“Artificial Intelligence” as we know it today is, at best, a misnomer. AI is in no way intelligent, but it is artificial. It remains one of the hottest topics in industry and is enjoying a renewed interest in academia. This isn’t new—the world has been through a series of AI peaks and valleys over the past 50 years. But what makes the current flurry of AI successes different is that modern computing hardware is finally powerful enough to fully implement some wild ideas that have been hanging around for a long time.
Back in the 1950s, in the earliest days of what we now call artificial intelligence, there was a debate over what to name the field. Herbert Simon, co-developer of both the logic theory machine and the General Problem Solver, argued that the field should have the much more anodyne name of “complex information processing.” This certainly doesn’t inspire the awe that “artificial intelligence” does, nor does it convey the idea that machines can think like humans.
However, “complex information processing” is a much better description of what artificial intelligence actually is: parsing complicated data sets and attempting to make inferences from the pile. Some modern examples of AI include speech recognition (in the form of virtual assistants like Siri or Alexa) and systems that determine what’s in a photograph or recommend what to buy or watch next. None of these examples are comparable to human intelligence, but they show we can do remarkable things with enough information processing.
Google has ignited a social media firestorm on the nature of consciousness after placing an engineer on paid leave who went public with his belief that the tech group’s chatbot has become “sentient.”
Blake Lemoine, a senior software engineer in Google’s Responsible AI unit, did not receive much attention last week when he wrote a Medium post saying he “may be fired soon for doing AI ethics work.”
But a Saturday profile in the Washington Post characterizing Lemoine as “the Google engineer who thinks the company’s AI has come to life” became the catalyst for widespread discussion on social media regarding the nature of artificial intelligence. Among the experts commenting, questioning or joking about the article were Nobel laureates, Tesla’s head of AI and multiple professors.
Welcome to the week after Ars Frontiers! This article is the first in a short series of pieces that will recap each of the day’s talks for the benefit of those who weren’t able to travel to DC for our first conference. We’ll be running one of these every few days for the next couple of weeks, and each one will include an embedded video of the talk (along with a transcript).
For today’s recap, we’re going over our talk with Amazon Web Services tech evangelist Dr. Nashlie Sephus. Our discussion was titled “Breaking Barriers to Machine Learning.”
New research from MIT explores fire from a whole series of new perspectives. The research uses deep-learning approaches that extract the vibrational features of flames as flickering objects and, in turn, renders them into sounds and materials.
The 19th-century physicist Michael Faraday was known not only for his seminal experimental contributions to electromagnetism but also for his public speaking. His annual Christmas lectures at the Royal Institution evolved into a holiday tradition that continues today. One of his most famous Christmas lectures concerned the chemical history of a candle. Faraday illustrated his points with a simple experiment: He placed a candle inside a lampglass in order to block out any breezes and achieve “a quiet flame.” Faraday then showed how the flame’s shape flickered and changed in response to perturbations.
“You must not imagine, because you see these tongues all at once, that the flame is of this particular shape,” Faraday observed. “A flame of that shape is never so at any one time. Never is a body of flame, like that which you just saw rising from the ball, of the shape it appears to you. It consists of a multitude of different shapes, succeeding each other so fast that the eye is only able to take cognizance of them all at once.”
Now, MIT researchers have brought Faraday’s simple experiment into the 21st century. Markus Buehler and his postdoc, Mario Milazzo, combined high-resolution imaging with deep machine learning to sonify a single candle flame. They then used that single flame as a basic building block, creating “music” out of its flickering dynamics and designing novel structures that could be 3D-printed into physical objects. Buehler described this and other related work at the American Physical Society meeting last week in Chicago.
Google DeepMind has collaborated with classical scholars to create a new AI tool that uses deep neural networks to help historians decipher the text of damaged inscriptions from ancient Greece. The new system, dubbed Ithaca, builds on an earlier text restoration system called Pythia.
Ithaca doesn’t just assist historians in restoring text—it can also identify a text’s location of origin and the date of creation, according to a new paper the research team published in the journal Nature. In fact, Ithaca has already been used to help resolve an ongoing debate among historians about the correct dates for a group of ancient Athenian decrees. An interactive version of Ithaca is freely available, and the team is making its code open source.
Many ancient sources—whether they be written on scrolls, papyri, stone, metal, or pottery—are so damaged that large chunks of text are often illegible. Determining where the texts originated can also be a challenge, since they have likely been moved multiple times. As for accurately determining when they were produced, radiocarbon dating and similar methods can’t be used since they can damage the priceless artifacts. So the daunting and time-consuming task of interpreting these incomplete texts falls to so-called epigraphists who specialize in those skills.
A team of researchers at Sony AI have used deep reinforcement learning to teach an artificial intelligence to play Gran Turismo at a world-class level. While previous experiments have taught AI how to drive very fast, this is the first time that one has learned to actually race. And to prove it, the AI beat some of the world’s best GT players in head-to-head competition, as described in a new paper published in Nature this week.
Racing is not easy, and it involves more than just knowing how to drive a car really fast. Car control is obviously important, but so too are tactics, strategy, and the somewhat nebulous concept of etiquette.
Or, as the authors put it, “[a]utomobile racing is a domain that poses exactly these challenges; it requires real-time control of vehicles with complex, non-linear dynamics while operating within inches of opponents.” Some drivers might have limited success through aggression and going for every overtaking opportunity they see. But knowing where to pass and when to wait for a better opportunity—so you don’t get re-passed at the end of the next straight, for instance—is at least as important, as is knowing when to cede to a rival so you don’t end up in the wall or a gravel trap.
