Challenges of Implementing Machine Learning/Artificial Intelligence Capabilities at GXP

Topic: Challenges of Implementing Machine Learning/Artificial Intelligence Capabilities at GXP

Introduction: Problem Statement and light background, Why is this topic so important, big picture, not as much detail as the L&R on the topic areas? (2-3 pages)

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Background on who GXP is

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Problems faced with

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Engineering at max already maintaining product lines

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Need to fill ML/AI across product lines

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The AI Scientists as a scarce and expensive resource, most of the best of the best have their own companies competing with us but do not have the user interface that we do.

Research Question: One or two sentences, with one being an open ended question that drives the body of the paper.

Q:  How can a software company best leverage the capabilities of a smaller ML/AI company to both grow in the market and limit the stress/demands on its already over-capacity engineering team?

Literature and Resource Review: Detailed information on the background which lead to the research question. (8-10 pages)

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Point 1 – Requirements driving implementing ML/AI across product lines.

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Point 2 – Technical challenges to add ML/AI across product lines

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AI Scientists as a scarce and expensive resource

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Point 3 – Business challenges in implementing ML/AI across product lines.

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Point 4 – Strategies available to meet the problems

Argument and Analysis: Provides a detailed answer to the research question and gets into “Coopetition” as the solution. (8-10 pages)

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Point 1 – Coopetition as the chosen Strategy/Solution

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Point 2 – Big and Small Companies, and Coopetition

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Point 3 – APIs, and Coopetition

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Point 4 – Cloud-Based Migration and Security as a result of Coopetition

Conclusion: Also includes a recommendations paragraph for further research. (2 pages)

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Recommendations:

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R1 – More info needed on how this would look business wise

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R2 – Successful Examples as case studies

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R3 – Unsuccessful Examples as case studies

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Conclusion

References: Requires 20 references min with at least 15 peer reviewed.

Some background for the writer:

GXP is referring to https://www.geospatialexploitationproducts.com/content/

Geospatial eXploitation Products (GXP) creates software for intelligence and targeting, we are rather small at 300 including all our coders but part of the greater BAE Systems. GXP has to go the route of “Coopetition”, the implementation of ML/AI across the product lines. This allows for the innovative small tech companies out there, the best in their breed a way to expand into larger contracts by teaming with GXP, and this allows GXP to not spend the money and resources to grow their product lines internally to keep up with the industry. Use of API’s in Swagger permit the innovative emerging tech that can meet our product roadmap items to quickly and cheaply integrate with the product lines. This permits GXP product development teams to focus on the maintenance of the core product lines and development of roadmap items that are manageable with the size, experiment and expertize of the permanent engineering staff. In other words we don’t need to recruit and hire AI scientists to work on our roadmap items or emerging areas when we can have the best scientists in the world integrate for free using our APIs and go into customer requirements as a team. We need them, they need us to ever meet the next level. To me this is similar to what Franklin Covey refers to as “Synergy” in the “Seven Habits of Highly Effective People”. One such example of this is that we (GXP), have been teaming with IDenTV to have them tackle ML/AI items we have on our product roadmaps, across multiple product lines as well as integrating additional innovative capabilities into our products and workflows that come with the power of having IDenTV “under the hood” of our user interface. This experimental integration effort has rocketed our product line to the forefront of the customers as we have been able to build capabilities that vastly our perform even the most innovative experiments in industry or academia and ours is done, ready for sale here and now and in a user interface customers already know and rely on as the premiere capability in the industry.

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Six section headings (Centered, bolded, Cap first letter:

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Introduction: why is this important and light background. 2-3 pages

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Research Question: One or two sentences, with one being an open ended question that drives the body of the paper

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Literature and Resource review: 8-10 pages of detailed background leading up to the research question

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Argument and Analysis: 8-10 pages of detailed answer to the research question

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Conclusion: 2-3 pages of takeaways and include a paragraph of Recommended Future Research. Only place in the paper for opinion is conclusion.

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References

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Every paragraph (except in Conclusion) should have one to several in-text references.

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Feel free to use second level headings (Bold, Cap first letter, Left justified) in the Lit review and A&A. It makes it easier for reader to track where they are. ALWAYS write for the reader.

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The core background information in the paper should come from peer reviewed journals, credible interviews, or reliable industry publications. Each paper should include at least 20 sources, with at least 15 being peer-reviewed. If there are a limited number of peer reviewed sources, you will be expected to augment your literature review with at least ten interviews with credible expert sources. The final paper should be no less than 20 pages and no more than 25 pages in length. The Cover sheet, references, and appendices do not count in the 20-25 page count. The document should be presented in 12 pt. Times New Roman font, double-spaced, with one-inch margins. I make this request to normalize the document length. Formatting and references should be in APA format.

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