On a long car ride, Linhda and Kyle record a short episode. This discussion is about transfer learning, a technique using in machine learning to leverage training from one domain to have a head start learning in another domain.

Transfer learning has some obvious appealing features. Take the example of an image recognition problem. There are now many widely available models that do general image recognition. Detecting that an image contains a "sofa" is an impressive feat. However, for a furniture company interested in more specific details, this classifier is absurdly general. Should the furniture company build a massive corpus of tagged photos, effectively starting from scratch? Or is there a way they can transfer the learnings from the general task to the specific one.

A general definition of transfer learning in machine learning is the use of taking some or all aspects of a pre-trained model as the basis to begin training a new model which a specific and potentially limited dataset.

Medical imaging is a highly effective tool used by clinicians to diagnose a wide array of diseases and injuries. However, it often requires exceptionally trained specialists such as radiologists to interpret accurately. In this episode of Data Skeptic, our host Kyle Polich is joined by Gabriel Maicas, a PhD candidate at the University of Adelaide, to discuss machine learning systems that can be used by radiologists to improve their accuracy and speed of diagnosis.

Thanks to our sponsor Galvanize

A Kalman Filter is a technique for taking a sequence of observations about an object or variable and determining the most likely current state of that object. In this episode, we discuss it in the context of tracking our lilac crowned amazon parrot Yoshi.

Kalman filters have many applications but the one of particular interest under our current theme of artificial intelligence is to efficiently update one's beliefs in light of new information.

The Kalman filter is based upon the Gaussian distribution. This distribution is described by two parameters:  (the mean) and standard deviation. The procedure for updating these values in light of new information has a closed form. This means that it can be described with straightforward formulae and computed very efficiently.

You may gain a greater appreciation for Kalman filters by considering what would happen if you could not rely on the Gaussian distribution to describe your posterior beliefs. If determining the probability distribution over the variables describing some object cannot be efficiently computed, then by definition, maintaining the most up to date posterior beliefs can be a significant challenge.

Kyle will be giving a talk at Skeptical 2018 in Berkeley, CA on June 10.

There's so much to discuss on the AI side, it's hard to know where to begin. Luckily,  Steve Guggenheimer, Microsoft’s corporate vice president of AI Business, and Carlos Pessoa, a software engineering manager for the company’s Cloud AI Platform, talked to Kyle about announcements related to AI in industry.

Today's interview is with the authors of the textbook Artificial Intelligence and Games.

Thanks to our sponsor The Great Courses.

This week's episode is a short primer on game theory.

For tickets to the free Data Skeptic meetup in Chicago on Tuesday, May 15 at the Mendoza College of Business (224 South Michigan Avenue, Suite 350), click here,

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.

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.

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.

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.