Trends in Systems Engineering

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Trends in Systems Engineering

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Table of Contents

Introduction

Counter Argument -

Argument

Assert point #1 Machine Learning

Assert point #2 Human Machine Teaming

Assert point # 3 Data Sources -

Conclusion –

References

Ivan Bolano () - Can everyone please write their name next to the slide they would like to be tasked with.

Introduction

Artificial Intelligence (AI) Trends exist across the scope of systems engineering and can be found in everything from security to databases. The development of self-improving systems has been trending in multiple industries but is sometime overlooked by IT professionals. “Too often, though, business and IT leaders take a limited view of AI. They often focus almost exclusively on machine learning (ML)—sometimes even using “ML” as a synonym for “AI.”” (Martinez, 2020) The development of AI in systems engineering includes the use of a variety of different tools including ML, data conditioning and sensors and sources. Using all these tools can create a diverse AI environment that can be used to advance systems engineering and make the process of developing advanced systems easier for the engineers and systems architects.

Thesis

With the development of these new highly complicated systems it will become necessary for the future success of system engineering that AI become standard to system design and development. AI presents a host of tools including ML and automation that increases the capabilities of the final system as well as ensures that the design process is as efficient as possible. By outlining the tools and capabilities of AI a simple step by step approach can be developed to further integrate AI processes into systems engineering and design.

Artificial Intelligence in System Engineering

Counter Argument

Artificial Intelligence isn’t necessary always the best proposal, unfortunately in the world we live in many people want to do harm to others as well as profit off of their weaknesses and vulnerabilities. If a machine can have software written to think similar to a human and accomplish predetermined task for everyday work, then it would only be reasonable to assume that it can also be used to do harm and etc.

Counter Argument

Attacks on systems such as corporations will become more sophisticated (Ashesh, A. 2019)

Large amounts of data can be quickly and easily scanned for Personally Identifiable Information (PII) (Ashesh, A. 2019)

Healthcare industry will become open to ransom and/or holding patient information.

Military R & D (Research and Development) systems working on confidential systems will become open to the world for adversaries to use against the vary country that was creating it.

Argument Machine Learning

AI integration into system engineering requires adaptive learning.

Machine Learning takes real world data and calculates best way to reach endpoint.

Creates most efficient path to completing tasks efficiently.

Analyzes current processes and recommends improvement.

Algorithms can be adjusted to meet desired outcomes.

Machine Learning Methods in System Engineering

AI integration into the success of systems engineering is an inevitable progression of the processes involved in developing working systems. At the heart of systems engineering is the goal of maximizing the efficiency of a designed system and ensuring that the scope of the project is equivalent to the expected performance of the system itself. By using one of the most powerful AI tool, Machine Learning (ML), systems engineers are able to incorporate the power of intelligence systems and data science. ML works by taking large samples of data to create statistical models to follow during AI tasks.

Human Machine Teaming

Artificial intelligence provides strong empowerment with information to promote effectiveness. So that human being is protected with the ability to control activities in various sector such as private and government applications within the entire society.

The effort of Human machine teaming process is needed to produce quality artificial intelligent models under cohesive strategy of ethnics.

The Human effort will help understand client’s needs and challenges which is incorporated in the development to assist all effort such as usable testing technique to know if the clients really understand the concept of AI systems in compliance of applicable framework.

Human Machine Teaming Developmental Process

Human machine teaming (HMT) uses ethical techniques and quality methods during the human computer interaction and development of software. The process is the most reliable for human client to depend on AI technology to perform as needed, securely and interactively.

Utilizing HMT standard to develop AI systems will provide resilience to clients and show different issues before they manifest providing great experience to humans. To provide quality experience for human client the teams must emerge from different background such as gender, education, race, including location. (Mike, 2020).

The selected teams must include machine learning expert, system developers, programming administrators and product managers with clients who will utilize the system and how to be trained. The main purpose of bringing specialized individuals of various backgrounds is to mitigate bias in the system development and foresee unintended consequences.

Human Machine Teaming - Liable to Humans

The Human machine teaming process requires that all Artificial intelligence systems must:

Liable to humans

Aware of advantages and disadvantages

Trustful and secure

Straightforward and functional

Liable to humans

The AI systems are required to be accountable to humans to have total control, especially choices that affect the human life, health status, or representation. All outcomes must be obligated to human ensuring that the choice is carefully made while AI support humans based on the system various team created.

