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AI in enterprise environments: The October 30 US government AI policy announcement.

Preamble: Study of the US Government's AI Policy in the Context of Virtual Server Center Administration

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In the dynamic realm of Information Technology, server administration, especially in virtualized environments, is rapidly evolving with the integration of Artificial Intelligence (AI).

As the backbone of modern digital infrastructures, virtual server centers play a critical role in managing, distributing, and storing vast amounts of data, ensuring efficiency, and facilitating swift computational processes. As AI becomes increasingly woven into these operations, understanding its ethical, practical, and policy implications becomes paramount.

The US government, recognizing the transformative power of AI, has crafted a comprehensive policy paper addressing various facets of AI integration, deployment, and regulation.

This policy will affect how you and your family live, work, and learn. does not merely touch upon traditional areas of AI application but delves deep into its implications for sectors as niche yet vital as virtual server center administration.

The objectives of studying this policy in our class on Virtual Server Center Administration include:

Relevance: As future administrators, engineers, or policy-makers, you will likely be at the forefront of implementing or regulating AI-driven solutions in virtual environments. Understanding the government's stance provides a foundational framework to make informed decisions.

Ethical Considerations: AI brings with it a plethora of ethical dilemmas, from data privacy to potential biases in algorithms. Exploring these in the context of server administration equips us with the foresight to preemptively address such issues.

Operational Excellence: Grasping the policy's guidelines aids in achieving operational excellence, ensuring that virtual server centers are not just efficient, but also transparent, ethical, and in line with national standards.

Future Preparedness: As AI continues its exponential growth, it will invariably bring about shifts in job roles, responsibilities, and required skill sets. A deep understanding of its policy implications prepares us for this imminent future, making us adaptable and resilient.

By analyzing the US government's AI policy through group research, we aim to foster a collaborative learning environment where diverse perspectives meet, debate, and innovate. Each team will tackle specific facets of the policy, bringing to light nuances and insights that will benefit us all.

Let the exploration begin!

Outputs from this Activity:

See the team assignments and the research questions for each team at the bottom of this Document.

With your team, analyze the questions you have been tasked to research.

In addition to your primary questions, research and present on foundational topics such as: What does Governance mean in the context of AI? What is a Neural Network?

Make a TRELLO BOARD detailing your Research questions research.

You will make a PREZI. com presentation → To make a visually engaging and appealing presentation of your Research.

Class Plan for November 2, 2023

Class Title: "AI in the Enterprise: Implications of the New US Government Policy"

Class Duration: 3 Hours

Class Objective: To explore the implications of the US government's new AI policy on enterprise-level AI systems, with the goal of understanding its impact on business strategies, technology deployment, and regulatory compliance. Our class here is about running Virtual Server Centers.

Class Structure:

Part 1: Introduction and Context Setting (30 minutes)

Brief Recap: A 10-minute overview of the US government's AI policy announcement.

Discussion: Engage students in a 20-minute open discussion to gauge their initial thoughts and understanding of the topic.

Part 2: Research and Team Formation (30 minutes)

Team Formation: Divide the class into teams of 3-4 students.

Research Assignment: Assign each team to research one aspect of the new AI policy and its potential impact on enterprises for 20 minutes.

Presentation Briefing: In the remaining 10 minutes, explain the Prezi presentation tool and the expected outcome for the session.

Part 3: Group Work for Presentation Preparation (1 hour)

Prezi Creation: Teams work on creating their presentations based on their research. They should focus on how AI systems hosted on virtual servers can be adapted to meet the new guidelines and regulations.

Part 4: Prezi Presentations and Discussion (1 hour)

Presentations: Each team gets 10 minutes to present their Prezi to the class.

Q&A: After each presentation, allow for a 5-minute question and answer session.

Put the URL to your TRELLO board in a text file. Name the file teamname.txt :

Upload this file to this Dropbox location:

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https://www.dropbox.com/request/lhonS1P3UVl1ZuTkKXU3⁠

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Make sure that

peter@petersigurdson.net⁠

is a member of the Board.

