Data Skeptic

By Kyle Polich

Data Skeptic is your source for a perspective of scientific skepticism on topics in statistics, machine learning, big data, artificial intelligence, and data science. Our weekly podcast and blog bring you stories and tutorials to help understand our data-driven world.

  1. 1.
    Video Anomaly Detection
    24:05
  2. 2.
    Fault Tolerant Distributed Gradient Descent
    36:05
  3. 3.
    Decentralized Information Gathering
    32:56
  4. 4.
    Leaderless Consensus
    27:24
  5. 5.
    Automatic Summarization
    27:56
  6. 6.
    Gerrymandering
    34:08
  7. 7.
    Even Cooperative Chess is Hard
    23:08
  8. 8.
    Consecutive Votes in Paxos
    30:10
  1. 9.
    Visual Illusions Deceiving Neural Networks
    33:42
  2. 10.
    Earthquake Detection with Crowd-sourced Data
    29:26
  3. 11.
    Byzantine Fault Tolerant Consensus
    35:32
  4. 12.
    Alpha Fold
    23:13
  5. 13.
    Arrow's Impossibility Theorem
    26:18
  6. 14.
    Face Mask Sentiment Analysis
    41:10
  7. 15.
    Counting Briberies in Elections
    37:54
  8. 16.
    Sybil Attacks on Federated Learning
    31:31
  9. 17.
    Differential Privacy at the US Census
    29:42
  10. 18.
    Distributed Consensus
    27:43
  11. 19.
    ACID Compliance
    23:46
  12. 20.
    National Popular Vote Interstate Compact
    30:35
  13. 21.
    Defending the p-value
    29:50
  14. 22.
    Retraction Watch
    32:03
  15. 23.
    Crowdsourced Expertise
    27:49
  16. 24.
    The Spread of Misinformation Online
    35:34
  17. 25.
    Consensus Voting
    22:56
  18. 26.
    Voting Mechanisms
    27:27
  19. 27.
    False Consensus
    33:05
  20. 28.
    Fraud Detection in Real Time
    38:23
  21. 29.
    Listener Survey Review
    23:11
  22. 30.
    Human Computer Interaction and Online Privacy
    32:37
  23. 31.
    Authorship Attribution of Lennon McCartney Songs
    30:57
  24. 32.
    GANs Can Be Interpretable
    26:38
  25. 33.
    Sentiment Preserving Fake Reviews
    28:38
  26. 34.
    Interpretability Practitioners
    32:06
  27. 35.
    Facial Recognition Auditing
    47:29
  28. 36.
    Robust Fit to Nature
    38:15
  29. 37.
    Black Boxes Are Not Required
    32:28
  30. 38.
    Robustness to Unforeseen Adversarial Attacks
    21:42
  31. 39.
    Estimating the Size of Language Acquisition
    25:05
  32. 40.
    Interpretable AI in Healthcare
    35:50
  33. 41.
    Understanding Neural Networks
    34:42
  34. 42.
    Self-Explaining AI
    32:02
  35. 43.
    Plastic Bag Bans
    34:50
  36. 44.
    Self Driving Cars and Pedestrians
    30:43
  37. 45.
    Computer Vision is Not Perfect
    26:07
  38. 46.
    Uncertainty Representations
    17:14
  39. 47.
    AlphaGo, COVID-19 Contact Tracing and New Data Set
    33:40
  40. 48.
    Visualizing Uncertainty
    32:52
  41. 49.
    Interpretability Tooling
    42:37
  42. 50.
    Shapley Values
    20:07

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