Enterprise

Should private sector startups get to use a federally funded AI research cloud?

A proposed federally funded cloud for AI research was expected by some to be a resource for academics. Now, in addition to likely involvement by the top commercial cloud providers, The National AI Research Resource might be available to private-sector startups too.

Hands on laptop

Just who will get to use the NAIRR remains in question.

Photo: ThisisEngineering RAEng/Unsplash

Rima Seiilova-Olson wasn’t sure why she was the only startup founder on a panel full of academics.

“I feel a little puzzled,” said Seiilova-Olson, co-founder and chief machine-learning scientist at a mental health AI startup Kintsugi, talking to Protocol about her participation in a Feb. 16 federal task force meeting about how she might use a federally funded AI research cloud.

The National AI Research Resource, or NAIRR, would be a repository of data and tools for AI research combined with access to the computing power necessary to develop machine learning and other AI systems. The proposed project is currently being evaluated by a task force overseen by the White House Office of Science and Technology Policy and the National Science Foundation.

Just who will get to use it remains in question. The National Artificial Intelligence Initiative, established by Congress in 2020, envisions the NAIRR as a research hub “for AI researchers and students across scientific fields and disciplines” including from “communities, institutions, and regions that have been traditionally underserved.”

That legislation, which established the task force planning the NAIRR, does not exclude private-sector researchers, but some believe they should not belong in that community. Researchers from the Stanford Institute for Human-Centered Artificial Intelligence, an influential proponent of the project, have indicated that the government-funded research hub should focus on the needs of academic and nonprofit researchers.

“Public investment in AI research for noncommercial purposes may help to address some of the issues of social harm we see presently in commercial contexts, as well as contribute to shifting the broader focus of the field toward technology developed in the public interest by the public sector and civil society, including academia,” wrote the institute in an October 2021 white paper.

The case for startup access

Amid representatives from five colleges and universities, Seiilova-Olson was the lone speaker representing the private sector at the virtual panel discussion addressing the needs of various potential users of the NAIRR.

She discussed her goals as a startup founder trying to compete with Big Tech. Kintsugi is developing machine-learning models to help detect clinical depression and anxiety based on voice data, and she said that means the company needs access to costly computing power to process large-scale, unstructured data.

But in addition to the nuts and bolts of building AI, Seiilova-Olson said the NAIRR also should provide training or community resources for people without access to traditional computer-science education.

“An open resource that is freely available for people, I think it’s extremely important. It played a huge role in my journey from being a regular software engineer to being a machine-learning scientist,” she said during the panel discussion, explaining her background using online training to advance her AI skills.

Rima Seiilova-Olson Rima Seiilova-Olson.Courtesy: Rima Seiilova-Olson

“There’s a big need for small players like myself to benefit from these resources, and I’m not talking about compute power and data,” she told Protocol last week, adding that she hopes NAIRR can provide access for STEM-related educational resources for underrepresented people.

After Seiilova-Olson spoke at the NAIRR meeting, Lynne Parker, task force co-chair and the founding director of the National AI Initiative Office at the OSTP, told her she made “a really strong case for how the availability of resources can really make a huge impact, including in particular for startups.”

Jen King, privacy and data policy fellow at the Stanford Institute, said use by private-sector researchers would pose legal and logistical issues, as well as distract from the core mission of the research cloud. “Overloading this resource out of the gate to address very different sets of users — small business and academic researchers — may jeopardize its development and ultimately its effectiveness,” she told Protocol.

Seiilova-Olson said she was personally invited by Parker to join the panel discussion, but she wasn’t sure why. Her company has ties to the National Science Foundation. Kintsugi has received around $2 million in NSF grant funding with another $1.5 million in funding on the way, according to a company spokesperson.

“In NAIRR task force discussions to date, a number of task force members have suggested that small business and startups, specifically those funded by federal [small business innovation] grants, have important perspectives that could help inform the task force’s work,” said Andres Anzola, press secretary at the OSTP. He said the task force co-chairs worked with federal subject matter experts to identify a grant recipient to participate in the panel discussion.