Former FBI special agent Vincent Pankoke was looking forward to a relaxing retirement hanging out at the beach when he left the agency. Instead, he was drawn into solving a famous cold case: the question of who betrayed Anne Frank and her family to the Nazis, leading to their arrest and deportation to a concentration camp. Only the father, Otto Frank, survived. To find his own answer to that question, Pankoke assembled his own crack team of dogged investigators. They spent five years poring over every bit of pertinent material, setting up an extensive online database, and developing an AI program to help them sift through it all and find new connections.
While admitting that the case is circumstantial and some reasonable doubt remains, Pankoke et al. believe the most likely culprit is a man named Arnold van den Bergh, a local Jewish leader who may have handed over lists of addresses where fellow Jews were hiding to the Nazis in order to protect his own family. The Pankoke team’s story was featured in a segment on 60 Minutes earlier this week (see video at end of post), and is covered in detail in a new book by Rosemary Sullivan, The Betrayal of Anne Frank: A Cold Case Investigation.
Millions of people have read The Diary of Anne Frank since it was first published posthumously in 1947. It’s been translated into 70 languages and inspired a theatrical play and subsequent Oscar-winning 1959 film, featuring Millie Perkins in the title role. Anne Frank was born in Frankfurt, Germany, but the family fled the country and settled in Amsterdam after Adolf Hitler came to power. They didn’t flee quite far enough: the Nazi occupation of the Netherlands began in May1940 and eventually forced the Franks (and many other Jews) into hiding.
deutsche-startups.de präsentiert heute wieder einmal einige junge Startups, die zuletzt, also in den vergangenen Wochen und Monaten an den Start gegangen sind, sowie Firmen, die zuletzt aus dem Stealth-Mode erwacht sind. Übrigens: Noch mehr neue Startups gibt es in unserem Newsletter Startup-Radar.
meteoIntelligence meteoIntelligence verwendet Wetterprognosen und meteorologische Daten, um mit Hilfe von maschinellen Lernverfahren branchenspezifische Prozessgrößen vorherzusagen. Die Umsetzung soll mit einem generalisierten Vorhersagemodell gelingen, das die Welt der Meteorologie mit Data Science verknüpfen soll.
SpeechText.AI SpeechText.AI bietet eine Software mit künstlicher Intelligenz für die Umwandlung von Sprache in Text und die Audiotranskription. Dabei soll die “Sprache-zu-Text-Konverter-Software” eine Genauigkeit von 96 % erreichen und damit fast so genau sein wie eine menschliche Transkription.
Avocargo Das Berliner Startup Avocargo, das von Marc Shakory Tabrizi und Matti Schurr gegründet wurde, setzt auf Cargobike-Sharing. Der Slogan dabei lautet: “Buch´ dir lieber ein Avocargo-Bike und bring´ deine Kinder dank der Sitzgurte ebenso sicher zur Kita und zum Spielplatz!”
Storydive Storydive bietet sogenannte Audiowalks, das sind Hörspaziergänge via App. Dabei können Nutzer:innen in eine fiktionale Geschichte eintauchen und diese aus Sicht der Hauptfigur erleben. Damit will Startup unter anderem der Verlags- und Medienbranche ermöglichen, Zielgruppen auf eine neue Art zu erreichen.
Truckerdata.io Die Jungfirma Truckerdata.io bündelt die für Berufskraftfahrer:innen wichtigen und relevanten Daten – etwa Daten zu Tank- und Rastplätzen, Reinigungsanlagen, Werkstätten sowie Informationen zu Verladern in einer App. Hinter dem Startup steckt insbesondere Sixtyone Minutes-Gründer Michael Gnamm.
Tipp: In unserem Newsletter Startup-Radar berichten wir einmal in der Woche über neue Startups. Alle Startups stellen wir in unserem kostenpflichtigen Newsletter kurz und knapp vor und bringen sie so auf den Radar der Startup-Szene. Jetzt unseren Newsletter Startup-Radar sofort abonnieren!
Startup-Jobs: Auf der Suche nach einer neuen Herausforderung? In der unserer Jobbörse findet Ihr Stellenanzeigen von Startups und Unternehmen.
The seed round will be used for accelerating product development and global launch of open beta for its AI-powered document management platform. The company opened an office in Santa Clara, California this year to spur its global expansion.
People are bombarded with information thanks to advances in technology that opens the doors to a wealth of information, but at the same time, too much information and a huge amount of data at one time leave the users confused and/or unable to make timely decisions.
Business Canvas, founded in July 2020 by CEO Woojin Kim, Brian Shin, Seungmin Lee, Dongjoon Shin and Clint Yoo, is hoping to solve the challenge that every knowledge worker and writer faces: spending more time on research and file organization than the actual content output they need to create.
“In fact, people commit over 30% of their working hours trying to search for that file we once saved in a folder that we just cannot find anymore,” Business Canvas CEO and co-founder Kim said.
Through a network that intelligently tracks and organizes files based on the user’s interactions, Typed brings all the knowledge from different websites and applications into one simple-to-use and quick-to-learn digital workspace.
Strictly keeping its users’ information and their confidential files uninterrupted, Typed does not access the content of users’ documents but utilize them as machine learning data in order to protect their information and data, Kim told TechCrunch. It simply collects users’ action driven data point and publicly available metadata of documents and resources under users’ permission, Kim added.
“Modern document writing has not changed since the 1980s,” Business Canvas co-founder Clint Yoo said. “While we have more knowledge at our fingertips than ever before, we use the same rudimentary methods to organize and make sense of it. We want any writer – from lawyers and entrepreneurs to researchers and students – to focus on creating great content instead of wasting time organizing their source material. We achieved this by making knowledge management more like the way our brain operates.”
