What is artificial intelligence (AI)?
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What is AI? 

Artificial intelligence (AI) is technology that enables computers and digital devices to learn, read, write, talk, see, create, play, analyze, make recommendations and do other things humans do.

In addition, AI refers to the field of computer science focused on developing these technologies. Yet, at its simplest form, artificial intelligence is a field 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 which seek to create expert systems to make predictions or classifications based on input data.

Artificial intelligence has gone through many cycles of hype, but even to skeptics, the release of OpenAI’s ChatGPT seems to mark a turning point. The last time generative AI loomed this large, the breakthroughs were in computer vision, but now the leap forward is in natural language processing (NLP). And it’s not just human language: Generative models can also learn the grammar of software code, molecules, natural images, and a variety of other data types. Some no-code interfaces enable people without coding skills to use visual interfaces and intuitive controls including drag-and-drop to create and modify applications quickly and efficiently while the actual code remains hidden in the background.

While a number of definitions for artificial intelligence have surfaced over the last few decades, John McCarthy proposed one of the most widely-cited: "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."

The applications for this technology are growing every day, and we’re just starting to explore the possibilities. But as the hype around the use of AI tools in business takes off, conversations around ethics become critically important. For more on where IBM stands within the conversation, please read What is AI ethics? and Building trust in AI.

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Types of artificial intelligence: weak AI vs. strong AI

Weak AI—also known as Narrow AI or Artificial Narrow Intelligence (ANI)—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 watsonx™, and self-driving vehicles.

Strong AI is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). AGI, or general AI, is a theoretical form of AI where a machine would have an intelligence equal to humans; it would be self-aware with a consciousness that would have the ability to solve problems, learn, and plan for the future. 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, that doesn't mean AI researchers aren't also exploring its development. In the meantime, the best examples of ASI might be from science fiction, such as HAL, the superhuman and 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 a sub-field of machine learning.

Deep learning is comprised of neural networks. The “deep” in deep learning refers to a neural network comprised of more than three layers—which would be inclusive of the inputs and the output—which can be considered a deep learning algorithm. This is generally represented using the diagram below.

The way in which deep learning and machine learning differ is in how each algorithm learns. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required and enabling the use of larger data sets. You can think of deep learning as "scalable machine learning," as Lex Fridman noted in same MIT lecture above. Classical, or "non-deep," machine learning is more dependent on human intervention to be trained. Human experts determine the hierarchy of features to understand the differences between data inputs, usually requiring more structured data to learn. Enterprise-grade GPUs will help power through the mathematically intensive workloads required for deep learning and machine learning.

"Deep" machine learning can leverage labeled datasets, also known as supervised learning, to inform its algorithm—but it can also conduct unsupervised learning using raw content (text or images, for example)—and it can automatically determine the hierarchy of features which distinguish different categories of data from one another. Unlike machine learning, it doesn't require human intervention to process data, enabling us to scale machine learning in more interesting ways.

 

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The rise of generative models

Generative AI refers to deep-learning models that can take raw data—say, all of Wikipedia or the collected works of Rembrandt—and “learn” to generate statistically probable outputs when prompted. At a high level, generative models encode a simplified representation of their training data and draw from it to create a new work that’s similar, but not identical, to the original data.

Generative models have been used for years in statistics to analyze numerical data. The rise of deep learning, however, made it possible to extend them to images, speech, and other complex data types. Among the first class of AI models to achieve this cross-over feat were variational autoencoders, or VAEs, introduced in 2013. VAEs were the first deep-learning models to be widely used for generating realistic images and speech.

“VAEs opened the floodgates to deep generative modeling by making models easier to scale,” said Akash Srivastava, an expert on generative AI at the MIT-IBM Watson AI Lab. “Much of what we think of today as generative AI started here.”

Early examples of models, including GPT-3, BERT, or DALL-E 2, have shown what’s possible. In the future, models will be trained on a broad set of unlabeled data that can be used for different tasks, with minimal fine-tuning. Systems that execute specific tasks in a single domain are giving way to broad AI systems that learn more generally and work across domains and problems. Foundation models, trained on large, unlabeled datasets and fine-tuned for an array of applications, are driving this shift.