Seiilova-Olson, who co-founded her company after a personal experience with postpartum depression in the hopes of helping other parents, said startups are important contributors to AI research in part because they develop AI that is intended to be commercialized, and therefore possibly meet people’s needs.

However, noting her respect for academic research, she also said, “academic researchers have their own ideas that may be a bit detached from the needs of regular citizens.”

King said NAIRR may not be the appropriate home for startup AI research. “Small business AI may be expressing legitimate needs and constraints with competing against big AI, but the NAIRR may not be the right solution for addressing them,” King said.

Private-sector presence on the task force

The NAIRR task force already has a private-sector presence, which is by design according to the legislation that established it.

Among its 12 members are Andrew Moore, Google Cloud’s vice president and general manager for AI and Industry Solutions; Daniela Braga, founder and CEO of AI startup Defined.ai, formerly DefinedCrowd; and Oren Etzioni, a venture partner at investment firm Madrona Venture Group who also works with new AI startups as part of an incubator fund affiliated with AI2, a nonprofit he leads that was founded by Microsoft co-founder Paul Allen.

Google has said it wants researchers who don’t need computing power from the research cloud to be able to access data in the NAIRR. For one thing, that would ensure that researchers from commercial cloud providers like Google, Amazon and Microsoft would be able to take advantage of the data flowing through the system.

Health care data giant Cerner, recently acquired by Oracle, also indicated interest in the NAIRR. The company, which helps health care customers manage patient data and is increasing its use of AI for hospital administration and patient care, emphasized public-private partnerships when it comes to how data in the resource is handled.

“It is critical that the governance structure for the NAIRR involve[s] representation from knowledgeable and relevant public-private entities for the types of data and purposes of collection that prevail,” the company wrote in an October 2021 response to a request for information by the task force. “Public policies and laws are government core competencies. However, administration of the data asset and associated technical aspects may be more suited for a public-private partner.”

Meanwhile, opponents of the project, including some advising AI policy inside the Federal Trade Commission, have raised concerns that the NAIRR will be designed primarily to enable massive-scale AI projects that, by default, would require assistance from big private-sector cloud providers.

Still, there’s no shortage of academics who see value in private cloud providers building and maintaining the NAIRR. “I would strongly urge that the NAIRR would be based on the existing cloud providers in the commercial space,” said Tom Dietterich, distinguished professor emeritus in the Collaborative Robotics and Intelligent Systems Institute at Oregon State University, during the panel discussion.

A task force working group focused on compute capabilities of the NAIRR is also weighing the possibility of working with commercial cloud providers.

During a separate session of the February task force meeting, research staff from the Science and Technology Policy Institute, a federally funded research and development center, said that partnering with established entities like commercial cloud providers could provide the NAIRR with a range of data sources, a workforce pipeline and timely updates to data architectures and data security technologies.

However, said Emily Grumbling, a research staff member with the Policy Institute, “relying on these resources could ultimately increase dependence on the private sector and for-profit based resources for the AI [research and development] ecosystem.”

This story was updated to correct the amount of funding that Kintsugi has received.

Fintech

Judge Zia Faruqui is trying to teach you crypto, one ‘SNL’ reference at a time

His decisions on major cryptocurrency cases have quoted "The Big Lebowski," "SNL," and "Dr. Strangelove." That’s because he wants you — yes, you — to read them.

The ways Zia Faruqui (right) has weighed on cases that have come before him can give lawyers clues as to what legal frameworks will pass muster.

Photo: Carolyn Van Houten/The Washington Post via Getty Images

“Cryptocurrency and related software analytics tools are ‘The wave of the future, Dude. One hundred percent electronic.’”

That’s not a quote from "The Big Lebowski" — at least, not directly. It’s a quote from a Washington, D.C., district court memorandum opinion on the role cryptocurrency analytics tools can play in government investigations. The author is Magistrate Judge Zia Faruqui.

Keep ReadingShow less
Veronica Irwin

Veronica Irwin (@vronirwin) is a San Francisco-based reporter at Protocol covering fintech. Previously she was at the San Francisco Examiner, covering tech from a hyper-local angle. Before that, her byline was featured in SF Weekly, The Nation, Techworker, Ms. Magazine and The Frisc.

