Data Science vs Artificial IntelligenceFrancesco Gadaleta, PhD.Artificial Intelligence ArchitectCDO Abe.ai

My name is Francesco Gadaleta. I am Chief Data Officer at Abe AI, where we streamline banking with financial AI

“I keep saying that the sexy job in the next

10 years will be statisticians, and I am not kidding

Hal Varian - Chief Economist Google

“Data science, the sexiest job of the 21st century

“...and definitely the most vulnerable one

“Data science AI is fundamentally changing every industry

1.Do you know data science?

DATA SCIENCE YESTERDAY

DATA SCIENCE YESTERDAY

This process is called feature engineering. In a financial transaction, a feature might be a date or an amount. Traditional machine learning relies on the data scientist to create features. This is time consuming and requires domain expertise.

1. Who wants to manually prepare features?

2. Prone to human error

3. Time consuming

Input Data Feature Engineering

ML algorithm

Algorithm 1

Algorithm 2

Algorithm 3

Finance

Healthcare

Media

Validation

Validation

Validation

Featureengineering

Featureengineering

Featureengineering

DATA SCIENCE YESTERDAY

How many neural networks do you think there are?

11

x1

x2

x3

b=+1

JUST ONE

Layers

Output: predict supervised target

Hidden: learn abstract representations

Input: raw sensory inputs.

THE CORE OF NEURAL NETWORKS

THE CORE OF NEURAL NETWORKS

x1

x2

x3

b=+1W1 W2

(Logistic regression) (Logistic regression)

b1 b2

THE CORE OF NEURAL NETWORKS

(Logistic regression)

SGD Stochastic Gradient Descent

Backpropagation (at each layer)

x1

x2

x3

b=+1

CAT OR DOG?

FEATURE ENGINEERING CHALLENGE

Cat- Four legs- Two eyes- Two ears- Many Whiskers- Multiple Colors

Dog- Four legs- Two eyes- Two ears- Many Whiskers- Multiple Colors

Cat- Four legs- Two eyes- Two ears- Many Whiskers- Multiple Colors

Dog- Four legs- Two eyes- Two ears- Many Whiskers- Multiple Colors

➔ Detecting the “right” features is challenging.

➔ Expert knowledge plays a fundamental role

➔ Handcrafted features usually do not generalize

across domains

FEATURE ENGINEERING CHALLENGE

Neural models learn to distinguish a dog from a cat just like the brain of a baby. Simply tell, don’t explain.

Input: images, text, numeric, etc.

Output: class, label, text, numeric, etc.

Learn higher level concepts of data

(eg. pixel->segments -> shapes -> objects -> scene -> ...)

DEEP LEARNING TODAY

Neural models learn to distinguish a dog from a cat just like the brain of a baby. Simply tell, don’t explain.

Input: images, text, numeric, etc.

Output: class, label, text, numeric, etc.

Learn higher level concepts of data

(eg. pixel->segments -> shapes -> objects -> scene -> ...)

DEEP LEARNING TODAY

BIGData

GPUPower

ALGOProgress

HAPPENING TODAY

BIG DATANot just large data. But integrated data.

POWERFUL GPUsMaking deep learning possible

Up to 100x faster than regular CPUs

BETTER ALGORITHMSBack-propagation, SGD, Optimization

Legitimate transaction

Fraudulent transaction

CATS AND DOGS IN FINANCE

Fraud Detection

TRANSACTION CLASSIFICATION FOREX1.29 Million Transactions

From, to, 23, USD, dateFrom, to, 12, USD, dateFrom, to, 56, USD, dateFrom, to, 84, USD, date

From, to, 23, USD, dateFrom, to, 12, USD, dateFrom, to, 56, USD, dateFrom, to, 84, USD, date

101C23.A42E42D

101C23.A42E42D

TRANSACTION DATA

(REAL-TIME) TRANSACTION DATA Abe AI Engine

Human Process CLASSIFICATION

REAL-TIME CLASSIFICATION

“Lack of Data

Deep learning needs a lot of data to work well.Data transformation can help

Think different TM

Transform the same problem into something deep learning can solve

Knowledge transfer

Train/Tune/Predict somewhere elseNLP, Speech, Image classification

OPPORTUNITIESML, Deep Learning, and AI are changing the way we solve problems

Thank you.

datascienceathome.com

@thisisFrag

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