A Technical Introduction to AI/ML for Business Professionals: Understanding AI Techniques with no prior ML background Required
English | October 9, 2026 | ASIN: B0HMHF1CKH | 355 Pages | Epub | 4.37 MB
Every day, business leaders approve multi-million-dollar AI budgets for technology they've been told is essentially magic. It isn't. AI is a scientific computing system governed by statistics, optimization, and strict mathematical rules - and when you treat it like ordinary software, it fails in expensive, predictable ways.No prior machine learning background required.The problem isn't that AI is too complex. It's that nobody explains it honestly.Traditional software has bugs you can patch. Machine learning systems have assumptions - and when the world violates those assumptions, nothing crashes. The model simply keeps producing confident, authoritative, wrong answers.That is how companies end up overpaying for redundant tools, building brittle architectures, and getting blindsided when a system hallucinates in front of a customer.This book is the antidote to the hype.Written for executives and business professionals, it translates the mechanics of machine learning into clear, actionable strategy. It doesn't water down the math - it makes the math legible, grounding abstract concepts in the business realities that actually cost money.Inside, you will learn:• The Overfitting Trap - why 99% training accuracy is a vanity metric, and the real reason enterprise AI projects collapse in production.• The Anatomy of Hallucination - the mathematical reason language models lie with absolute confidence, and the architectural fixes required for high-stakes deployments.• Build, Buy, or Tune - how scaling laws dictate your cloud bill, and why tuning a small open model often beats licensing a frontier system.• The Truth About "AI Agents" - the critical difference between true reinforcement learning, the "personality layer" of RLHF, and the orchestrated, deterministic workflows vendors are actually selling.• The Meaningful Map - how embeddings and cosine similarity power modern search, and why retrieval-augmented generation usually beats fine-tuning on company data.• The Reality Gap of Physical AI - why autonomous vehicles and robotics advance far more slowly than digital AI, and how the "Delusion Box" limits offline learning.From educated guesses to rigorous decisions.By the final chapter, you will have the vocabulary to see straight through a vendor pitch, the frameworks to size a compute budget, and the technical intuition to know why your team's agentic workflow keeps breaking - and what to do about it.About the author: C. Friederich Li is an AI researcher and industry leader who advises companies across finance, biotech, education, and social media. The book opens with the moment a carefully calibrated set of financial models stopped working overnight - and the assumption, buried in every AI system running today, that explains why.Whether you are evaluating a vendor, planning a deployment, or simply tired of nodding along in meetings, start reading and master the mechanics of enterprise AI.
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