Request for Information (RFI) on Advancing Privacy Enhancing Technologies by Joseph Wehbe

National governments need to contribute to grassroots AI ecosystem. Each stakeholder in an AI ecosystem adds to build a value network from the local government up to federal policy. An AI ecosystem is comprised of eight stakeholders that all have different goals. For these stakeholders to achieve their goals, they require government support.

The role of national governments specifically starts in providing recognition to AI degrees and education immediately at the graduate school level, but also in the k-12 curriculum. Imagine if government departments of education don’t recognize medical, legal, or teaching degrees? The education system wouldn’t function. But the paradox is that departments of education require to build internal expertise to be able to recognize such AI degrees.

The AI ecosystem and the approach to building the next generation of AI talent, can’t be compartmentalized. AI workshops, certifications, and bootcamps have no educational merit. They don’t build practitioner level skills.

DISCLAIMER: Please note that the RFI public responses received and posted do not represent the views or opinions of the U.S. Government. We bear no responsibility for the accuracy, legality, or content of the responses and external links included in this document.

Office of Science and Technology Policy (OSTP)

on behalf of the Fast Track Action Committee on Advancing Privacy-Preserving Data Sharing
and Analytics of the Subcommittee on Networking and Information Technology Research
and Development (NITRD) of the National Science and Technology Council,
the National Artificial Intelligence Initiative Office,
and the NITRD National Coordination Office

Re: Request for Information on Advancing Privacy-Enhancing Technologies
(Document Number: 2022-12432)

Submitted by:

Joseph Wehbe

Joseph Wehbe is an American artificial intelligence ecosystem builder. Led the #1 winning team of a Massachusetts Institute of Technology (MIT) Challenge (knowledge-economy) in 2020. He received an AI master’s degree recognized by the leading research institute in Canada in which Dr. Geoffrey Hinton (the Godfather of AI) is the Chief Scientific Advisor. Joseph is also an ambassador for Stanford Women in Data Science in Canada.

Dear Sir/Madam,

Effectively developing a national strategy on privacy-preserving data sharing and analytics, and associated policy initiatives requires:

1. Operationalizing the strategy at a Federal, State, & Local Government using a software / infrastructure layer.

2. Building a pipeline of talent & projects as a Government to Grassroots AI value network to execute on this strategy.

3. Redesigning the entry margin for verified stakeholders, marginalized and underrepresented groups along with the non-consumers to have a way to participate.

Context for my response:

According to the AI Index Report 2022, the US ranks 1st globally in Talent, Research, Development, and Commercial. (see graphic below)

While we rank 35th in operating environment, and 17th in government strategy. Our low ranking in these 2 areas are the basis of all my feedback. This translates into the USA having the best talent, entrepreneurs, innovators, and companies in the world, but the strategies do not effectively impact the local government level. Why? Because the innovation and artificial intelligence ecosystems in the US are built around the greatest learning institutions in the world, who carry the responsibility. They don’t revolve around the people. In AI development, while technology and algorithms have been commoditized, skilled workers that are able to create solutions to AI problems are the most important factor. There is a demand for a whole generation of workers with capacities in artificial intelligence. This will be the generation of talent that will support national interest in aerospace, defense, education, housing, transportation, public safety, supply chain, manufacturing, and many other industries critical to the American homeland. We can improve the 35th and 17th running using a software and infrastructure layer.

USA
Global
Index
Ranking

*source
https://www.tortoisemedia.com/
intelligence/global-ai/

Intelligence | Global Al Index

United States
of America

My Responses to Question 9 & 10 combined below:

1. [Response by JW] Existing barriers, not covered above, to PETs adoption:
   Information about technical, sociotechnical, usability, and socioeconomic barriers that have inhibited wider adoption of PETs, such as a lack of public trust. This includes recommendations on how such barriers could be overcome. Responses that focus on increasing equity for underserved or marginalized groups are especially welcome.
2. [Response by JW] Other information that is relevant to the adoption of PETs:
   Information that is relevant to the adoption of PETs that does not fit into any of the topics enumerated above.

