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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
AI-Powered Banking