Episode 1: Intrusive Face Detection, Kaggle Cheaters, AlphaFold, and Becoming an A.I. Researcher

For our inaugural episode of A4N — the "Artificial Neural Network
News Network", a lighthearted podcast covering A.I. advances — we
discuss real-time face recognition by London police, cheating in
the famed Kaggle data-science competition, the landmark AlphaFold
model for predicting protein structure from DNA, and how to
become (or at least hire!) an A.I. researcher.


Below’s a detailed breakdown of the episode’s four segments, with
time stamps for all of the references that we mentioned.


Segment 1 — Global headline news: Intrusive real-time
face recognition


In this segment, we discuss a controversial new approach by the
Met police force in London, which is to use a (low-performing)
facial-recognition system to flag “known criminals” in real-time
in train stations.


Segment host: Vince Petaccio II


Reference news article from The Guardian (1:10)


Citizen App (9:10)


Segment 2 — “Sports”: Kaggle
Cheating


In this segment, we introduce what the Kaggle data-science
competition platform is and how folks (now formerly!) working at
the well-known firm H2O.ai cheated to perform well. How
unsportspersonlike!


Segment host: Andrew Vlahutin


Reference news article from Towards Data Science (14:38)


Segment 3 — Health: AlphaFold


In this segment, we introduce how DNA encodes proteins that do
all of the work in our bodies. We then describe the new AlphaFold
algorithm that crushes all of the existing approaches at
predicting protein structure from DNA-sequence data. In the
benchmark “CASP” competition, AlphaFold correctly predicted the
structure of 58.1% of the proteins while the second-best
algorithm correctly predicted 7.0% of them.


Segment host: Grant Beyleveld


Reference blog post from DeepMind (26:31)


CASP (30:05)


AlphaGo documentary film (37:20)


Segment 4 — Classifieds: How to become
(or hire!) an A.I. Researcher


In this segment, we list ways that you can find hidden-gem A.I.
researchers to hire from within a high-demand field. We also list
approaches for breaking into the field of A.I. if you come from a
non-traditional background, e.g., you don’t already have a PhD in
machine learning or statistics.


Segment host: Jon


How to Hire Smarter than the Market (38:33)


Getting Hired in AI as Self-Taught Researcher (40:39)


Deep Learning book by Goodfellow et al. (41:30)