The creation of AI technologies could be challenging and time consuming, the teaming member may report the system is generating different algorithms which is called black box in such case the entire system must be shut down by the team members. Because teaming members must be liable to the output of the AI system and hold total control every time.

Human Machine Teaming - Benefits and Risks

Disadvantages such as Risks to client’s personal information may affect their way of life, health or representation which must be address long ago before usage. As mentioned the team must include various members based on education, race, skills sets and problem solving techniques. (Benson, 2018).

The team members will appropriate the given time to locate and extensively assess blind spot within data input which the AI system did not notice e.g. certain language in English have same spellings but different origin could be complex for the AI system to recognize.

It is important to explain the outcome of the risk to stakeholders and users of system, which include resources for backup, maybe team members can restore previous mode all this procedure helps create quality response to further enhance the system functionalities.

Human Machine Teaming - Functions

The human machine teaming will help ensure the AI system is pronounced in simple text.

The data sources and preparation technique must have explicit background and provable (Andre,2013).

Self-reliance and proper situations are known to human to determine conclusions.

Transparency must be included in all decisions.

The AI technologies will have direction, detection and representation.

AI: Data Sources

Artificial intelligence is amazing technology, but it is essentially useless without data

AI exists today that can get valuable data and make predictions about systems based on the information gathered from the data source

Artificial Intelligence uses a combination of technologies that succeed at extracting insights and patterns from large sets of data.

Artificial uses the data gathered to make predictions based on the information obtained from the large data sets

Therefore it is imperative that the data sources artificial intelligence pulls from should be credible and reliable

Data is critical to the success of a system thinking organization, because of this companies need to get as much value out of data.

Artificial intelligence can help with this business objective

AI: Data Sources

Today we can teach machines to improve our systems based on data from reliable sources.

Machine learning and data extraction work in conjunction to support using data to improve systems

Netflix and Amazon use AI to offer products and services to improve the quality of their systems (Kaput, 2019). They gather data based on their customer preferences and use that data to recommend product offerings.

The international monetary fund publishes data on international finances, debt rates, foreign exchange reserves, commodity prices and investments.

The FBI reports crime using datasets gathered to compile and publish national crime statistics (Marr, 2019).

Regardless of the data source, a system engineering approach that encompasses trustworthy data sensors, sources, appropriate conditioning processes, and a balance between machine and human interactions (Martinez, 2020).

References

Kaput, M. (2019, October 17). How is artificial intelligence used in analytics? Retrieved from https://www.marketingaiinstitute.com/blog/how-to-use-artificial-intelligence-for-analytics Martinez, D. (2020, June 30) A systems engineering approach to AI. Retrieved from https://www.machinedesign.com/automation-iiot/article/21135512/a-systems-engineering-approach-to-ai Marr, B. (2018, February 26). Big data and AI : 30 Amazing (and free) public data sources for 2018. Retrieved from https://www.forbes.com/sites/bernardmarr/2018/02/26/big-data-and-ai-30-amazing-and-free-public-data-sources-for-2018/?sh=1ad5c6125f8a

Summary / Conclusion

References

Ashesh, A. (2019, February 04). Artificial Intelligence its use and misuse. https://www.seqrite.com/blog/artificial-intelligence-its-use-and-misuse/#:~:text=In%20fact%2C%20there%20can%20be,where%20AI%20could%20be%20misused.&text=Ultimately%2C%20AI%20is%20all%20about%20data.&text=Cybercriminals%20and%20hackers%20are%20increasingly,forms%20of%20cyberattacks%20being%20automated.

Martinez, D. (2020, June 30). A System Engineers Approach to AI. Retrieved from https://www.machinedesign.com/automation-iiot/article/21135512/a-systems-engineering-approach-to-ai

Andre, S. (2013). Human machine teaming.An Interim Report on Space Applications", Multiagent Systems, Artificial Societies, and Simulated Organizations, Springer US, pp. 243–280.

David, N (2015). “Quenching the Thirst for Human-Machine Teaming Guidance:Helping Military Systems Acquisition Leverage Cognitive Engineering Research.” I

Benson, A (2018). “Beyond usability evaluation: Analysis ofhuman-robot interaction at a major robotics competition..

Mike, A. (2020). Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection, Springer International Publishing, pp. 262–274.