Since the launch of the closed beta test in February 2021, Typed saw significant user growth including more than 10,000 users on the waitlist, with 25,000 files uploaded and 350% month-over-month active user growth, the company said in its statement. Typed will be available through a freemium model and is currently accepting beta registrations on its website.
“When we’ve tested our closed beta, our metrics show top traction among students as well as journalists, writers and lawyers, who require heavy research and document work on a frequent basis. We opened up access earlier this month for the waitlists in over 50 countries. These are primarily B2C users,” Kim told TechCrunch. “As for B2B, we are currently in the process of proof-of-concept (POC) for one of the largest conglomerates in South Korea. Smaller teams like startups, boutique law, consulting firms, venture capitals and government institutions also have been adopting Typed as well.”
“While the company is still in its nascent stage in its development, Typed has the potential to fundamentally change how we work individually or as a team. If there is a business to take on our outdated way of writing content, it’s them [Typed],” Shina Chung, Kakao Ventures CEO said.
REvil is a symptom, not the cause. I advise Tony Stark and his fellow Avengers to look past any one criminal organization — because there is no evil mastermind. Ransomware is just the latest in the 50,000-year evolution of petty criminals discovering get-rich-quick schemes.
The massive boom in the number of ransomware occurrences arises from the lack of centralized control. More than 304 million ransomware attacks hit global businesses last year, with costs surpassing $178,000 per event. Technology has created a market where countless petty criminals can make good money fast. The best way to fight this kind of threat is with a market-based approach.
The spike in global ransomware attacks reflects a massive “dumbing down” of criminal activity. People looking to make an illicit buck have many more options available to them today than they did even two years ago. Without technical chops, people can steal your data, hold it for ransom and coerce you to pay to get it back. Law enforcement has not yet responded to combat this form of cybercrime, and large, sophisticated criminal networks have likewise not yet figured out how to control the encroaching upstarts.
The spike in ransomware attacks is attributable to the “as a service” economy. In this case, we’re talking about RaaS, or ransomware as a service. This works because each task in the ransomware chain benefits from the improved sophistication enabled by the division of labor and specialization.
Someone finds a vulnerable target. Someone provides bulletproof infrastructure outside of the jurisdiction of responsible law enforcement. Someone provides the malicious code. The players all come together without knowing each other’s names. No need to meet in person as Mr. Pink, Mr. Blonde and Mr. Orange because the ability to coordinate tasks has become simple. The rapid pace of technological innovation created a decentralized market, enabling amateurs to engage in high-dollar crimes.
There’s a gig economy for the underworld just like there is for the legal business world. I’ve built two successful software companies, even though I’m an economist. I use open source software and rent infrastructure via cloud technologies. I operated my first software company for six years before I sought outside capital, and I used that money for marketing and sales more than technology.
This tech advancement is both good and bad. The global economy did better than expected during a global pandemic because technology enabled many people to work from anywhere.
But the illicit markets of crime also benefited. REvil provided a service — a piece of a larger network — and earned a share of proceeds from ransomware attacks committed by others — like Jeff Bezos and Amazon get a share of my company’s revenues for the services they provide to me.
To fight ransomware attacks, appreciate the economics — the markets that enable ransomware — and change the market dynamics. Specifically, do three things:
1. Analyze the market like a business executive
Any competitive business thinks about what’s allowing competitors to succeed and how they can outcompete. The person behind a ransomware strike is an entrepreneur or a worker in a firm engaged in cybercrime, so start with good business analytics using data and smart business questions.
Can the crypto technologies that enable the crime also be used to enable entity resolution and deny anonymity/pseudonymity? Can technology undermine a criminal’s ability to recruit, coordinate or move, store and spend the proceeds from criminal activities?
2. Define victory in market terms
Doing the analytics to understand competing firms allows one to more clearly see the market for ransomware. Eliminating one “firm” often creates a power vacuum that will be filled by another, provided the market remains the same.
REvil disappeared, but ransomware attacks persist. Victory in market terms means creating markets in which criminals choose not to engage in the activity in the first place. The goal is not to catch criminals, but to deter the crime. Victory against ransomware happens when arrests drop because attempted attacks drop to near zero.
3. Combat RaaS as an entrepreneur in a competitive market
To prevent ransomware is to fight against criminal entrepreneurs, so the task requires one to think and fight crime like an entrepreneur.
Crime-fighting entrepreneurs require collaboration — networks of government officials, banking professionals and technologists in the private sector across the globe must come together.
Through artificial intelligence and machine learning, the capability to securely share data, information and knowledge while preserving privacy exists. The tools of crime become the tools to combat crime.
No evil mastermind sits in their lair laughing at the chaos inflicted on the economy. Instead, growing numbers of amateurs are finding ways to make money quickly. Tackling the ransomware industry requires the same coordinated focus on the market that enabled amateurs to enter cybercrime in the first place. Iron Man would certainly agree.
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.
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.
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.
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 85-80% 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 over 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, and 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 Zuzuki. “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.
An ad tech veteran who has logged time at Google and The Rubicon Project (now Magnite), Casey Saran is co-founder and CEO of Spaceback.
2021 has been a good year to be an ad tech 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 ad tech 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 ad tech 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 ad tech 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.
With supply chains under constant stress because of the pandemic, freight forwarding has become one of the hottest startup sectors in the last two years. Indeed, International freight forwarding is now a $199 billion market. And the evidence is mounting.
