machine learning
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The responsibilities of AI-first investors
Investors in AI-first technology companies serving the defense industry, such as Palantir, Primer and Anduril, are doing well. Anduril, for one, reached a valuation of over $4 billion in less than four years. Many other companies that build general-purpose, AI-first technologies — such as image labeling — receive large (undisclosed) portions of their revenue from the defense industry.
Investors in AI-first technology companies that aren’t even intended to serve the defense industry often find that these firms eventually (and sometimes inadvertently) help other powerful institutions, such as police forces, municipal agencies and media companies, prosecute their duties.
Most do a lot of good work, such as DataRobot helping agencies understand the spread of COVID, HASH running simulations of vaccine distribution or Lilt making school communications available to immigrant parents in a U.S. school district.
The first step in taking responsibility is knowing what on earth is going on. It’s easy for startup investors to shrug off the need to know what’s going on inside AI-based models.
However, there are also some less positive examples — technology made by Israeli cyber-intelligence firm NSO was used to hack 37 smartphones belonging to journalists, human-rights activists, business executives and the fiancée of murdered Saudi journalist Jamal Khashoggi, according to a report by The Washington Post and 16 media partners. The report claims the phones were on a list of over 50,000 numbers based in countries that surveil their citizens and are known to have hired the services of the Israeli firm.
Investors in these companies may now be asked challenging questions by other founders, limited partners and governments about whether the technology is too powerful, enables too much or is applied too broadly. These are questions of degree, but are sometimes not even asked upon making an investment.
I’ve had the privilege of talking to a lot of people with lots of perspectives — CEOs of big companies, founders of (currently!) small companies and politicians — since publishing “The AI-First Company” and investing in such firms for the better part of a decade. I’ve been getting one important question over and over again: How do investors ensure that the startups in which they invest responsibly apply AI?
Let’s be frank: It’s easy for startup investors to hand-wave away such an important question by saying something like, “It’s so hard to tell when we invest.” Startups are nascent forms of something to come. However, AI-first startups are working with something powerful from day one: Tools that allow leverage far beyond our physical, intellectual and temporal reach.
AI not only gives people the ability to put their hands around heavier objects (robots) or get their heads around more data (analytics), it also gives them the ability to bend their minds around time (predictions). When people can make predictions and learn as they play out, they can learn fast. When people can learn fast, they can act fast.
Like any tool, one can use these tools for good or for bad. You can use a rock to build a house or you can throw it at someone. You can use gunpowder for beautiful fireworks or firing bullets.
Substantially similar, AI-based computer vision models can be used to figure out the moves of a dance group or a terrorist group. AI-powered drones can aim a camera at us while going off ski jumps, but they can also aim a gun at us.
This article covers the basics, metrics and politics of responsibly investing in AI-first companies.
The basics
Investors in and board members of AI-first companies must take at least partial responsibility for the decisions of the companies in which they invest.
Investors influence founders, whether they intend to or not. Founders constantly ask investors about what products to build, which customers to approach and which deals to execute. They do this to learn and improve their chances of winning. They also do this, in part, to keep investors engaged and informed because they may be a valuable source of capital.
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News aggregator SmartNews raises $230 million, valuing its business at $2 billion
SmartNews, a Tokyo-headquartered news aggregation website and app that’s grown in popularity despite hefty competition from built-in aggregators like Apple News, today announced it has closed on $230 million in Series F funding. The round brings SmartNews’ total raise to date to over $400 million and values the business at $2 billion — or as the company touts in its press release, a “double unicorn.” (Ha!)
The funding included new U.S. investors Princeville Capital and Woodline Partners, as well as JIC Venture Growth Investments, Green Co-Invest Investment, and Yamauchi-No.10 Family Office in Japan. Existing investors participating in this round included ACA Investments and SMBC Venture Capital.
Founded in 2012 in Japan, the company launched to the U.S. in 2014 and expanded its local news footprint early last year. While the app’s content team includes former journalists, machine learning is used to pick which articles are shown to readers to personalize their experience. However, one of the app’s key differentiators is how it works to pop users’ “filter bubbles” through its “News From All Sides” feature, which allows its users to access news from across a range of political perspectives.
