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Pedro Domingos

16 quotesComputer Scientist · Male

Pedro Domingos is Professor at University of Washington. He is a researcher in machine learning and known for markov logic network enabling uncertain inference. 2Biography Domingos received an undergraduate degree and M.S. from Instituto Superior Técnico (IST). And then at University of California, Irvine, he received an M.S. and Ph.D. After spending two years as an assistant professor at IST, he joined University of Washington in 1999 and now is a professor. 2Research 3Markov logic network 2Awards and honors 2014. ACM SIGKDD Innovation Award. for his foundational research in data stream analysis, cost-sensitive classification, adversarial learning, and Markov logic networks, as well as applications in viral marketing and information integration. 2010. AAAI Fellow. For significant contributions to the field of machine learning and to the unification of first-order logic and probabilit

All Machine Learning (9)Artificial intelligence (3)Dumb But Right Enough (3)Computer Science (2)Data Science (2)Future Predictions (2)Metadata is the Answer (2)Three Is The Number (1)Overfitting (1)Robots Will Take Care of Us? (1)Bias (1)If You Can't Do It Right (Do It Wrong) (1)How to Teach A Naïve Bayes Classifier (1)Go Basic (1)Multiplicity of Learners = More Consciousness (1)

From “A Few Useful Things to Know about Machine Learning”

Teams from all over the world competed to build the best video recommender system for Netflix. As the competition progressed, teams found that they obtained the best results by combining their learners with other teams’, and merged into larger and larger teams. The winner and runner up were both…

Multiplicity of Learners = More Consciousness Learning Stacks Netflix

Learners can be divided into two major types: those whose representation has a fixed size, like linear classifiers, and those whose representation can grow with the data, like decision trees.

Machine Learning Types of Learners

As a rule of thumb, a dumb algorithm with lots and lots of data beats a clever one with modest amounts of it.

Metadata Machine Learning Data Science Metadata is the Answer Dumb But Right Enough

Every learner must embody some knowledge or assumptions beyond the data it’s given in order to generalize beyond it.

Machine Learning Learning

From “The Master Algorithm”

Everything is connected, but not directly.

Connectionism Relationalism Conditional Dependence

Learning a Naïve Bayes classifier is just a matter of counting how many times each attribute co-occurs with each class

Naïve Bayes How to Teach A Naïve Bayes Classifier

You don’t need to understand the details of the message; it’s enough to get the gist of it by seeing which words it contains.

Dumb But Right Enough Reductionism Reading When Simple Works Defeating The Complexity Monster

Someday there’ll be a robot in every house, doing the dishes, making the beds, even looking after the children while the parents work.

Robots Future Predictions Artificial intelligence Computer Science Robots Will Take Care of Us?

Machine learning will bring about not just a new era of civilization, but a new stage in the evolution of life on Earth.

Machine Learning Evolution of Civilization Artificial intelligence Brand New World (Of Learning Machines)Future Predictions

Machine learning takes many different forms and goes by many different names: pattern recognition, statistical modeling, data mining, knowledge discovery, predictive analytics, data science, adaptive systems, self-organizing systems, and more.

Machine Learning Artificial intelligence Computer Science Knowledge Bases Statistical Modeling

In general, the more rules and facts we start out with, the more opportunities we have to induce new rules using ‘inverse deduction.’ And the more rules we induce, the more rules we can induce. It’s a virtuous circle of knowledge creation, limited only by overfitting risk and computational cost.

Metadata is the Answer Induction Machine Learning

If you try to come up with a set of rules that makes an exception, you’ll probably wind up with a worse answer than if you’d just ignored it. Learning a set of rules that gets [hyper-contextual instances] right is actually counterproductive: you’re better off ‘misclassifying’ it.

Smart Enough Data Science Machine Learning Rule-Based Learning If You Can't Do It Right (Do It Wrong)

It’s even been said that data mining means torturing the data until it confesses.

Pattern Recognition Data Hallucination Data Mining Statistical Subjectivity Bias

Overfitting happens when you have too many hypotheses and not enough data to tell them apart.

Machine Learning Overfitting

In ordinary life, bias is a pejorative word: preconceived notions are bad. But in machine learning, preconceived notions are indispensable.

Stupid Is Better Than Smart Machine Learning Go Basic

Every algorithm, no matter how complex, can be reduced to just these three operations: AND, OR, and NOT.

Three Is The Number

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