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Warren gets $1.4 million to help local cloud infrastructure providers compete against the giants

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Started as a side project by its founders, Warren is now helping regional cloud infrastructure service providers compete against Amazon, Microsoft, IBM, Google and other tech giants. Based in Tallinn, Estonia, Warren’s self-service distributed cloud platform is gaining traction in Southeast Asia, one of the world’s fastest-growing cloud service markets, and Europe. It recently closed a $1.4 million seed round led by Passion Capital, with plans to expand in South America, where it recently launched in Brazil.

Warren’s seed funding also included participation from Lemonade Stand and angel investors like former Nokia vice president Paul Melin and Marek Kiisa, co-founder of funds Superangel and NordicNinja.

The leading global cloud providers are aggressively expanding their international businesses by growing their marketing teams and data centers around the world (for example, over the past few months, Microsoft has launched a new data center region in Austria, expanded in Brazil and announced it will build a new region in Taiwan as it competes against Amazon Web Services).

But demand for customized service and control over data still prompt many companies, especially smaller ones, to pick local cloud infrastructure providers instead, Warren co-founder and chief executive officer Tarmo Tael told TechCrunch.

“Local providers pay more attention to personal sales and support, in local language, to all clients in general, and more importantly, take the time to focus on SME clients to provide flexibility and address their custom needs,” he said. “Whereas global providers give a personal touch maybe only to a few big clients in the enterprise sectors.” Many local providers also offer lower prices and give a large amount of bandwidth for free, attracting SMEs.

He added that “the data sovereignty aspect that plays an important role in choosing their cloud platform for many of the clients.”

In 2015, Tael and co-founder Henry Vaaderpass began working on the project that eventually became Warren while running a development agency for e-commerce sites. From the beginning, the two wanted to develop a product of their own and tested several ideas out, but weren’t really excited by any of them, he said. At the same time, the agency’s e-commerce clients were running into challenges as their businesses grew.

Tael and Vaaderpass’s clients tended to pick local cloud infrastructure providers because of lower costs and more personalized support. But setting up new e-commerce projects with scalable infrastructure was costly because many local cloud infrastructure providers use different platforms.

“So we started looking for tools to use for managing our e-commerce projects better and more efficiently,” Tael said. “As we didn’t find what we were looking for, we saw this as an opportunity to build our own.”

After creating their first prototype, Tael and Vaaderpass realized that it could be used by other development teams, and decided to seek angel funding from investors, like Kiisa, who have experience working with cloud data centers or infrastructure providers.

Southeast Asia, one of the world’s fastest-growing cloud markets, is an important part of Warren’s business. Warren will continue to expand in Southeast Asia, while focusing on other developing regions with large domestic markets, like South America (starting with Brazil). Tael said the startup is also in discussion with potential partners in other markets, including Russia, Turkey and China.

Warren’s current clients include Estonian cloud provider Pilw.io and Indonesian cloud provider IdCloudHost. Tael said working with Warren means its customers spend less time dealing with technical issues related to infrastructure software, so their teams, including developers, can instead focus on supporting clients and managing other services they sell.

The company’s goal is to give local cloud infrastructure providers the ability to meet increasing demand, and eventually expand internationally, with tools to handle more installations and end users. These include features like automated maintenance and DevOps processes that streamline feature testing and handling different platforms.

Ultimately, Warren wants to connect providers in a network that end users can access through a single API and user interface. It also envisions the network as a community where Warren’s clients can share resources and, eventually, have a marketplace for their apps and services.

In terms of competition, Tael said local cloud infrastructure providers often turn to OpenStack, Virtuozzo, Stratoscale or Mirantis. The advantage these companies currently have over Warren is a wider network, but Warren is busy building out its own. The company will be able to connect several locations to one provider by the first quarter of 2021. After that, Tael said, it will “gradually connect providers to each other, upgrading our user management and billing services to handle all that complexity.”

Lyron Foster is a Hawaii based African American Musician, Author, Actor, Blogger, Filmmaker, Philanthropist and Multinational Serial Tech Entrepreneur.

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Mike Cagney is testing the boundaries of the banking system for himself — and others

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Founder Mike Cagney is always pushing the envelope, and investors love him for it. Not long sexual harassment allegations prompted him to leave SoFi, the personal finance company that he cofounded in 2011, he raised $50 million for new lending startup called Figure that has since raised at least $225 million from investors and was valued a year ago at $1.2 billion.

