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Series: AI Books · Book deep dive

Rolf Pfeifer and Josh Bongard argue that intelligence emerges through interactions among the brain, the body, and the environment.

Robot experiments show how physical form and sensory-motor coordination can contribute to behavior that otherwise appears to require complex computation.

The authors develop design principles for complete agents, including ecological balance, redundancy, and coordination among loosely coupled processes.

They connect embodiment to development, evolution, collective intelligence, and the emergence of cognition.

Applications to memory, everyday robots, and intelligent organizations explore how these principles extend beyond individual machines.

Source: How the Body Shapes the Way We Think: A New View of Intelligence · Rolf Pfeifer and Josh Bongard

Explore: AI & Computer Science · AI Books

Related episodes: Neuromorphic Intelligence (Yang & Chen) · Bayesian Theories of Cognition (Griffiths, Chater & Tenenbaum)

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Series: Weekly AI Papers · Recent research

Any Time, Any Space tackles the problem of keeping generated video shots consistent across changes of place, viewpoint, and time.

The framework turns a screenplay into shot requests with explicit scene, camera, and event-time references.

Scene memory stores registered static geometry, while event memory retains dynamic observations indexed by scene, event, and time.

Before generating a shot, the system retrieves relevant evidence; afterward, review and state checks decide whether new observations enter memory.

The authors report improvements in cross-shot consistency, spatial revisiting, and event replay across four video generators.

Source: Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation · Weijie Wang, Haomin Zhang, Ye Li et al.

Explore: AI & Computer Science · AI Papers

Related episodes: V-JEPA (LeCun) · VisionHOPE: Visual Backbones as Self-Modifying Learning Systems

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Series: Mathematics & Science

Book: An Advanced Course in Probability and Stochastic Processes by Dirk P. Kroese and Zdravko I. Botev.

Kroese and Botev build a rigorous account of probability from measurable spaces, integration, and random variables.

They compare different forms of convergence and develop the law of large numbers and central limit theorem.

Conditional expectation connects information and independence to Markov chains and other stochastic processes.

Martingales, stopping times, and Brownian motion reveal how random systems evolve over time.

The book brings these ideas together through stochastic integration, Itô calculus, and stochastic differential equations.

Source: Authors’ official book page · Chapman & Hall/CRC · 2023 · ISBN 9781032320465. Read the free author-provided book.

Explore: More Mathematics & Science episodes · Browse all series

Related episodes: First Course in Causal Inference (Peng Ding) · Graph Theory (Diestel)

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Series: AI Books · Book deep dive

Book: Human-in-the-Loop Machine Learning: Active Learning and Annotation for Human-Centered AI by Robert (Munro) Monarch.

Robert Monarch explains how human judgment and machine learning can work together to build better models.

Active learning uses uncertainty and diversity sampling to select examples that most need human feedback.

The book examines how to recruit annotators, write instructions, and measure agreement and annotation quality.

It connects reliable training and evaluation data with transfer learning and other ways to improve labeling workflows.

Annotation interfaces and human-computer interaction bring these methods together in practical machine learning products.

Source: Manning Publications · 2021 · 424 pages · ISBN 9781617296741.

Explore: AI & Computer Science · AI Books · AI Papers

Related episodes: Learning Representations by Back-Propagating Errors (Rumelhart, Hinton & Williams) · Machines That Think Like People (Lake et al.)

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Series: History & Judaism

Book: The Final Solution to the Jewish Question: A Love Story for the Living by Dara Horn.

Dara Horn examines why antisemitism persists and why familiar responses often fail to confront it.

She combines historical research, reporting, and personal experience to trace recurring myths about Jews.

The book questions prevailing approaches to Holocaust education and examines anti-Zionism and American Jewish assimilation.

Horn also considers how anti-Jewish propaganda circulates through contemporary institutions and artificial intelligence.

She argues for a response centered on freedom, responsibility, and the flourishing of living Jewish communities.