It has also developed new products, like its COVID-19 vaccine dashboard and U.S. election dashboard, that provide critical information at a glance. With the additional funds, the company says it plans to develop more features for its U.S. audience — one of its largest, in addition to Japan — that will focus on consumer health and safety. These will roll out in the next few months and will include features for tracking wildfires and crime and safety reports. It also recently launched a hurricane tracker.
The aggregator’s business model is largely focused on advertising, as the company has said before that 85-90% of Americans aren’t paying to subscribe to news. But SmartNews’ belief is that these news consumers still have a right to access quality information.
In total, SmartNews has relationships with more than 3,000 global publishing partners whose content is available through its service on the web and mobile devices.
To generate revenue, the company sells inline ads and video ads, where revenue is shared with publishers. Over 75% of its publishing partners also take advantage of its “SmartView” feature. This is the app’s quick-reading mode, an alternative to something like Google AMP. Here, users can quickly load an article to read, even if they’re offline. The company promises publishers that these mobile-friendly stories, which are marked with a lightning bolt icon in the app, deliver higher engagement — and its algorithm rewards that type of content, bringing them more readers. Among SmartView partners are well-known brands like USA Today, ABC, HuffPost and others. Currently, over 70% of all SmartNews’ pageviews are coming from SmartView first.
SmartNews’ app has proven to be very sticky, in terms of attracting and keeping users’ attention. The company tells us, citing App Annie July 2021 data, that it sees an average time spent per user per month on U.S. mobile devices that’s higher than Google News or Apple News combined.
Image Credits: App Annie data provided by SmartNews
The company declined to share its monthly active users (MAUs), but had said in 2019 it had grown to 20 million in the U.S. and Japan. Today, it says its U.S. MAUs doubled over the last year.
According to data provided to us by Apptopia, the SmartNews app has seen around 85 million downloads since its October 2014 launch, and 14 million of those took place in the past 365 days. Japan is the largest market for installs, accounting for 59% of lifetime downloads, the firm noted.
“This latest round of funding further affirms the strength of our mission, and fuels our drive to expand our presence and launch features that specifically appeal to users and publishers in the United States,” said SmartNews co-founder and CEO Ken Suzuki. “Our investors both in the U.S. and globally acknowledge the tremendous growth potential and value of SmartNews’s efforts to democratize access to information and create an ecosystem that benefits consumers, publishers and advertisers,” he added.
The company says the new funds will be used to invest in further U.S. growth and expanding the company’s team. Since its last fundraise in 2019, where it became a unicorn, the company more than doubled its headcount to approximately 500 people globally. it now plans to double its headcount of 100 in the U.S., with additions across engineering, product, and leadership roles.
The Wall Street Journal reports SmartNews is exploring an IPO, but the company declined to comment on this.
The SmartNews app is available on iOS and Android across more than 150 countries worldwide.
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Creative adtech is on the cusp of a revolution, and VCs should take note
2021 has been a good year to be an adtech investor. Valuations are surging, Wall Street is happy and exits are frequent and satisfying. It’s the perfect time to double down and invest in an area that has been largely ignored but is poised for major upside in the next few years: Digital creative ad technology.
Think about it. When was the last time we saw a major adtech funding round that was directed at the actual ads themselves — the messages people actually see everyday? I’d argue that now is the perfect time.
The adtech startups that can figure out how to adapt ads that can interact with the remote control, a synced smartphone or voice commands — maybe even make them shoppable — can theoretically produce a game-changer.
Here are five reasons why VCs should consider ratcheting up their investment into adtech startups building the next generation of creative tools:
Creative tech is far from being saturated
Consider how much has been spent over the 15 years on digital advertising mechanics such as targeting, serving, measuring and verification. Not to mention the trillions that have gone toward helping brands keep track of customer data and interactions — the marketing clouds, DMPs and CDPs.
Yet you can count the number of creative-centric adtech companies on one hand. This means there is a lot of room for innovation and early leaders. VideoAmp, which helps brands make ads for various social platforms, pulled in $75 million earlier this year. Given how fast platforms like TikTok and Snap are growing, it won’t be the last.
Digital ad targeting is being squeezed
Ads need to do more work today. Between regulation, cookies going away and Apple locking down data collection, we’ve seen a renewed interest in contextual advertising, including funding for the likes of GumGum, as well as identity resolution firms like InfoSum.
But the digital ad ecosystem can’t get by only using broader data-crunching techniques to replace “retargeting.” The medium is practically crying out for a creative revival that can only be sparked by scalable tech. The recent funding for creative testing startup Marpipe is a start, but more focus is needed on actual tech-driven ideation and automation.