Now, Cagney is trying to do something unprecedented with Figure, which says it uses a blockchain to more quickly facilitate home equity, mortgage refinance, and student and personal loan approvals. The company has applied for a national bank charter in the U.S., wherein it would not take FDIC-insured deposits but it could take uninsured deposits of over $250,000 from accredited investors.

Why does it matter? The approach, as American Banker explains it, would bring regulatory benefits. As it reported earlier this week, “Because Figure Bank would not hold insured deposits, it would not be subject to the FDIC’s oversight. Similarly, the absence of insured deposits would prevent oversight by the Fed under the Bank Holding Company Act. That law imposes restrictions on non-banking activities and is widely thought to be a deal-breaker for tech companies where banking would be a sidelight.”

Indeed, if approved, Figure could pave the way for a lot of fintech startups — and other retail companies that want to wheel and deal lucrative financial products without the oversight of the Federal Reserve Board or the FDIC — to nab non-traditional bank charters.

As Michelle Alt, whose year-old financial advisory firm helped Figure with its application, tells AB: “This model, if it’s approved, wouldn’t be for everyone. A lot of would-be banks want to be banks specifically to have more resilient funding sources.” But if it’s successful, she adds, “a lot of people will be interested.”

One can only guess at what the ripple effects would be, though the Bank of Amazon wouldn’t surprise anyone who follows the company.

In the meantime, the strategy would seemingly be a high-stakes, high-reward development for a smaller outfit like Figure, which could operate far more freely than banks traditionally but also without a safety net for itself or its customers. The most glaring danger would be a bank run, wherein those accredited individuals who are today willing to lend money to the platform at high interest rates began demanding their money back at the same time. (It happens.)

Either way, Cagney might find a receptive audience right now with Brian Brooks, a longtime Fannie Mae executive who served as Coinbase’s chief legal officer for two years before jumping this spring to the Office of the Comptroller of the Currency (OCC), an agency that ensures that national banks and federal savings associations operate in a safe and sound manner.

Brooks was made acting head of the agency in May and green-lit one of the first national charters to go to a fintech, Varo Money, this past summer. In late October, the OCC also granted SoFi preliminary, conditional approval over its own application for a national bank charter.

While Brooks isn’t commenting on speculation around Figure’s application, in July, during a Brookings Institution event, he reportedly commented about trade groups’ concerns over his efforts to grant fintechs and payments companies charters, saying: “I think the misunderstanding that some of these trade groups are operating under is that somehow this is going to trigger a lighter-touch charter with fewer obligations, and it’s going to make the playing field un-level . . . I think it’s just the opposite.”

Christopher Cole, executive vice president at the trade group Independent Community Bankers of America, doesn’t seem persuaded. Earlier this week, he expressed concern about Figure’s bank charter application to AB, saying he suspects that Brooks “wants to approve this quickly before he leaves office.”

Brooks’s days are surely numbered. Last month, he was nominated by President Donald to a full five-year term leading the federal bank regulator and is currently awaiting Senate confirmation. The move — designed to slow down the incoming Biden administration — could be undone by President-elect Joe Biden, who can fire the comptroller of the currency at will and appoint an acting replacement to serve until his nominee is confirmed by the Senate.

Still, Cole’s suggestion is that Brooks still has enough time to figure out a path forward for Figure — and if its novel charter application is approved, and it stands up to legal challenges — a lot of other companies, too.

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We read the paper that forced Timnit Gebru out of Google. Here’s what it says

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On the evening of Wednesday, December 2, Timnit Gebru, the co-lead of Google’s ethical AI team, announced via Twitter that the company had forced her out. 

Gebru, a widely respected leader in AI ethics research, is known for coauthoring a groundbreaking paper that showed facial recognition to be less accurate at identifying women and people of color, which means its use can end up discriminating against them. She also cofounded the Black in AI affinity group, and champions diversity in the tech industry. The team she helped build at Google is one of the most diverse in AI, and includes many leading experts in their own right. Peers in the field envied it for producing critical work that often challenged mainstream AI practices.

A series of tweets, leaked emails, and media articles showed that Gebru’s exit was the culmination of a conflict over another paper she co-authored. Jeff Dean, the head of Google AI, told colleagues in an internal email (which he has since put online) that the paper “didn’t meet our bar for publication” and that Gebru had said she would resign unless Google met a number of conditions, which it was unwilling to meet. Gebru tweeted that she had asked to negotiate “a last date” for her employment after she got back from vacation. She was cut off from her corporate email account before her return.

Online, many other leaders in the field of AI ethics are arguing that the company pushed her out because of the inconvenient truths that she was uncovering about a core line of its research—and perhaps its bottom line. More than 1,400 Google staff and 1,900 other supporters have also signed a letter of protest.