Based on my proprietary frameworks, in building artificial intelligence ecosystems there are 8 stakeholders that effectively connect as a value network. While government policy and advocates will call to increase diversity of talent by lowering the barriers of participation for all regardless of organizational affiliation, this is a recipe for disaster.

If this happens, it means we are removing educational merit if we want security, and accountability. My frameworks redesign the entry marging / the entry barrier to Artificial intelligence (and other technologies) not lower the barrier. The existing barrier to PETs adoption can be solved by redesigning the entry barrier for each stakeholder.

In my experience as a World Economic Forum artificial intelligence and entrepreneurship expert, I believe there’s a demand for a whole generation of workers skilled in technology. These technologists must emerge from innovation ecosystems. There are systematic, structural and institutional barriers that many times and almost always limit opportunities that are also applicable to PETs. Once we identify the attributes of each innovation ecosystem, we can use artificial intelligence to identify the specific barriers affecting PETs.

We can’t generalize or compartmentalize the barriers. Successful innovation ecosystems that we know of in Boston, Silicon Valley, and Seattle all have educational merit. Why? Because they are anchored by the most successful entrepreneurs and ventures in the world.

Innovators from underrepresented backgrounds and underserved communities do not have a pathway to achieve their innovation goals. Capital is not the barrier in these ecosystems. It’s the lack of intellectual infrastructure in the region that’s the main barrier. Using advanced technology and artificial intelligence, we can identify the attributes of each community. Generalizing the results is doing a dis-service to the community. And relying on human knowledge alone does not do the work / results justice.

Using a software layer, the US Federal government must identify the stakeholders who are eligible to participate in the innovation ecosystem. Think of those “eligible” as the “total addressable market.” From this pool of eligible stakeholders, we identify those who have shown a willingness to become entrepreneurs, innovators, or technologists using various artificial intelligence methods that we can identify. To provide context, a Harvard Business School professor’s definition of entrepreneurship is “the pursuit of opportunity beyond resources controlled.” In this case, my definition of underrepresented, underserved, or marginalized is an individual or group who has knowledge and education of the said technology or innovation but lacks the intellectual infrastructure to realize their goals. The education component is critical because everything we do, must have educational merit. If our targets are truly innovators/technologists, these stakeholders must have educational merit. We are not expecting them to have all the knowledge, but should have access to the intellectual infrastructure. Using artificial intelligence, we can identify those most likely to succeed innovators/technologists and unlock their potential to succeed. Unfortunately, using human knowledge alone to realize this goal is difficult.

Entrepreneurs/technologists from marginalized groups and underserved communities all exist / live in communities with community colleges, technical schools, vocation schools, colleges, universities, and high schools. Using technology and a software layer, we can leverage these institutions to identify the specific barriers. But we must “redesign the entry barrier” so that these stakeholders (innovators or specific user type) understand “how” to achieve their goals, otherwise, they won’t have the willingness to share and engage. Again, we can’t “lower” the barrier to engage, because that won’t have educational merit, we must redesign the barrier.

I will reinforce again that capital is NOT the barrier for marginalized or underserved innovators to achieve their goals. If we throw capital at the problem, but the intellectual infrastructure does not exist, then we don’t achieve our goal. Let us use technology and artificial intelligence to solve this problem, redesign the entry barrier for stakeholders, and build a pipeline of
innovators so they are no more underrepresented, underserved, or marginalized. Our goal is to have upward mobility for each of these stakeholders so that they are no more identified as such.

Highlights of AI expertise I offer:
– The benefits of AI ecosystems are distributed unevenly across the US & don’t exist
in the heartland.
– Dismantle institutional, & systematic barriers that limit opportunities for stakeholders
in AI & bring educational merit to the AI workforce.
– Redesign entry margin for stakeholders so the US can build a pipeline of AI talent.

