Q: How many machine learning specialists does it take to change a light bulb?

A: Just one, but they require a million light bulbs to train properly.

Q: How many machine learning specialists does it take to change a fluorescent light bulb?

A: That wasn't in the training data!

Neural Network are not black boxes. They are a big pile of linear algebra :

image from xkcd

1. If you torture data long enough, it will tell you whatever you want to hear.




2. Statistics shows that statistics cannot be trusted.

Frequentists vs. Bayesians

Transcript:

Did the sun just explode?

(It's night, so we're not sure)

[[Two statisticians stand alongside an adorable little computer that is suspiciously similar to K-9 that speaks in Westminster typeface]]
Frequentist Statistician: This neutrino detector measures whether the sun has gone nova.
Bayesian Statistician: Then, it rolls two dice. If they both come up as six, it lies to us. Otherwise, it tells the truth.
FS: Let's try. [[to the detector]] Detector! Has the sun gone nova?
Detector: <<roll>> YES.

Frequentist Statistician:
FS: The probability of this result happening by chance is $frac1{36}=0.027$. Since $p< 0.05$, I conclude that the sun has exploded.

Bayesian Statistician:
BS: Bet you $50 it hasn't.

Title text:

'Detector! What would the Bayesian statistician say if I asked him whether the–' [roll] 'I AM A NEUTRINO DETECTOR, NOT A LABYRINTH GUARD. SERIOUSLY, DID YOUR BRAIN FALL OUT?' [roll] '... yes.'

I find this funny because it's true.

source

Cute funny...

This one always cracks me up for no reason...

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Unsure whether they qualify, but there are some fun facts taken from various sources:

Beginning from Yann Lecun:

• Geoff Hinton doesn't need to make hidden units. They hide by
  themselves when he approaches.




• Geoff Hinton doesn't disagree with you, he contrastively diverges
  
  (from Vincent Vanhoucke)




• Shakespeare and Bayes are in a boat, fishing. Bayes is trying to figure out which net to cast when Shakespeare says:
  "loopy or not loopy? that is the question".




• Deep Belief Nets actually believe deeply in Geoff Hinton.




• Geoff Hinton discovered how the brain really works. Once a year for
  
  the last 25 years.



• 
  

  Bayesians are the only people who can feel marginalized after being integrated

  
  
  

  And now the legend:

  
  
  

  

  
  

One from Reddit:

YOLO: you only LEARN once

P.S: Ian Goodfellow and Jurgen Schmidhuber are co-authoring a paper (to be presented at NIPS 2019) on Inverse GANs (More jokes on the topic here)

Let me embrace thee, sour adversity, for wise men say it is the wisest course.

Yann Le Trump! 😂😂😂

Question: What's the different between machine learning and AI?

Answer:

If it's written in Python, then it's probably machine learning.

If it's written in PowerPoint, then it's probably AI.

A Machine Learning algorithm walks into a bar.

The bartender asks, "What'll you have?"

The algorithm says, "What's everyone else having?"

A: What is machine learning sir?
B: It is not machine learning! It is machine burning, man.

by Davide Mazzini

"Predictions are hard -- especially about the future."

(Yogi Berra or Neils Bohr, depending whether you prefer physics or baseball)

In 2006, a common joke was that you would get an award for writing a paper that would either have "Karl Marx" or "Neural Network" in the title and get accepted at NIPS. Now that's become the standard for the latter... :D