In November last year, digital freight forwarder Forto raises another $50M in a round led by Inven Capital. In April this year, Nuvocargo raised $12M to digitize the freight logistics industry. In May, Zencargo, with a freight forwarding platform, raised $42 million. In June, freight forwarder sennder raised $80M at a $1B+ valuation. In July Freightify landed $2.5M to make rate management easier for freight forwarders.
And today, Vector.ai, which says it helps freight forwarders improve productivity via its AI platform, has raised $15 million in a Series A led by US VC Bessemer Venture Partners. It was joined by existing investors Dynamo Ventures and Episode 1. Bessemer’s investment is yet another sign that US VC continues to make incursions into the UK and European tech scene.
Vector now plans to accelerate its international expansion plans as an automated system for freight forwarders.
The problem it’s tackling is this: Freight forwarders lose time to repetitive administrative tasks as they execute shipments, such as hunting through customer emails etc, rather than concentrating on higher-value activities. Vector.ai says it’s machine learning platform can automate these tasks.
Its customers now include Fracht, EFL, NNR Global Logistics, The Scarbrough Group, Steam Logistics and Navia Freight, as well as other top-10 freight forwarders.
James Coombes, Co-Founder, and CEO of Vector.ai, commented: “Most employees within freight forwarders spend the majority of their time communicating with the 10-25 different entities that might be associated with a given shipment and coordinating freight movement and documentation. Communication usually runs through email and attachments… The volume of freight continues to rise globally – and with the added burden of Brexit and pandemic disruptions such as the recent port closure in China – freight forwarders are facing staffing shortages, steep wage increases, and shipping delays that continue to cost companies money in lost revenue and spoiled goods. They cannot afford to keep wasting time on low-level processing, which is why we created the technology to automate basic tasks.”
Mike Droesch, Partner at Bessemer Venture Partners, said: “Vector.ai is one of the early leaders in an emerging category of freight forwarding workflow automation and digitization tools. It has built an intuitive and industry-focused product – which is already winning over some of the largest freight forwarders.”
Vector competes with Shipamax out of the UK which has raised $9.5M, RPA Labs out of the US which has raised $1.2M and slync.io also in the US which has raised $75.9M.
Cresicor, a consumer packaged goods trade management platform startup, raised $5.6 million in seed funding to further develop its tools for more accurate data and analytics.
The company, based remotely, focuses on small to midsize CPG companies, providing them with an automated way to manage their trade promotion, a process co-founder and CEO Alexander Whatley said is done primarily manually using spreadsheets.
Here’s what happens in a trade promotion: When a company wants to run a discount on one of their slower-selling items, the company has to spend money to do this — to have displays set up in a store or have that item on a certain shelf. If it works, more people will buy the item at the lower price point. Essentially, a trade promotion is the process of spending money to get more money in the future, Whatley told TechCrunch.
Figuring out all of the trade promotions is a complicated process, Whatley explained. Companies receive data feeds on the promotions from several different places, revenue data from retailers, accounting source data to show how many units were shipped and then maybe data directly from retailers. All of that has to be matched against the promotion.
“No API is bringing this data back to brands, so our software helps to automate and track these manual processes so companies can do analytics to see how the promotions are doing,” he added. “It also helps the finance team understand expenses, including which are valid and those that are not.”
What certain companies spend on trade promotions can represent their second-largest cost behind manufacturing, and companies often end up reinvesting between 20% and 30% of their revenue into trade promotions, Whatley said. This is a big market, representing untapped growth, especially with U.S. CPG sales topping $720 billion in 2020.
“You can see how messy the whole industry is, which is why we have a bright future and huge TAM,” he added. “With this new funding, we can target other parts of the P&L like supply chain and salaries. We also provide analytics for their strategy and where they should be spending it — which store, on which supply. By allocating resources the right way, companies typically see a 10% boost in sales as a result.”
Whatley started the company in 2017 with his brother, Daniel, Stuart Kennedy and Nikki McNeil while a Harvard undergrad. Since raising the funding back in February, the company has grown 2.5x in revenue, while employee headcount grew 4x over the past 12 months to 20.
Costanoa Ventures led the investment and was joined by Torch Capital and a group of angel investors including Fivestars CTO Matt Doka and Hu’s Kitchen CEO Mark Ramadan.
John Cowgill, partner at Costanoa, said though Cresicor raised a seed round, the company was already acquiring brands and capital before releasing a product and grew to almost a Series A company without any outside capital, saying it “blew me away.”
Cresicor is the “perfect example” of a company that Costanoa would get excited about — a vertical software company using data or machine learning to augment a pain point, Cowgill added.
“The CPG industry is in the middle of a rapid change where we see all of these emerging, digital native and mission-driven brands rapidly eating share from incumbents,” he added. “For the next generation of brands to compete, they have to win in trade promotion management. Cresicor’s opportunity to go beyond trade is significant. It is just a starting point to build a company that is the core enabler of great brands.”
The new funding will be used mainly to hire more talent in the areas of engineering and customer success so the company can hit its next benchmarks, Alexander Whatley said. He also intends to use the funding to acquire new brands and on software development. Cresicor boasts a list of customers including Perfect Snacks, Oatly and Hint Water.
The retail industry is valued at $5.5 trillion, and one-fifth of it is CPG, Whatley said. As a result, he has his eye on going after other verticals within CPG, like electronics and pet food, and then expanding into other areas.
“We are also going to work with enterprise companies — we see an opportunity to work with companies like P&G and General Mills, and we also want to build an ecosystem around trade promotion and launch into other profit and loss areas,” Whatley said.
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.
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.
