Artificial Intelligence Machine learning and Deep

Learning: Trends and Tools

Dr. Shaona Ghosh

@shaonaghosh

What is Machine Learning?

Computer algorithms that learn

patterns in data automatically from

large quantities of data

Use the learnt model for prediction

on new data

Machine Learning: Classification

Source:

Microsoft Azure Machine

Learning

Machine Learning: Regression

Source: Microsoft Azure

Machine Learning

Machine Learning: Housing data

David

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015

Data drives machine learning?

Data collection infrastructure

sensors, mobile phone, internet connectivity

Data management

Private cloud servers, government clusters

Data pre-processing

Data Annotation

Crowdsourcing, hiring annotators, openly available data

Rise of AI (2012 - present)

•Due to advent of deep (new) neural networks(old) ~ huge models

•Availability of huge amounts of training data ~ millions/ billions of samples

•Powerful computational infrastructure~ 1920 CUDA cores on GPU

Notable Results

Surpassed Human Level Performance in Image Classification or Face Recognition

(Kaiming He et al. 2015, Yaniv Taigman et al, 2014)

Matched Human Level Performance in Machine Translation,Speech Recognition

(Google Translate and Baidu Research)

Beat the world’s top human player in the ancient game of Go

(Google DeepMind)

Adam W. Harley. The AI learns to focus on features

Convolution Neural Networks (CNNs)

Source: Andrej Karpathy Stanford

Network identifies features in image data that human eyes

cannot

Semantic Segmentation

by Shuai Zheng et. al. University of Oxford, ICCV 2015

Instance Segmentation

by Romera Paredes et. al, University of Oxford, 2016

Industry Start-up Sustainability Projects

Ca

pe A

naly

tics (

US

A)

Detect the property condition, square footage, roof condition, solar panels and

other details.

Industry Start-up Sustainability Projects

• Propera (Israel): precision agriculture company that uses

computer vision, deep learning, and agtech to figure out

exactly how much water to deliver to plants in particular

locations so as to improve crop yields while conserving

resources.

• The Climate Corporation (USA) : field health view and

surveillance using deep learning

• Peat (Germany) : precision agriculture, surveillance,

disease diagnostics using deep learning

Will machine learning potentially change

the approaches to development?

Given enough annotated/labelled data is available

• Speed of analysis – terabytes of data in hours

• Accuracy of prediction – human level accuracy or even

better in particular perceptual data: images, text, video,

speech

• Automatically discover features – no more manual and

slow feature engineering, biases

Infrastructure: Hardware

Infrastructure: Hardware

Infrastructure: Distributed Hardware

•Amazon AWS Server with GPU support

•Google Cloud with TPU support

•Google Cloud with GPU support

•Microsoft Azure with ML support

Infrastructure: Software

• Deep Learning Libraries

Infrastructure: Open Data

ImageNet – natural images

Pascal VOC – natural images

OpenStreetMap – geospatial

Biometric Recognition dataset

Uber Ride dataset

DataUSA

EU Gender statistics database

Netherlands National Georegister

Many more

https://deeplearning4j.org/opendata

Infrastructure: Policy

• ASILOMAR AI Principles by Future of Life Institute:

principles to empower people with AI

• Partnership in AI

Amazon, Apple, Google, FB, IBM, Microsoft for best

practices and open platform in AI

• Future of Humanity Institute (Oxford)

Strategic Implications of Openness in AI Development by

Nick Bostrom Report

Leverhulme Centre for the Future of Intelligence (Cambridge)

make the most of machine intelligence

Infrastructure: Policy

• Future of Humanity Institute (Oxford) Report on

When Will AI Exceed Human Performance? Evidence from

AI Experts

Strategic Implications of Openness in AI Development by

Nick Bostrom Technical Report 2016

• Leverhulme Centre for the Future of Intelligence

(Cambridge) brings together the best of human

intelligence so that we can make the most of machine

intelligence

Thanks for listening.