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DataRobot CEO Dan Wright coming to TC Sessions: SaaS to discuss role of data in machine learning
Just about every company is sitting on vast amounts of data, which they can use to their advantage if they can just learn how to harness it. Data is actually the fuel for machine learning models, and with the proper tools, businesses can learn to process this data and build models to help them compete in a rapidly changing marketplace, to react more quickly to shifting customer requirements and to find insights faster than any human ever possibly could.
Boston-based DataRobot, a late-stage startup that has built a platform to help companies navigate the machine learning model lifecycle, has been raising money by the bushel over the last several years, including $206 million in September 2019 and another $300 million in July. DataRobot CEO Dan Wright will be joining us on a panel to discuss the role of data in business at TC Sessions: SaaS on October 27th.
The company covers the gamut of the machine learning lifecycle, including preparing data, operationalizing it and finally building APIs to make it useful for the organization as it attempts to build a soup-to-nuts platform. DataRobot’s broad platform approach has appealed to investors.
As we wrote at the time of the $206 million round:
The company has been catching the attention of these investors by offering a machine learning platform aimed at analysts, developers and data scientists to help build predictive models much more quickly than it typically takes using traditional methodologies. Once built, the company provides a way to deliver the model in the form of an API, simplifying deployment.
DataRobot has raised a total of $1 billion on $6.3 billion post valuation, according to PitchBook data, and it’s been putting that money to work to add to its platform of services. Most recently the company acquired Algorithmia, which helps manage machine learning models.
As the pandemic has pushed more business online, companies are always looking for an edge, and one way to achieve that is by taking advantage of AI and machine learning. Wright will be joined on the data panel by Monte Carlo co-founder and CEO Barr Moses and AgentSync co-founder and CTO Jenn Knight to discuss the growing role of data in business operations
In addition to our discussion with Wright, the conference will also include Microsoft’s Jared Spataro, Amplitude’s Olivia Rose, as well as investors Kobie Fuller and Laela Sturdy, among others. We hope you’ll join us. It’s going to be a thought-provoking lineup.
Buy your pass now to save up to $100. We can’t wait to see you in October!
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Affinity, a relationship intelligence company, raises $80M to help close deals
Relationships ultimately close deals, but long-term relationships come with a lot of baggage, i.e. email interactions, documents and meetings.
Affinity wants to take what Ray Zhou, co-founder and CEO, refers to as “data exhaust,” all of those daily interactions and communications, and apply machine learning analysis and provide insights on who in the organization has the best chance of getting that initial meeting and closing the deal.
Today, the company announced $80 million in Series C funding, led by Menlo Ventures, which was joined by Advance Venture Partners, Sprints Capital, Pear Ventures, Sway Ventures, MassMutual Ventures, Teamworthy and ECT Capital Partners’ Brian N. Sheth. The new funding gives the company $120 million in total funding since it was founded in 2014.
Affinity, based in San Francisco, is focused on industries like investment banking, private equity, venture capital, consulting and real estate, where Zhou told TechCrunch there aren’t customer relationship management systems or networking platforms that cater to the specific needs of the long-term relationship.
Stanford grads Zhou and co-founder Shubham Goel started the company after recognizing that while there was software for transactional relationships, there wasn’t a good option for the relationship journeys.
He cites data that show up to 90% of company profiles and contact information living in traditional CRM systems are incomplete or out of date. This comes as market researcher Gartner reported the global CRM software market grew 12.6% to $69 billion in 2020.
“It is almost bigger than sales,” Zhou said. “Our worldview is that relationships are the biggest industries in the world. Some would disagree, but relationships are an asset class, they are a currency that separates the winners from the losers.”
Instead, Affinity created “a new breed of CRM,” Zhou said, that automates the inputting of that data constantly and adds information, like revenue, staff size and funding from proprietary data sources, to assign a score to a potential opportunity and increase the chances of closing a deal.
Affinity people profile. Image Credits: Affinity
He intends to use the new funding to expand sales, marketing and engineering to support new products and customers. The company has 125 employees currently; Zhou expects to be over 200 by next year.
