Data Skeptic

Latest Episodes:

Eugene Goostman Apr 13, 2018

Show notes

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.

The Theory of Formal Languages Apr 06, 2018

Show notes

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.

The Loebner Prize Mar 30, 2018

Show notes

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.

Chatbots Mar 23, 2018

Show notes

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.

The Master Algorithm Mar 16, 2018

Show notes

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.

The No Free Lunch Theorems Mar 09, 2018

Show notes

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.

ML at Sloan Kettering Cancer Center Mar 02, 2018

Show notes

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.

Optimal Decision Making with POMDPs Feb 23, 2018

Show notes

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.

AI Decision-Making Feb 16, 2018

Show notes

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.

[MINI] Reinforcement Learning Feb 09, 2018

Show notes

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.

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