Yearly Archives: 2014

LM101-019 (Rerun): How to Enhance Intelligence with a Robotic Body (Embodied Cognition)

Robot with human head playing racquetball and human with robot body.

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Episode Summary: Embodied cognition emphasizes the design of complex artificially intelligent systems may be both vastly simplified and vastly enhanced if we view the robotic bodies of artificially intelligent systems as important contributors to intelligent behavior. Show Notes: Hello everyone! Welcome to the ninth podcast in the podcast series Learning Machines 101. In this series of podcasts my… Read More »

LM101-018 (Rerun): Can computers think? A mathematician’s response (Super Turing Computation)

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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 brains and other non-standard computing machines. (NOTE: This is a rerun of Episode… Read More »

LM101-017 (rerun): How to Decide if a Machine is Artificially Intelligent (The Turing Test)

Robot and human participating in the Turing Test.

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Episode Summary: In this rerun of episode 5 we discuss the Turing Test for Artificial Intelligence which is designed to determine if the behavior of a computer is indistinguishable from the behavior of a thinking human being. The chatbot A.L.I.C.E. (Artificial Linguistic Internet Computer Entity) is interviewed and basic concepts of AIML (Artificial Intelligence Markup Language) are introduced. Show… Read More »

LM101-016: How to Analyze and Design Learning Rules using Gradient Descent Methods

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Episode Summary: In this episode we introduce the concept of gradient descent which is the fundamental principle underlying learning in the majority of machine learning algorithms. Show Notes: Hello everyone! Welcome to the sixteenth podcast in the podcast series Learning Machines 101. In this series of podcasts my goal is to discuss important concepts of artificial intelligence and… Read More »

LM101-015: How to Build a Machine that Can Learn Anything (The Perceptron)

Multilayer feedforward neural network for binary classification.

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Episode Summary: In this episode we describe how to build a machine that can learn any given pattern of inputs and generate any desired pattern of outputs when it is possible to do so! Show Notes: Hello everyone! Welcome to the fifteenth podcast in the podcast series Learning Machines 101. In this series of podcasts my goal is… Read More »

LM101-014: How to Build a Machine that Can Do Anything (Function Approximation)

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Episode Summary: In this episode we describe how to build a machine that can take any given pattern of inputs and generate any desired pattern of outputs! Show Notes: Hello everyone! Welcome to the fourteenth podcast in the podcast series Learning Machines 101. In this series of podcasts my goal is to discuss important concepts of artificial intelligence… Read More »

LM101-013: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)

Android classifying a flower in the Iris Dataset.

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Episode Summary: In this episode we describe how to download and use free linear machine learning software to make predictions for classifying flower species using a famous machine learning data set. Show Notes: Hello everyone! Welcome to the thirteenth podcast in the podcast series Learning Machines 101. In this series of podcasts my goal is to discuss important… Read More »

LM101-012: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)

There is no correct interpretation of the identity of an ambiguous letter.

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Episode Summary: In this episode we discuss the problem of how to evaluate the ability of a learning machine to make generalizations and construct abstractions given the learning machine is provided a finite limited collection of experiences. Show Notes: Hello everyone! Welcome to the twelfth podcast in the podcast series Learning Machines 101. In this series of podcasts… Read More »

LM101-011: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)

How do you compute the probability of rare and unseen events?

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Episode Summary: Today we address a strange yet fundamentally important question. How do you predict the probability of something you have never seen? Or, in other words, how can we accurately estimate the probability of rare events? Show Notes: Hello everyone! Welcome to the eleventh podcast in the podcast series Learning Machines 101. In this series of podcasts… Read More »

LM101-010: How to Learn Statistical Regularities (MAP and maximum likelihood estimation)

Cartoon describing statistical learning with priors.

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Episode Summary: In this podcast episode, we discuss fundamental principles of learning in statistical environments including the design of learning machines that can use prior knowledge to facilitate and guide the learning of statistical regularities. Show Notes: Hello everyone! Welcome to the tenth podcast in the podcast series Learning Machines 101. In this series of podcasts my goal… Read More »

LM101-009: How to Enhance Intelligence with a Robotic Body (Embodied Cognition)

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LM101-008: How to Represent Beliefs using Probability Theory

Robot is using probabilistic inference to make deductions about world.

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Episode Summary: This episode focusses upon how an intelligent system can represent beliefs about its environment using fuzzy measure theory. Probability theory is introduced as a special case of fuzzy measure theory which is consistent with classical laws of logical inference. Show Notes: Hello everyone! Welcome to the eighth podcast in the podcast series Learning Machines 101. In… Read More »

LM101-007: How to Reason About Uncertain Events using Fuzzy Set Theory and Fuzzy Measure Theory

Example of Logical Reasoning in an Environment Characterized by Uncertainty.

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Episode Summary: In real life, there is no certainty. There are always exceptions. In this episode, two methods are discussed for making inferences in uncertain environments. In fuzzy set theory, a smart machine has certain beliefs about imprecisely defined concepts. In fuzzy measure theory, a smart machine has beliefs about precisely defined concepts but some beliefs are stronger… Read More »

LM101-006: How to Interpret Turing Test Results

Searle's Chinese Room argument against the validity of the Turing Test.

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Episode Summary: In this episode, we briefly review the concept of the Turing Test for Artificial Intelligence (AI) which states that if a computer’s behavior is indistinguishable from that of the behavior of a thinking human being, then the computer should be called “artificially intelligent”. Some objections to this definition of artificial intelligence are introduced and discussed. At… Read More »

LM101-005: How to Decide if a Machine is Artificially Intelligent (The Turing Test)

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Episode Summary: This episode we discuss the Turing Test for Artificial Intelligence which is designed to determine if the behavior of a computer is indistinguishable from the behavior of a thinking human being. The chatbot A.L.I.C.E. (Artificial Linguistic Internet Computer Entity) is interviewed and basic concepts of AIML (Artificial Intelligence Markup Language) are introduced. Show Notes: Hello everyone!… Read More »

LM101-004: Can computers think? A mathematician’s response

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LM101-003: How to Represent Knowledge using Logical Rules

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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, these challenges include: issues of feature representation, having an… Read More »

LM101-002: How to Build a Machine that Learns to Play Checkers

Would anyone like to play a game of checkers?

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Episode Summary: In this episode, we explain how to build a machine that learns to play checkers. The solution to this problem involves several key ideas which are fundamental to building systems which are artificially intelligent. Show Notes: Hello everyone! Welcome to the second podcast in the podcast series Learning Machines 101. In this series of podcasts my… Read More »

LM101-001: Welcome to the Big Artificial Intelligence Magic Show!

Artificial Intelligence is like a magic show.

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LM101-001: Welcome to the Big Artificial Intelligence Magic Show! Episode Summary: This episode discusses the similarities between designing an android that can command a starship and designing an android that can play a game of checkers. In addition, the mystery underlying both artificial intelligence and biological intelligence is discussed. Show Notes: Artificial Intelligence (AI) is a field of… Read More »