Artificial Intelligence: A Primer
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Artificial Intelligence: A Primer

May 17, 2023 | Report

Artificial intelligence is a widely used, and often misused, term first mentioned in a 1956 college seminar exploring the potential of technology for problem solving. AI’s meaning has continued to evolve, which has contributed to the concept’s apparent fuzziness. Still, AI experts would agree on this basic definition, refined by The Conference Board:

» AI is technology that mimics human thinking by making assumptions, learning, reasoning, problem solving, or predicting with a high degree of autonomy.

While this definition might seem quite straightforward, concepts of AI continue to change in response to new technologies. Viewing AI as a concept whose parameters are perpetually redefined is not the same thing as saying, “AI is a fuzzy term,” or “No one can agree on what AI means.” Those statements are untrue. Most people who work in AI, evaluate AI products, or observe its social and business impacts can clearly articulate what it is and isn’t. Business executives must be able to do so, too.

Think of AI as an umbrella term that covers a variety of leading-edge capabilities. Under today’s AI umbrella, this includes machine learning, deep learning, natural language processing, text analytics, voice recognition, speech recognition, andcomputer vision.

Rather than thinking about AI as a binary (i.e., yes/no) concept, it’s more useful to imagine it as a range, or a spectrum, with assisted intelligence at one end and autonomous intelligence at the other.

The model below can help. It presents shades, or gradations, of “AI-ness.” Importantly, the model also captures what’s not AI: automation; that is, the tools and systems that can perform simple, repetitive tasks independently, although they cannot think or learn in the process. Automation is effectively off the AI grid, therefore, but we include it in our model, set apart from AI, because the two are so often conflated.

 Shades of Artificial Intelligence

A Glossary of AI Terms

  • Machine learning (ML) is arguably the most common kind of AI in use today. Technology that uses ML continuously learns from new data and experience, becoming smarter and more capable over time. It does this autonomously, in contrast to analytics, where humans need to refresh the model as the data change and decide what questions to pursue. Computer vision (technology that can process visual information), voice recognition, and speech recognition use ML to analyze data.
  • Deep learning is an advanced form of ML that identifies patterns and trends in data beyond what humans are capable of—for example, by analyzing a continuous stream of real-time big data and instantaneously adjusting its models as it deems necessary.
  • Natural language processing (NLP) is a tool for extracting the meaning from the words that humans speak or write. Ideally, it factors in the context to capture nuances and subtleties of language. Some companies’ human capital analytics teams, for example, can use sentiment analysis, a form of NLP, to identify employees’ feelings or attitudes from the aggregated, anonymous data the analysis scrapes from online discussions and open-ended survey questions.
  • Generative AI is a type of AI or category of AI algorithm that generates new outputs based on the data it has been trained on. It can perform tasks that typically require human-like intelligence, such as problem-solving, learning, perception, understanding language, and making decisions. It has a wide range of applications, including creating content—text, imagery, audio, and synthetic data—and information that is artificially manufactured rather than generated by real-world events.

Additional Resources

For a deep dive into early AI thought leadership and some current definitions (including ChatGPT’s own) see:

  • Alan Turing:Perhaps best known for his contribution to breaking the Enigma code, the British mathematician and computer scientist delivered a 1947 lecture during which he noted that “what we want is a machine that can learn from experience,” arguably jump-starting the concept of AI.
  • Stanford University Human-Centered Artificial IntelligenceArtificial Intelligence Definitions. “Artificial intelligence (AI), is a term coined…by John McCarthy, Stanford’s first faculty member in AI, who defined it as ‘the science and engineering of making intelligent machines.’ Much research has human program software agents with the knowledge to behave in a particular way, like playing chess, but today, we emphasize agents that can learn, just as human beings navigating our changing world.”
  • Dartmouth UniversityArtificial Intelligence Coined at Dartmouth. “… a small group of scientists gathered for the Dartmouth Summer Research Project on Artificial Intelligence, which was the birth of this field of research…The initial meeting was organized by John McCarthy, then a mathematics professor at the College. In his proposal, he stated that the conference was ‘to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.’”
  • John McCarthy: The computer scientist and cognitive scientist who is one of the founders of AI research structured his 2007 article What Is Artificial Intelligence? as a Q&A directed to lay readers. In his view, AI 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.” For McCarthy, the central question is not so much “what is AI?” as “what is intelligence?”
  • World Economic ForumWhat is Generative AI? An AI Explains. “Everything in the infographic—from illustrations and icons to the text descriptions?—was created using generative AI tools such as Midjourney. Everything that follows in this article was generated using ChatGPT based on specific prompts.”
  • QuantumBlack, AI by McKinseyAn Executive’s Guide to AI. “From machine learning operations and organizational change to ethical considerations and emerging use cases, this is QuantumBlack, AI by McKinsey’s latest thinking on how organizations can most effectively and responsibly use AI to create business value.”
  • US Department of StateArtificial Intelligence. This page lays out the current administration’s views of AI as it pertains to national security and foreign policy. “The term ‘artificial intelligence’ means a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments.” – National Artificial Intelligence Act of 2020 (introduced March 12, 2020; has not to date moved forward through Congress).
  • Organisation for Economic Co-operation and DevelopmentArtificial Intelligence. This site introduces the OECD’s AI principles, policies, classifications, and aims. It also includes an up-to-date list of its AI-centered publications.



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