References:

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https://www.linkedin.com/pulse/october-30-us-government-policy-statement-use-ai-peter-sigurdson-ullge?trackingId=VN5XToOyL4378DAZrDMr6Q%3D%3D&lipi=urn%3Ali%3Apage%3Ad_flagship3_profile_view_base_recent_activity_content_view%3BcbFbw%2FwqQQGrZ6YBVS08HQ%3D%3D⁠

Research tools:

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You.com⁠

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Perplexity.ai⁠

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Merlin.ai⁠

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ChatGPT

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LLama2.ai⁠

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2 pieces of hand-in work for this activity:

Create a TRELLO board. Include

peter@petersigurdson.net⁠

: Hand-in your TRELLO Board URL.

Make a Prezi Presentation summarizing the answers to your Questions.

Your Workflow on how to do this activity:

Trello.com: Create a team board. Any team member can do this and it.

Prezi.com: Make a Prezi Presentation of the answers to your research questions.

Presentation Questions:

Each team will address one of the following questions in their Prezi presentation:

Policy Interpretation: How does the new US government AI policy define the secure and ethical use of AI systems in enterprises?

Impact Analysis: What are the potential impacts of this policy on AI deployment strategies within businesses, especially regarding virtual server environments?

Compliance Strategy: How might enterprises adapt their existing AI systems to comply with the new US government regulations?

Innovation and Advancement: How could the policy potentially drive innovation in AI development and application within the enterprise sector?

International Considerations: What are the implications of the US AI policy for multinational enterprises, and how might it affect global operations and competitiveness?

Ethics and Equity: How does the policy address issues of ethics, equity, and transparency in AI applications within enterprises?

Post-Class Assignment (To be completed after the class):

Reflection Paper: Each student will submit a short reflection on what they learned from the exercise and their personal perspective on the new AI policy's implications for future enterprise AI development.

Materials Needed: Internet access for research, Prezi accounts for presentation creation, a projector for presentations, and a timer to keep track of presentation lengths.

With this plan, students will not only delve into the recent policy changes but also get to collaborate, research, and present their findings, fostering a deeper understanding of the subject matter through active participation.

Team Assignments:

Team Neural Networks

202104108 Abhijeet

202103210 Amritpal

202103241 Amritpal

202103510 Arshdeep

Team Deep Learners

202103565 Arshdeep

202104120 Arshdeep

202103741 Bavanpreet Singh

202104161 Gursimran

Team Tensor Titans

202104740 Harkamal Kaur

202102379 Harman

202103361 Harmandeep

202104565 Harmanpreet Singh

Team Algorithm Architects

202104518 Harshdeep

202104245 Jagjit Singh

202103825 Jagmeet

202003625 Jarman

Team Machine Minds

202004719 Jashanpreet

202103831 Khushdeep

202103964 Komalpreet

202102087 Mehakdeep

Team Data Dynamos

202103395 Mehakdeep

202104484 Navdeep Kaur

202104331 Navjot

202104362 Navjot

Team AI Avengers

202104466 Parminder

202102507 Ramanpreet

202101509 Rupinder

202104496 Sahilpreet

Team Code Commandos

202104181 Sanam Dev

202104490 Sehajpreet Singh

202103586 Simranjeet

202104375 Sukhbeer

Team Bot Builders

202104492 Taranpreet

202102729 Ekamjot

202100708 Manpreet Kaur

202103845 Krishnam

Team Quantum Questers

202103822 Karanvir

202103252 Amitoj

202102129 Ujjwal

202102644 Aashima

202101210 Mayank

Team Neural Networks

How does the US government's AI policy address the ethical use of neural networks in public domains?

In what ways does the policy advocate for transparency in neural network-based AI systems?

What challenges does the policy anticipate regarding the international deployment of neural networks?

How does the policy paper emphasize the importance of security in neural network architectures?

Team Deep Learners

How does the US government's AI policy differentiate between deep learning and other AI techniques in terms of regulation?

What measures does the policy recommend for ensuring fairness in deep learning applications?

How does the policy propose to handle biases in deep learning models?

What provisions does the policy make for advancing research in deep learning while ensuring public safety?

