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1 Object-Oriented Data Governance Overview Global IT Solutions Intuitive, Cost Effective, Data- Centric, Scalable Solutions Global IT Solutions (GITS) Presents: Machine Learning and Language v2 Intuitive, Cost-Effective, Scalable Solutions

Machine learning and language v2

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Page 1: Machine learning  and language v2

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Object-Oriented Data Governance Overview

Global IT SolutionsIntuitive, Cost Effective, Data-Centric, Scalable Solutions

Global IT Solutions (GITS) Presents: Machine Learning and Language v2

Global IT Solutions

Intuitive, Cost-Effective, Scalable Solutions

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Machine Learningand the importance of Language

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Genetics

Pharmaceuticals Medicine

Aquaculture

Agriculture

Mining

Different Business Areas have a Common Denominator

Solving a common puzzle…

Question:

What do the above areas of science have in common?

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Answer is - Untapped Potential!

Like other industries, 80% of information is Unstructured, and buried in Artifacts:o Journalso Websiteso Publications

Scientists create Artifacts to share knowledge

If Knowledge is power and Information fuels Knowledge - then logic dictates that vast opportunities are being missed

So you’ve digitized/scanned your documents…

You’ve provided document-to-document links on the Web…Information stored in documents (unstructured data) is still ‘buried’ – you can’t link it to structured/geospatial data

Thus, you’re still not getting the results you were looking for…

The Problem

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It doesn’t have to be this way…

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You can build a Roadmap that leverages both Structured and Unstructured Data Strategies

You can forge Interoperability/Collaboration, Globally

You can enlist Machines to exceed the limits of humans..

The Future is hereThe contents of Unstructured documents from various scientists can be shared and linked in a meaningful way

Can you measure the benefits of your improvements?Do your plans include building and implementing the framework that is necessary to sustain 'Machine Learning'?

Let's talk a little bit about Machine Learning benefits and hurdles...

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You may not know that much about Machine Learning (ML) …But you know enough to know you don’t want what's behind Doors #2 and #3…You also know that nothing is as easy as they say it isQuestion: So, who’s right?

The Experts say that ‘Machine Learning’ can achieve your objectives….

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The Answer: You both are (you and the experts)

You can ‘teach the Machine’ to learn and help:Discover patterns and similarities across millions of Artifacts

Impart Knowledge contained in Unstructured Text and Structured Data

Make Inferences and Extrapolations on what you provide

Aid in making decisions

Exceed the limits of humans

But you are also right, there will be hurdles…

The hurdles are rooted in both the Machine and Humans

GITS uses the term ‘hurdles’ deliberately – the following items are not ‘problems’, they are just realities that have to be addressed

Machine LearningSlide7 of 13

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Hurdle #1: Machine Learning is a gradual process…

Reality #1 – When teaching new concepts to the Machine, assume it thinks like a Child

Reality #2 -- You also must think like a Child, to understand the ML process

Reality #3 – You can’t assume the Machine has grasped a concept, you have to prove it

Reality #4 - Machine Learning Maturity is obtained through trial-and-error – you need to conduct ‘experiments’

Reality #5 – You don’t need to be a genius to conduct experiments, for trial-and-error ML

Reality #6 – You do need to keep track of your experiments to determine how the Machine has matured.

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Reality #8 -- People work in SilosThis is a fact of life. You can't change it. People like their Silos.Within a given Silo, as Unstructured/Structured Data is captured, Reality #7 is not a problemIn an Integrated Environment, Reality #7 is a problem

Reality #7 -- There is a ‘Vernacular’, collectively - among Colleagues, and independently - amongst AuthorsWhen individuals speak, it is common to use Synonyms, Homonyms and Homographs (e.g., ‘Duff’, instead of ‘Coal Duff’; ‘Duff’, instead of ‘Soil Duff’

Hurdle #2: The Human Language is fluid..

Reality #9 -- Enterprises rarely understand the importance of having Ontologies/Taxonomies

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Taxonomy ExampleThe GITS Methodology:

Provides visual representations of Taxonomies (e.g., Venn, Hierarchy) specific to the language of the businessStores Taxonomies as Meta-DataProvides the ability to linkUnstructured DataStructured DataGeospatial Data

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GITS doesn’t attempt to change these Realities, our Methodology accommodates themBefore you teach the machine, GITS can show you how to manage the languageGITS will develop a Framework that can sustain Machine LearningGITS will help you to ‘Practice what you teach’ the MachinesIf you manage the language properly, you can exceed your expectations

GITS is realistic about the hurdles…

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The GITS Methodology:– Mitigates ‘Untapped Potential’ – Uses Ontologies/Taxonomies (as diagrams and Metadata)– Links meaningful content from Unstructured Documents to

Structured Data/Geospatial Information– Creates an environment amenable to efficient Machine Learning– Facilitates Machine Learning

GITS understands how to:– Use Machines to exceed the limits of humans– Provide Cost-Effective, Data-Centric Solutions

GITS provides The SolutionSlide12 of 13

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• The GITS Methodology:– Mitigates ‘Untapped Potential’ – Uses Ontologies/Taxonomies (as diagrams and Metadata)– Links meaningful content from Unstructured Documents to

Structured Data/Geospatial Information– Creates an environment amenable to efficient Machine Learning– Facilitates Machine Learning

• GITS understand how to:– Use Machines to exceed the limits of humans– Provide Cost-Effective, Data-Centric Solutions

GITS provides The SolutionSlide13 of 8

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Are the following part of your Solutions Framework?

Bi-Temporal Time Series Solutions

Interoperability, Collaboration and Operational Efficiency

Unstructured/Structured Data AnalyticsMultifaceted Business Intelligence (i.e., Unstructured/Structured Data, Geospatial)

Leveraging Social Media and Big Data

Ontology/Taxonomy Management and Implementation

Data Architecture/Data Science

Cost-Based Data Governance

Preparation for and usage of Machine Learning

If not, discover why they should be – contact us for a free Consultation Session

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