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 Kobey Fuller and Laela Sturdy, among others. We hope you’ll join us. It’s going to be a thought-provoking lineup.
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.
“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.”
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. You can read more about Fin’s origins at the link below.
In 2020, the company pivoted again to the company it is today. 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.”
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 to 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 it 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 increasinglycriticized 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 A.I. 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.
The website, app and integrations are rolling out starting today. (If you aren’t able to find the app yet, you can try scanning the QR code from your mobile device.)
Margery Mayer served for 25 years as president of education at Scholastic.
Speech recognition technology is finally working for kids.
That wasn’t the case back in 1999, when my colleagues at Scholastic Education and I launched a reading intervention program called READ 180. We’d hoped to incorporate voice-enabled capabilities: Children would read to a computer program, which would provide real-time feedback on their fluency and literacy. Teachers, in turn, would receive information about their students’ progress.
Unfortunately, our idea was 20 years ahead of the technology, and we moved ahead with READ 180 without speech-recognition capabilities. Even at the height of the dot-com bubble, speech recognition for classrooms was still largely the stuff of science fiction.
Artificial intelligence and machine learning hadn’t enabled us to map the terabytes of data required to block out ambient noise in busy classrooms. Nor had it evolved to grasp the complexity of children’s voices, which have different pitches and speech patterns than those of adults, much less recognize a variety of dialects and accents, and — last but not least — manage children’s less-than-predictable behaviors when engaging with technology.
At Scholastic, we didn’t want to tell kids they were mastering something when they weren’t, and we understood the profound implications of telling a young student they got something wrong when they were actually right.
Fast forward to today. Speech recognition has advanced to the point where it can recognize and process children’s speech and account for differences in accents or dialects. Companies like Dublin-based SoapBox Labs have developed speech-recognition technology that is modeled on the diversity of children’s voices you’d find in a busy playground or classroom. Thanks to the high accuracy and performance of this technology, elementary school educators can now rely on it to help them gauge students’ progress with more regularity and offer more personalized approaches to their instruction.
Such advances could not have come at a more crucial moment.
Even before the pandemic, more than 80% of children from economically disadvantaged families failed to reach reading proficiency by fourth grade. After a year of separation from skilled educators, fumbling with technology designed for adults and vast gaps in digital equity, students had learned just 87% of the reading that they would have in a typical year, according to a report from McKinsey & Co. They lost an average of three months of learning during spring school closures.
Not surprisingly, reading losses were especially acute in schools that serve mostly students of color, where reading scores were just 77% of the historical average.
As students return to classrooms, speech recognition can revolutionize education — not to mention remote learning and entertainment in the home — by transforming the way children interact with technology. Voice-enabled literacy, as well as math and language programs, can further professionalize the field by taking the administrative work out of measuring a child’s learning rate and acquisition of foundational skills.
For example, speech recognition can generate regular and valuable insights into a student’s reading progress, pick up on patterns or isolate areas where improvement is needed. Teachers can review the progress and assessment data generated by voice-enabled tools, adapt the learning paths for each child’s needs, screen for challenges such as dyslexia and schedule timely interventions when necessary.
Voice-enabled reading tools allow every child to spend time reading aloud and receiving feedback during the school day, something that simply isn’t practical for one teacher to offer. To put the challenge in perspective: 15 minutes of individual time per child in a class of 25 would eat up more than six hours of a teacher’s day, every day. That sort of individualized observation and assessment was a persistent challenge for teachers before COVID-19. It becomes even more challenging with the emergence of remote learning and as students return to school with unprecedented educational and emotional issues.
Speech recognition technology also has the potential to increase equity in the classroom. Human reading assessment is, after all, highly subjective, and recent studies have shown variances of up to 18% caused by assessor bias. The child-centered high-accuracy speech recognition available today overcomes inevitable human bias by ensuring that every child’s voice is understood regardless of accent or dialect.
In a few years, this technology will be part of every classroom instruction, accelerating the reading — and math and language — skills of young students. Educators will find it enables them to be more strategic in their instruction. And it holds tremendous promise for something desperately needed in the era of COVID-19: technology that can significantly improve reading outcomes and tackle the global literacy crisis in a real and profound way.
Bringing order and understanding to unstructured information located across disparate silos has been one of 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.
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 over 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 are 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.
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.”
“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.”
Last week, Kathryn Zealand shared some insight on the eve of Women’s Equality Day. The post highlighted an issue that’s been apparent to everyone in and around the robotics industry: there’s a massive gender gap. It’s something we try to be mindful of, particularly when programming events like TC Sessions: Robotics. Zealand cites some pretty staggering figures in the piece.
According to the stats, around 9% of robotics engineers are female. That’s bad. That’s, like, bad even by the standards of STEM fields in general — which is to say, it’s really, really bad. (The ethnic disparities in the same source are worth drawing attention to, as well.)
Zealand’s piece was published on LinkedIn — fitting, given that the overarching focus here is on hiring. Well worth your time, if you’re involved in the hiring process at a robotics firm and are concerned about broader diversity issues (which hopefully go hand in hand for most orgs). Zealand offers some outside of the box thinking in terms of what, precisely, it means to be a roboticist, writing:
We have a huge opportunity here! Women and other under-represented groups are untapped pools of talented people who, despite not thinking of themselves as “roboticists,” could be vital members of a world-changing robotics team.
I’m going to be real with you for a minute, and note what really caught my eye was that above image. See, Zealand is a Project Lead at Alphabet X. And what you have there is a robotic brace — or, rather, what appears to be a component of a soft exosuit.