Team Tensor Titans

How does the US government's AI policy envision the future of AI frameworks like TensorFlow in the tech industry?

In the context of the policy, how crucial is data representation (like tensors) for AI transparency?

How might tensor-based computations impact compliance with the AI policy's data security guidelines?

What potential challenges does the policy foresee with the widespread adoption of tensor-based AI in governmental operations?

Team Algorithm Architects

How does the US government's AI policy propose to regulate the design and deployment of new AI algorithms?

What ethical considerations does the policy highlight regarding AI algorithm design?

How does the policy address potential monopolies in the AI algorithm market?

In the light of the policy, how should proprietary algorithms be evaluated for transparency and fairness?

Team Quantum Questers

In what ways does the US government's AI policy anticipate the convergence of quantum computing and AI?

How might the policy address potential security concerns arising from quantum-enhanced AI?

What opportunities does the policy see in the intersection of quantum technologies and AI for the nation's growth?

How does the policy suggest preparing the workforce for advancements in quantum and AI?

Team Policy Pioneers

How does the US government's AI policy address international collaborations and treaties on AI?

What frameworks does the policy propose for monitoring AI's economic impact on various sectors?

How does the policy emphasize the role of public opinion and feedback in shaping AI governance?

What strategies does the policy recommend for fostering AI literacy and education at all levels?

Team Data Defenders

How does the US government's AI policy tackle data privacy concerns, especially in the context of AI?

What measures does the policy propose to ensure AI's responsible access to and use of big data?

How does the policy address data rights, especially in scenarios of cross-border data flow?

In light of the policy, how are companies encouraged to maintain data integrity while employing AI?

Team Ethical Explorers

How does the US government's AI policy framework ensure the ethical deployment of AI across sectors?

What are the policy's guidelines on preventing discrimination and bias in AI applications?

How does the policy propose to balance AI innovations with ethical considerations?

In what ways does the policy suggest holding AI developers and deployers accountable for ethical lapses?

Team Futuristic Federals

How does the policy envision the role of AI in future federal operations and services?

What are the policy's projections on the job market changes due to widespread AI adoption in government operations?

How does the policy emphasize the importance of upskilling federal employees in the wake of AI advancements?

What steps does the policy recommend for ensuring the robustness and reliability of AI systems in federal operations?

Team Governance Guardians

How does the US government's AI policy ensure consistent governance across AI deployments in different sectors?

What are the policy's stipulations on audit trails and transparency in AI systems?

How does the policy tackle the challenge of keeping AI regulations up-to-date with rapid technological advancements?

In what ways does the policy suggest fostering a collaborative governance model involving both public and private sectors?

AI Avengers

How does the US government's AI policy promote fairness and prevent systemic biases in AI applications?

What are the policy's provisions for promoting inclusivity and ensuring underrepresented communities benefit from AI advancements?

How does the policy tackle challenges associated with the explainability and transparency of AI models?

What roles do public-private partnerships play in achieving the objectives set out in the AI policy?

Team Commandos

What measures does the US government's AI policy recommend to ensure national security in the age of AI?

How does the policy address concerns of AI-driven cyber threats and warfare?

What frameworks does the policy propose for international cooperation in AI-related defense matters?

How does the policy envision the future of AI in military applications, training, and strategy?

Bot Builders

With the advent of AI, how does the US government's policy view the future of automation and bots in various sectors?

What ethical considerations does the policy highlight in the context of AI-driven bots interacting with humans?

How does the policy address job displacements and the economic implications of widespread bot implementations?

What standards does the policy set for ensuring the safety and reliability of bots, especially in critical applications?

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Team Machine Minds

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Members:

202004719 Jashanpreet

202103831 Khushdeep

202103964 Komalpreet

202102087 Mehakdeep

Research Questions:

How does the US government's AI policy approach the integration of AI with existing machine-based systems in virtual server centers?

In the policy framework, what is the significance of machine learning in enhancing virtual server administration tasks?

What are the provisions set by the policy for ensuring that machine-driven AI solutions remain transparent and accountable in a virtual server environment?

How does the policy address potential scalability challenges when machines incorporate advanced AI techniques in server centers?

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