Many details of the exact sequence of events that led up to Gebru’s departure are not yet clear; both she and Google have declined to comment beyond their posts on social media. But MIT Technology Review obtained a copy of the research paper from  one of the co-authors, Emily M. Bender, a professor of computational linguistics at the University of Washington. Though Bender asked us not to publish the paper itself because the authors didn’t want such an early draft circulating online, it gives some insight into the questions Gebru and her colleagues were raising about AI that might be causing Google concern.

Titled “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” the paper lays out the risks of large language models—AIs trained on staggering amounts of text data. These have grown increasingly popular—and increasingly large—in the last three years. They are now extraordinarily good, under the right conditions, at producing what looks like convincing, meaningful new text—and sometimes at estimating meaning from language. But, says the introduction to the paper, “we ask whether enough thought has been put into the potential risks associated with developing them and strategies to mitigate these risks.”

The paper

The paper, which builds off the work of other researchers, presents the history of natural-language processing, an overview of four main risks of large language models, and suggestions for further research. Since the conflict with Google seems to be over the risks, we’ve focused on summarizing those here. 

Environmental and financial costs

Training large AI models consumes a lot of computer processing power, and hence a lot of electricity. Gebru and her coauthors refer to a 2019 paper from Emma Strubell and her collaborators on the carbon emissions and financial costs of large language models. It found that their energy consumption and carbon footprint have been exploding since 2017, as models have been fed more and more data.

Strubell’s study found that one language model with a particular type of “neural architecture search” (NAS) method would have produced the equivalent of 626,155 pounds (284 metric tons) of carbon dioxide—about the lifetime output of five average American cars. A version of Google’s language model, BERT, which underpins the company’s search engine, produced 1,438 pounds of CO2 equivalent in Strubell’s estimate—nearly the same as a roundtrip flight between New York City and San Francisco.

Gebru’s draft paper points out that the sheer resources required to build and sustain such large AI models means they tend to benefit wealthy organizations, while climate change hits marginalized communities hardest. “It is past time for researchers to prioritize energy efficiency and cost to reduce negative environmental impact and inequitable access to resources,” they write.

Massive data, inscrutable models

Large language models are also trained on exponentially increasing amounts of text. This means researchers have sought to collect all the data they can from the internet, so there’s a risk that racist, sexist, and otherwise abusive language ends up in the training data.

An AI model taught to view racist language as normal is obviously bad. The researchers, though, point out a couple of more subtle problems. One is that shifts in language play an important role in social change; the MeToo and Black Lives Matter movements, for example, have tried to establish a new anti-sexist and anti-racist vocabulary. An AI model trained on vast swaths of the internet won’t be attuned to the nuances of this vocabulary and won’t produce or interpret language in line with these new cultural norms.

It will also fail to capture the language and the norms of countries and peoples that have less access to the internet and thus a smaller linguistic footprint online. The result is that AI-generated language will be homogenized, reflecting the practices of the richest countries and communities.

Moreover, because the training datasets are so large, it’s hard to audit them to check for these embedded biases. “A methodology that relies on datasets too large to document is therefore inherently risky,” the researchers conclude. “While documentation allows for potential accountability, […] undocumented training data perpetuates harm without recourse.”

Research opportunity costs

The researchers summarize the third challenge as the risk of “misdirected research effort.” Though most AI researchers acknowledge that large language models don’t actually understand language and are merely excellent at manipulating it, Big Tech can make money from models that manipulate language more accurately, so it keeps investing in them. “This research effort brings with it an opportunity cost,” Gebru and her colleagues write. Not as much effort goes into working on AI models that might achieve understanding, or that achieve good results with smaller, more carefully curated datasets (and thus also use less energy).

Illusions of meaning

The final problem with large language models, the researchers say, is that because they’re so good at mimicking real human language, it’s easy to use them to fool people. There have been a few high-profile cases, such as the college student who churned out AI-generated self-help and productivity advice on a blog, which went viral.

The dangers are obvious: AI models could be used to generate misinformation about an election or the covid-19 pandemic, for instance. They can also go wrong inadvertently when used for machine translation. The researchers bring up an example: In 2017, Facebook mistranslated a Palestinian man’s post, which said “good morning” in Arabic, as “attack them” in Hebrew, leading to his arrest.

Why it matters

Gebru and Bender’s paper has six co-authors, four of whom are Google researchers. Bender asked to avoid disclosing their names for fear of repercussions. (Bender, by contrast, is a tenured professor: “I think this is underscoring the value of academic freedom,” she says.)

