London, May 2019

Moty FaniaPrinciple Engineer (CTO), Advanced Analytics

Sales

Legal notices

This presentation is for informational purposes only. INTEL MAKES NO WARRANTIES, EXPRESS OR IMPLIED, IN THIS SUMMARY.

For more complete information about performance and benchmark results, visit www.intel.com/benchmarks

Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries.

* Other names and brands may be claimed as the property of others.

Copyright © 2019, Intel Corporation. All rights reserved.

2

3

Descriptive Analytics

Diagnostic Analytics

Predictive Analytics

Prescriptive Analytics

Cognitive Analytics

Operational Analytics

AdvancedAnalytics

ForesightWhat Will Happen,

When, and Why

HindsightWhat Happened

Self-Learning and Automation

Computerized human thought and actions

InsightWhat Happened

and Why

Va

lue

Difficulty

Simulation Driven Analysis and

Decision Making

10

Harnessing Analytics

Narrow AI

Also referred to as “weak AI.” This is AI that works within a very limited context, and can’t take on tasks beyond its field. It is the only form of Artificial Intelligence achieved so far.

Artificial intelligence is about replacing human decision making

▪ These are not repetitive tasks, but rather judgment-based decisions

▪ Measured by the quality of decisions & actions taken to achieve a desired objective

▪ Machine learning as the key technology to use a variety of inputs , recognize patterns, predict future outcomes and make decisions.

4

AI in a Nutshell

What is

AI?

About Us – Advanced Analytics @ IntelHo

wVa

lue

Embed Learning HW Validation Product Dev Sales Industrial IOT Health

Improve products power/

performance

Cut product time to market

Increaserevenue

Reduce Manufacturing

cost

Improve clinical trials outcome

Reduce test cost and improve

quality

Adaptive & personalized

HW

Automated validation with

Context

Autonomous accounts coverage

Proactive actuation to

changes

Continuance Monitoring at

home

Personalized units testing

-----------------

5

Vision : Put AI to work for human experts

Why AI Platforms?

• Developing an excellent AI model(s) that solve a business problem is essential but not sufficient

• Successful AI implementation requires deep integration into business processes and the ability to continuously and rapidly deploy AI models to the production environment of a biz domain

Vertical Specific AI Platforms

Infrastructure (on premise or Public Cloud)

Platforms / SW “Building Blocks”

• Dedicated, Specialized AI platforms are built using standard, open source building blocks to enable this continuous, closed feedback loop and offer better TTM and lower TCO

SalesHealthHW Validation

-----------------

Product Dev Industrial AI

The Characteristics of AI Platforms

Key Characteristics:

• Deep integration to vertical's biz processes

• Continuous delivery / deployment

• On-Prem, Cloud, Hybrid• Closed feedback loop &

Actuation

• Designed for experts• No GUI • Code based• Open Source

AI Platforms enable a self-sustained, on-going AI Service in production with built-in integration points for different kinds of models

Sales AI

Sense Reason Interact Learn

Information Action Results

Real time scan of customer information

Detect intent to buy and potential opportunities

Automate a personalized best

action

Automatically learn based on customer

response

Sales AI Platform – What is required?

Online streaming

system

Agile Micro-services,

architecture

Specialized data stores –

Knowledge Graph, Search, Big data

object store

NLU and auto labeling

Real time DL inference

service

Scalable computation Engine

Online streaming system

TwitterService Message Bus

SQLData Stores

CrawlService

Seed Queue

Handles Queue

Reasoning Agents

* Intel’s Sales AI uses APIs provided by Twitter to collect data only from accounts the user has designated as public and adheres to individual websites’ terms of use. The only data collected is what is permitted by the site owner under their terms of use agreement.

Micro-services, architecture

Message Bus - Broker

Information extraction …

Interactive Agents

Inference Agent

GraphBuilder

Flexible Data Store(s)

Search(Engine)

SQL Big data Store Knowledge Graph

Why Knowledge Graphs?

• We are surrounded by entities , which are connected by relations

• Graphs are a natural way to represent entities and their relationships

• We need a structured and formal representation of knowledge over time

• Graphs can be managed efficiently

• Enable reasoning to infer new knowledge

Works forCompany B

CompanyA

CompanyC

Person

David Smith

Acquired

Partners with

ProductX

Buys

Semantic descriptions of entities and their relationships

14

• Microservice architecture

• Asynchronous stream processing using Kafka as the message bus

• Easily deployable with Docker, K8S and Helm

• Configuration driven - configurable schema vs. code

• Decoupled from specific Graph technology

• Support incremental updates + ongoing refresh process

“Graphier” – A service for building and sustaining graphs

Message Bus

Asynchronous Graph Transformers

• Always ON (Docker containers)

• Stateless

• Configuration driven behavior

• Each handles a certain graph entity

• May produce of work for others!

