Understanding Artificial Intelligence: Of Minds and Machines

by Patrick Grim, The Great Courses

Long before we were immersed in artificial intelligence, we met it in the movies—in HAL, R2D2, and the Terminator. Obsequious robots, homicidal mainframes, and uncanny virtual companions prepared us for a future that has now arrived. Today, AI shapes daily life through chatbots like ChatGPT, autonomous systems that navigate our streets, and financial algorithms that track our spending. With AI everywhere, we need to understand how these systems work, what their limits are, and where they may take us next.

In Understanding Artificial Of Minds and Machines, philosopher Patrick Grim traces the story of AI from ancient legends to the neural networks behind today’s breakthroughs. You see how modern systems became so powerful so quickly.

You also explore the very meaning of “intelligence,” discovering how modern AI succeeds by drawing on three vast datasets that let systems learn from examples; deep-learning architectures loosely modeled on the brain’s layers; and feedback loops that help models refine their behavior. Together, these give AI the flexibility that lets it mimic intelligence.

But there are downsides that the course brings these into sharp black-box opacity, where systems offer answers with no explanation; deepfakes that blur the boundary between real and fabricated; and the much-debated “Singularity,” the point at which machines might one day outpace human intelligence.

Knowing how AI works is the best strategy for navigating what comes next. If AI follows the pattern of past technological revolutions, it may eventually slow and plateau. Or future advances, such as quantum computing, may push it far beyond where it stands today. As Jean-Jacques Rousseau warned over 250 years ago, it’s crucial “to foresee that some things cannot be foreseen.”

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Published January 23, 2026

About the author

Dr. Patrick Grim is Distinguished Teaching Professor of Philosophy at the State University of New York at Stony Brook.

He graduated with highest honors in anthropology and philosophy from the University of California, Santa Cruz. He was named a Fulbright Fellow to the University of St. Andrews, Scotland, from which he earned his B.Phil. He earned his Ph.D. from Boston University.

Professor Grim is the recipient of several honors and awards. In addition to being named SUNY Distinguished Teaching Professor, Dr. Grim has been awarded the President and Chancellor’s awards for excellence in teaching and was elected to the Academy of Teachers and Scholars. The Weinberg Distinguished Visiting Professor at the University of Michigan in 2006, Professor Grim has also held visiting fellowships at the Center for Complex Systems at Michigan and at the Center for Philosophy of Science at the University of Pittsburgh.

Professor Grim, author of The Incomplete Universe: Totality, Knowledge, and Truth; coauthor of The Philosophical Computer: Exploratory Essays in Philosophical Computer Modeling; and editor of the forthcoming Mind and Consciousness: 5 Questions, is widely published in scholarly journals. He is the founder and coeditor of 25 volumes of The Philosopher’s Annual, an anthology of the best articles published in philosophy each year.

Reviews

[Irena Pasvinter · Rating 4 out of 5]

Somehow I naively expected the lecturer's background would be a little bit more technical, while in fact Patrick Grim's credentials are in anthropology and philosophy. Mea colpa -- I didn't really bother to check as this course was free for me with my audible.it monthly subscription. The philosophical leaning of these 12 half-hour lectures became obvious soon after I began listening. That was when I discovered I already had a few courses on philosophy by Patrick Grim in my TBR list.

All in all, this short course is a good introduction into the topic of AI, especially for those who have no technical background whatsoever.

I don't think the history of AI equals the history of computing and should be told starting from Lord Byron, Ada Lovelace (his daughter) and Charles Babbage, but well, at least Patrick Grim knows how to tell a good story.


Charles Babbage case at the Science Museum, London. (Image credit: Marcin Wichary from San Francisco, U.S.A., CC BY 2.0 , via Wikimedia Commons).

I also didn't appreciate weird audio enhancements -- for example, the mention of chimpanzees is accompanied with chimpanzee noises in the background. There are many more similar "enhancements", which I found annoyingly distracting.

On the positive side, the mention of singularity in AI was followed by a discussion of different opinions on inevitability of singularity, which I found interesting and thought-provoking.



Portrait of Ada King, Countess of Lovelace (Ada Lovelace). (Image credit: Alfred Edward Chalon, Public domain, via Wikimedia Commons.)

Another of many thought-provoking highlights dealt with creativity. According to Alang Turing, before asking whether machines can “think,” we should clarify what we mean by “thinking.” We could reapply this statement to creativity -- what is creativity and how do we define it? Does human creativity exist in a vacuum? Isn't it connected to the world of human experience and to all the previous human creations the same way Generative AI's creativity is based on the input data used for its training? I personally am not interested in AI generated artistic output -- for me an artistic output devoid of person, of a living being with emotions, doubts, joys, sufferings, has no meaning and interest. Will AI ever be able to become self-aware and feel emotions? More food for thought.


AI's relation to Generative Models subset, Venn diagram. (Image credit: The Original Benny C, CC BY-SA 4.0 , via Wikimedia Commons.)

[Raisa · Rating 5 out of 5]

[Audiobook] Learned a lot about the workings and history of AI from these lectures. Great work

[amber · Rating 4 out of 5]

Interesting exploration on the Philosophy of AI and how the ultimate goal is to replicate ‘human’ and ‘intelligence.’  Intelligence comprised of the ability to learn, reason, understand, and be environmentally flexible.

Brought forth interesting questions:
✰ Is the only way to obtain genuine understanding by way of consciousness?
✰ Can AI be creative, or is it just pattern recognition?
✰ Are humans also just combining inputs? Are we actually creating anything new?
✰ If AI systems are only being quantified and observed based on System 2 thinking (logical and analytical calculations) will they ever be able to truly replicate human intelligence that also utilizes System 1 thinking (automatic and unconscious)?
✰ How will information cascade ultimately affect AI and what will the end result look like?
 
Side notes:
✰ Wish there was a further discussion on the broader scope of ethics of AI, particularly environmental.
✰ Strategy game with unchanging rules: AI system training on logic only > AI trained using a human model > human

[Kevin · Rating 4 out of 5]

This was a good dive into AI; its history, its current state, its future, and its ethics.