Data Skeptic

Data Skeptic

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  • Typical duration

    37 min

    per episode

Based on Podcast App listening data

Data Skeptic episodes

  • The Experimental Design of Paranormal Claims

    In this episode of Data Skeptic, Kyle chats with Jerry Schwarz from the Independent Investigations Group (IIG)'s SF Bay Area chapter about testing claims of the paranormal. The IIG is a volunteer-based organization dedicated to investigating paranormal or extraordinary claim from a scientific viewpoint. The group, headquartered at the Center for Inquiry-Los Angeles in Hollywood, offers a $100,000 prize to anyone who can show, under proper observing conditions, evidence of any paranormal, supernatural, or occult power or event.

    CHICAGO Tues, May 15, 6pm. Come to our Data Skeptic meetup.

    CHICAGO Saturday, May 19, 10am. Kyle will be giving a talk at the Chicago AI, Data Science, and Blockchain Conference 2018.

    28 min
  • Winograd Schema Challenge

    Our guest this week, Hector Levesque, joins us to discuss an alternative way to measure a machine's intelligence, called Winograd Schemas Challenge. The challenge was proposed as a possible alternative to the Turing test during the 2011 AAAI Spring Symposium. The challenge involves a small reading comprehension test about common sense knowledge.

    37 min
  • The Imitation Game

    This week on Data Skeptic, we begin with a skit to introduce the topic of this show: The Imitation Game. We open with a scene in the distant future. The year is 2027, and a company called Shamony is announcing their new product, Ada, the most advanced artificial intelligence agent. To prove its superiority, the lead scientist announces that it will use the Turing Test that Alan Turing proposed in 1950. During this we introduce Turing's "objections" outlined in his famous paper, "Computing Machinery and Intelligence."

    Following that, we talk with improv coach Holly Laurent on the art of improvisation and Peter Clark from the Allen Institute for Artificial Intelligence about question and answering algorithms.

    1 hr 1 min
  • Eugene Goostman

    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.

    18 min
  • The Theory of Formal Languages

    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

    24 min
  • The Loebner Prize

    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.

    34 min
  • Chatbots

    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.

    28 min
  • The Master Algorithm

    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.

    47 min
  • The No Free Lunch Theorems

    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.

    28 min
  • ML at Sloan Kettering Cancer Center

    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 Sloan Kettering Cancer Center to talk about how data and technology can be used to prevent, control and ultimately cure cancer.

    39 min

About Data Skeptic

From the publisher's feed

The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying…

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