The companies that landed awards under Interior’s Foundation Cloud Hosting Services II contract are Accenture Federal Services, IBM, CGI Federal, SAIC, Cognosante, Zivaro and Smartronix. Those seven vendors will compete for task orders under the greater indefinite-delivery, indefinite-quantity contract. 

The announcements of the seven awards detail Interior’s statement of work for the contract, requiring cloud license and support services for infrastructure, platform and software in a cloud environment. 

This contract comes as the initial iteration of the Foundation Cloud Hosting Services vehicle is set to expire later this year. Awarded in 2013 to 10 contractors, the initial contract has a $10 billion ceiling.

In the department’s initial statement of work, it wrote about the latest contract: “This follow-on FCHS contract is shifting to multiple service provider focus and integration among our solutions and a hybrid model hosting environment vision. Providing interoperability and data integrations between multiple technologies and services across the Department bureaus and offices.”

It also complements Interior’s $1 billion cloud contract award to Peraton last year for its Cloud Hosting Solutions III acquisition, which enlists the IT contractor to manage the department’s portfolio of cloud services.

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The pilot for the resource, referred to as the NAIRR, is composed of contributions from 11 federal agencies and 25 private sector partners, including Microsoft, Amazon Web Services, NVIDIA, Intel, and IBM. Those contributions range from use of the Department of Energy’s Summit supercomputer to datasets from NASA and the National Oceanic and Atmospheric Administration to access for models from OpenAI, Anthropic, and Meta.

“A National AI Research Resource, simply put, has the potential to change the trajectory of our country’s approach to AI,” NSF Director Sethuraman Panchanathan told reporters on a call ahead of the launch. “It will lead the way for a healthy, trustworthy U.S. AI ecosystem.”

The idea for a NAIRR has been under discussion for some time as a way to provide researchers with the resources needed to carry out their work on AI, including advanced computing, data, software, and AI models. Supporters say a NAIRR is needed because the computational resources that AI demands aren’t often attainable for prospective academic researchers.

Katie Antypas, director of NSF’s Office of Advanced Cyberinfrastructure, underscored that need on the call with reporters, saying “the pilot is the first step to bridging this gap and will provide access to the research and education community across our country — all 50 states and territories.”

The launch comes ahead of a requirement in President Joe Biden’s Oct. 30 AI executive order for NSF to establish a pilot project for the resource within 90 days. According to an NSF release and accompanying call with reporters, the two-year pilot will serve as a “proof of concept” for the full-scale resource. 

Creating a pilot that would run parallel to a full buildout was among the options the NAIRR Task Force, which was co-chaired by NSF and the Office of Science and Technology Policy, presented in its implementation framework for the resource roughly a year ago. 

The pilot is divided into four focus areas: “NAIRR Open,” which will provide access to resources for AI research on the pilot’s portal; “NAIRR Secure,” an AI privacy- and security-focused component co-led by DOE and the National Institutes of Health; “NAIRR Software,” which will facilitate and explore the interoperable use of pilot resources; and “NAIRR Classroom,” which focuses on education, training, user support, and outreach.

Antypas said anticipated uses of the pilot might include a researcher seeking access to large models to investigate validation and verification or an educator from a community college, rural, or minority-serving institution who’s able to obtain AI resources for the students in their classroom.

When asked how resources are being vetted for the NAIRR, Antypas said there will be a process for datasets that become part of the resource. “We are going to be standing up an external ethics advisory committee to be providing independent advice on, you know, what are those standards? How do we develop those with a pilot?” Antypas said.

Quality of datasets came into focus recently after a Stanford report flagged the existence of child sexual abuse material on a popular AI research dataset known as LAION-5B. FedScoop previously reported that NSF doesn’t know if or how many researchers had used that dataset — it doesn’t track this aspect of principal investigators’ work — but highlighted the need for a NAIRR to provide researchers with trusted resources.

Among the support from industry, Microsoft is contributing $20 million in compute credits for its cloud computing platform Azure, in addition to access to its models, and NVIDIA is contributing $30 million in support, including $24 million in computing access on its DGX platform.