Image Credits: Bryce Durbin/TechCrunch
Exosuits/exoskeletons are a booming category for robotics right now that really run the gamut from Sarcos’ giant James Cameron-esque suit to far subtler, fabric-based systems. Some key names in the space include Ekso Bionics, ReWalk and SuitX. Heck, even Samsung has shown off a solution as part of a robotics department that appears to be largely ornamental at the moment.
Image Credits: Harvard Biodesign Lab
Most of these systems aim to tackle one of two issues: 1) Augmenting workers to assist with difficult or repetitive tasks for work and 2) Provide assistance to those with impaired mobility. Many companies have offers for both. Here’s what Harvard’s Biodesign Lab has to say on the matter:
As compared to a traditional exoskeleton, these systems have several advantages: the wearer’s joints are unconstrained by external rigid structures, and the worn part of the suit is extremely light. These properties minimize the suit’s unintentional interference with the body’s natural biomechanics and allow for more synergistic interaction with the wearer.
Alphabet loves to give the occasional behind-the-scenes peak at some of its X projects, and it turns out we’ve had a couple of glimpses of the Smarty Pants project. Zealand and Smarty Pants make a cameo in a Wired UK piece that ran early last year about the 10th anniversary of Google/Alphabet X. The piece notes that that the project was inspired by her experience with her 92-year-old grandmother’s mobility issues.
Image Credits: Alphabet X
The piece highlights a very early Raspberry Pi-controlled setup created by a team that includes costume designers and deep learning specialists (getting back to that earlier discussion about outside the box thinking when it comes to what constitutes a roboticist). The system is using sensors in an attempt to effectively predict movement in order to anticipate where force needs to be applied for tasks like walking up stairs. The piece ends on a fittingly somber note, “Fewer than half of X’s investigations become Projects. By the time this story is published it will probably have been killed.”
My suspicion is that the team is looking to differentiate itself from other exosuit projects by leveraging Google’s knowledge base of deep learning and AI to build out those predictive algorithms.
Alphabet declined to offer additional information on the project, noting that it likes to give its moonshot teams, “time to learn and iterate out of the spotlight.” But last October, we got what is probably our best look at Smarty Pants, in the form of a video highlighting Design Kitchen, Alphabet X’s lab/design studio.
Image Credits: Alphabet X
The Wired piece mentions a “pearlescent bumbag,” holding the aforementioned Raspberry Pi and additional components. For you yanks, that’s a fanny pack, which are not referred to as such in the U.K., owing to certain regional slang. Said fanny pack also makes an appearance in the video, providing, honestly, a very clever solution to the issue of hanging wires for an early-stage wearable prototype.
“One of the things that’s really helped the team is being really focused on a problem. Even if you spent months on something, if it’s not actually going to achieve that goal, then sometimes you honor the work that’s been done and say, ‘we’ve learned a ton of things during the process, but this is not the one that’s actually going to solve that problem.’ ”
The most notable takeaway from the video is some additional footage of prototypes. One imagines that, by the time Alphabet feels confident sharing that sort of stuff with the world, the team has moved well beyond it. “It doesn’t matter how janky and cardboard-and-duct-tape it is, as long as it helps you learn — and everyone can prototype, even while working from home,” the X team writes in an associated blog post.
The one other bit of information we have at the moment is a granted patent application from last year, which comes with all of the standard patent warnings. Seeing a patent come to fruition is often even more of a longshot (read: moonshot) than betting on an Alphabet X project to graduate. But they can offer some insight into where a team is headed — or at least some of the avenues it has considered.
Image Credits: Alphabet X
The patent highlights similar attempts to anticipate movement as those highlighted above. It effectively uses sensors and machine learning to adjust the tension on regions of the garments designed to assist the wearer.
Image Credits: Alphabet X
The proposed methods and systems provide adaptive support and assistance to users by performing intelligent dynamic adjustment of tension and stiffness in specific areas of fabric or by applying forces to non-stretch elements within a garment that is comfortable enough to be suitable for frequent, everyday usage. The methods include detecting movement of a particular part of a user’s body enclosed within the garment, determining an activity classification for that movement, identifying a support configuration for the garment tailored to the activity classification, and dynamically adjusting a tension and/or a stiffness of one or more controllable regions of the garment or applying force to non-stretch fabric elements in the garment to provide customized support and assistance for the user and the activity the user is performing.
It’s nice seeing Alphabet take a more organic approach to developing robotics startups in-house, rather than the acquisitions and consolidations that occurred several years back that ultimately found Boston Dynamics briefly living under the Google umbrella. Of course, we saw the recent graduation of the Wendy Tan White-led Intrinsic, which builds software for industrial robotics.
All right, so there’s a whole bunch of words about a project we know next to nothing about! Gotta love the startup space, where we’re definitely not spinning wild speculation based on a thin trail of breadcrumbs.
I will say for sure that I definitely know more about Agility Robotics than I did this time last week, after speaking with the Oregon-based company’s CEO and CTO. The conversation was ostensibly about a new video the team released showcasing Digit doing some menial tasks in a warehouse/fulfillment setting.
Some key things I learned:
Agility sold a dozen Cassie robots, largely to researchers.
It’s already sold “substantially more” Digits.
The team includes 56 people, primarily in Oregon (makes sense, as an OSU spinout), with plans to expand operations into Pittsburgh, everyone’s favorite rustbelt robotics hub.
Agility is consulting with “major logistics companies.”
In addition to the Ford delivery deal, the company has its sights set on warehouse tasks in hopes of offering a more adaptable solution than ground-up warehouse automation companies like Berkshire Gray.
