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johnhormanHPE

Augmented Intelligence:Helping humans make smarter decisionsHPE Big Data Cognitive Analytics Platform for Text and Rich MediaJohn Horman Chief Field Technologist, HPE Big Data

A (very brief) history of AI

HavenOnDemand.com@HavenOnDemand

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Great Expectations...

Asimovs Three Laws of Robotics:A robot may not injure a human being or, through inaction, allow a human being to come to harm.A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.5

Logic, rule-based problem solving approachGreat early progress, but very difficult to extend to wider variety or harder problems.-> combinatorial explosion, hardware limitations as well as programming

Chess example

If we still dont fully know how the human brain works5

Next attempt: neural networksAttempt to imitate human learningMuch closer match to human intelligence, contrasted well against rigid logic-based approachesCaptures our fuzziness, ability to fail gracefully (i.e. guess)Very effective but unfortunately still only applicable to specific applications like speech recognition, image recognition.6

Simple neural network models known since the 50sBut took the recent advancement of computing power to make them practically feasible6

Which brings us to todaySmart machines, but very specific domainsVery often better than humans, but only in the specific tasks for which they were builtHowever, they lack general intelligence, eg.Learn abstract conceptsThink cleverly about strategyCompose flexible plansMake a wide range of ingenious logical deductionsImmense social change needed for human acceptance of AI and delegating control7

IBM Watson great if you want to play Jeopardy, but struggle to apply this to other applicationsTesla Autopilot fantastic at driving, almost certainly better than humans, but not that great at conversation

Social acceptance: Tesla, travel booking example7

Machine Learning at the Service of BusinessAugmented Intelligence

Broad idea of AI is an abstraction, and maps down lower and lower to a wide set of precise functionalityVery clearly not a mature, commoditised area. Therefore success in Augmented Intelligence is achieved through agility and being able to leverage these many functions quickly and easily. Build fast, fail fast. Open architecture is key.8

How do you bridge the gap between data and outcomes?

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How do you consume any data generated or understood by humans?How do you identify key aspects and patterns to determine outcomes?How do you automate to take action?

Data sourcesDiverse Modern AppsQ1Q2Q3

Augmented Intelligence power apps for competitive advantage

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Augmented Intelligence powered by HPE

Artificial intelligence, machine learning and natural language processing using advanced analytics functions.

Connect: Thousands of connectors for text, video, image and audio available designed to handle enterprise and government scale volumes of data. Available in over 155 languages + 55 languages for speech to text

Process & Analyze - Established and proven technology to determine outcomes bas

Build - Portfolio of hundreds of advanced analytics functions and APIs to automate and take actioned on machine learning and deep neural networks to enrich data findings, identify key aspects and patterns10

Human data

Connected people, apps and things generating massive data in many forms

Machine data

Business datafaster growth than traditional business data10x

Today, being data driven is about: Harnessing all the relevant data available today and in the future including business, human and machine

Democratizing the data by empowering and delivering insights for all stakeholders collaboratively in your organization from LOB leadership, operations, line workers, etc. irrespective of level or function, in-real time, at the moments that matter

Operationalizing analytics through many applications, resulting in better results across your entire business/operations

Achieving greater value through insight and foresight analytics answering why did something happen? or what will happen? instead of just reactively what happened, so you can take action and be proactive

In yesterdays data driven enterprises, analytics and insights were limited to (and for) traditional business data the data generated from business-process applications like CRM, ERP, HRM, and supply chain. But as we have all seen, the data landscape has been radically changing over the past few years 90% of the data available today was created in just the last 2 years - and the landscape will continue to change due to the fastest growing data segments: Human and Machine.

Human data includes all the content we create some of which is highly regulated for compliance purposes (contracts, legal docs) , but much of it is social media, emails, call logs, images, audio, and video.

Machine data is the complete opposite of Human. Its the high-velocity information generated by the computers, networks, and sensors embedded in just about everythingthe Internet of Things.

Together, Human Data and Machine Data are growing 10x faster than traditional Business Data, and organizations that are data-driven are not only able to leverage this data to create new value, but they are able to bridge the interconnection of data across the silos and repositories for integrated intelligence.

For example, in retail retailers can maximize customer loyalty across multiple channel by integrating data from real-time inventory, in-store location positioning sensors, RFID, and social media.11

Whats so difficult about human information?

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Human Information is made up of ideas, is diverse and has contextWhy is processing human data different?Ideas dont exactly match like data does; they have distance.Human Information is not static its dynamic and lives everywhere.Legacy techniques have all fallen short.