The paper’s goal, Bender says, was to take stock of the landscape of current research in natural-language processing. “We are working at a scale where the people building the things can’t actually get their arms around the data,” she said. “And because the upsides are so obvious, it’s particularly important to step back and ask ourselves, what are the possible downsides? … How do we get the benefits of this while mitigating the risk?”

In his internal email, Dean, the Google AI head, said one reason the paper “didn’t meet our bar” was that it “ignored too much relevant research.” Specifically, he said it didn’t mention more recent work on how to make large language models more energy-efficient and mitigate problems of bias. 

However, the six collaborators drew on a wide breadth of scholarship. The paper’s citation list, with 128 references, is notably long. “It’s the sort of work that no individual or even pair of authors can pull off,” Bender said. “It really required this collaboration.” 

The version of the paper we saw does also nod to several research efforts on reducing the size and computational costs of large language models, and on measuring the embedded bias of models. It argues, however, that these efforts have not been enough. “I’m very open to seeing what other references we ought to be including,” Bender said.

Nicolas Le Roux, a Google AI researcher in the Montreal office, later noted on Twitter that the reasoning in Dean’s email was unusual. “My submissions were always checked for disclosure of sensitive material, never for the quality of the literature review,” he said.

Dean’s email also says that Gebru and her colleagues gave Google AI only a day for an internal review of the paper before they submitted it to a conference for publication. He wrote that “our aim is to rival peer-reviewed journals in terms of the rigor and thoughtfulness in how we review research before publication.”

Bender noted that even so, the conference would still put the paper through a substantial review process: “Scholarship is always a conversation and always a work in progress,” she said. 

Others, including William Fitzgerald, a former Google PR manager, have further cast doubt on Dean’s claim: 

Google pioneered much of the foundational research that has since led to the recent explosion in large language models. Google AI was the first to invent the Transformer language model in 2017 that serves as the basis for the company’s later model BERT, and OpenAI’s GPT-2 and GPT-3. BERT, as noted above, now also powers Google search, the company’s cash cow.

Bender worries that Google’s actions could create “a chilling effect” on future AI ethics research. Many of the top experts in AI ethics work at large tech companies because that is where the money is. “That has been beneficial in many ways,” she says. “But we end up with an ecosystem that maybe has incentives that are not the very best ones for the progress of science for the world.”

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Daily Crunch: Slack and Salesforce execs explain their big acquisition

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We learn more about Slack’s future, Revolut adds new payment features and DoorDash pushes its IPO range upward. This is your Daily Crunch for December 4, 2020.

The big story: Slack and Salesforce execs explain their big acquisition

After Salesforce announced this week that it’s acquiring Slack for $27.7 billion, Ron Miller spoke to Slack CEO Stewart Butterfield and Salesforce President and COO Bret Taylor to learn more about the deal.

Butterfield claimed that Slack will remain relatively independent within Salesforce, allowing the team to “do more of what we were already doing.” He also insisted that all the talk about competing with Microsoft Teams is “overblown.”

“The challenge for us was the narrative,” Butterfield said. “They’re just good [at] PR or something that I couldn’t figure out.”

Startups, funding and venture capital

Revolut lets businesses accept online payments — With this move, the company is competing directly with Stripe, Adyen, Braintree and Checkout.com.

Health tech venture firm OTV closes new $170M fund and expands into Asia — This year, the firm led rounds in telehealth platforms TytoCare and Lemonaid Health.

Zephr raises $8M to help news publishers grow subscription revenue — The startup’s customers already include publishers like McClatchy, News Corp Australia, Dennis Publishing and PEI Media.

Advice and analysis from Extra Crunch

DoorDash amps its IPO range ahead of blockbuster IPO — The food delivery unicorn now expects to debut at $90 to $95 per share, up from a previous range of $75 to $85.

Enter new markets and embrace a distributed workforce to grow during a pandemic — Is this the right time to expand overseas?

Three ways the pandemic is transforming tech spending — All companies are digital product companies now.

(Extra Crunch is our membership program, which aims to democratize information about startups. You can sign up here.)

Everything else

WH’s AI EO is BS — Devin Coldewey is not impressed by the White House’s new executive order on artificial intelligence.

China’s internet regulator takes aim at forced data collection — China is a step closer to cracking down on unscrupulous data collection by app developers.

Gift Guide: Games on every platform to get you through the long, COVID winter — It’s a great time to be a gamer.

The Daily Crunch is TechCrunch’s roundup of our biggest and most important stories. If you’d like to get this delivered to your inbox every day at around 3pm Pacific, you can subscribe here.

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