To date, the company’s platform has analyzed over 18 trillion emails and 213 million calendar events and currently drives over 500,000 new introductions and tracks 450,000 deals per month. It also has more than 1,700 customers in 70 countries, boasting a list that includes Bain Capital Ventures, Kleiner Perkins, SoftBank Group, Nike, Qualcomm and Twilio.
Tyler Sosin, partner at Menlo Ventures, said he met Zhou and Goel at a time when the firm was looking into CRM companies, but it wasn’t until years later that Affinity came up again when Menlo itself wanted to work with a more modern platform.
As a user of Affinity himself, Sosin said the platform gives him the data he cares about and “removes the manual drudgery of entry and friction in the process.” Affinity also built a product that was intuitive to navigate.
“We have always had an interest in getting CRMs to the next generation, and Affinity is defining itself in a new category of relationship intelligence and just crushing it in the private capital markets,” he said. “They are scaling at an impressive growth rate and solving a hard problem that we don’t see many other companies in the space doing.”
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Fin names former Twilio exec Evan Cummack as CEO, raises $20M
Work insights platform Fin raised $20 million in Series A funding and brought in Evan Cummack, a former Twilio executive, as its new chief executive officer.
The San Francisco-based company captures employee workflow data from across applications and turns it into productivity insights to improve the way enterprise teams work and remain engaged.
Fin was founded in 2015 by Andrew Kortina, co-founder of Venmo, and Facebook’s former VP of product and Slow Ventures partner Sam Lessin. Initially, the company was doing voice assistant technology — think Alexa but powered by humans and machine learning — and then workplace analytics software in 2020. You can read more about Fin’s origins at the link below.
The new round was led by Coatue, with participation from First Round Capital, Accel and Kleiner Perkins. The original team was talented, but small, so the new funding will build out sales, marketing and engineering teams, Cummack said.
“At that point, the right thing was to raise money, so at the end of last year, the company raised a $20 million Series A, and it was also decided to find a leadership team that knows how to build an enterprise,” Cummack told TechCrunch. “The company had completely pivoted and removed ‘Analytics’ from our name because it was not encompassing what we do.”
Fin’s software measures productivity and provides insights on ways managers can optimize processes, coach their employees and see how teams are actually using technology to get their work done. At the same time, employees are able to manage their workflow and highlight areas where there may be bottlenecks. All combined, it leads to better operations and customer experiences, Cummack said.
Graphic showing how work is really done. Image Credits: Fin
Fin’s view is that as more automation occurs, the company is looking at a “renaissance of human work.” There will be more jobs and more types of jobs, but people will be able to do them more effectively and the work will be more fulfilling, he added.
Particularly with the use of technology, he notes that in the era before cloud computing, there was a small number of software vendors. Now with the average tech company using over 130 SaaS apps, it allows for a lot of entrepreneurs and adoption of best-in-breed apps so that a viable company can start with a handful of people and leverage those apps to gain big customers.
“It’s different for enterprise customers, though, to understand that investment and what they are spending their money on as they use tools to get their jobs done,” Cummack added. “There is massive pressure to improve the customer experience and move quickly. Now with many people working from home, Fin enables you to look at all 130 apps as if they are one and how they are being used.”
As a result, Fin’s customers are seeing metrics like 16% increase in team utilization and engagement, a 25% decrease in support ticket handle time and a 71% increase in policy compliance. Meanwhile, the company itself is doubling and tripling its customers and revenue each year.
Now with leadership and people in place, Cummack said the company is positioned to scale, though it already had a huge head start in terms of a meaningful business.
Arielle Zuckerberg, partner at Coatue, said via email that she was part of a previous firm that invested in Fin’s seed round to build a virtual assistant. She was also a customer of Fin Assistant until it was discontinued.
When she heard the company was pivoting to enterprise, she “was excited because I thought it was a natural outgrowth of the previous business, had a lot of potential and I was already familiar with management and thought highly of them.”
She believed the “brains” of the company always revolved around understanding and measuring what assistants were doing to complete a task as a way to create opportunities for improvement or automation. The pivot to agent-facing tools made sense to Zuckerberg, but it wasn’t until the global pandemic that it clicked.
“Service teams were forced to go remote overnight, and companies had little to no visibility into what people were doing working from home,” she added. “In this remote environment, we thought that Fin’s product was incredibly well-suited to address the challenges of managing a growing remote support team, and that over time, their unique data set of how people use various apps and tools to complete tasks can help business leaders improve the future of work for their team members. We believe that contact center agents going remote was inevitable even before COVID, but COVID was a huge accelerant and created a compelling ‘why now’ moment for Fin’s solution.”
