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Data Skeptic Podcast

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The Data Skeptic Podcast features conversations with researchers and other professionals active in applying data science to real world problems. The topics relate to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches. The podcast has an alternating format with even episodes featuring long for conversations, and odd episodes featuring short discussions about topics related to data science which are aimed at listeners who might not be familiar with some of the topics discussed on the show.

The Data Skeptic Podcast features conversations with researchers and other professionals active in applying data science to real world problems. The topics relate to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches. The podcast has an alternating format with even episodes featuring long for conversations, and odd episodes featuring short discussions about topics related to data science which are aimed at listeners who might not be familiar with some of the topics discussed on the show.
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Location:

United States

Description:

The Data Skeptic Podcast features conversations with researchers and other professionals active in applying data science to real world problems. The topics relate to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches. The podcast has an alternating format with even episodes featuring long for conversations, and odd episodes featuring short discussions about topics related to data science which are aimed at listeners who might not be familiar with some of the topics discussed on the show.

Language:

English


Episodes

Eugene Goostman

4/13/2018
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In this episode, Kyle shares his perspective on the chatbot Eugene Goostman which (some claim) "passed" the Turing Test. As a second topic Kyle also does an intro of the Winograd Schema Challenge.

Duration:00:17:19

The Theory of Formal Languages

4/6/2018
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In this episode, Kyle and Linhda discuss the theory of formal languages. Any language can (theoretically) be a formal language. The requirement is that the language can be rigorously described as a set of strings which are considered part of the language. Those strings are any combination of alphabet characters in the given language. Read more

Duration:00:31:25

The Loebner Prize

3/30/2018
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The Loebner Prize is a competition in the spirit of the Turing Test. Participants are welcome to submit conversational agent software to be judged by a panel of humans. This episode includes interviews with Charlie Maloney, a judge in the Loebner Prize, and Bruce Wilcox, a winner of the Loebner Prize.

Duration:00:42:01

Chatbots

3/23/2018
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In this episode, Kyle chats with Vince from iv.ai and Heather Shapiro who works on the Microsoft Bot Framework. We solicit their advice on building a good chatbot both creatively and technically. Our sponsor today is Warby Parker.

Duration:00:33:42

The Master Algorithm

3/16/2018
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In this week’s episode, Kyle Polich interviews Pedro Domingos about his book, The Master Algorithm: How the quest for the ultimate learning machine will remake our world. In the book, Domingos describes what machine learning is doing for humanity, how it works and what it could do in the future. He also hints at the possibility of an ultimate learning algorithm, in which the machine uses it will be able to derive all knowledge — past, present, and future.

Duration:00:52:33

The No Free Lunch Theorems

3/9/2018
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What's the best machine learning algorithm to use? I hear that XGBoost wins most of the Kaggle competitions that aren't won with deep learning. Should I just use XGBoost all the time? That might work out most of the time in practice, but a proof exists which tells us that there cannot be one true algorithm to rule them.

Duration:00:36:16

ML at Sloan Kettering Cancer Center

3/2/2018
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For a long time, physicians have recognized that the tools they have aren't powerful enough to treat complex diseases, like cancer. In addition to data science and models, clinicians also needed actual products — tools that physicians and researchers can draw upon to answer questions they regularly confront, such as “what clinical trials are available for this patient that I'm seeing right now?” In this episode, our host Kyle interviews guests Alex Grigorenko and Iker Huerga from Memorial...

Duration:00:29:49

Optimal Decision Making with POMDPs

2/23/2018
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In a previous episode, we discussed Markov Decision Processes or MDPs, a framework for decision making and planning. This episode explores the generalization Partially Observable MDPs (POMDPs) which are an incredibly general framework that describes most every agent based system.

Duration:00:24:10

AI Decision-Making

2/16/2018
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Making a decision is a complex task. Today's guest Dongho Kim discusses how he and his team at Prowler has been building a platform that will be accessible by way of APIs and a set of pre-made scripts for autonomous decision making based on probabilistic modeling, reinforcement learning, and game theory. The aim is so that an AI system could make decisions just as good as humans can. At the moment Prowler is focusing on multi-agent systems for the video game industry, smart city...