Your faithfully,

Joseph Wehbe
“AI Ecosystem Builder”

“The recipe is straightforward, let us invest in AI Education, AI Research & Development.”-JW

White House Office of Science and Technology Policy- National Artificial Intelligence Initiative Office
Request for Information (RFI) on Implementing the Initial Findings and Recommendations of the National Artificial Intelligence Research Resource Task Force: Response by Joseph Wehbe

DISCLAIMER: Please note that the RFI public responses received and posted do not represent the views
or opinions of the U.S. Government nor those of the National AI Research Resource Task Force, and/or
any other Federal agencies and/or government entities. We bear no responsibility for the accuracy,
legality, or content of the responses and external links included in this document.

Dr. Lynne Parker

Director, National Artificial Intelligence Initiative Office, White House Office of Science and Technology Policy

Re: Request for Information (RFI) on Implementing Initial Findings and Recommendations of the National Artificial Intelligence Research Resource Task Force (Document Number: 2022-11223)

Submitted by:

Joseph Wehbe

Joseph Wehbe is an American artificial intelligence ecosystem builder. Led the #1 winning team of a Massachusetts Institute of Technology (MIT) Challenge (knowledge-economy) in 2020. He received an AI master’s degree recognized by the leading research institute in Canada in which Dr. Geoffrey Hinton (the Godfather of AI) is the Chief Scientific Advisor. Joseph is also an ambassador for Stanford Women in Data Science in Canada.

Dear Dr. Parker,

There’s a demand for a generation of workers skilled in AI, and it’s my mission to build that by focusing on 3 areas:

1. Operationalizing Federal, State, & Local Govt AI strategies.
2. Building a pipeline of talent & projects as a Government to Grassroots AI value network.
3. Redesigning the entry margin into AI & allowing the non-consumers of AI to participate.

I hereby submit my feedback based on 2+ years of being the class president of an Artificial Intelligence Masters program in Canada led by Dr. Geoffrey Hinton as the Chief Scientific Advisor, and as an American participating in building the Canadian AI ecosystem.

Summary of my NAIRR
feedback & level setting:

1. We must define the eligibility of students & researchers who are earning an education in AI. There are only 17 AI focused Master’s programs in the US. For us to be inclusive of all students we must redesign the entry barrier to AI for participants in academia, industry, research, entrepreneurship, investors, government, & practitioners. In AI, there’s so much public opinion & policy, AI students themselves receive very little say about their own discipline, at the same time, we bear the burden to deliver on the potential of the future while trying to navigate through it all. Let’s build an environment that gives back to students what belongs to students, and to seed a culture of learning, innovation, and research.
2. While scientific merit is important as mentioned, educational merit is required of the stakeholders accessing the NAIRR and the AI education development to build a pipeline of AI talent. To address DEIA, we must solve the AI education problem. The barrier now is “those with AI knowledge” and “those without it”.
3. We must integrate an infrastructure and software layer to operationalize the NAIRR plan. According to the Global AI Index Report 2022, the US ranks 35th globally in “Operating Agreement”, and ranks 17th in “Government Strategy”; this is reflected in our nation’s AI strategy execution. The NAIRR plan has the ability to evolve into a Government to Grassroots AI value network for the benefit for American Federal,
   State, and Local Government stakeholders. We must fix this! Our low ranking in these 2 positions are the basis of all my feedback.