The financial technology transformation is driving competition, creating consumer choice, and shaping the future of finance. Hear from seven fintech leaders who are reshaping the future of finance, and join the inaugural Financial Technology Association Fintech Summit to learn more.

Keep ReadingShow less
FTA
The Financial Technology Association (FTA) represents industry leaders shaping the future of finance. We champion the power of technology-centered financial services and advocate for the modernization of financial regulation to support inclusion and responsible innovation.
Enterprise

AWS CEO: The cloud isn’t just about technology

As AWS preps for its annual re:Invent conference, Adam Selipsky talks product strategy, support for hybrid environments, and the value of the cloud in uncertain economic times.

Photo: Noah Berger/Getty Images for Amazon Web Services

AWS is gearing up for re:Invent, its annual cloud computing conference where announcements this year are expected to focus on its end-to-end data strategy and delivering new industry-specific services.

It will be the second re:Invent with CEO Adam Selipsky as leader of the industry’s largest cloud provider after his return last year to AWS from data visualization company Tableau Software.

Keep ReadingShow less
Donna Goodison

Donna Goodison (@dgoodison) is Protocol's senior reporter focusing on enterprise infrastructure technology, from the 'Big 3' cloud computing providers to data centers. She previously covered the public cloud at CRN after 15 years as a business reporter for the Boston Herald. Based in Massachusetts, she also has worked as a Boston Globe freelancer, business reporter at the Boston Business Journal and real estate reporter at Banker & Tradesman after toiling at weekly newspapers.

Image: Protocol

We launched Protocol in February 2020 to cover the evolving power center of tech. It is with deep sadness that just under three years later, we are winding down the publication.

As of today, we will not publish any more stories. All of our newsletters, apart from our flagship, Source Code, will no longer be sent. Source Code will be published and sent for the next few weeks, but it will also close down in December.

Keep ReadingShow less
Bennett Richardson

Bennett Richardson ( @bennettrich) is the president of Protocol. Prior to joining Protocol in 2019, Bennett was executive director of global strategic partnerships at POLITICO, where he led strategic growth efforts including POLITICO's European expansion in Brussels and POLITICO's creative agency POLITICO Focus during his six years with the company. Prior to POLITICO, Bennett was co-founder and CMO of Hinge, the mobile dating company recently acquired by Match Group. Bennett began his career in digital and social brand marketing working with major brands across tech, energy, and health care at leading marketing and communications agencies including Edelman and GMMB. Bennett is originally from Portland, Maine, and received his bachelor's degree from Colgate University.

Enterprise

Why large enterprises struggle to find suitable platforms for MLops

As companies expand their use of AI beyond running just a few machine learning models, and as larger enterprises go from deploying hundreds of models to thousands and even millions of models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems.

As companies expand their use of AI beyond running just a few machine learning models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems.

Photo: artpartner-images via Getty Images

On any given day, Lily AI runs hundreds of machine learning models using computer vision and natural language processing that are customized for its retail and ecommerce clients to make website product recommendations, forecast demand, and plan merchandising. But this spring when the company was in the market for a machine learning operations platform to manage its expanding model roster, it wasn’t easy to find a suitable off-the-shelf system that could handle such a large number of models in deployment while also meeting other criteria.

Some MLops platforms are not well-suited for maintaining even more than 10 machine learning models when it comes to keeping track of data, navigating their user interfaces, or reporting capabilities, Matthew Nokleby, machine learning manager for Lily AI’s product intelligence team, told Protocol earlier this year. “The duct tape starts to show,” he said.

Keep ReadingShow less
Kate Kaye

Kate Kaye is an award-winning multimedia reporter digging deep and telling print, digital and audio stories. She covers AI and data for Protocol. Her reporting on AI and tech ethics issues has been published in OneZero, Fast Company, MIT Technology Review, CityLab, Ad Age and Digiday and heard on NPR. Kate is the creator of RedTailMedia.org and is the author of "Campaign '08: A Turning Point for Digital Media," a book about how the 2008 presidential campaigns used digital media and data.

Latest Stories
Bulletins