In this episode, we discuss a study that recruits human researchers to try to predict how computers classify images. We then highlight a number of examples of natural language processing techniques applied to the Mueller Report.

In this episode, we discuss Microsoft's handy phone application for scanning and reporting on our surroundings, as a way of helping vision impaired individuals better interact with the world around them. We then talk about how AI can be useful in detecting exoplanets (or extrasolar planets).

We discuss a survey designed to analyze the extent and root cause of statistical anxiety in the classroom, discussing the methods/limitations of the study. We then talk about yet another crusade against hypothesis testing, this time around the concept of "statistical significance".

In this episode, Susan Wang is joined by guest Natalie Doss to consider the statistical sins committed by Theranos, the former blood testing unicorn. From arbitrary data manipulation to inappropriate data aggregation, we discuss what they did and why these practices were particularly bad. Then, we weigh in on how Theranos could have done worse, making it harder for the public to find out about their faulty tests.

We discuss NVIDIA's AI-generated faces that look incredibly authentic, and relatedly, OpenAI's text generator that is so capable that it has to be kept under wraps. We then assess the study design of a recent research article that considered how health outcomes vary amongst African Americans of different skin tones.

We discuss opportunities for machines and humans in the prediction of protein structures, a necessary task in new drug discovery. Google's DeepMind has taken the prize in the recent iteration of CASP, a protein folding prediction challenge. We also discuss how AI has begun to revolutionize journalism.

538 has provided a free, online personality test that might make more sense than your typical online clickbaity quiz. We talk about why it calls itself the only personality test that isn't junk science. We then discuss the results of a recent study on Spotify data. Does it know too much about us (and you)? We'll let you know. 

On February 11, IBM showcased its Project Debater in a face-off against debate champion Harish Natarajan. We talk about how this machine vs. human competition went. Then, we discuss a Harvard Business Review article citing a survey that discovered companies are not becoming data-oriented quickly enough.

Three topics are featured in this episode: first, statistics about Super Bowl LIII, including what was in the bowls as the game happened; second, a fun activity for teaching confidence intervals; finally, we present some online sources for data.

AI and ML algorithms are growing popular -- but they can actually perpetuate cognitive biases in our daily lives. We discuss the state of the problem and possible solutions. We also present a favorable job outlook for aspiring (or continuing!) data scientists.