Connect with us

Uncategorized

Spacemaker, AI software for urban development, is acquired by Autodesk for $240M

Published

on

Autodesk, the U.S. publicly listed software and services company that targets engineering and design industries, has acquired Norway’s Spacemaker, a startup that has developed AI-supported software for urban development.

The price of the acquisition is $240 million in a mostly all-cash deal. Spacemaker’s VC backers include European firms Atomico and Northzone, which co-led the company’s $25 million Series A round in 2019. Other investors on the cap table include Nordic real estate innovator NREP, Nordic property developer OBOS, U.K. real estate technology fund Round Hill Ventures and Norway’s Construct Venture.

Founded by Håvard Haukeland, Carl Christensen and Anders Kvale, and based in Oslo, Norway — but with a number of other outposts around the globe — the 115-person Spacemaker team develops and sells cloud-based software that utilises AI to help architects, urban designers and real estate developers make more informed design decisions. By having Spacemaker look over a designer’s shoulder, as CEO Haukeland likes to say, the software aims to augment the work of humans and not only speed up the urban development design and planning process but also improve outcomes, including around sustainability and quality of life for the people who will ultimately live in the resulting spaces.

To do this, the platform enables users to quickly “generate, optimize, and iterate on” design alternatives, taking into account design criteria and data like terrain, maps, wind, lighting, traffic and zoning, etc. Spacemaker then returns design alternatives optimized for the full potential of the site.

“It was never our plan in the beginning of 2020 to sell the company,” Haukeland told me on a call last week. “But when we started talking to Autodesk, who have reached out for a while, we realized they share our vision. And we understood that this can put our vision on steroids and we can really reach that vision much faster. And that’s what drives us, that’s what we want to do: We want to realize our vision and get our offering out in the world, at the hands of millions of architects and engineers and developers”.

During a call late Friday, Andrew Anagnost, CEO and president of Autodesk, said the acquisition of Spacemaker is in line with the company’s long-term strategy of using the power of the cloud, “cheap compute” and machine learning to evolve and change the way people design things.

“This is something strategically we’ve been working towards, both with the products we make internally with the capabilities we roll out that are more cutting edge, and also our initiative when we look at companies we’re interested in acquiring,” he said.

“We’ve been watching this space for a while; the application that Spacemaker has built we would characterize it, from our terminology, as ‘generative design’ for urban planning, meaning the machine generating options and option explorations for urban planning-type applications.

“Spacemaker really stands out in terms of applying cloud computing, artificial intelligence, data science, to really helping people explore multiple options and come up with better decisions”.

Image Credits: Spacemaker

Post-acquisition, the plan is to keep Spacemaker as an autonomous unit within Autodesk and (hopefully) not interfere too much with the formula and startup ethos that has seemingly been working, while also enabling the team to have the resources needed to continue on their mission.

“They want to let Spacemaker be Spacemaker; they’re not [just] acquiring our product, they’re acquiring the potential and the journey we’re on as a team,” says Haukeland. “They’re acquiring the mission we’re on, the way we work, the knowledge we have, [and] all our failed attempts along the way… so it’s much more than just swallowing the product”.

That knowledge and those “failed attempts” span not only the Spacemaker CEO’s own background as an architect, but the path to product-market-fit and the technology itself.

“Initially they targeted architects directly, but realised that they have relatively small budgets,” recalls Michiel Kotting, who led the startup’s Series A round on behalf of Northzone. “From Håvards experience in the industry they decided to pivot to serving [property] developers who then give the software to their in-house and external architects. They were surprised to see that they could get significant six-figure deals per project out of the gate”.

He also says the team was convinced early on that generative design is the future. “Rather than be software that can do what architects used to do on paper, the full power of modern day compute is put at the disposal of architects,” he told me. “The path to get there has been a bit like Deep Mind’s AlphaGo project — a myriad of different techniques, ML, AI, rules based optimisation etc. that jointly provide the most powerful result, rather than just ‘lets just throw the latest deep learning model at the project and see what sticks’ “.

“They were actually solving a problem, a problem that our customers were telling us that they wanted solved and liked the way they were solving it,” says Anagnost. “So it wasn’t just a great team with a great idea and some great technology, they actually solved the problem. And I think this is really important: You can play with technology all you like, but if you can’t find the intersection of either creating a whole new opportunity or market or solving an existing problem in a completely new and disruptive way, then you really haven’t created something useful. They’ve created something useful”.

