explain it like i’m 5 · analytics! “big data”! supervised and unsupervised learning....

Kathryn Hume, Sales & Marketing @humekathryn | kathryn@fastforwardlabs.com

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

Thank you!

@Humekathryn | kathryn@fastforwardlabs.com