Presentación del libro en españolinteligencia artificial

JEV AND SYSTEM ONE AI

Name: JEV AND SYSTEM ONE AI
Author: Faramarz Kowsari

Building Fast, Calibrated, Decision-Native Software Beyond Generative LLMs

“JEV AND SYSTEM ONE AI” es un libro de Faramarz Kowsari dentro del área de inteligencia artificial. Esta página de descubrimiento en español organiza la información pública del libro para que sus temas, público, idioma real de publicación y vías oficiales de lectura o compra sean más fáciles de encontrar.

Los metadatos públicos sitúan este título en temas de inteligencia artificial, herramientas modernas de IA o flujos de trabajo basados en prompts.

Idioma de publicación: inglés. Esta es una página de orientación en español. El libro se publica en inglés; el título, la vista previa y las opciones de compra de Google Books corresponden a esa edición.

Ver en Google Books Abrir vista previa gratuita Página original del libro

¿De qué trata este libro?

Los metadatos públicos sitúan este título en temas de inteligencia artificial, herramientas modernas de IA o flujos de trabajo basados en prompts.

Temas destacados

Decision-native AIJev and System One modelsTyped probabilistic decisionsState design for decision modelsSemantic yes/no probabilityClosed decision spaces and choice modelsOrdered scoring and bounded judgmentProbability versus confidenceCalibration and threshold designParallel intelligence and speculative fan-outLearned if-statementsConfidence-gated automationJev and LLM hybrid architecturesModel, tool and agent routing

Descripción pública original del libro

inglés

JEV AND SYSTEM ONE AI examines a different way to place machine intelligence inside production software: not as a conversational destination that must generate prose, but as a fast, typed, probabilistic decision component. Using Jev and TypeSafe AI's System One framing as a case study, the book asks what changes when software needs a route, score, probability, gate, category, or bounded judgment rather than an essay. Its central architectural idea is compositional: deterministic code handles what is exact, decision models handle bounded semantic ambiguity, generative models handle language and open-ended reasoning, and people remain responsible for novelty, accountability, and high-consequence judgment. The book develops this idea from first principles through state design, semantic yes/no decisions, closed choice spaces, ordered scoring, parallel fan-out, calibration, confidence-gated automation, learned if-statements, model/tool/agent routing, guardrails, verification, retrieval, customer-service workflows, production architecture, observability, security, privacy, adversarial state, cost, throughput, governance, and human review. It also distinguishes probability from confidence, explains why type safety removes invalid output shapes without eliminating semantic error, and treats calibration, thresholds, fallback policies, evaluation sets, and operational traces as core engineering artifacts rather than optional afterthoughts. The treatment is deliberately skeptical of hype. The manuscript separates company claims from independently established facts, explicitly notes that Jev's internal architecture and training corpus are undisclosed, and emphasizes that fast or schema-valid decisions can still be wrong, drift over time, or be inappropriate for reasoning-heavy…

¿A quién va dirigido?

El público objetivo se basa en la descripción pública del libro. Para lectores hispanohablantes, conviene tener presente que la edición disponible está publicada en inglés.

For software engineers, AI engineers, ML practitioners, technical founders, product architects, platform teams and engineering leaders who are building production AI systems and want lower-latency, more testable and more controllable alternatives to routing every decision through a generative LLM.

Objetivos de aprendizaje publicados