Going forward, Coatue sees Fin as “a process mining company that is focused on service teams.” By initially focusing on customer support and contact center use case — a business large enough to support a scaled, standalone business — rather than joining competitors in going after Fortune 500 companies where implementation cycles are long and there is slow time-to-value, Zuckerberg said Fin is better able to “address the unique challenges of managing a growing remote support team with a near-immediate time-to-value.”
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Microsoft launches a personalized news service, Microsoft Start
Microsoft today is introducing its own personalized news reading experience called Microsoft Start, available as both a website and mobile app, in addition to being integrated with other Microsoft products, including Windows 10 and 11 and its Microsoft Edge web browser. The feed will combine content from news publishers, but in a way that’s tailored to users’ individual interests, the company says — a customization system that could help Microsoft better compete with the news reading experiences offered by rivals like Apple or Google, as well as popular third-party apps like Flipboard or SmartNews.
Microsoft says the product builds on the company’s legacy with online and mobile consumer services like MSN and Microsoft News. However, it won’t replace MSN. That service will remain available, despite the launch of this new, in-house competitor.
To use Microsoft Start, consumers can visit the standalone website MicrosoftStart.com, which works on both Google Chrome and Microsoft Edge (but not Safari), or they can download the Microsoft Start mobile app for iOS or Android.
The service will also power the News and Interests experience on the Windows 10 taskbar and the Widgets experience on Windows 11. In Microsoft Edge, it will be available from the New Tab page, too.
Image Credits: Microsoft
At first glance, the Microsoft Start website is very much like any other online portal offering a collection of news from a variety of publishers, alongside widgets for things like weather, stocks, sports scores and traffic. When you click to read an article, you’re taken to a syndicated version hosted on Microsoft’s domain, which includes the Microsoft Start top navigation bar at the top and emoji reaction buttons below the headline.
Users can also react to stories with emojis while browsing the home page itself.
This emoji set is similar to the one being offered today by Facebook, except that Microsoft has replaced Facebook’s controversial laughing face emoji with a thinking face. (It’s worth noting that the Facebook laughing face has been increasingly criticized for being used to openly ridicule posts and mock people — even on stories depicting tragic events, like COVID deaths, for instance.)
Microsoft has made another change with its emoji, as well: After you react to a story with an emoji, you only see your emoji instead of the top three and total reaction count.
Image Credits: Microsoft
But while online web portals tend to be static aggregators of news content, Microsoft Start’s feed will adjust to users’ interests in several different ways.
Users can click a “Personalize” button to be taken to a page where they can manually add and remove interests from across a number of high-level categories like news, entertainment, sports, technology, money, finance, travel, health, shopping and more. Or they can search for categories and interests that could be more specific or more niche. (Instead of “parenting,” for instance, “parenting teenagers.”) This recalls the recent update Flipboard made to its own main page, the For You feed, which lets users make similar choices.
As users then begin to browse their Microsoft Start feed, they can also click a button to thumbs up or thumbs down an article to better adjust the feed to their preferences. Over time, the more the user engages with the content, the better refined the feed becomes, says Microsoft. This customization will leverage AI and machine learning, as well as human moderation, the company notes.
The feed, like other online portals, is supported by advertising. As you scroll down, you’ll notice every few rows will feature one ad unit, where the URL is flagged with a green “Ad” badge. Initially, these mostly appear to be product ads, making them distinct from the news content. Since Microsoft isn’t shutting down MSN and is integrating this news service into a number of other products, it’s expanding the available advertising real estate it can offer with this launch.
According to the iOS app’s privacy label, the data being used to track users across websites and apps owned by other companies includes the User ID. By comparison, Google News does not include a tracking section. Both Microsoft Start and Google News collect a host of “data linked to you,” like location, identifiers, search history, usage data, contact info, and more. The website itself, however, only links to Microsoft’s general privacy policy.
The website, app, and integrations are rolling out starting today. (If you aren’t able to find the new app yet — it replaces Microsoft News — you can try scanning the QR code from your mobile device. We currently found the app had rolled out on iOS but the link pointed us to Microsoft News on Android. Your mileage may vary.)
