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Thinking AI: How Artificial Intelligence Emulates Human Understanding
Can a computer program think like a human?
“Can machines think?” Ever since Alan Turing posed this question in an influential 1950 paper, it has been central to research in artificial intelligence. More than seventy-five years after Turing’s paper, we grapple with it every time we wonder if Watson was actually smarter than Jeopardy! champions, or if ChatGPT really knows what it’s talking about. In Thinking AI, computer scientist John MacCormick explores Turing’s question from a perspective informed by a detailed understanding of the way modern AI systems work. MacCormick explains, in accessible fashion, the ideas behind the two main pillars of the twenty-first century AI deep neural networks and reinforcement learning.
MacCormick offers a tour of the most famous AI systems, including AlexNet and VGG16, deep neural networks for object recognition that led to a Nobel prize; DeepMind’s AlphaGo, which shocked AI researchers with its superhuman performance in the game of Go; and OpenAI’s ChatGPT, which stunned the world with its natural language capabilities. He describes how each system works, and points to parallels with human brain processes. Both human minds and computer programs, MacCormick explains, can induce intelligence through emergence: the capability for new phenomena to emerge from the interactions of many small, simple components. Does this mean that a computer program can think like a human? In many ways, MacCormick argues, the answer is yes. In Thinking AI, he reveals a new landscape of emergent intelligence—a world in which computer programs can emulate many or all aspects of human thinking but humanity retains its meaning and purpose.
“Can machines think?” Ever since Alan Turing posed this question in an influential 1950 paper, it has been central to research in artificial intelligence. More than seventy-five years after Turing’s paper, we grapple with it every time we wonder if Watson was actually smarter than Jeopardy! champions, or if ChatGPT really knows what it’s talking about. In Thinking AI, computer scientist John MacCormick explores Turing’s question from a perspective informed by a detailed understanding of the way modern AI systems work. MacCormick explains, in accessible fashion, the ideas behind the two main pillars of the twenty-first century AI deep neural networks and reinforcement learning.
MacCormick offers a tour of the most famous AI systems, including AlexNet and VGG16, deep neural networks for object recognition that led to a Nobel prize; DeepMind’s AlphaGo, which shocked AI researchers with its superhuman performance in the game of Go; and OpenAI’s ChatGPT, which stunned the world with its natural language capabilities. He describes how each system works, and points to parallels with human brain processes. Both human minds and computer programs, MacCormick explains, can induce intelligence through emergence: the capability for new phenomena to emerge from the interactions of many small, simple components. Does this mean that a computer program can think like a human? In many ways, MacCormick argues, the answer is yes. In Thinking AI, he reveals a new landscape of emergent intelligence—a world in which computer programs can emulate many or all aspects of human thinking but humanity retains its meaning and purpose.
296 pages, Hardcover
Published April 21, 2026
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Displaying 1 - 3 of 3 reviews
April 28, 2026
John MacCormick geht in Thinking AI der grundlegenden Frage nach, ob Maschinen tatsächlich denken können – und in welchem Maß moderne KI menschliches Verstehen zu imitieren vermag. Das Buch bietet eine gut zugängliche Einführung in zentrale Konzepte wie tiefe neuronale Netze und bestärkendes Lernen, die den aktuellen KI-Boom tragen.
Dabei zieht MacCormick immer wieder Parallelen zu menschlichen Hirnprozessen und zeigt, wie Intelligenz als emergentes Phänomen aus komplexen Systemen hervorgehen kann. In Kapitel 2: Der Turing-Test, damals und heute zeichnet er die Entwicklung dieses klassischen Prüfsteins maschineller Intelligenz nach, während Kapitel 10: Generative KI: Beispiellose Skalierung, überraschende Einfachheit die Funktionsweise moderner Systeme wie ChatGPT verständlich erläutert.
MacCormicks zentrale These bleibt dabei ausgewogen: Computerprogramme können zahlreiche Facetten des Denkens überzeugend simulieren, doch die Frage nach Bewusstsein, Bedeutung und menschlicher Einzigartigkeit bleibt damit keineswegs erledigt.
MacCormick gelingt das seltene Kunststück, den KI-Hype auf seine mathematischen Grundlagen zurückzuführen, ohne die philosophische Tiefe der Frage aus dem Blick zu verlieren, ob aus bloßer Statistik jemals echtes Verstehen hervorgehen kann. Er macht deutlich, dass wir zwar Maschinen entwickelt haben, die den Turing-Test scheinbar mühelos bestehen, damit jedoch weniger die Rätsel des Bewusstseins gelöst als vielmehr die Musterhaftigkeit unserer eigenen Sprache offengelegt haben.