USA
Global
Index
Ranking

*source
https://www.tortoisemedia.com/
intelligence/global-ai/

lntelliigence | Global Al Index

United States
of America

1. From NAIIR Page 2 Line 4Going from AI to “organize their days, find the best routes to work and school, select the items they buy, and remind them of upcoming appointments”
   [Response by JW] As a nation we should think about moving from using AI to “organize our days” to work on projects of National interest building American Dynamism in Aerospace, Defense, Education, Housing, Transportation, Public Safety, Supply Chain, Manufacturing and beyond.
2. From NAIIR Page 1-1The “growing divide” in computational and data resources
   [Response by JW] The divide is created by those that “have knowledge about AI”
   and those that don’t. There isn’t a researcher or AI student in the US that has the AI
   formal education, proprietary data sources, AI use case knowledge AND has a barrier
   to start their AI journey. The growing divide is ignited by the knowledge gap. Let us
   build AI education capacity at the K-12 and university level and that will eliminate the
   growing divide.
3. From NAIIR page 1-2
   “new pathways to participation”

   [Response by JW] We must redesign the entry margin for the underserved
   communities to participate. We can’t lower the barrier to AI. AI education is difficult.
   It must be earned from a university to have educational merit. Bootcamps and
   certificates are not the solution to finding new pathways to participation.

4. From NAIIR page 1-2
   “american researchers to access computational and data resources”

   [Response by JW] The definition of an American researcher must include a
   researcher that has an AI education, affiliated to a university in the US, part of an AI
   degree granting program, affiliated to an AI center of excellence, or an AI research
   lab. Not every American researcher has AI knowledge to execute, it’s not the NAIRR’s
   role to educate them, it’s the role of the academic institution they belong to.

5. From NAIIR page 1-3
   “National AI Initiative Act of 2020”, the 8-point National AI R&D Strategic Plan

   [Response by JW] Neither mention the educational merit required of the
   stakeholders accessing the NAIRR or the AI education development to build a
   pipeline of AI talent. We must bring back educational merit to AI education and
   subsequently the stakeholders that benefit from NAIRR.

6. From NAIIR page 1-3
   “better understanding the national AI R&D workforce needs”

   [Response by JW] An AI researcher in the workforce belongs to either a well
   resourced large scale enterprise (i.e. FAANG or similar company), a well resourced AI
   non-profit lab (i.e. AI Allen Institute), a venture funded startup, an SMB with limited to
   no AI expertise on the team, an early stage startup that is not funded nor has the
   scientific/AI educational merit to work on AI research.

   This group of stakeholders do NOT need access to NAIRR. This is an oversimplification of the landscape, but I argue that the focus for AI R&D workforce needs should
   be on building AI educational merit for stakeholders from all backgrounds that want
   to participate in AI.

7. From NAIIR page 1-4 & 1-5
   

   “…required elements of the NAIRR roadmap and implementation plan”

   [Response by JW] There is an infrastructure and software layer missing from
   operationalizing the plan. According to the AI Index Report 2022, the US ranks 35th
   globally in “Operating Agreement”, and ranks 17th in “Government Strategy” and this
   is reflected in this report. We must build an infrastructure and software layer to
   operationalize the plan as a Government to Grassroots AI value network for the
   benefit of American Federal, State, and Local Government Stakeholders.

8. From NAIIR page 2-1, Recommendation 2-1
   

   “NAIRR should support early experimentation by students learning how to build and apply AI”

   [Response by JW] We must define the eligibility of students & researchers who
   are earning an education in AI. There are only 17 AI focused Master’s programs in
   the US. A computer science degree that covers AI classes is different from a
   student earning an AI degree. AI bootcamps and certificates don’t give students
   practitioner level AI skills with educational merit. For us to be inclusive of all students
   we must redesign the entry barrier to AI for participants in academia, industry,
   research, entrepreneurship, investors, government, & practitioners.

9. From NAIIR page 2-2
   

   Increase diversity of talent- “by lowering the barriers of participation for all” regardless
   of “organizational affiliation”

   [Response by JW] Means we are removing educational merit if we want security,
   and accountability…We must redesign the entry margin/barrier to AI not lower the
   barrier. Organizational affiliation in this case should mean that stakeholders belong
   to an AI lab, and not any American organization.

10. From NAIIR page 2-3
    

    Mentions “the system should take advantage of existing campus” resources…

    [Response by JW] We don’t need to add new resources, but connect existing
    campuses and launch AI centers of excellence.

