploring its development. In the meantime, the best examples of ASI might be from science fiction, such as HAL, the rogue computer assistant in 2001: A Space Odyssey.
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.
The way in which deep learning and machine learning differ is in how each algorithm learns. "Deep" machine learning can use labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn’t necessarily require a labeled dataset. Deep learning can ingest unstructured data in its raw form (e.g. text, images), and it can automatically determine the set of features which distinguish different categories of data from one another. This eliminates some of the human intervention required and enables the use of larger data sets. You can think of deep learning as "scalable machine learning" as Lex Fridman notes in the same MIT lecture from above. Classical, or "non-deep", machine learning is more dependent on human intervention to learn. Human experts determine the set of features to understand the differences between data inputs, usually requiring more structured data to learn.
Deep learning (like some machine learning) uses neural networks. The “deep” in a deep learning algorithm refers to a neural network with more than three layers, including the input and output layers. This is generally represented using the following diagram:
The rise of deep learning has been one of the most significant breakthroughs in AI in recent years, because it has reduced the manual effort involved in building AI systems. Deep learning was in part enabled by big data and cloud architectures, making it possible to access huge amounts of data and processing power for training AI solutions.
Artificial intelligence applications
There are numerous, real-world applications of AI systems today. Below are some of the most common examples:
History of artificial intelligence: Key dates and names
Since the advent of electronic computing, some important events and milestones in the evolution of artificial intelligence include the following:
The future of AI
While Artificial General Intelligence remains a long way off, more and more businesses will adopt AI in the short term to solve specific challenges. Gartner predicts (link resides outside IBM) that 50% of enterprises will have platforms to operationalize AI by 2025 (a sharp increase from 10% in 2020).
Knowledge graphs are an emerging technology within AI. They can encapsulate associations between pieces of information and drive upsell strategies, recommendation engines, and personalized medicine. Natural language processing (NLP) applications are also expected to increase in sophistication, enabling more intuitive interactions between humans and machines.
Artificial intelligence and IBM Cloud
IBM has been a leader in advancing AI-driven technologies for enterprises and has pioneered the future of machine learning systems for multiple industries. Based on decades of AI research, years of experience working with organizations of all sizes, and on learnings from over 30,000 IBM Watson engagements, IBM has developed the AI Ladder for successful artificial intelligence deployments:
IBM Watson gives enterprises the AI tools they need to transform their business systems and workflows, while significantly improving automation and efficiency. For more information on how IBM can help you complete your AI journey, explore the IBM portfolio of managed services and solutions
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