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 critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.
Orders of Magnitude
Today’s show in two parts. First, Linhda joins us to review the episodes from Data Skeptic: Pilot Season and give her feedback on each of the topics.
Second, we introduce our new segment “Orders of Magnitude”. It’s a statistical game show in which participants must identify the true statistic hidden in a list of statistics which are off by at least an order of magnitude. Claudia and Vanessa join as our first contestants. Below are the sources of our questions.
https://en.wikipedia.org/wiki/Willis_Tower https://en.wikipedia.org/wiki/Eiffel_Tower https://en.wikipedia.org/wiki/GreatPyramidof_Giza https://en.wikipedia.org/wiki/InternationalSpaceStation Bird Statistics
Birds in the US since 2000 Causes of Bird Mortality Amounts of Data
Our statistics come from this post
They're Coming for Our Jobs
AI has, is, and will continue to facilitate the automation of work done by humans. Sometimes this may be an entire role. Other times it may automate a particular part of their role, scaling their effectiveness. Unless progress in AI inexplicably halts, the tasks done by humans vs. machines will continue to evolve. Today’s episode is a speculative conversation about what the future may hold.
Co-Host of Squaring the Strange Podcast, Caricature Artist, and an Academic Editor, Celestia Ward joins us today! Kyle and Celestia discuss whether or not her jobs as a caricature artist or as an academic editor are under threat from AI automation.
Mentions https://squaringthestrange.wordpress.com/ https://twitter.com/celestiaward The legendary Dr. Jorge Pérez and his work studying unicorns Supernormal stimulus International Society of Caricature Artists Two Heads Studios
Pandemic Machine Learning Pitfalls
Today on the show Derek Driggs, a PhD Student at the University of Cambridge. He comes on to discuss the work Common Pitfalls and Recommendations for Using Machine Learning to Detect and Prognosticate for COVID-19 Using Chest Radiographs and CT Scans.
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Flesch Kincaid Readability Tests
Given a document in English, how can you estimate the ease with which someone will find they can read it? Does it require a college-level of reading comprehension or is it something a much younger student could read and understand?
While these questions are useful to ask, they don't admit a simple answer. One option is to use one of the (essentially identical) two Flesch Kincaid Readability Tests. These are simple calculations which provide you with a rough estimate of the reading ease.
In this episode, Kyle shares his thoughts on this tool and when it could be appropriate to use as part of your feature engineering pipeline towards a machine learning objective.
For empirical validation of these metrics, the plot below compares English language Wikipedia pages with "Simple English" Wikipedia pages. The analysis Kyle describes in this episode yields the intuitively pleasing histogram below. It summarizes the distribution of Flesch reading ease scores for 1000 pages examined from both Wikipedias.
Fairness Aware Outlier Detection
Today on the show we have Shubhranshu Shekar, a Ph. D Student at Carnegie Mellon University, who joins us to talk about his work, FAIROD: Fairness-aware Outlier Detection.
Life May be Rare
Today on the show Dr. Anders Sandburg, Senior Research Fellow at the Future of Humanity Institute at Oxford University, comes on to share his work “The Timing of Evolutionary Transitions Suggest Intelligent Life is Rare.”
“The Timing of Evolutionary Transitions Suggest Intelligent Life is Rare.”by Andrew E Snyder-Beattie, Anders Sandberg, K Eric Drexler, Michael B Bonsall
I also want to leave a five star review to counterbalance the fool who left a one star review because the host has not made a comment about BLM! This is a podcast about machine learning and AI. I’m grateful for this excellent resource and the huge amount of work that obviously goes into it.
Just listened to Kyle speaking with Adrian Martin about CNN and cutting edge advancements in neural nets. Love getting this insiders perspective.
Agree with the comments on the co-hosts dynamic; he lectures, she plays dumb, podcast grates.