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Seqera Labs grabs $5.5M to help sequence COVID-19 variants and other complex data problems
Bringing order and understanding to unstructured information located across disparate silos has been one of the more significant breakthroughs of the big data era, and today a European startup that has built a platform to help with this challenge specifically in the area of life sciences — and has, notably, been used by labs to sequence and so far identify two major COVID-19 variants — is announcing some funding to continue building out its tools to a wider set of use cases, and to expand into North America.
Seqera Labs, a Barcelona-based data orchestration and workflow platform tailored to help scientists and engineers order and gain insights from cloud-based genomic data troves, as well as to tackle other life science applications that involve harnessing complex data from multiple locations, has raised $5.5 million in seed funding.
Talis Capital and Speedinvest co-led this round, with participation also from previous backer BoxOne Ventures and a grant from the Chan Zuckerberg Initiative, Mark Zuckerberg and Dr. Priscilla Chan’s effort to back open source software projects for science applications.
Seqera — a portmanteau of “sequence” and “era”, the age of sequencing data, basically — had previously raised less than $1 million, and quietly, it is already generating revenues, with five of the world’s biggest pharmaceutical companies part of its customer base, alongside biotech and other life sciences customers.
Seqera was spun out of the Centre for Genomic Regulation, a biomedical research center based out of Barcelona, where it was built as the commercial application of Nextflow, open source workflow and data orchestration software originally created by the founders of Seqera, Evan Floden and Paolo Di Tommaso, at the CGR.
Floden, Seqera’s CEO, told TechCrunch that he and Di Tommaso were motivated to create Seqera in 2018 after seeing Nextflow gain a lot of traction in the life science community, and subsequently getting a lot of repeat requests for further customization and features. Both Nextflow and Seqera have seen a lot of usage: the Nextflow runtime has been downloaded more than 2 million times, the company said, while Seqera’s commercial cloud offering has now processed more than 5 billion tasks.
The COVID-19 pandemic is a classic example of the acute challenge that Seqera (and by association Nextflow) aims to address in the scientific community. With COVID-19 outbreaks happening globally, each time a test for COVID-19 is processed in a lab, live genetic samples of the virus get collected. Taken together, these millions of tests represent a goldmine of information about the coronavirus and how it is mutating, and when and where it is doing so. For a new virus about which so little is understood and that is still persisting, that’s invaluable data.
So the problem is not if the data exists for better insights (it does); it is that it’s nearly impossible to use more legacy tools to view that data as a holistic body. It’s in too many places, and there is just too much of it, and it’s growing every day (and changing every day), which means that traditional approaches of porting data to a centralized location to run analytics on it just wouldn’t be efficient, and would cost a fortune to execute.
That is where Segera comes in. The company’s technology treats each source of data across different clouds as a salient pipeline which can be merged and analyzed as a single body, without that data ever leaving the boundaries of the infrastructure where it already exists. Customised to focus on genomic troves, scientists can then query that information for more insights. Seqera was central to the discovery of both the Alpha and Delta variants of the virus, and work is still ongoing as COVID-19 continues to hammer the globe.
Seqera is being used in other kinds of medical applications, such as in the realm of so-called “precision medicine.” This is emerging as a very big opportunity in complex fields like oncology: cancer mutates and behaves differently depending on many factors, including genetic differences of the patients themselves, which means that treatments are less effective if they are “one size fits all.”
Increasingly, we are seeing approaches that leverage machine learning and big data analytics to better understand individual cancers and how they develop for different populations, to subsequently create more personalized treatments, and Seqera comes into play as a way to sequence that kind of data.
This also highlights something else notable about the Seqera platform: it is used directly by the people who are analyzing the data — that is, the researchers and scientists themselves, without data specialists necessarily needing to get involved. This was a practical priority for the company, Floden told me, but nonetheless, it’s an interesting detail of how the platform is inadvertently part of that bigger trend of “no-code/low-code” software, designed to make highly technical processes usable by non-technical people.
It’s both the existing opportunity and how Seqera might be applied in the future across other kinds of data that lives in the cloud that makes it an interesting company, and it seems an interesting investment, too.
