Fossasia ai-ml technologies and application for product development-chetan khatri

AI / ML Technologies and
Application for Product
Development
Chetan Khatri
FOSSASIA Summit, 2018
@khatri_chetan

Accionlabs Artificial Intelligence | Machine Learning
WHO AM I ?
Lead - Data Science, Technology Evangelist @ Accion labs India Pvt. Ltd.
Committer @ Apache Spark, Apache HBase, Elixir Lang.
Co-Authored University Curriculum @ University of Kachchh.
Software Engineering @: Nazara Games, Eccella Corporation.
M.Sc. - Computer Science from University of Kachchh.

What is Artificial Intelligence ?
“Artificial intelligence is intelligence demonstrated by machines, in contrast to the natural intelligence
displayed by humans and other animals”. - Wikipedia
The Original definition of A.I:
“Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a
machine can be made it simulate it. An attempt will be made to find how to make machines use language,
form abstractions and concepts, solve kinds of problems now reserved for humans, and improve
themselves”.
- John McCarthy at Dartmouth Conference. 1955
AI is new electricity!

“Software is eating the world, and A.I is eating
software !”
- GPUs / TPUs are eating Linear Algebra.
- Linear Algebra is eating Deep Learning.
- Deep Learning is eating Machine Learning.
- Machine Learning is eating Artificial Intelligence (AI).
- AI is eating Software.
- Software is eating the world.

Machine learning
It is a subfield of AI concerned with algorithms that allow computer to learn from examples/data and
experience. Machine
Learning Machine
Learning
Supervised
Learning
Unsupervised
Learning
Reinforcement
Learning
Deep Neural
Network / Deep
learning

How deep learning is different ?
Data
Feature
engineering
Statistical
Machine learning
Model
Features
Unseen Sample
Data
Deep Learning kind of
Machine learning
Model
Automatically identifies
features !
Prediction
Prediction
Unseen Sample

What is an Artificial Neural Network ?
BEACH
SEA
SKY
...

Neural Networks have been around for a while ...

… But then this happened
1
WEB-SCALE
DATA
Data volumes double
every year

2
WEB-SCALE
DATA
Massive Adoption of GPU
and TPU.

3
ADVANCED NEURAL
NETWORK DESIGNS

Supervised Learning !
A (INPUT) B (RESPONSE)
EMAIL SPAM ? (0/1)
IMAGE
OBJECT(1,...,10
00)
AUDIO TEXT
ENGLISH FRENCH

Machine learning / AI: Use Cases
Regression &
Classification
Real-time Data
Analysis
Character
Recognition (OCR)
ML Workload
Automation
Recommendation
& Personalization
Natural Language
Processing
Computer Vision
Data
Visualization
Information
Extraction
Deep Neural
Network
Conversational
Dialog Agent
(Bots)
Speech
Recognition
Forecasting
Predictions
Reinforcement
Learning
Scaling Prediction
services
Federated
Learning

TOOLS &
TECHNOLOGIES
Languages
Data Quality

Predictive
Modelling
Deep
Learning / AI
TOOLS &
TECHNOLOGIES

Data
Visualization
Containerized Scheduling
TOOLS &
TECHNOLOGIES

Scheduling
GPU Enabled
TOOLS &
TECHNOLOGIES

Artificial Intelligence and Machine learning
Automating the organization
Improving Decision making &
Reducing Inefficiencies

Machine learning process
Collect and
prepare
training data
Choose and
optimize your
ML algorithm
Setup and manage
environments for
training
Train and tune model
(trial and error)
Deploy model
in production
Scale and manage the
production environment

High level Architecture
BUILD TRAIN
Machine learning as
Service
ALGORITHMS
FRAMEWORKS
K-Means Clustering
Principal Component Analysis
Neural Topic Modelling
Factorization Machines
Linear Learner - Regression
Deep Reinforcement Learner
Convolutional Neural Network
XGBoost
Latent Dirichlet Allocation
Seq2Seq , LSTM
Recurrent Neural Network
Linear Learner - Classification
TensorFlow
PyTorch
Torch
Caffe2
CNTK
Caffe
Apache Spark MLlib
Apache Flink
Setup and manage
environments for
training
Train and tune
model
(trial and error)
Deploy model
in production
Scale & manage
the production
environment

Applications of AI : Computer Vision
Self-Driving
Cars
Convolution Neural
Network
Recurrent Neural
network
Vision i.e Camera Object recognition and identification
Real time, Per pixel Object Segmentation
Centimeter-accurate positioning

Visual Search &
Recommendation
Convolution Neural
Network
Recurrent Neural
network
Camera

Ad / User Click ? (0/1) Whether user will click to adv. or not ?
UI Wireframe UI Screen
Possible ???
Screenshot Source Code

Ad / User Click ? (0/1) Whether user will click to adv. or not ?
Possible ???
DEMO

Applications of AI : Computer Vision - DEMO

Natural Language Processing
Entities
Key Phrases
Language
Sentiment
Topics

Application of AI: Natural Language Processing
Business Applications
Entity Recognition
Sentiment Analysis
Content Classification
Language Translation, Speech Recognition
Conversational Dialog Agent / Chatbots
Syntax Analysis (Key Phrases, Language
Understanding)
Techniques
Recurrent Neural Network
Latent Dirichlet Allocation (LDA)
Neural Topic Modeling
Neural Recursive Network / Attention Network
Transfer Learning
Seq-to-Seq + LSTM

Challenges
- Interoperability of Deep Learning frameworks.
- Scale Inference at Production.
- Distribution and Parallelism of ML / AI Models.
- Build reusable components that work well together (across frameworks) - UNIX Philosophy
- Train at GPU / CPU - Inference @ Mobile, Raspberry PI, Drone, Streaming Application

Facial Emotion Recognition: DEMO
1.
Facial
Emotion
Recognition
Convolution Neural
Network
Recurrent Neural
network
Vision i.e Camera

Real time object recognition : DEMO
Real time
Object
recognition
Convolution Neural
Network
Recurrent Neural
network
Vision i.e Camera

Everyone has own choice of Deep Learning
Frameworks

Research to Production
Everyone has own choice of Deep Learning
Frameworks
Reimplementation takes
Weeks or months

Deep Learning Framework Zoo
...
Framework
backends
Vendor and numeric libraries
...
O (n2
) pairs
Apple CoreML Nvidia TensorRT
Intel/Nervana
ngraph
Qualcomm SNPE

Open Neural Network Exchange (ONNX)
...
Framework
backends
Vendor and numeric libraries
...
Apple CoreML Nvidia TensorRT
Intel/Nervana
ngraph
Qualcomm SNPE
Shared model and operator representation
From O(n2
) to O(n) pairs

Train at GPU, Inference at Mobile App !
...
Apple CoreML TensorFlow Light

Thanks !
@khatri_chetan chetan.khatri@accionlabs.com

Fossasia ai-ml technologies and application for product development-chetan khatri

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