Image Credits: Agility Robotics
Oh, and a good quote about job loss from CEO Damion Shelton:
The conversation around automation has shifted a bit. It’s viewed as an enabling technology to allow you to keep the workforce that you have. There are a lot of conversations around the risks of automation and job loss, but the job loss is actually occurring now, in advance of the automated solutions.
Agility hopes to start rolling out its robots to locations in the next year. More immediate than that, however, is this deal between Simbe Robotics and midwestern grocery chain, Schnuks. The food giant will be bringing Simbe’s inventor robots to all of its 111 stores, four years after it began piloting the tech.
Schnuck Markets deploys Tally robot by Simbe Robotics to its stores – bringing shelf insights for better shopping experience. Photographed on Friday, Aug. 13, 2021, in Des Peres, Mo.
Simbe says its Tally robot can reduce out of stock items by 20-30% and detect 14x more missing inventor than standard human scanning.
Carbon Robotics (not to be confused with the prosthetic company of the same name that made it onto our Hardware Battlefield a few years back) just raised $27 million. The Series B brings its total funding to around $36 million. The Seattle-based firm builds autonomous robots that zap weeds with lasers. We highlighted their most recent robot in this column back in April.
Segway’s first robotic lawnmower is designed for a lawn area of up to 3,000 square meters, has several features of a smart helper in the garden and is the quietest mower on the market with only 54 dB. The Frequent Soft Cut System (FSCS) ensures that the lawn is cut from above and the desired height is reached gradually. Offset blades allow cutting as close as possible to edges and corners.
That’s it for the week. Don’t forget to sign up to get the upcoming free newsletter version of Actuator delivered to your inbox.
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 6 early employees, a number that has grown to 17 recently and 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.
How many of us have not switched insurance carriers because we don’t want to deal with the hassle of comparison shopping?
A lot, I’d bet.
Today, Insurify, a startup that wants to help people make it easier to get better rates on home, auto and life insurance, announced that it has closed $100 million in an “oversubscribed” Series B funding round led by Motive Partners.
Existing backers Viola FinTech, MassMutual Ventures, Nationwide, Hearst Ventures and Moneta VC also put money in the round, as well as new investors Viola Growth and Fort Ross Ventures. With the new financing, Cambridge, Massachusetts-based Insurify has now raised a total of $128 million since its 2013 inception. The company declined to disclose the valuation at which the money was raised.
Since we last covered Insurify, the startup has seen some impressive growth. For example, it has seen its new and recurring revenue increase by “6x” since it closed its Series A funding in the 2019 fourth quarter. Over the last three years, Insurify has achieved a CAGR (compound annual growth rate) of 151%, according to co-founder and CEO Snejina Zacharia. It has also seen consistent “2.5x” year-over-year revenue growth, she said.
Insurify has built a machine learning-based virtual insurance agent that integrates with more than 100 carriers to digitize — and personalize — the insurance shopping experience. There are others in the insurtech space, but none that we know of currently tackling home, auto and life insurance. For example, Jerry, which has raised capital twicethis year, is focused mostly on auto insurance, although it does have a home product. The Zebra, which became a unicorn this year, started out as a site for people looking for auto insurance via its real-time quote comparison tool. Over time, it has also evolved to offer homeowners insurance with the goal of eventually branching out into renters and life insurance. But it too is mostly focused on auto.
Zacharia said that since Insurify’s Series A funding, it has expanded its home insurance marketplace, deepened its carrier integrations to provide users an “instant” purchase experience and launched its first two embedded insurance products through partnerships with Toyota Insurance Management Solutions and Nationwide (the latter of which also participated in the Series B funding round).
Image Credits: Insurify
Last year, when SkyScanner had to lay off staff, Insurify scooped up much of its engineering team and established an office in Sofia, Bulgaria.
Zacharia, a former Gartner executive, was inspired to start the company after she was involved in a minor car accident while getting her MBA at MIT. The accident led to a spike in her insurance premium and Zacharia was frustrated by the “complex and cumbersome” experience of car insurance shopping. She teamed up with Chief Product Officer Tod Kiryazov and her husband KAYAK President Giorgos Zacharia to build Insurify, which they describe as a virtual insurance agent that offers real-time quotes.
“We decided to build the most trusted virtual insurance agent in the industry that allows for customers to easily search, compare and buy fully digitally — directly from their mobile phone, or desktop, and really get a very smart, personalized experience based on their unique preferences,” Zacharia told TechCrunch. “We leverage artificial intelligence to be able to make recommendations on both coverage as well as carrier selection.”
Notably, Insurify is also a fully licensed agent that takes over the fulfillment and servicing of the policies. Since the company is mostly working as an insurance agent, it gets paid new and renewed commission. So while it’s not a SaaS business, its embedded insurance offerings have SaaS-like monetization.
“Our goal is to provide an experience for the end consumer that allows them to service and manage all of their policies in one place, digitally,” Zacharia said. “We think that the data recommendations that the platform provides can really remove most of the friction that currently exists in the shopping experience.”
Insurify plans to use its fresh capital to continue to expand its operations and accelerate its growth plans. It also, naturally, wants to add to its 125-person team.
“We want to build into our API integrations so customers can receive real-time direct quotes with better personalization and a more tailored experience,” Kiryazov said. “We also want to identify more embedded insurance opportunities and expand the product functionality.”
The company also down the line wants to expand into other verticals such as pet insurance, for example.
Insurify intends to use the money in part to build brand awareness, potentially through TV advertising.
“Almost half of our revenue comes from self-directed traffic,” Zacharia said. “So we want to explore more inorganic growth.”