“Advancements in machine learning, and the proliferation of volumes and types of data, are leading to increasingly more applications of computer science in life sciences and biology,” said Kirill Tasilov, principal at Talis Capital, in a statement. “While this is incredibly exciting from a humanity perspective, it’s also skyrocketing the cost of experiments to sometimes millions of dollars per project as they become computer-heavy and complex to run. Nextflow is already a ubiquitous solution in this space and Seqera is driving those capabilities at an enterprise level – and in doing so, is bringing the entire life sciences industry into the modern age. We’re thrilled to be a part of Seqera’s journey.”
“With the explosion of biological data from cheap, commercial DNA sequencing, there is a pressing need to analyse increasingly growing and complex quantities of data,” added Arnaud Bakker, principal at Speedinvest. “Seqera’s open and cloud-first framework provides an advanced tooling kit allowing organisations to scale complex deployments of data analysis and enable data-driven life sciences solutions.”
Although medicine and life sciences are perhaps Seqera’s most obvious and timely applications today, the framework originally designed for genetics and biology can be applied to any a number of other areas: AI training, image analysis and astronomy are three early use cases, Floden said. Astronomy is perhaps very apt, since it seems that the sky is the limit.
“We think we are in the century of biology,” Floden said. “It’s the center of activity and it’s becoming data-centric, and we are here to build services around that.”
Seqera is not disclosing its valuation with this round.
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Mobius Labs nabs $6M to help more sectors tap into computer vision
Berlin-based Mobius Labs has closed a €5.2 million (~$6.1M) funding round off the back of increased demand for its computer vision training platform. The Series A investment is led by Ventech VC, along with Atlantic Labs, APEX Ventures, Space Capital, Lunar Ventures plus some additional angel investors.
The startup offers an SDK that lets the user create custom computer vision models fed with a little of their own training data — as an alternative to off-the-shelf tools which may not have the required specificity for a particular use-case.
It also flags a ‘no code’ focus, saying its tech has been designed with a non-technical user in mind.
As it’s an SDK, Mobius Labs’ platform can also be deployed on premise and/or on device — rather than the customer needing to connect to a cloud service to tap into the AI tool’s utility.
“Our custom training user interface is very simple to work with, and requires no prior technical knowledge on any level,” claims Appu Shaji, CEO and chief scientist.
“Over the years, a trend we have observed is that often the people who get the maximum value from AI are non technical personas like a content manager in a press and creative agency, or an application manager in the space sector. Our no-code AI allows anyone to build their own applications, thus enabling these users to get close to their vision without having to wait for AI experts or developer teams to help them.”
Mobius Labs — which was founded back in 2018 — now has 30 customers using its tools for a range of use cases.
Uses include categorisation, recommendation, prediction, reducing operational expense, and/or “generally connecting users and audiences to visual content that is most relevant to their needs”. (Press and broadcasting and the stock photography sector have unsurprisingly been big focuses to date.)
But it reckons there’s wider utility for its tech and is gearing up for growth.
It caters to businesses of various sizes, from startups to SMEs, but says it mainly targets global enterprises with major content challenges — hence its historical focus on the media sector and video use cases.
Now, though, it’s also targeting geospatial and earth observation applications as it seeks to expand its customer base.
The 30-strong startup has more than doubled in size over the last 18 months. With the new funding it’s planning to double its headcount again over the next 12 months as it looks to expand its geographical footprint — focusing on Europe and the US.
Year-on-year growth has also been 2x but it believes it can dial that up by tapping into other sectors.
“We are working with industries that are rich in visual data,” says Shaji. “The geospatial sector is something that we are focussing on currently as we have a strong belief that vast amounts of visual data is being produced by them. However, these huge archives of raw pixel data are useless on their own.
“For instance, if we want to track how river fronts are expanding, we have to look at data collected by satellites, sort and tag them in order to analyse them. Currently this is being done manually. The technology we are creating comes in a lightweight SDK, and can be deployed directly into these satellites so that the raw data can be detected and then analysed by machine learning algorithms. We are currently working with satellite companies in this sector.”
On the competitive front, Shaji names Clarifai and Google Cloud Vision as the main rivals it has in its sights.
“We realise these are the big players but at the same time believe that we have something unique to offer, which these players cannot: Unlike their solutions, our platform users can be outside the field of computer vision. By democratising the training of machine learning models beyond simply the technical crowd, we are making computer vision accessible and understandable by anyone, regardless of their job titles,” he argues.
