Machine Learning for Non-technical

People

Slater Victoroff

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YOU!

The non-technical audience interested

in Learning about

Machine Learning!

Who is this talk for?

Who am I?• Slater Victoroff

• Olin College of Engineering

• Typical young hoodie, flip-flop wearing entrepreneur

• Someone who cares very deeply about machine learning

• CEO of indico

What is Machine Learning?

Such a big buzzword.

Here’s what it comes down to in a human definition:

A class of computer algorithms and mathematical models that allow machines to perform general tasks, like identifying human faces in photos. The models are used to make predictions and decisions, which you can then use to solve real world problems, such as understanding how your customers feel about your brand across various social media channels. The neat thing is that instead of hiring 100 people to analyze 1,000 data points each, you could get a single machine to do it in a fraction of the time.

Quick Poll

Can you use machine learning in the following industries?

Factories

Smart Phones

Robots

Human Robots

NOT HUMAN ROBOTS

Machine Learning is Blurry

Language is blurry — sarcasm, etc.Where there’s a gray area,

machine learning can solve the issue.

Computers are bad at the world when there is inconsistency.

Say you’re a brand and you want to know what people are saying about your brand.

You look through everyone talking about your brand on Twitter, Facebook, etc..

Now you want to look at how popular those people are to find your influencers.

And finally, you want to know… what are they talking about?

In the old spreadsheet way, we have always just ignored these problems as they were in a gray area we couldn’t

access.

A social media example

Machine learning is

born in very ordinary

circumstances

• Marty McFly ended up in 1955 which is the same year that the first branch of ML came out (AI movie to come later)

• Georgetown and IBM Cold War found ML to be useful as they wanted to translate a large amount of Russian text to analyze

• MIT went after the image side, teaching computers to recognize objects and scenes. They tried to teach the computer to look at a picture and determine a bird or a plant.

Machine Translation will be a Solved

Problem in Three to Five Years

- Optimistic Researcher 1954

CSAIL• The Computer Science and Artificial

Intelligence Laboratory – known as CSAIL is the largest research laboratory at MIT and one of the world’s most important centers of information technology research.

• Founded in the 1940’s by Marvin Minsky

We’re pretty sure we bit off more than we

can chew here- ALPAC 1966

• Committees were spun up to precise translation and recognition.

• In one solid decade, we effectively made no progress. We had one-off ML systems.

• We could teach a computer to understand one sentence by showing it that one sentence.

• We made no progress, spent a lot of money, and cut the research. It was the death of an era.

During that time…

Time Passes

Arnold brings us back!

Machine Learning Goes Mainstream

Thumbs up? Sentiment classification

usingmachine learning

techniques.Bo Pang, Lillian Lee, and Shivakumar

Vaithyanathan.

Sentiment analysis = determine if a piece of text is positive or negative.

How do we do it? Well, we map each word to its sentiment and give the words a score.

AKA: A Lexicon-based approach

Sentiment Analysis

Word Positivity

Great 0.9

Terrible 0.1

Alright 0.6

Mediocre 0.4

This sandwich isn’t bad

Words Positivity

Isn’t bad 0.6

Isn’t good 0.3

Ain’t half-bad 0.73

Above average 0.7

“I have to say, that while most of my experiences at tourists traps have been horrendous,

the one I recently went to broke the pattern.”

• Many humans can’t figure out the sentiment of this sentence

• Gray areas of language = why sentiment analysis is quite a difficult problem for computers to solve

How do we know how well we’re doing?

How do we know how good AI is?

• Well, it’s hard• Take a spreadsheet• Label each piece of text for positive vs. negative• Guess which words made it positive or negative• Train the model on half of the spreadsheet and

then make predictions on the other half

Then what.

Train

Test

Still, it’s not that simple

Performance metrics

Overfitting

Customer Did they buy?1 No2 No3 No4 No5 No6 Yes7 No8 No9 No

10 No11 No12 Yes13 No14 No

Performance Metrics

- Accuracy isn’t necessarily the best performance metric

- Predicting sentiment is a very different problem depending on whether the text you’re making predictions on consists of Amazon reviews, tweets, or medical journals

- It also depends on how much data you’ve got

- When you teach a computer what sentiment is, you end up showing it a huge number of examples. Depending on the data you’ve got, the number of examples you might use range from a few hundred to hundreds of millions

- It’s not fair to use those examples to check your model’s accuracy — you already know the answers

Performance Metrics

Learn more about sentiment analysis and performance metrics:

What Even Is Sentiment Analysis?

Precision: fraction of retrieved instances that are relevant

Recall: fraction of relevant instances that are retrieved

Precision vs Recall

OverfittingThis product left me with a deep feeling of regret.

This film left me with a deep feeling of regret, love, and hopelessness for a life not lived.

I #love these new @nike shoes

Overfitting• Overfitting means you “fail to generalise to examples

outside of your training set”• In other words…you’re living under a rock. You’re

great at recognizing everything under your rock, but you don’t understand the rest of the world

• Domain is a factor — there are so many different kinds of text (scientific journal articles vs. tweets)• No one model is going to be the best at every kind of

text

KNOWLEDGE = POWEREmail us: contact@indico.io