Some contributions are tied to specific uses. OpenAI, for example, will contribute “up to $1 million in credits for model access for research related to AI safety, evaluations, and societal impacts, and up to $250,000 in model access and/or ChatGPT accounts to support applied research and coursework at Historically Black Colleges and Universities and Minority Serving Institutions,” according to information provided by NSF. Anthropic, meanwhile, is providing 10 researchers working on climate change-related projects with API access to its Claude model.

The list of partners could grow as time goes on. Tess deBlanc-Knowles, special assistant to the director for AI in the Office of the Director at NSF, noted on the call with reporters that the pilot came together on “a really ambitious timeline” and said “it’s important to note that this is just the beginning.”

deBlanc-Knowles said NSF hopes to bring on more partners and add more resources after the launch “so that we can serve more researchers, educators, and more places, and start to really make progress towards that bigger vision of the NAIRR of democratizing AI.”

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AI-trained employees who have been upskilled and certified through intensive training programs rather than earning a degree from a four-year institution can be considered unqualified to work for the federal government, according to testimony from Timi Hadra, an IBM client partner and the company’s senior state executive for West Virginia. 

Despite the call to action from the White House through the AI executive order, Hadra said that the government’s efforts so far to hire more talent from diverse educational backgrounds are “not enough.”

Subcommittee Chair Nancy Mace, R-S.C., said in an interview with FedScoop after the hearing that Hadra’s answer was illuminating.

“Hearing that testimony today and asking that question of IBM is certainly very helpful to understand what the real world and the reality is like, on the ground with tech companies that have these federal contracts,” Mace said. “If 20% of the workforce, or more, doesn’t have that four-year degree, it’s clearly hindering our ability to meet the demands that we have in the tech, cyber and innovation AI space.”

Hadra noted that IBM has a six-month curriculum for its cybersecurity apprenticeship program that trains employees in these disciplines. She said that the workers are “ready to hit the ground running on those programs, and because they don’t meet those minimum qualifications, we are not able to put them on that contract.”

Mace added that the more recently trained and upskilled employees could be “more qualified” than those who hold a degree because “they put that skillset into practice.” 

“We have a shortage of 700,000 cybersecurity workers across the private and public sectors,” Mace said during the hearing. “We know that our traditional education system doesn’t produce nearly enough degreed graduates in the field to fill the need. We also know that that shortfall would be much worse if not for the appearance of nimble educational alternatives. That includes short-term ‘boot camp’ programs that issue non-degree credentials like certifications and badges.”

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Types of clouds

Strategy development relies on understanding basic cloud architecture. There are four main types of cloud computing architectures: private clouds, public clouds, hybrid clouds, and multi-clouds. Multi-clouds can have multiple public cloud service providers (CSPs); hybrid-clouds have an on-premises data center as well as a public cloud; and hybrid multi-clouds have a mixture of private and public cloud services. Each architecture has a different impact on an agency.

The intersection of AI and cloud computing

The synergy between AI and cloud computing has been evident since the inception of these technologies. Cloud computing facilitated the rise of AI, but in recent years, attention has shifted to leveraging AI for cloud management and cybersecurity. AI has played a pivotal role in the field of AIOPs (AI for Operations), aiding in the creation of various AI-driven copilots for tasks like security and modernization.

Utilizing AI to tackle cloud modernization challenges in the federal market

Throughout the cloud modernization journey, AI and generative AI can help ensure leaders make informed decisions and expedite the elimination of technical debt. Here are key challenges these technologies can help overcome.

Challenge #1: Legacy applications

Over the past few years, cloud adoption has become the default choice for new applications. However, a significant number of legacy applications in the U.S. federal market remains unmigrated for various reasons, which increases technical debt. There is a strong case for using AI to expedite the elimination of this technical debt to align with mission objectives. Additionally, there is tremendous potential for generative AI to migrate on-premises infrastructures to public or private clouds.