“Another core value that differentiates us is the way we treat client data. Our solutions are delivered in the form of a Software Development Kit (SDK), which runs on-premise, completely locally on clients’ systems. No data is ever sent back to us. Our role is to empower people to build applications, and make them their own.”
Computer vision startups have been a hot acquisition target in recent years and some earlier startups offering ‘computer vision as a service’ got acquired by IT services firms to beef up their existing offerings, while tech giants like Amazon and (the aforementioned) Google offer their own computer vision services too.
But Shaji suggests the tech is now at a different stage of development — and primed for “mass adoption”.
“We’re talking about providing solutions that empower clients to build their own applications,” he says, summing up the competitive play. “And that [do that] with complete data privacy, where our solutions run on-premise, and we don’t see our clients data. Coupled with that is the ease of use that our technology offers: It is a lightweight solution that can be deployed on many ‘edge’ devices like smartphones, laptops, and even on satellites.”
Commenting on the funding in a statement, Stephan Wirries, partner at Ventech VC, added: “Appu and the team at Mobius Labs have developed an unparalleled offering in the computer vision space. Superhuman Vision is impressively innovative with its high degree of accuracy despite very limited required training to recognise new objects at excellent computational efficiency. We believe industries will be transformed through AI, and Mobius Labs is the European Deep Tech innovator teaching machines to see.”
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Explosion snags $6M on $120M valuation to expand machine learning platform
Explosion, a company that has combined an open source machine learning library with a set of commercial developer tools, announced a $6 million Series A today on a $120 million valuation. The round was led by SignalFire, and the company reported that today’s investment represents 5% of its value.
Oana Olteanu from SignalFire will be joining the board under the terms of the deal, which includes warrants of $12 million in additional investment at the same price.
“Fundamentally, Explosion is a software company and we build developer tools for AI and machine learning and natural language processing. So our goal is to make developers more productive and more focused on their natural language processing, so basically understanding large volumes of text, and training machine learning models to help with that and automate some processes,” company co-founder and CEO Ines Montani told me.
The company started in 2016 when Montani met her co-founder, Matthew Honnibal in Berlin where he was working on the spaCy open source machine learning library. Since then, that open source project has been downloaded over 40 million times.
In 2017, they added Prodigy, a commercial product for generating data for the machine learning model. “Machine learning is code plus data, so to really get the most out of the technologies you almost always want to train your models and build custom systems because what’s really most valuable are problems that are super specific to you and your business and what you’re trying to find out, and so we saw that the area of creating training data, training these machine learning models, was something that people didn’t pay very much attention to at all,” she said.
The next step is a product called Prodigy Teams, which is a big reason the company is taking on this investment. “Prodigy Teams is [a hosted service that] adds user management and collaboration features to Prodigy, and you can run it in the cloud without compromising on what people love most about Prodigy, which is the data privacy, so no data ever needs to get seen by our servers,” she said. They do this by letting the data sit on the customer’s private cluster in a private cloud, and then use Prodigy Team’s management features in the public cloud service.
Today, they have 500 companies using Prodigy including Microsoft and Bayer in addition to the huge community of millions of open source users. They’ve built all this with just six early employees, a number that has grown to 17 recently (they hope to reach 20 by year’s end).
She believes if you’re thinking too much about diversity in your hiring process, you probably have a problem already. “If you go into hiring and you’re thinking like, oh, how can I make sure that the way I’m hiring is diverse, I think that already shows that there’s maybe a problem,” she said.
“If you have a company, and it’s 50 dudes in their 20s, it’s not surprising that you might have problems attracting people who are not white dudes in their 20s. But in our case, our strategy is to hire good people and good people are often very diverse people, and again if you play by the [startup] playbook, you could be limited in a lot of other ways.”
She said that they have never seen themselves as a traditional startup following some conventional playbook. “We didn’t raise any investment money [until now]. We grew the team organically, and we focused on being profitable and independent [before we got outside investment],” she said.
But more than the money, Montani says that they needed to find an investor that would understand and support the open source side of the business, even while they got capital to expand all parts of the company. “Open source is a community of users, customers and employees. They are real people, and [they are not] pawns in [some] startup game, and it’s not a game. It’s real, and these are real people,” she said.
“They deserve more than just my eyeballs and grand promises. […] And so it’s very important that even if we’re selling a small stake in our company for some capital [to build our next] product [that open source remains at] the core of our company and that’s something we don’t want to compromise on,” Montani said.
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