“When we led Spacemaker’s Series A round less than two years ago, we saw a world-leading product and a company with the DNA to push the boundaries of what was possible in applying AI to architecture and property development,” says Atomico’s Ben Blume . “As the global leader in architecture, engineering and construction (AEC) software, and with products that set the standard across the industry, Autodesk’s acquisition validates our belief that world-class AI products are being built here in Europe”.

Image Credits: Spacemaker

In building out the product iteratively, Northzone’s Kotting says the Spacemaker team “honed the art of ‘human in the loop’ “. “The generative design calculates the possible solution space, and the architect can then navigate that space and figure out interesting starting points and see the impact of design choices. So you can design something that is both beautiful/fit for purpose and optimal”.

He also doesn’t think the team would have been able to do that if it wasn’t for a combination of architectural talent and “bleeding-edge” software designers. This is where founding the company in Norway may have been an advantage. “It might not be so obvious you’d find a lot of those in Norway, but some of the hard-core optimisation problems in oil and gas are very similar to the Spacemaker problem, so it is actually a very fertile country for that,” adds Kotting.

The challenge then wasn’t Norway’s talent pool but persuading the most talented people to work for a startup. This is where Spacemaker’s mission, and Nordic culture more generally, was also a strength.

Reflects Haukeland: “What we experienced in the early days is that when you’re trying to solve such a hard problem, [with] such an ambitious journey, you need incredibly talented people who are able to get a lot of autonomy and solve problems, because there are so many problems you need to solve. And I think what we experienced in Norway four years ago was that a lot of the really good people went into either oil and gas or, you know, consulting. And what we saw was that people really want to join a mission where they can have a positive impact, and they can use their capacity and their talent and their brains to solve difficult problems. We were lucky to get so much incredible talent to join us because of that”.

Anagnost also cites Spacemaker’s culture and its European vantage-point as a differentiator. “This is a European high technology company using cutting-edge algorithms and approaches in the cloud and they start it from an ethical framework that might not be as common as startups in other places,” he tells me. “So if you were to ask me what was differentiating here, I think the ethical framework they’re coming in with this is, ‘we’re going to use this data to enable this audience to do a better job of what they do every day. And we’re going to do it in such a way that we’re partnering with the customers, and we’re also creating better outcomes, not just for them but for the whole ecosystem of stakeholders… and one of the stakeholders is the environment of the area. That ethos from a technology company, probably, you know, rose up faster in the European market than it might have in some of the U.S. markets where it’s more about, ‘let’s plow through things,’ and not so much about what is my ethical foundation here and what I’m trying to accomplish?”

However, with Europe’s current infatuation with unicorns — and a growing track record of producing companies valued at $1 billion dollars (or a lot more) — one legitimate question that can be asked is did the Norwegian startup sell too early?

“I think that’s a very VC-oriented perspective, because what it’s really about is, are they selling out earlier on the return for the VCs?” argues the Autodesk CEO. “I think if you look at it through the lens of what the employees and the company is trying to accomplish, they’re going to be able to accomplish more working closely inside of Autodesk than they would have, even if they continue to accept dollars and have their valuation increase. Maybe the VCs might see a smaller return, [but] I don’t think the employees are going to see a bigger net return to their vision. And if you’ve talked to these people, they’re very passionate about what they do”.

“Even though for our taste this exit comes early in the journey, we share the enthusiasm for achieving maximum impact fast, and have seen in the process how important Autodesk believes the Spacemaker product is in their future,” says Kotting.

Meanwhile, Haukeland maintains that Spacemaker has only built “5% of what can be built” and says the industry as a whole is at the beginning of a huge transformation in the way people work. “When you go from designing something and checking how it works to asking your computer for help and having the computer advising you on your shoulder, it’s really changing the game. That is such a fundamental change that it’s more than just putting a product out there. It’s really a shift that’s going to be changing the industry over the years”.

“We’re going to continue to encourage them and drive them to build out that product,” says Anagnost, “but they’re also going to have other avenues to extend their technology and other places where they can link their technology to parts of the Autodesk ecosystem”.

Continue Reading
Comments

Uncategorized

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

Published

on

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.

Continue Reading

Uncategorized

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

Published

on

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.”

Continue Reading

Uncategorized

Daily Crunch: Slack and Salesforce execs explain their big acquisition

Published

on

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.

Continue Reading

Trending