The primary challenge with legacy applications in the federal sector is the discovery and analysis process. To overcome this, deep learning tools can be used to crawl through legacy environments, collecting data to create a comprehensive application overview within complex infrastructures. The true value of such deep learning techniques emerges when applied at an enterprise scale. For years, creating an enterprise-wide view of the application portfolio has been a challenge for enterprise architects involved in modernization. But deep learning can create a reporting system that provides a digital modernization roadmap for the enterprise. Generative AI can then leverage these deep learning datasets to facilitate informed modernization decisions through interactive techniques.

Challenge #2: Choosing a path forward

Another critical concern in cloud modernization is selecting the optimal path. Binary choices between modernization and migration may not always serve the best interests of the organization or agency. While certain migration strategies make sense at the application level, adopting a portfolio view can lead to more optimal decisions for enterprises. 

Various generative AI-based techniques can resolve this dilemma at the portfolio level through an automated application rationalization framework. A generative-AI-based query system will greatly assist in making appropriate migration choices that produce the desired result.

Challenge #3: Cost management

While the true power of the cloud lies in its elasticity, over time, this elasticity can result in “cloud sprawl,” which increases cost and can increase technical debt.

Implementing AI and generative AI tools that perform cloud cost management through regular recommendation optimization of infrastructure can help enterprises save substantially and ensure compliance with federal contracts. A continuous, intelligent view of costs can aid in planning project budgets to minimize the impact of migration costs.

Challenge #4: Compliance

Ensuring cloud compliance is crucial in federal markets where regulations and security measures are, by necessity, always top of mind.

Generative AI can produce template-based infrastructure-as-code, forming the foundation for consistent, secure, and best-practice cloud environments. Following the creation of cloud infrastructure by a cloud AI companion, another Generative AI tool can autonomously scan for threats, compliance with corporate policies, adherence to industry best practices, and alignment with cloud provider frameworks such as the AWS Well-Architected Framework. Scanning for misconfigurations is especially vital, as those account for a significant portion of cloud security challenges.

Beyond the dashboard

Managing a cloud infrastructure that consists of millions of resources is a daunting task when relying solely on dashboards and spreadsheets, and it carries significant risks. Many cloud operation management vendors now offer AI-driven solutions specifically for managing hybrid and multi-cloud environments, a well-established use case in the realm of AI in cloud computing known as AIOPs. These generative AI use cases include:

1. Workload management strategy through AI
2. Financial operations (FinOps) management strategy through deep analytics
3. Formulating policies for data management in the cloud
4. Defining security postures and addressing workload-specific security requirements
5. Identifying and training staff to develop a knowledgeable workforce

In summary, AI and generative AI hold great potential to assist federal agencies that need to reduce technical debt and accelerate cloud modernization initiatives at mission-critical speeds. AI and generative AI capabilities span every phase, from assessment to operations. While this market is still emerging and evolving, many independent software vendors (ISVs) and service providers are already offering solutions in this domain.

For clients looking to modernize on Amazon Web Services, IBM Consulting plans to integrate generative AI services into its proprietary IBM Consulting Cloud Accelerator to help accelerate the cloud transformation process.

Sandeep Shilawat is vice president of cloud technology at Octo, an IBM company.

For more information, contact Sandeep.Shilawat@octo.us or visit the IBM Booth #930 at the  AWS re:Invent Conference in Las Vegas, Nevada, beginning November 27, 2023.

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The product, watsonx.governance, is aimed at providing organizations with “nutrition label”-like detail about where and how any AI model gets the information for its outputs, so they can meet anticipated transparency and safety regulations, the company said. 

Rob Thomas, IBM’s chief commercial officer and senior vice president of IBM Software, told reporters Monday evening that while AI presents opportunities, it also poses a problem. 

“The problem is that if AI is a black box, and people are just inserting questions and answers come out, that’s a significant issue,” Thomas said. “Because nobody really believes that we can just trust what we’re told blindly.”

