Prediction Machines
About this book
Agrawal, Gans and Goldfarb frame machine learning in economic terms as a drop in the cost of prediction. When prediction gets cheap, its complements of judgement, data and action become more valuable. The book works through where prediction fits in a decision, how to identify tasks worth automating, the tradeoff between prediction accuracy and cost, and the strategic and organisational consequences.
Description via Product Digest.
Topics
Related concepts
- AI in project managementWhat a language model genuinely does for a project manager today, what it does not, and the accountability, confidentiality and data questions that come with putting project material into one.
- What generative AI is good and bad atThe capabilities worth building on, the failure modes that are properties of the technology and will outlast any model, and how to tell a suitable problem from an unsuitable one.
- What AI safety meansThe three kinds of risk the field separates, why each of them requires a different remedy, and where the disagreements inside the field actually lie.
- When not to ship an AI featureThe decision that prevents more harm than any control does, and the four situations where the honest answer is that this should not be built.
- AI, machine learning and deep learningArtificial intelligence, machine learning and deep learning are three nested categories, and the difference between them decides what a team can promise about a system.
- Adopting generative AI in a businessMatching a solution type to a business need, the constraints that decide it, the steps to get it into an organisation, and how to measure whether it worked.