explain it like i’m 5 · analytics! “big data”! supervised and unsupervised learning....
TRANSCRIPT
Kathryn Hume, Sales & Marketing @humekathryn | [email protected]
Explain it like I’m 5 AI, ML, NLP, and Deep Learning
“Artificial intelligence is whatever computers cannot do
until they can.”
Artificial intelligence is uncoupled from consciousness
Artificially intelligent systems are idiot savants, not Renaissance Men
“Machine learning is the study of computer systems that
automatically improve with experience.”
AI?!
Machine!Learning!
Data Science!
Analytics!
“Big Data”!
Supervised and Unsupervised Learning
Unsupervised Learning
Supervised Learning
Find a function that defines a correlation between P and C
Use this function to make guesses about C
Find a proxy (P) for something hard to know (C)
Use square footage (P) to predict housing prices (C)
Use “Nigerian Prince” (P) to predict if emails are spam (C)
Use past behavior (P) to predict future preferences (C)
What P should we pick to decide if it’s a cat or dog?
Deep Learning
• Use layers to transform complex input into mathematical expressions • Remove need for human to select which features matter
Universal Approximation Theorem
Neural networks can approximate arbitrary functions
Dog!
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 ….
W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 ….
“x” =Y1 Y2 Y3 …
X “x” W = Y
one equation three variables
X “x” W = YKnown Known Unknown
X “x” W = YKnown Unknown Known
2 x 3 = Y
2 x w = 6
w = 6 / 2
w = 6 / 2
0 = 2 x w - 6
Error = |2 x w - 6|
6
4
2
1
0.50.2 0.1 0.06 0.02 0.00020
1
2
3
4
5
6
7
1 2 3 4 5 6 7 8 9 10
w = 2.999 (close enough)
Supervised Learning: Recap
Find a function that describes how these two things are correlated. (Solve for W through iteration)
Use this function to make guesses about the thing that’s hard to know. (Use W to solve for new Ys)
Identify a correlation between something easy to know and hard to know. (X and Y)
Natural Language Processing
The real impact lies in making complex data simple.
There’s been a rise in sales!
Developments in Language Processing
Traditional NLP N-grams Word Embeddings
Bolukbasi, Chang, Zou, Saligrama, Kalai, 2016
Man : King :: Woman : Queen
Man : Computer Programmer :: Woman : Homemaker
Black Male : Assaulted :: White Male: Entitled To
Inherent Bias in Word Embeddings