Social MediaVideoAudioEmailTextsMobileTransactional DataIT/OTDocumentsSearch EngineImages

What do we mean by unstructured or human information? Well, anything that is produced by a human to be understood by a human any of the information sources that we ourselves easily and naturally interact with every single day. The problem is, human information is very difficult for computers to process, and there are two key reasons for this:1. Different words can have totally different meanings depending on the context, and these meanings can even change over time or completely new meanings can appear suddenly. For example, consider the term ground zero before 9/11 this would most likely have referred to the centre of a nuclear test site, but immediately after took on a new meaning as the former site of the World Trade Centre in New York. Now both meanings exist, but the most likely reference is now to the latter concept or meaning.2. Ideas and concepts expressed in human information have an idea of distance from each other, for example we understand that a coat and a jacket are almost the same thing, but are very distant from the concept of a tree, and may even be synonyms in certain contexts (eg. a police report, where some witnesses describe a suspect as wearing a dark coat and other a dark jacket, should be interpreted as the same thing). Understanding how closely related or otherwise different human information is essential to analysis and full understanding.

Both of these challenges are impossible to address with any keyword-based analysis platform, and as a result nearly all Big Data analysis solutions continue to focus on the traditional structured business data / BI analysis approach and just ignore the unstructured or human information. But human information makes up about 90% of the data in any organisation, so it is foolish to ignore such a significant proportion of data. Furthermore, human information is usually the key part of the data set that allows us to uncover and explain the why behind any patterns or trends discovered in the structured data for example, hospital mortality figures may reveal a different survival rate for an operation over two sets of people, but only analysis of the relevant written consultant reports can reveal why that is and allow us to take action.

That is why there was a need to develop a new engine, from the ground up, specifically to process and understand human information in the same way that we as human do the IDOL engine. Because of this, IDOL is able to understand the actual different meaning of different words and concepts in the unstructured data, and to build an understanding of how and in what way these different concepts are related to each other.

So how does it do it? There are two key algorithms that were used to develop this unique capability Bayesian Inference and Shannons Information Theory. Many of you will have heard of the first, but Ill give a very simple outline of each and how weve developed them to work together to achieve this human understanding of unstructured data.13

Using Cognitive Analysis to form a human-like understanding of contentHPE Natural Language Processing (NLP) engineFundamentally created to understand naturalhuman language using probabilistic modelingand NLP algorithmsAllows incoming data to dictate the model, not pre-defined rules, dictionaries, or semantic websSelf-Learning / Machine LearningUpdates as more data is added or removedAdapts to changing definitions or meaningFundamentally language-independentTreats words as symbolsOptimized with language packsEduction, sentiment analysis, speech analytics

Information Theory and Bayesian Inference

So that is how we use those two algorithms to gain a human-like understanding of unstructured information in just the same way that the human brain does. The probabilistic approach to Natural Language Processing and Machine Learning has several advantages over other approaches such as linguistic analysis or semantic webs.

Firstly, linguistic and semantic web approaches both have significant limitations in that they need to have a set of predefines rules, dictionaries or tuples (semantic web) in order to make any sense of what they are processing, and these rules need to be constantly . So while they can sometimes perform well in very specific, niche domains or use cases, as a general analysis tool they are extremely limited. In contrast, IDOL applies machine learning over the data itself, allowing it to dictate the model and as new words appear, or existing words take on new meanings, these changes are immediately identified, learned and understood (in just the same way you can learn the meaning of a new word through its context, seeing it used several times). In Big Data analysis applications, this is absolutely essential because its very rare that you know exactly what is in the data you want to analyse (hence why you're analyzing it), and indeed its very often these new, unusual concepts or outliers that are of particular value.

Secondly, IDOLs probabilistic model also has the advantage of making the analysis process entirely language independent. This is because IDOL is not trying to break down a sentence into nouns and verbs in a completely language-dependent way, but rather IDOL looks at each word as a symbol or black-box and builds up its understanding around how it is related to other symbols to determine its meaning. This means a single IDOL engine can natively index and understand human information in any of over 160 languages, irrespective of character set and so on.14

But we are all fine with structured data, right?

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Unfortunately, most existing structured data solutions are full of compromiseTraditional Enterprise DatabasesThe original SQL databases did not envision todays data volumesVendors scrambling to handle bigger data volumes by tacking on Hadoop technologies and retrofitting legacy technologiesEither use reduced data sets or eye-watering costs

Hadoop-Based SolutionsMajor Hadoop vendors strive to meet the standard with SQL on HadoopNoSQL is incomplete SQLAnalytics Performance is very limited Not a substitute for a full implementation of SQL

Compromise

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HPE Augmented Intelligence Real-Time Analytics

Leverages BI, ETL, Hadoop/MapReduce and OLTP investmentsNo disk I/O bottleneck simultaneously load & queryNative DB-aware clustering on low-cost x86 Linux nodesML algorithms, such as k-means and regression, built into the core engineAutomatic setup, optimization, and DB managementUp to 90% space reduction using 10+ algorithms50x 1000x faster than traditional RDBMSScales from TB to PB with industry-standard hardware Simple integration with existing ETL and BI solutionsSQL-99+ compliant Ultimate deployment flexibilityExtended advanced analytics24/7 Load & Query17

Alison

Coupled with this, we offer rapid deploy services to help you eliminate challenges and get it right the first time. We are moving away from the build from scratch model to create

Our Methodology includes four phases:

Assess & Align: HPE consultants help you to identify key customer metrics that truly impact your bottom line and the data sources that contribute to these. We create an effective project plan based on blueprints that have been created based on best practices.