Duration:00:53:09

[MINI] Reinforcement Learning

2/9/2018
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In many real world situations, a person/agent doesn't necessarily know their own objectives or the mechanics of the world they're interacting with. However, if the agent receives rewards which are correlated with the both their actions and the state of the world, then reinforcement learning can be used to discover behaviors that maximize the reward earned.

Duration:00:29:06

Evolutionary Computation

2/2/2018
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In this week’s episode, Kyle is joined by Risto Miikkulainen, a professor of computer science and neuroscience at the University of Texas at Austin. They talk about evolutionary computation, its applications in deep learning, and how it’s inspired by biology. They also discuss some of the things Sentient Technologies is working on in stock and finances, retail, e-commerce and web design, as well as the technology behind it-- evolutionary algorithms.

Duration:00:24:48

[MINI] Markov Decision Processes

1/26/2018
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Formally, an MDP is defined as the tuple containing states, actions, the transition function, and the reward function. This podcast examines each of these and presents them in the context of simple examples. Despite MDPs suffering from the curse of dimensionality, they're a useful formalism and a basic concept we will expand on in future episodes.

Duration:00:20:24

Neuroscience Frontiers

1/19/2018
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Last week on Data Skeptic, we visited the Laboratory of Neuroimaging, or LONI, at USC and learned about their data-driven platform that enables scientists from all over the world to share, transform, store, manage and analyze their data to understand neurological diseases better. We talked about how neuroscientists measure the brain using data from MRI scans, and how that data is processed and analyzed to understand the brain. This week, we'll continue the second half of our two-part...

Duration:00:33:31

Neuroimaging and Big Data

1/12/2018
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Last year, Kyle had a chance to visit the Laboratory of Neuroimaging, or LONI, at USC, and learn about how some researchers are using data science to study the function of the brain. We’re going to be covering some of their work in two episodes on Data Skeptic. In this first part of our two-part episode, we'll talk about the data collection and brain imaging and the LONI pipeline. We'll then continue our coverage in the second episode, where we'll talk more about how researchers can gain...

Duration:00:26:40

The Agent Model of Artificial Intelligence

1/5/2018
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In artificial intelligence, the term 'agent' is used to mean an autonomous, thinking agent with the ability to interact with their environment. An agent could be a person or a piece of software. In either case, we can describe aspects of the agent in a standard framework.

Duration:00:22:23

Artificial Intelligence a Podcast Approach

12/29/2017
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This episode kicks off the next theme on Data Skeptic: artificial intelligence. Kyle discusses what's to come for the show in 2018, why this topic is relevant, and how we intend to cover it.

Duration:00:40:10

Holiday reading 2017

12/22/2017
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We break format from our regular programming today and bring you an excerpt from Max Tegmark's book "Life 3.0". The first chapter is a short story titled "The Tale of the Omega Team". Audio excerpted courtesy of Penguin Random House Audio from LIFE 3.0 by Max Tegmark, narrated by Rob Shapiro. You can find "Life 3.0" at your favorite bookstore and the audio edition via penguinrandomhouseaudio.com. Kyle will be giving a talk at the Monterey County SkeptiCamp 2018.

Duration:00:12:37

Complexity and Cryptography

12/15/2017
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This week, our host Kyle Polich is joined by guest Tim Henderson from Google to talk about the computational complexity foundations of modern cryptography and the complexity issues that underlie the field. A key question that arises during the discussion is whether we should trust the security of modern cryptography.

Duration:00:35:51

Mercedes Benz Machine Learning Research

12/14/2017
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This episode features an interview with Rigel Smiroldo recorded at NIPS 2017 in Long Beach California. We discuss data privacy, machine learning use cases, model deployment, and end-to-end machine learning.

Duration:00:27:04

[MINI] Parallel Algorithms

12/8/2017
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When computers became commodity hardware and storage became incredibly cheap, we entered the era of so-call "big" data. Most definitions of big data will include something about not being able to process all the data on a single machine. Distributed computing is required for such large datasets. Getting an algorithm to run on data spread out over a variety of different machines introduced new challenges for designing large-scale systems. First, there are concerns about the best strategy...

Duration:00:20:35

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