The announcement comes as interest in generative AI has soared with tools like ChatGPT, and governments race to create their own frameworks for how the nascent technology should be regulated. Last month, President Joe Biden announced his much-anticipated executive order on AI safety and security.

As interest in AI has grown for both the public and private sectors, so too have concerns about how the models produce their answers. The Government Accountability Office, for example, which is in the process of developing its own large language model, recently noted that it wants to avoid an “auto magical” interface that doesn’t explain its reasoning.

Thomas on Monday said he envisions both business and government use of the tool.

“I see application at every level of government,” he said. “Federal, obviously, state, local, anywhere that an organization is going to be using AI to drive some level of productivity, or … some level of better service to citizens. You really have to have the capability to explain what is going on, and why it’s happening when it is.”

Thomas said IBM has been in conversation with government entities primarily in the U.S., including state and local governments across the country, but he noted that this will be new territory for the government. 

Where companies have over 15 years of experience in meeting reporting requirements for their systems after the financial crisis, “it’s a bit newer in government,” he said. “So I think it will take some time. It will be a bit of a learning curve, but I expect it will happen pretty quickly.”

Chris Padilla, vice president of IBM government regulatory affairs, also noted the company’s recent conversations with government entities at the Monday event.

Padilla said IBM briefed financial officials Monday, including those from the Federal Reserve, the Federal Deposit Insurance Corp., and the Treasury Department. The company will also brief officials in Europe, including the European Parliament, on Tuesday. 

Watsonx.governance will be generally available for businesses to use in early December and can be used with AI models from IBM or other providers, including open source, according to a Tuesday release.

DefenseScoop reporter Brandi Vincent contributed to this article.

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The National Semiconductor Technology Center, which is expected to launch this fall, was a key component of the CHIPS Act that focused on promoting semiconductor manufacturing. The members announced Wednesday will now set up the nonprofit that will operate the NSTC and hire executive leaders.

“The members of the board of trustees will help to establish an NSTC that is visionary, agile and responsive to the needs of the semiconductor ecosystem,” Laurie E. Locascio, director of the National Institute of Standards and Technology and Under Secretary of Commerce for Standards and Technology, said in a release.

New members of the board from the private sector include retired Intel Corp. CEO Craig Barrett and retired IBM technology leader Nick Donofrio, as well as Donna Dubinsky, the former CEO of handheld electronics companies Palm Computing and Handspring; and Robin Abrams, former CEO of microcontroller product provider ZILOG Inc.

Board members from academia and research include Erica Fuchs, professor of engineering and public policy at Carnegie Mellon University; Jim Plummer, a professor of electrical engineering and former dean at Stanford University; and Reggie Brothers, principal with MIT Lincoln Laboratory and the former CEO of BigBear.ai and former CTO of Peraton.

The announced board members also have experience in government. Brothers was previously Under Secretary for Science and Technology at the Department of Homeland Security and served in roles at the Department of Defense and the Defense Advanced Research Projects Agency (DARPA). Dubinsky recently worked as a senior counselor to the Commerce Secretary.

According to a press release from the Department of Commerce‘s NIST, the agency “expects to enter into a funding agreement with the newly formed nonprofit so that it can begin to operate NSTC.”

The center is one of several research and development programs established under the CHIPS Act that is being overseen by NIST’s CHIPS Research and Development Office. The others are the National Advanced Packaging Manufacturing Program, the CHIPS R&D Metrology Program and up to three new Manufacturing USA institutes focused on semiconductors.

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The companies — Adobe, Cohere, IBM, Nvidia, Palantir, Salesforce, Scale AI, and Stability — join an initial seven that convened at the White House in July to sign on to the set of voluntary commitments overseeing how the emerging technology is developed and used. Representatives from the cohort met with Secretary of Commerce Gina Raimondo, White House Chief of Staff Jeff Zients, and other senior administration officials at the White House on Tuesday.

The first companies to accept the commitments were Amazon, Anthropic, Google, Inflection, Meta, Microsoft, and OpenAI.