Map & Integrate: Our experts do the heavy lifting by normalizing and mapping your data into the Voice of the Customer solution. Our Rapid Deploy Package includes aggregating data from three data sources (social media, CRM, web analytics) in order to get you started. We configure the system to aggregate six months of historical data from each of these.

Configure & Connect: We configure your unique end user environment to meet the business needs identified in the Assess phase. We leverage our prebuilt reference queries to create various visualizations are the most meaningful to you and connect the reference platform configuration to your chosen data sources.

Test & Deploy: Before go-live, we ensure that your solution is up and running properly. After a series of User Acceptance Testing, the solution is ready for production. HPE consultants will provide comprehensive documentation and hand-off. User training is conducted at the facility or remotely. Knowledge transfer is completed so that your team fully understands their implementation.

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HPE Haven OnDemand is a self- service cloud platform that provides augmented intelligence through cognitive analytics, machine learning APIs and services.

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Over 70 APIsConnect, extract, index, search and analyze

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Real life challenges20eDiscoveryFileshareanalysisCall dataBroadcastmonitoringWebsite search

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HPE Haven OnDemand Combination APIsReusable Machine Learning building blocks for cognitive apps and services

Machine Learning API CombinationsReduce Implementation Effort and Accelerate Development 75% faster to build appsYour Apps

2. Copy & Paste1. Select

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Search video as easily as textTransform rich media into intelligent assets

InquireSearch your data

InvestigateAnalyze your data

InteractPersonalize your data

ImproveEnhance your data

Live video or playback from archived footageOn-screen text recognition

Face identificationAutomatically generated transcript using speech recognitionSpeaker identificationTimecode synchronizationAutomatic keyframe generationAutomateAutomatically create metadata, keyframes, transcriptionsUnderstandUnderstand video footage and audio streams in real timeActApply advanced analytics such as clustering and categorization, and link with other file types

Intuitive Knowledge Discovery for Self-Service Analytics 23

Visualization to simplify analytics workflowTopics MapSunburst Result ComparisonRich Contextual View

Business Intelligence for Human Information (BIFHI)

What is it? A new end-user GUI provides a straightforward analytics workflow for diverse use cases. BIFHI incorporates visualization functionality such as topic map to highlight key concepts, sunburst diagram to enable easy filtering based upon extracted entities (e.g. people, place, company),result set comparison to examine how a change of search parameter may impact the outcome and rich contextual view where the query result includes not only the document itself, it shows the metadata and other relevant information such as documents by the same author or documents from around the same period. Why does it matter? The intuitive interface enables business users to perform self-service analytics and shortens time to insight. For example, the user can search for a topic, visualize the result breakdowns on the main panel, and refine the search parameters on the side panel with automated guidance based upon IDOLs deep understanding of queried data, and see real-time result changes, all within the same window.

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HPE Virtual Assistant Cognitive Chat BotAn illustrative case studyA few ways to approach this:Build a big long list of 5,000-10,000 Q&A pairsNot really cognitive AI though is it?

2. Build a cognitive solution that automatically extracts answers from dataConceptually understands the ideas and meaning. Seamlessly combines multiple analysis techniques (Probabilistic Conceptual Analysis, Machine Learning, Neural Networks, etc.)

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This kind of full automation requires a platform with a few pre-requisites:Universal connectivity, out of the boxAutomatic processing and fact extractionCognitive Analytics platform supporting all data formats and including a broad range of algorithms

25HPE Augmented Intelligence automatically identifies and extracts facts from documents

ASOS Annual Report 2015ASOS SummaryChief Executive Officer = Nick BeightonTotal revenue growth = 18%Profit before tax = 47.5mCash position = 119.2mUK Retail sales = 473,885,000Group total revenues = 1,1550,788,000

Language independentAutomatic table recognition and field extractionHPE Augmented Intelligence

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HPE cognitive analytics is trained to understand user dialogue, and continues to learn from each user interaction26

LoanTrade FinanceIm interested in borrowing money to invest in a new production line

Im not sure I completely understood you. Did you want a loan; or were you asking for a credit line, or securities account and brokerage service?"IntentScoreLoan0.72Credit Line0.58Investing0.49

UserHPE VirtualAssistant

Case study Cognitive Chat Bot

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So this is what we built 2-3 resources, 3-4 weeksSimplicity of what you see hides the intelligence not scriptedFully cognitive chat botAsk it anything (across the data domain)No manual data loading27

Summary

Artificial Intelligence is not here yet, and likely will not be for some decades at leastInstead, the focus in on Augmented Intelligence using machines to make people smarter and more effectiveThe key to success and achieving business value is agility and innovation. Build fast, fail fast.Everything is derived from the data never underestimate the importance of being able to access, ingest and process the raw dataA broad range of analytic tools and algorithms are key to this agility and innovation. An open and transparent architecture is critical for futureproofing and allowing for further innovation.Only HPEs pioneering AI platform is uniquely able to facilitate all of the above through connectivity, breadth of analysis, and ease of application development and innovation.28