Yearly Archives: 2018

LM101-075: Can computers think? A Mathematician’s Response using a Turing Machine Argument (remix)

LM101-075: Can computers think? A Mathematician’s Response using a Turing Machine Argument (remix) Episode Summary: In this episode, we explore the question of what can computers do as well as what computers can’t do using the Turing Machine argument. Specifically, we discuss the computational limits of computers and raise the question of whether such limits pertain to biological… Read More »

LM101-074: How to Represent Knowledge using Logical Rules (remix)

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LM101-074: How to Represent Knowledge using Logical Rules (remix) Episode Summary: In this episode we will learn how to use “rules” to represent knowledge. We discuss how this works in practice and we explain how these ideas are implemented in a special architecture called the production system. The challenges of representing knowledge using rules are also discussed. Specifically,… Read More »

LM101-073: How to Build a Machine that Learns Checkers (remix)

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LM101-073: How to Build a Machine that Learns Checkers (remix) Episode Summary: This is a remix of the original second episode Learning Machines 101 which describes in a little more detail how the computer program that Arthur Samuel developed in 1959 learned to play checkers by itself without human intervention using a mixture of classical artificial intelligence search… Read More »

LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (LM101-001+LM101-002 remix)

Robot holding a magic wand, hat, and rabbit!

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LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (LM101-001+LM101-002 remix) Episode Summary: This podcast is basically a remix of the first and second episodes of Learning Machines 101 and is intended to serve as the new introduction to the Learning Machines 101 podcast series. The search for common organizing principles which could support the foundations of machine… Read More »

LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets

Self-driving car doesn't see the difference between hitting a baby carriage or hitting a garbage can because it does not have common sense.

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LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets Episode Summary: 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… Read More »

LM101-070: How to Identify Facial Emotion Expressions Using Stochastic Neighborhood Embedding

Robot looking at a facial image from a security camera.

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LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding Episode Summary: This 70th episode of Learning Machines 101 we discuss how to identify facial emotion expressions in images using an advanced clustering technique called Stochastic Neighborhood Embedding. We discuss the concept of recognizing facial emotions in images including applications to problems such as: improving… Read More »