The Biden administration says these commitments are an “immediate step and an important bridge to government action,” according to a fact sheet, as the White House develops an upcoming executive order and lawmakers consider legislation focused on AI. The fact sheet acknowledges the in-the-works executive order, saying the Office of Management and Budget will “soon release draft policy guidance for federal agencies to ensure the development, procurement, and use of AI systems is centered around safeguarding the American people’s rights and safety.”

“These commitments, which the companies have chosen to undertake immediately, underscore three principles that must be fundamental to the future of AI—safety, security, and trust—and mark a critical step toward developing responsible AI,” the White House said in the fact sheet. “As the pace of innovation continues to accelerate, the Biden-Harris Administration will continue to take decisive action to keep Americans safe and protect their rights.”

In total, the companies agreed to eight commitments, which range from opening their algorithms to security testing and sharing information across industry on them prior to launch to a variety of measures that put security, transparency and responsibility at the forefront of their products.

“We applaud the Administration for making this a priority — open discussions between industry and policymakers like today are foundational to enacting safeguards without stopping AI development,” Akash Jain, president of Palantir U.S. government and an attendee at the meeting, said in a statement. “Today, Palantir, along with other leading AI companies, made a set of voluntary commitments to advance effective and meaningful AI governance, which is essential for open competition and maintaining US leadership in innovation and technology.”

These commitments complement the actions of the U.S.’s allies, such as Japan’s G-7 Hiroshima Process, the United Kingdom’s Summit on AI Safety, and India’s leadership as Chair of the Global Partnership on AI. 

Short of forthcoming policy or legislation, the Biden administration in the past year issued its foundational Blueprint for an AI ‘Bill of Rights,’ which is meant to work in tandem with the AI Risk Management Framework published by the National Institute of Standards and Technology. However, some policy and tech experts say those leading frameworks are inherently contradictory and provide confusing guidance for tech companies working to develop innovative products and the necessary safeguards around them.

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“Even when companies are good and want to have some of the protections that we need, if their competitors aren’t doing it, they’re going to be under huge pressure not to do it themselves,” said Schumer. “That’s why Congress must join the AI revolution. The federal government — we have no choice.”

Speaking alongside IBM CEO Arvind Krishna at an event in downtown New York, the senator repeated his support for his SAFE Innovation Framework. That framework, which was introduced last month, aims to focus on regulatory questions related to competition, open-source technology, and federal financial incentives that are raised by AI.

Schumer said he was particularly concerned with AI explainability — the idea that the technology must be able to articulate why it makes one decision, and not another, which he called one of the most “difficult” technical issues in AI. “You want the system to spit back some kind of satisfying answer,” he remarked.

Stalled immigration reform has also exacerbated technology workforce challenges, Schumer added.

The Senate Majority leader is now planning nine different forums, which will occur later this fall, that will focus on potential avenues for regulating the technology. The idea is to include members of private industry, but also skeptics and critics of the technology. These panels, called “Insight Forums,” will focus on issues including national security, privacy, and high-risk applications and bias, and the implications of AI for the workforce.

Schumer played a critical role in passing the Chips and Science package passed last year — Krishna cited that legislation as an critical milestone for US tech competitiveness. Notably, IBM’s semiconductor business, along with several New York fabs, upstate could be major beneficiaries of that package.

Schumer’s comments also come as federal officials, along with Congress, weigh myriad approaches to regulating AI.  There’s growing pressure on the US to catch up to the European Union, which recently passed a draft law called the AI Act. At the same time, federal officials are also searching for ways to push US companies to the forefront of global AI technology development — particularly as China continues to invest in the technology, too.

As the quest to regulate the tech ramps up, AI experts, activists, and civil rights groups have continued to highlight  the myriad harms that artificial intelligence can create or exacerbate, including misinformation, bias and discrimination, intellectual property issues, and data privacy and cybersecurity risks. 