11. From NAIIR page 2-3 (recommendation 2-6: support needs students) point 3
    

    Those studying who are “learning about AI, experimenting with the development of AI
    models and tools”

    [Response by JW] The AI programs should be explicit, vetted, recognized by the
    Department of Education, and have a Chief Scientific Advisor. FYI- there are only 17
    AI master’s programs in the US.

12. From NAIIR page 2-4
    
    (Access to Startups or SMBs) have federal grants, or SBIR, or STTR

    [Response by JW] Startups are known to offshore work, we should not grant
    access. The NAIRR can’t control a startup’s or SMB’s offshore / outsourced resources.

13. From NAIIR page 2-4
    
    (access to Private Sector researchers with Federal funding)

    [Response by JW] Such researchers should be affiliated to an AI center of excellence, or vetted technology hub / program to prevent bad actors. There are 68 such
    centers in the US. We can build an AI value network, digitally. Unlike an AI ecosystem,
    the proposed AI value network is a collection of upstream resources, downstream
    stakeholders, and subsidiary providers/services supporting a shared business model
    within our ecosystem. Each node adds value to the end goal of that particular AI
    stakeholder. This AI value network also serves the non-consumers of AI so that they
    have a pathway to achieve their goals.

14. From NAIIR page 3-1
    
    Sustainability and long term funding or revenue sources.

    [Response by JW] By establishing the value network in each community and
    determining their willingness to pay, we can build several revenue streams and
    business models.

15. From NAIIR page 3-2
    
    Ownership and Administration “other options may exist”
    [Response by JW] An infrastructure software layer to operationalize the NAIRR p
    across all stakeholders.
16. From NAIIR page 3-3
    
    The day-to-day operations “employ permanent and diverse staff”

    [Response by JW] What about qualified AI staff, managers of AI? There’s no
    mention of such in the report. Can the NAIRR employ enough qualified staff with
    AI masters degrees?

17. From NAIIR page 3-3
    
    Ownership and Administration “other options may exist”

    [Response by JW] An infrastructure software layer to operationalize the NAIRR p
    across all stakeholders.

18. From NAIIR page 3-4
    
    NAIRR management Entity “scientific merit” is mentioned

    [Response by JW] There should be educational merit to the AI stakeholders
    accessing. Why should there be educational merit to healthcare/doctors but not for
    AI practitioners?

19. From NAIIR page 3-4
    
    “resource providers” not duplicate resources

    [Response by JW] All AI programs have platform companies and resource
    providers seeking their attention, and offer free resources. We must include them
    into our value network.

20. From NAIIR page 3-5
    
    “day to day” operations

    [Response by JW] There are 8 stakeholders in an AI ecosystem, they should all
    have a path to contribute, not necessarily all be a user.

21. From NAIIR page 3-5
    
    “Governance and performance”

    [Response by JW] The scientific advisors from the AI labs should all have a seat
    at the table.

    For new research proposals, there should be mechanisms for industry / manufacturing / stakeholders in the heartland and emerging frontier hubs to participate

22. From NAIIR page 3-6 recommendation 3-11
    
    “students, startups”

    [Response by JW] Access should be given to those with educational merit. p
    Connected to AI programs, labs, or other vetted stakeholder groups.

23. From NAIIR page 3-7 recommendation 3-14
    
    “private entities”

    [Response by JW] The private entities should be connected to an AI lab or center
    of excellence in their local AI value network

    They can contribute data from industry but should be connected to AI centers of
    excellence at their Local or State Government levels.

24. From NAIIR page 3-7 recommendations 3-15
    
    “NAIRR evaluation methods”

    [Response by JW] Each stakeholder has a different goal in AI, and the outcome /
    impact on each varies, the measurements should reflect such. There is an
    8-stakeholder AI ecosystem model that underpins the performance.

