Nick Polson – författare

När en teknologi blir tillräckligt avancerad går den inte att skilja från magi, konstaterade en gång författaren Arthur C Clarke i ”Clarkes tredje lag”. Och när det gäller AI – artificiell intelligens – kan det onekligen vara lätt att börja tänka på magi. Svart magi inte minst, som när det gäller den straffrättsliga algoritmen COMPAS, som på egen hand utvecklade rasfördomar, eller de apokalyptiska farhågorna om att maskinerna kommer att ta över kontrollen och göra människor överflödiga. AIQ. Hur artificiell intelligens fungerar rymmer emellertid inga apokalyptiska visioner och tillskriver heller inte algoritmerna någon magisk förmåga. Datavetarna Nick Polson och James Scott vill begripliggöra och avmystifiera AI och berätta en delvis annorlunda historia än den vanliga – och inte minst sätta in AI i ett historiskt sammanhang. Boken handlar förstås om teknologi, men i lika hög grad om idéer och om människorna och problemen bakom idéerna. Även om AI innebär något nytt i mänsklighetens historia, bygger den på framsteg inom matematik och statistik under tidigare epoker. AIQ rymmer många överraskande exempel på sådana framsteg. Författarna reder ut snåriga samband och har ett tydligt pedagogiskt mål: att läsaren ska förstå vad AI är, hur det uppstått, hur det fungerar och vad det betyder för oss idag. En sådan grundläggande kunskap om AI har alla glädje av. Nick Polson är professor i ekonometri och statistik vid Chicago Booth School of Business och forskar om djupinlärning och maskinintelligens.James Scott är biträdande professor i statistik vid University of Texas i Austin och har som statistiker och datavetare verkat som konsult åt företag inom en mängd branscher.

From Alan Turing breaking the Enigma code to ChatGPT reshaping how we work and create, artificial intelligence has always been powered by a single unifying principle: learning from data under uncertainty. This book reveals the mathematical machinery behind modern AI, weaving together Bayesian probability, statistical learning, and deep neural networks into a coherent intellectual framework. Whether you seek to understand why large language models hallucinate, how recommendation systems predict your preferences, or what makes reinforcement learning agents master complex games, this book equips you with both the theoretical foundations and practical intuitions that define the modern AI playbook.Key Features:Builds Bayesian reasoning from first principles through historical examples like submarine search and WWII code-breakingBridges classical statistics and deep learning, connecting linear regression to transformersCovers the complete AI stack: probability, decision theory, Gaussian processes, neural networks, CNNs, NLP, and LLMsEmphasizes uncertainty quantification—building systems that know what they don't knowPractical applications across finance, healthcare, operations, and autonomous systemsEmerging topics: AI agents, RLHF, and retrieval-augmented generationWritten for graduate students, data scientists, and quantitatively-minded practitioners, this book assumes comfort with calculus and basic probability while building sophisticated intuitions progressively. Business analysts will appreciate the decision-theoretic framing; engineers will value the architectural insights; researchers will find rigorous foundations for further study. Whether used as a course textbook, professional reference, or intellectual companion for understanding the AI revolution transforming every industry, this book delivers the rare combination of mathematical depth and accessible exposition that makes complex ideas genuinely understandable.

From Alan Turing breaking the Enigma code to ChatGPT reshaping how we work and create, artificial intelligence has always been powered by a single unifying principle: learning from data under uncertainty. This book reveals the mathematical machinery behind modern AI, weaving together Bayesian probability, statistical learning, and deep neural networks into a coherent intellectual framework. Whether you seek to understand why large language models hallucinate, how recommendation systems predict your preferences, or what makes reinforcement learning agents master complex games, this book equips you with both the theoretical foundations and practical intuitions that define the modern AI playbook.Key Features:Builds Bayesian reasoning from first principles through historical examples like submarine search and WWII code-breakingBridges classical statistics and deep learning, connecting linear regression to transformersCovers the complete AI stack: probability, decision theory, Gaussian processes, neural networks, CNNs, NLP, and LLMsEmphasizes uncertainty quantification—building systems that know what they don't knowPractical applications across finance, healthcare, operations, and autonomous systemsEmerging topics: AI agents, RLHF, and retrieval-augmented generationWritten for graduate students, data scientists, and quantitatively-minded practitioners, this book assumes comfort with calculus and basic probability while building sophisticated intuitions progressively. Business analysts will appreciate the decision-theoretic framing; engineers will value the architectural insights; researchers will find rigorous foundations for further study. Whether used as a course textbook, professional reference, or intellectual companion for understanding the AI revolution transforming every industry, this book delivers the rare combination of mathematical depth and accessible exposition that makes complex ideas genuinely understandable.

____________________What is AIQ? How does it work? Most importantly, how can it help us? Two leading data scientists offer an up-close and user-friendly look at artificial intelligence and how to harness its power for a better world. 'A positive and entertaining look at the great potential unlocked by marrying human creativity with powerful machines.' Steven D. Levitt, co-author of Freakonomics____________________Dozens of times per day, we all interact with intelligent machines that are constantly learning from the wealth of data now available to them. These machines, from smart phones to talking robots to self-driving cars, are remaking the world in the twenty first century in the same way that the Industrial Revolution remade the world in the nineteenth.AIQ is based on a simple premise: if you want to understand the modern world, then you have to know a little bit of the mathematical language spoken by intelligent machines. AIQ will teach you that language but in an unconventional way, anchored in stories rather than equations.