Amid calls to both accelerate and rein in AI development, tech companies have — unsurprisingly — advocated for their own preferred regulatory paradigms. IBM has extensively promoted a “precision regulation” approach to artificial intelligence, which would involve focusing on particular tools and particular applications. The company has supported frameworks developed by agencies like NIST — and has opposed the notion of creating a new federal agency to focus on the technology. 

“We also believe one must not try to regulate the actual algorithms — or what we call the underlying computer science — all that is going to make it go to a place where the regulations are not there,” said Krishna.  “But you must regulate use cases because those are what drive the benefit and the harm that is there.”

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The tech behemoths reiterated their support for flexible risk based AI regulatory frameworks like the National Institutes of Standards and Technology (NIST)’s AI Risk Management Framework rather than more horizontal, rigid, top down regulatory approaches like the proposed EU AI Act that’s currently being debated. 

Google and IBM were responding to a public consultation launched in May by the Biden administration to gather evidence from industry and researchers on the major threats and opportunities presented by AI. It is one of several recent inquiries launched to examine the technology, including a request for information from the National Telecommunications and Information Administration in April.

“IBM urges the Administration to adopt a “precision regulation” posture towards AI. This means establishing rules to govern the technology’s deployment in specific use-cases, not regulating the technology itself,” the company said in its comments submitted to the White House Office of Science and Technology Policy regarding national priorities for AI.

“IBM supports leveraging existing authorities to regulate AI. As such, we recommend that the Administration support an approach to regulating AI that prioritizes empowering every agency to be an AI agency,” the company said.

IBM in its comment to the OSTP added that the White House should push for the greater resources and the expansion of the GSA’s AI Center of Excellence, the National AI Research Resource (NAIRR), and agencies with high compute needs like the Commerce Department and the Energy Department.  

Google in its comment to the OSTP reiterated the importance of NIST taking the lead on trustworthy AI policies, standards and best practices in the U.S., and highlighted the need to ensure government acquisition policies are reformed to require AI training for acquisition workforce, remove barriers to data governance that harness the power of AI, and push federal agencies to use AI systems to enhance operations and decision making. 

The search giant also pushed for the White House to establish an AI competitiveness council in the form of a National AI Security & Competitiveness Council, or reactivate the National Security Commission on AI (NSCAI), to assess research and development (R&D) gaps and AI deployment to ensure that the US government is equipped to address security and defense challenges from foreign rivals and advocate for aligned international governance. 

IT global trade association, the Information Technology Industry Council (ITI), also submitted a comment to the OSTP calling for NIST to be at the forefront of AI regulatory technical standards. 

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Under terms of the contract, the two companies will compete for future task orders alongside Booz Allen Hamilton and IBM, which also have spots on the contract.

The contract vehicle supports the IRS applications development office, which is the IRS bureau responsible for building, testing and delivering integrated information application systems. It is intended to help bring more than 400 legacy IRS systems under one contract to modernize existing systems, build out analytics and improve cybersecurity.

Enterprise Development, Operations Services has a ceiling of up to $2.6 billion and a performance period of seven years.

Details of the contract award come after the agency earlier this month tested a prototype of a free tax filing system that could allow Americans to file tax returns digitally and free of charge.

IRS on May 16 submitted a report to Congress, undertaken in collaboration with nonprofit New America, to evaluate a direct file option for taxpayers. Following this feasibility study, the agency said it would look to begin a pilot project to test the e-filing system during the 2024 tax filing season.

Commenting on the contract award, AFS Managing Director and IRS Client Lead Jessica Powell said: “The IRS had the foresight and vision to create a contract vehicle with the ability to keep up with, or even ahead of, new legislative demands.” She added: “These reforms will result in faster processing of returns and quicker distributions of the credits and benefits that are vital to so many Americans. We’re thrilled to offer support to the IRS as they make these technology enhancements happen.”

Maximus Federal General Manager Teresa Weipert said: “The IRS laid out an ambitious plan to transform how the agency provides services and capabilities to improve the taxpayer experience as well as IRS internal operations. Maximus, through our excellent IRS team and modernization expertise, will deliver on that plan and taxpayer experience.”

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