25. From NAIIR page 3-8 recommendation 3-16
    
    “qualified external evaluators”

    [Response by JW] SAME AS ABOVE

26. From NAIIR page 3-9 recommendation 3-19
    
    “publicly accessible platform”

    [Response by JW] The definition of the user roles should all be enabled to AI
    centers of excellence, accredited AI programs, and not open to the world. A vetted AI
    stakeholder in the US should belong to one of these institutions. This is an
    oversimplification but I’m available to explain further.

27. From NAIIR page 3-9, recommendation 3-20
    
    “establish mechanisms” for evaluation…

    [Response by JW] Activity based costing and balance score cards should be integrated
    into the oversight and transparency to inform improvements to the activities.

28. From NAIIR page 4-1
    
    “…user interface portal”

    [Response by JW] There is an infrastructure and software layer missing for the
    NAIRR to effectively reach the grassroots. Regardless of the user interface portal,
    how do vetted AI stakeholders interact through the proposed “user interface portal?”

29. From NAIIR page 4-1
    
    “…set of resources for the AI R&D Community”

    [Response by JW] The eligibility and definition of the AI R&D Community must follow an
    8-stakeholder model and exclude startups and those not connected to AI centers of
    excellence. The reason for startup exclusion is mentioned in this document.

30. From NAIIR page 4-1
    

    “…increasing availability of data… AI-ready data, ethical, privacy, security, and usability”

    [Response by JW] There’s no mention of proprietary data, how do we manage the
    intellectual property for the owner, and provide assurance to the owner that data
    which could belong to a manufacturer that’s willing to share based on their set
    objective (which was their reason to share it to begin with)?

    If all researchers are working on open data sets, who’s working on proprietary AI
    projects? AI researchers must understand the context and domain of the problem
    they are trying to solve. Hence the 8-stakeholder AI model is required.

31. From NAIIR page 4-1, finding 4-1
    

    “…Rigorous AI R&D is often not possible without high-quality, trusted, dense, and
    transparent data resources.”

    [Response by JW] I argue that rigorous AI R&D is NOT possible without talent
    having the educational merit, scientific AI advisors, AI labs, and qualified team
    support. This component is missing from the report.

32. From NAIIR page 4-2, Finding 4-2
    

    “There are substantial data quality challenges within and across most research domains”

    [Response by JW] I believe there are substantial proprietary data availability
    challenges within and across most research domains. AI researchers don’t
    understand the business use cases / business value of industry, and industry does
    not understand the importance of the data. For example: an AI researcher seeking to
    solve a problem in healthcare, finance, or manufacturing in which they don’t have
    domain expertise. We can and must fix this problem.

33. From NAIIR page 4-2, finding 4-3
    

    “…data curation is a substantial challenge for researchers in all domains”

    [Response by JW] Data curation is not the responsibility of NAIRR. We must
    design a pathway for the private sector to contribute data via their local AI center of
    excellence.

34. From NAIIR page 4-2, finding 4-4
    
    “There are substantial costs to combining and linking heterogeneous data.”

    [Response by JW] This is not the responsibility of NAIRR nor its expertise. The
    concern about R&D data relating to privacy concerns can be managed via the p
    AI centers of excellence.

35. From NAIIR page 4-4, “Recommendation 4-5
    

    “…incentivizing the contribution of high-quality data for AI R&D to the Federated System”

    Recommendation 3-13 on page 3-7
    

    “…incentivize contributions to the NAIRR user community or to the public good”

    [Response by JW] Rewarding contributors of data “in kind” is beyond the scope of
    NAIRR. There are too many factors, and considerations to assess. Which can be
    explained to you at your convenience. By creating a pathway for these contributors
    via their local AI center of excellence, there must be educational merit to any and all
    activities relating to data and the proposed federated system.