As to the future of AI, when it comes to generative AI, it is predicted that foundation models will dramatically accelerate AI adoption in enterprise. Reducing labeling requirements will make it much easier for businesses to dive in, and the highly accurate, efficient AI-driven automation they enable will mean that far more companies will be able to deploy AI in a wider range of mission-critical situations. For IBM, the hope is that the computing power of foundation models can eventually be brought to every enterprise in a frictionless hybrid-cloud environment.

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Artificial Intelligence 
  • Customer service: Online virtual agents and chatbots are replacing human agents along the customer journey. They answer frequently asked questions (FAQ) around topics, like shipping, or provide personalized advice, cross-selling products or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms. Examples include messaging bots on e-commerce sites with virtual agents, messaging apps, such as Slack and Facebook Messenger, and tasks usually done by virtual assistants and voice assistants. See how Autodesk Inc. used IBM watsonx Assistant to speed up customer response times by 99% with our case study.

  • Computer vision: This AI technology enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and based on those inputs, it can take action. This ability to provide recommendations distinguishes it from image recognition tasks. Powered by convolutional neural networks, computer vision has applications within photo tagging in social media, radiology imaging in healthcare, and self-driving cars within the automotive industry. See how ProMare used IBM Maximo to set a new course for ocean research with our case study.
  • Supply chain: Adaptive robotics act on Internet of Things (IoT) device information, and structured and unstructured data to make autonomous decisions. NLP tools can understand human speech and react to what they are being told. Predictive analytics are applied to demand responsiveness, inventory and network optimization, preventative maintenance and digital manufacturing. Search and pattern recognition algorithms—which are no longer just predictive, but hierarchical—analyze real-time data, helping supply chains to react to machine-generated, augmented intelligence, while providing instant visibility and transparency. See how Hendrickson used IBM Sterling to fuel real-time transactions with our case study.
 

 

History of artificial intelligence: Key dates and names

The idea of "a machine that thinks" dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of artificial intelligence include the following:

  • 1950: Alan Turing publishes Computing Machinery and Intelligence (link resides outside ibm.com)In this paper, Turing—famous for breaking the German ENIGMA code during WWII and 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.
  • 1956: John McCarthy coins the term "artificial intelligence" at the first-ever AI conference at Dartmouth College. (McCarthy would go on to invent the Lisp language.) Later that year, Allen Newell, J.C. Shaw, and Herbert Simon create the Logic Theorist, the first-ever running AI software program.
  • 1967: Frank Rosenblatt builds the Mark 1 Perceptron, the first computer based on a neural network that "learned" though trial and error. Just a year later, Marvin Minsky and Seymour Papert publish a book titled Perceptrons, which becomes both the landmark work on neural networks and, at least for a while, an argument against future neural network research projects.
  • 1980s: Neural networks which use a backpropagation algorithm to train itself become widely used in AI applications.
  • 1995: Stuart Russell and Peter Norvig publish Artificial Intelligence: A Modern Approach (link resides outside ibm.com), which becomes one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiates computer systems on the basis of rationality and thinking vs. acting.
    • Human approach:  Systems that think like humans and
      Systems that act like humans
    • Ideal approach: Systems that think rationally and
      Systems that act rationally

      Alan Turing’s definition would have fallen under the category of “systems that act like humans.” 
       
  • 1997: IBM's Deep Blue beats then world chess champion Garry Kasparov, in a chess match (and rematch).

  • 2004: John McCarthy writes a paper, What Is Artificial Intelligence? (link resides outside ibm.com), and proposes an often-cited definition of AI.
  • 2011: IBM Watson beats champions Ken Jennings and Brad Rutter at Jeopardy!
  • 2015: Baidu's Minwa supercomputer uses a special kind of deep neural network called a convolutional neural network to identify and categorize images with a higher rate of accuracy than the average human.
  • 2016: DeepMind's AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champion Go player, in a five-game match. The victory is significant given the huge number of possible moves as the game progresses (over 14.5 trillion after just four moves!). Later, Google purchased DeepMind for a reported USD 400 million.
  • 2023: A rise in large language models, or LLMs, such as ChatGPT, create an
    enormous change in performance of AI and its potential to drive enterprise value. With these new generative AI practices, deep-learning models can be pre-trained on vast amounts of raw, unlabeled data.

 

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