Machine Learning and Artificial Intelligence: Two Fellow Travelers on the Quest for Intelligent Behavior in Machines - Specialty Grand Challenge

Aug 5, 2026

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Machine Learning (ML) and Artificial Intelligence (AI) are presented as complementary approaches for modeling intelligent behavior, with deep learning (DL) viewed as a specific instance within ML rather than a strict equivalence. The discussion argues that big data has accelerated practical progress by shifting the main challenge from collecting data to turning it into knowledge, decisions, and actions across diverse domains such as medicine, search, climate science, agriculture, robotics, and games. It also clarifies conceptual definitions of AI and ML and highlights why equating AI=ML=DL oversimplifies the field.

LanguageEnglish

CategoryTechnology

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Pages4

Reading Time10 min

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Big Data Is Boosting Intelligent Behavior in Machines

Artificial Intelligence and Machine Learning

SPECIALTY GRAND CHALLENGE published: 19 November 2018 doi: 10.3389/fdata.2018.00006 Edited and reviewed by: Dan Roth, University of Pennsylvania, United States *Correspondence: Kristian Kersting [kersting@cs.tu-darmstadt.de](kersting@cs.tu-darmstadt.de) Specialty section: This article was submitted to Machine Learning andArti􀀀cial Intelligence, a section of the journal Frontiers in Big Data Received: 14 June 2018 Accepted: 24 October 2018 Published: 19 November 2018 Citation: Kersting K (2018) Machine Learning andArti􀀀cial Intelligence: Two Fellow Travelers on the Quest for Intelligent Behavior in Machines. Front. Big Data 1:6. doi: 10.3389/fdata.2018.00006 Machine Learning and Arti􀀀cial Intelligence: Two Fellow Travelers on the Quest for Intelligent Behavior in Machines Kristian Kersting * Machine Learning Lab, CS Department and Centre for Cognitive Science, Darmstadt University of Technology, Darmstadt, Germany Keywords: machine learning, arti􀀀cial intelligence, deep learning, computation, learning methods 1. BIG DATA IS BOOSTING INTELLIGENT BEHAVIOR IN MACHINES Machine learning (ML) and arti􀀂cial intelligence (AI) are becoming dominant problem-solving techniques in many areas of research and industry, not least because of the recent successes of deep learning (DL) . However, the equation AI=ML=DL, as recently suggested in the news, blogs, and media, falls too short. These 􀀂elds share the same fundamental hypotheses: computation is a useful way to model intelligent behavior in machines. What kind of computation and how to program it? This is not the right question. Computation neither rules out search, logical, and probabilistic techniques, nor (deep) (un)supervised and reinforcement learning methods, among others, as computational models do include all of them. They complement each other, and the next breakthrough lies not only in pushing each of them but also in combining them. Big Data is no fad. The world is growing at an exponential rate and so is the size of the data collected across the globe. Data is becoming more meaningful and contextually relevant, breaking new grounds for machine learning (ML), in particular for deep learning (DL) and arti􀀂cial intelligence (AI), moving them out of research labs into production (Jordan and Mitchell, 2015) . The problem has shifted from collecting massive amounts of data to understanding it—turning it into knowledge, conclusions, and actions. Multiple research disciplines, from cognitive sciences to biology, 􀀂nance, physics, and social sciences, as well as many companies believe that data-driven and “intelligent” solutions are necessary to solve many of their key problems. High-throughput genomic and proteomic experiments can be used to enable personalized medicine. Large data sets of search queries can be used to improve information retrieval. Historical climate data can be used to understand global warming and to better predict weather. Large amounts of sensor readings and hyperspectral images of plants can be used to identify drought conditions and to gain insights into when and how stress impacts plant growth and development and in turn how to counterattack the problem of world hunger. Game data can turn pixels into actions within video games, while observational data can help enable robots to understand complex and unstructured environmentsand to learn manipulation skills. However, is AI, ML, and DL really synonymous, as recently suggested in the news, blogs, and media? For example, when AlphaGo (Silver et al., 2016) defeated South Korean Master Lee Se-dol in the board game Go in 2016, the terms AI, ML, and DL were used by the media to describe how AlphaGo won. In addition to this, even Gartner’s list (Panetta, 2017) of top 10 Strategic Trends for 2018 places (narrow) AI at the very top, specifying it as “consisting of highly scoped machine-learning solutions that target a speci􀀂c task.” 2. ARTIFICIAL INTELLIGENCE AND MACHIN

FAQs

Why does the article argue that AI=ML=DL is an oversimplification?

It explains that AI, ML, and DL share foundational ideas about computation but should not be treated as identical. They include different modeling techniques that can complement each other, and progress may come from combining them rather than collapsing them into one equation.

How does big data change the nature of the AI/ML challenge?

The text states that data availability grows exponentially, but the key shift is from collecting massive datasets to understanding them—transforming data into knowledge, conclusions, and actions for real production systems.

What definitions does the article use for AI and ML?

AI is described using McCarthy’s framing as the science and engineering of making intelligent machines and programs. ML is defined as constructing programs that automatically improve with experience, distinguishing it from AI when learning from data is not involved.

Aug 5, 2026

Upload by Eliana