Sublime
An inspiration engine for ideas
has its own master algorithm, a general-purpose learner that you can in principle use to discover knowledge from data in any domain. The symbolists’ master algorithm is inverse deduction, the connectionists’ is backpropagation, the evolutionaries’ is genetic programming, the Bayesians’ is Bayesian inference, and the analogizers’ is the support
... See morePedro Domingos • The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
A base de dados sobre os clientes aliados a modelos de previsão e segmentação de mercado, integrados em sistemas de suporte à decisão, possibilita à empresa criar uma base de conhecimento e diferenças, que se traduz em vantagem competitiva. O database marketing possui, portanto, dois movimentos característicos: • Para dentro (qualitativo e
... See moreLuiz Claudio Zenone • CRM (Customer Relationship Management): Marketing de Relacionamento, Fidelização de Clientes e Pós-Venda (Portuguese Edition)
When the algorithms now in the lab make it to the front lines, Bill Gates’s remark that a breakthrough in machine learning would be worth ten Microsofts will seem conservative. And if the ideas that really put a glimmer in researchers’ eyes bear fruit, machine learning will bring about not just a new era of civilization, but a new stage in the
... See morePedro Domingos • The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
he posted on the Stanford site in 1995, he talked about “a new project” to generate personalized movie ratings. “The way it works is as follows,” he wrote. “You rate the movies you have seen. Then the system finds other users with similar tastes to extrapolate how much you like other movies.”
Steven Levy • In The Plex: How Google Thinks, Works, and Shapes Our Lives
“There’s no data like more data,” Mercer told a colleague, an expression that became the firm’s hokey mantra.
Gregory Zuckerman • The Man Who Solved the Market
This “blessing of nonuniformity,” whereby data is not spread uniformly in (hyper) space, is often what saves the day.
Pedro Domingos • The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

Bien entendu, Amazon ne réussira pas tout le temps. C’est impossible. Les algorithmes répéteront donc des erreurs à cause de données insuffisantes, de programmation défaillante, d’objectifs confusément définis et de la nature chaotique de la vie(10). Mais Amazon n’aura pas besoin d’être parfait. Il lui suffira de faire mieux, en moyenne, que nous
... See more