    Any and all contributors should be vetted stakeholders belonging to an AI center
    of excellence. Otherwise, we can’t control the access to sensitive data. AI is about
    the people, and each stakeholder has an “individual” behind it who must be vetted
    and with AI educational merit

36. From NAIIR page 4-4, recommendation 4-8
    

    “…the NAIRR should provide high-value, core data sets to establish a value proposition
    and jump-start search and discovery”

    [Response by JW] This statement is not congruent with previous statement on
    page 4-3 (recommendation 4-1) that the “sheer volume and variety of data of
    interest will make it impossible for the NAIRR to curate any of all of it”

37. From NAIIR page 4-3, recommendation 4-1
    

    “…data resources could be contributed by researchers, non-profit or commercial
    organizations, government agencies, state, local, and/or tribal government, academic
    institutions, and citizen scientists”

    [Response by JW] If we treat the field of artificial intelligence with the
    same academic merit as healthcare, then we can identify who is a stakeholder or
    citizen scientist. Doctors need a medical degree to practice medicine, but they also
    have physician’s assistants, they have nurses and other medical support specialists.
    Citizen scientists should belong to an academic institution, AI center of excellence, AI
    lab, or other vetted AI community/ecosystem. The idea is not to raise the barrier for
    users/stakeholders, rather the goal is to redesign the entry margin so that
    everything is done with educational merit.

38. From NAIIR, page 4-5,
    

    “Government data sets… key domains in which the Federal Government could help drive
    AI-based innovation are transportation, healthcare, and natural hazards research, among
    many others…”

    [Response by JW] Each of these domains requires contextual understanding of the
    AI problem to solve with the said data set owned by the particular Federal Government
    agency. For example, the proposed AI center of excellence in a region can be supply p
    chain, clean energy or healthcare etc focused to allow the connectivity of resources
    into the NAIRR system

39. From NAIIR page 4-6, recommendation 4-10,
    “data generated by Federally funded research”

    [Response by JW] Despite research that’s been federally funded, the day to day
    employees or stakeholders that are involved in a project might not be American
    citizens in America. Many projects often outsource/offshore their work, and allowing
    access to such resources might compromise the integrity of the NAIRR. There are
    many considerations with a startup being given access that I am ready to share at
    the appropriate time.

40. From NAIIR page 5-2
    “Zero trust architecture presumes that no actor, system, network, or service operating
    outside or within the security perimeter is trusted”

    [Response by JW] Why do we adhere to a “zero trust architecture” but do not have
    a “zero trust AI educated stakeholder” policy? AI is about the people, the technology
    and algorithms have been commoditized, without the AI educated workforce, we can’t
    undertake cutting edge research and solve real-world problems. Access to NAIRR
    should be inclusive of those with AI degrees, AI formal education, and other reasons
    previously mentioned in this submission. We don’t want to exclude anyone, at the
    same time, users should have the AI educational merit from vetted institutions. Let us
    build the next generation of AI talent so that we remain #1 with AI talent globally.

CONCLUSION BY JOSEPH WEHBE & FURTHER CONTRIBUTION TO NAIRR

I am ready to serve my country in building the American AI ecosystem. I believe we’re at an
inflection point in history to execute otherwise we’ll lose the AI war. The Government has given
us all a platform to act now & thereby ignited a passion in me to believe that there’s a call to
action to build the next generation of AI talent.

Highlights of AI expertise I offer:
– The benefits of AI ecosystems are distributed unevenly across the US & don’t exist
in the heartland.
– Dismantle institutional, & systematic barriers that limit opportunities for stakeholders
in AI & bring educational merit to the AI workforce.
– Redesign entry margin for stakeholders so the US can build a pipeline of AI talent.

I have both the educational & technical expertise to serve my country in any AI project that will keep the US as a world AI leader. “Until the mayor or superintendent in small town New Jersey understands they must introduce an AI K-12 curriculum, we have alot of work to do.”
–Joseph Wehbe

Your faithfully,

Joseph Wehbe
“AI Ecosystem Builder”

“The recipe is straightforward, let us invest in AI Education, AI Research & Development.”-JW