• Implemented with Kafka streams

15

Consume from Work Queue

Extract Data

Convert to Graph Semantic

Produce to Broker

* Other names and brands may be claimed as the property of others.

• Transformer – Transforms raw string data from message bus to specific graph semantics

• Extractor - Implements the logic for a specific entity extraction from an external source

16

API

GraphBuilderTransformers ExtractorsData Loaders

Data Refresh

Message Bus

High level architecture

* Other names and brands may be claimed as the property of others.

Externalsources

17

GraphBuilder

“Company”Transformer

“Company”Extractor

Graph Builder Queue

Company Queue

Sample Flow

* Other names and brands may be claimed as the property of others.

CRM Companies Data

Data Loader

{Company: ABC,Country: US

}

{Nodes: [],Edges: []

}

Pro

du

ce

Co

nsu

me

“Product”Extractor

Company Extract Queue

Product Queue

{Product: X,

}

{Nodes: [],Edges: []

}

Pro

du

ce

{Nodes: [],Edges: []

}

18

Semantic Data Converter

• Enables adding new entities’ definitions without code changes

• Converts Business entities into Graph semantics

• YAML configurable

Data Converter{

BizName: ABC,Country: US

}

{Nodes: [],Edges: []

}

Company

Implemented as a generic module, consumed by Transformers and Extractors

Deep Learning Inference System

Inference Service

… ……… … ………

… ……… … ………

ML1 ML1

Classification Combiner

1

2

…

n

Cache

Request(POST)

Predict (GET)

• Many AI capabilities involve usage of Deep Learning

• NLP / NLU

• Visual inspection

• Recommendation Systems

• Implemented production grade, managed, inference system that serves DL models and enables online, closed feedback loop

Archelon

Reduces TCO and TTM

• Production Inference service for DL models

• Smart in-memory cache for data batching , sequencing

• Fast, scalable APIs for data ingestion & real time responses

• Full Scalability

Natural Language Understanding

• Named-entity recognition (NER)

• Text-classification M&A

• Relations extraction Amazon To Acquire Ring

• Intent classification Amazon Enters Industry Home Security

• Sentiment analysis Positive

Many (text-related) business problems require supervised methods

Snorkel is an open-source framework capable of training models for supervised problems without manual-labeling

Helps avoid rule-based classification and Amazon Mechanical Turk for labeling

We’re excited to report that Amazon buys Ring to get into home security

business.

RAY – A Scalable computation engine

• Ray is a flexible, high-performance, distributed execution framework for Emerging AI Applications (UC Berkeley)

• A scalable system for parallel and distributed Python.

• Shortens path to production (scalability, throughput, tolerance)

• Dev & DS friendly – Powerful yet easy actor system

• Superior performance to Spark in certain use cases

Ray on standalone mode is on an average 3x faster and on cluster mode is on an average 18x faster than the scenarios performed without Ray

22

Putting it all together

Consumer

Consumer

Consumer

Databases

Graph Builder

Relations analytics

Training

Action service - Points

Message Bus

Crawler

Deep LearningInference

NLU / Information extraction

API

Computation Engine

Action service - Training

Action service – Sales Assists

Action service Matchmaking

…

Archelon RAY

news

* Intel’s Sales AI uses APIs provided by Twitter to collect data only from accounts the user has designated as public and adheres to individual websites’ terms of use. The only data collected is what is permitted by the site owner under their terms of use agreement.

CRM

Sales Assist User Interface

Sales Assist provides relevant customer activity to the account manager with details that provide additional information.

Sales Assist User Interface – Assist Detail

With the “Details” for a “Viewed Intel product” assist, the account manager can see the specific pages the customer visited on intel.com and Sales Assist highlights that the customer has not previously purchased the product.

An Autonomous sales Example

Sense direct Intent

Intel.

com

Train

ing

Customer DataSINGLE

CUSTOMER VIEW

Recommender System

Reasoning Interact Learn

Communication

in 15 different languages

~150K

Contacts

• Open rate• Recommended

SKUs purchased in relevant period

A/B Testing

Sent to ~3% of contacts

IT@INTEL

Learn more about Intel IT’s initiatives at: www.intel.com/IT26

Sharing Intel IT Best Practices With the World

Copy protected with Online-PDF-No-Copy.com