Ein kurzer Impuls dazu:
Es ist die Ernüchterung nach dem Staunen: MacCormick zeigt, dass Systeme wie ChatGPT keine denkenden Subjekte sind, sondern eher wie gewaltige Spiegel funktionieren, die kollektive Sprach- und Wissensbestände in neuer Form anordnen. Die „überraschende Einfachheit“, die er im zehnten Kapitel beschreibt, wirkt dabei wie ein heilsamer Dämpfer für menschliche Selbstgewissheit – denn wenn Maschinen uns so überzeugend imitieren können, rückt unweigerlich die Frage in den Vordergrund, wie stark auch unser eigenes Denken von Routinen und Mustern geprägt ist.
Dabei zieht MacCormick immer wieder Parallelen zu menschlichen Hirnprozessen und zeigt, wie Intelligenz als emergentes Phänomen aus komplexen Systemen hervorgehen kann. In Kapitel 2: Der Turing-Test, damals und heute zeichnet er die Entwicklung dieses klassischen Prüfsteins maschineller Intelligenz nach, während Kapitel 10: Generative KI: Beispiellose Skalierung, überraschende Einfachheit die Funktionsweise moderner Systeme wie ChatGPT verständlich erläutert.
MacCormicks zentrale These bleibt dabei ausgewogen: Computerprogramme können zahlreiche Facetten des Denkens überzeugend simulieren, doch die Frage nach Bewusstsein, Bedeutung und menschlicher Einzigartigkeit bleibt damit keineswegs erledigt.
MacCormick gelingt das seltene Kunststück, den KI-Hype auf seine mathematischen Grundlagen zurückzuführen, ohne die philosophische Tiefe der Frage aus dem Blick zu verlieren, ob aus bloßer Statistik jemals echtes Verstehen hervorgehen kann. Er macht deutlich, dass wir zwar Maschinen entwickelt haben, die den Turing-Test scheinbar mühelos bestehen, damit jedoch weniger die Rätsel des Bewusstseins gelöst als vielmehr die Musterhaftigkeit unserer eigenen Sprache offengelegt haben.
Ein kurzer Impuls dazu:
Es ist die Ernüchterung nach dem Staunen: MacCormick zeigt, dass Systeme wie ChatGPT keine denkenden Subjekte sind, sondern eher wie gewaltige Spiegel funktionieren, die kollektive Sprach- und Wissensbestände in neuer Form anordnen. Die „überraschende Einfachheit“, die er im zehnten Kapitel beschreibt, wirkt dabei wie ein heilsamer Dämpfer für menschliche Selbstgewissheit – denn wenn Maschinen uns so überzeugend imitieren können, rückt unweigerlich die Frage in den Vordergrund, wie stark auch unser eigenes Denken von Routinen und Mustern geprägt ist.
May 10, 2026
I really enjoyed reading Thinking AI: How Artificial Intelligence Emulates Human Understanding by John MacCormick. The book does an excellent job of explaining artificial intelligence in a way that feels clear, engaging, and easy to follow, even for readers who are not from a technical background.
What I appreciated most was how the author connects AI concepts with human thinking and everyday experiences. Instead of making the subject feel intimidating, the book encourages curiosity and thoughtful reflection about how machines learn, reason, and interact with the world around us. The explanations are practical, well-structured, and filled with interesting examples that keep the reader interested throughout.
The writing style is smooth and approachable, making complex ideas feel surprisingly accessible. I also liked that the book maintains a balanced view of AI—highlighting both its exciting possibilities and its current limitations. It feels informative without being overly academic.
Overall, this is a smart, well-written, and thought-provoking read for anyone interested in understanding artificial intelligence and its growing role in our lives. Highly recommended for curious readers, students, and technology enthusiasts alike.
What I appreciated most was how the author connects AI concepts with human thinking and everyday experiences. Instead of making the subject feel intimidating, the book encourages curiosity and thoughtful reflection about how machines learn, reason, and interact with the world around us. The explanations are practical, well-structured, and filled with interesting examples that keep the reader interested throughout.
The writing style is smooth and approachable, making complex ideas feel surprisingly accessible. I also liked that the book maintains a balanced view of AI—highlighting both its exciting possibilities and its current limitations. It feels informative without being overly academic.
Overall, this is a smart, well-written, and thought-provoking read for anyone interested in understanding artificial intelligence and its growing role in our lives. Highly recommended for curious readers, students, and technology enthusiasts alike.
May 18, 2026
I’ve been thinking about this book since I finished it, which seems like the best review you can give. I think the target audience is probably on the narrow side (non-specialists but people who know some math and CS) but his reframing of the question from “Can machines think like people?” To “In what ways can machines emulate human thought?” Was a great mental shift and his ponderings on this were fascinating.
Displaying 1 - 3 of 3 reviews