Source: Avid Reader Press/Simon & Schuster · September 1, 2026 · 384 pages · ISBN 9781668090169.

Explore: More History & Judaism episodes · Browse all series

Related episodes: Adorno on Antisemitism · Eichmann vs. Arendt

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Series: Weekly AI Papers · Historical classic

Rumelhart, Hinton, and Williams explain how backpropagation trains hidden units to learn useful internal representations.

A forward pass computes the network's outputs; a backward pass uses the chain rule to calculate how each weight contributes to the error.

The weights are adjusted to reduce squared output error, with momentum helping the learning process converge.

Experiments on mirror symmetry and family relationships show hidden units discovering structure that was not supplied as a training target.

The paper extends the method to recurrent networks by unfolding their computation across time.

This 1986 Nature paper also discusses local minima and distinguishes its procedure from a biologically plausible model of learning.

Source: Learning representations by back-propagating errors · David E. Rumelhart, Geoffrey E. Hinton and Ronald J. Williams

Explore: AI & Computer Science · AI Papers

Related episodes: Machines That Think Like People (Lake et al.) · V-JEPA (LeCun)

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Series: Weekly AI Papers · Recent research

VisionHOPE explores how a vision model can change both its memory and its learning rules while processing an image.

Its design connects five memories: content, keys, values, learning rate, and retention.

The paper develops controls that keep these coupled updates stable under the conditions established by its analysis.

Four directional scans adapt the method to the two-dimensional structure of images, with computation scaling linearly in the number of image tokens.

The authors report competitive results on ImageNet classification, COCO object detection, and ADE20K semantic segmentation.

This 2026 preprint connects associative memory and Nested Learning to the design of visual backbones.

Source: VisionHOPE: Visual Backbones as Self-Modifying Learning Systems · Siran Peng, Tianshuo Zhang et al.

Explore: AI & Computer Science · AI Papers

Related episodes: V-JEPA (LeCun) · Machines That Think Like People (Lake et al.)

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Series: Mathematics & Science · AI Books · Book deep dive

A First Course in Causal Inference — Peng Ding

Peng Ding introduces causal inference from a statistical perspective.

The book explains the foundations needed to reason about causal effects rather than associations alone.

Examples and applications draw on biostatistics and econometrics.

Mathematical arguments are accompanied by exercises, data, and practical R code.

The course assumes basic probability, statistical inference, and familiarity with regression.

Publication source

Explore: Mathematics & Science · AI & Computer Science

Related episodes: Graph Theory (Diestel) · Lie Groups, Lie Algebras, and Representations (Brian Hall)

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Series: Mathematics & Science

Graph Theory — Reinhard Diestel

Reinhard Diestel develops the central ideas and proofs of modern graph theory.

The book studies connectivity, matchings, planarity, coloring, and flows.

Further chapters examine extremal, infinite, random, and Ramsey-theoretic questions.

Graph minors and related structures connect the foundations to more advanced methods.

The text supports introductory study while offering a path toward deeper mathematical results.

Publication source

Explore: Mathematics & Science

Related episodes: Lie Groups, Lie Algebras, and Representations (Brian Hall) · First Course in Causal Inference (Peng Ding)

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Series: Language & Linguistics · AI Books · Book deep dive

Syntactic Theory: A Formal Introduction — Ivan A. Sag, Thomas Wasow, and Emily M. Bender

Sag, Wasow, and Bender develop precise grammars whose predictions can be tested.

The book builds an account of central syntactic patterns through successive problems and analyses.

Lexical information and constraints help explain how grammatical structures are licensed.

Syntax is considered alongside meaning and evidence from language processing.

Exercises from multiple languages encourage readers to evaluate competing grammatical hypotheses.

Publication source

Explore: Language & Linguistics · AI & Computer Science

Related episodes: Formal Properties of Grammars (Chomsky) · Implicit Prosody (Van Handel)