In this podcast, we provide some insights into the complexity of common sense. First, we discuss the importance of building common sense into learning machines. Second, we discuss how first-order logic can be used to represent common sense knowledge. Third, we describe a large database of common sense knowledge where the knowledge is represented using first-order logic which is free for researchers in machine learning. We provide a hyperlink to this free database of common sense knowledge. Fourth, we discuss some problems of first-order logic and explain how these problems can be resolved by transforming logical rules into probabilistic rules using Markov Logic Nets. And finally, we have another book review of the book “Markov Logic: An Interface Layer for Artificial Intelligence” by Pedro Domingos and Daniel Lowd which provides further discussion of the issues in this podcast. In this book review, we cover some additional important applications of Markov Logic Nets not covered in detail in this podcast such as: object labeling, social network link analysis, information extraction, and helping support robot navigation. Finally, at the end of the podcast we provide information about a free software program which you can use to build and evaluate your own Markov Logic Net! For more information check out: www.learningmachines101.com
LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
LM101-065: How to Design Gradient Descent Learning Machines (Rerun)
LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun)
LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine
LM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine
LM101-061: What happened at the Reinforcement Learning Tutorial? (RERUN)
LM101-060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms
LM101-059: How to Properly Introduce a Neural Network
LM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis
LM101-057: How to Catch Spammers using Spectral Clustering
LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications
LM101-055: How to Learn Statistical Regularities using MAP and Maximum Likelihood Estimation (Rerun)
LM101-054: How to Build Search Engine and Recommender Systems using Latent Semantic Analysis (RERUN)
LM101-053: How to Enhance Learning Machines with Swarm Intelligence (Particle Swarm Optimization)
LM101-052: How to Use the Kernel Trick to Make Hidden Units Disappear
LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning[Rerun]
LM101-050: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]
LM101-049: How to Experiment with Lunar Lander Software
LM101-048: How to Build a Lunar Lander Autopilot Learning Machine (Rerun)
LM101-047: How Build a Support Vector Machine to Classify Patterns (Rerun)
Create your
podcast in
minutes
It is Free
Insight Story: Tech Trends Unpacked
Zero-Shot
Fast Forward by Tomorrow Unlocked: Tech past, tech future
The Unbelivable Truth - Series 1 - 26 including specials and pilot
A Prairie Home Companion: News from Lake Wobegon