James “Jim” O’Neill, founding partner at Motive Partners and industry partner Andy Rear point out that online purchasing now accounts for almost all of the growth in U.S. auto insurance.
“The lesson from other markets which have been through this transition is that customers prefer choice, presented as a simple menu of products and prices from different insurers, and a straightforward online purchasing process,” they wrote via email. “The U.S. auto market is huge: even a slow transition to online means a massive opportunity for Insurify.”
In conducting their due diligence, the pair said they were impressed with how the startup is building a business model “that works for customers, insurers and white-label partners.”
Harel Beit-On, founder and general partner at Viola Growth, believes that the quantum leap in e-commerce due to COVID-19 will completely transform the buying experience in almost every sector, including insurance.
“It is time to bring the frictionless purchasing experience that customers expect to the insurance space as well,” she said. “Following our fintech fund’s recent investment in the company, we watched Insurify’s immense growth, excellent execution with customer acquisition and building a brand consumers trust.”
In the customer service industry, your accent dictates many aspects of your job. It shouldn’t be the case that there’s a “better” or “worse” accent, but in today’s global economy (though who knows about tomorrow’s) it’s valuable to sound American or British. While many undergo accent neutralization training, Sanas is a startup with another approach (and a $5.5M seed round): using speech recognition and synthesis to change the speaker’s accent in near real time.
The company has trained a machine learning algorithm to quickly and locally (that is, without using the cloud) recognize a person’s speech on one end and, on the other, output the same words with an accent chosen from a list or automatically detected from the other person’s speech.
Image Credits: Sanas.ai
It slots right into the OS’s sound stack so it works out of the box with pretty much any audio or video calling tool. Right now the company is operating a pilot program with thousands of people in locations from the USA and UK to the Philippines, India, Latin America, and others. Accents supported will include American, Spanish, British, Indian, Filipino and Australian by the end of the year.
To tell the truth, the idea of Sanas kind of bothered me at first. It felt like a concession to bigoted people who consider their accent superior and think others below them. Tech will fix it… by accommodating the bigots. Great!
But while I still have a little bit of that feeling, I can see there’s more to it than this. Fundamentally speaking, it is easier to understand someone when they speak in an accent similar to your own. But customer service and tech support is a huge industry and one primarily performed by people outside the countries where the customers are. This basic disconnect can be remedied in a way that puts the onus of responsibility on the entry-level worker, or one that puts it on technology. Either way the difficulty of making oneself understood remains and must be addressed — an automated system just lets it be done more easily and allows more people to do their job.
It’s not magic — as you can tell in this clip, the character and cadence of the person’s voice is only partly retained and the result is considerably more artificial sounding:
But the technology is improving and like any speech engine, the more it’s used, the better it gets. And for someone not used to the original speaker’s accent, the American-accented version may very well be more easily understood. For the person in the support role, this likely means better outcomes for their calls — everyone wins. Sanas told me that the pilots are just starting so there are no numbers available from this deployment yet, but testing has suggested a considerable reduction of error rates and increase in call efficiency.
It’s good enough at any rate to attract a $5.5M seed round, with participation from Human Capital, General Catalyst, Quiet Capital, and DN Capital.
“Sanas is striving to make communication easy and free from friction, so people can speak confidently and understand each other, wherever they are and whoever they are trying to communicate with,” CEO Maxim Serebryakov said in the press release announcing the funding. It’s hard to disagree with that mission.
While the cultural and ethical questions of accents and power differentials are unlikely to ever go away, Sanas is trying something new that may be a powerful tool for the many people who must communicate professionally and find their speech patterns are an obstacle to that. It’s an approach worth exploring and discussing even if in a perfect world we would simply understand one another better.
Heroes, one of the new wave of startups aiming to build big e-commerce businesses by buying up smaller third-party merchants on Amazon’s Marketplace, has raised another big round of funding to double down on that strategy. The London startup has picked up $200 million, money that it will mainly be using to snap up more merchants. Existing brands in its portfolio cover categories like baby, pets, sports, personal health and home and garden categories — some of them, like PremiumCare dog chews, the Onco baby car mirror, gardening tool brand Davaon and wooden foot massager roller Theraflow, category best-sellers — and the plan is to continue building up all of these verticals.
Crayhill Capital Management, a fund based out of New York, is providing the funding, and Riccardo Bruni — who co-founded the company with twin brother Alessio and third brother Giancarlo — said that the bulk of it will be going towards making acquisitions, and is therefore coming in the form of debt.
Raising debt rather than equity at this point is pretty standard for companies like Heroes. Heroes itself is pretty young: it launched less than a year ago, in November 2020, with $65 million in funding, a round comprised of both equity and debt. Other investors in the startup include 360 Capital, Fuel Ventures and Upper 90.
Heroes is playing in what is rapidly becoming a very crowded field. Not only are there are tens of thousands of businesses leveraging Amazon’s extensive fulfillment network to sell goods on the e-commerce giant’s Marketplace; but some days it seems we are also rapidly approaching a state of nearly as many startups launching to consolidate these third-party sellers.
Many a roll-up play follows a similar playbook, which goes like this: Amazon provides the Marketplace to sell goods to consumers, and the infrastructure to fulfill those orders, by way of Fulfillment By Amazon and its Prime service. Meanwhile, the roll-up business — in this case Heroes — buys up a number of the stronger companies leveraging FBA and the Marketplace. Then, by consolidating them into a single tech platform that they have built, Heroes creates better economies of scale around better and more efficient supply chains, sharper machine learning and marketing and data analytics technology, and new growth strategies.
What is notable about Heroes, though — apart from the fact that it’s the first roll-up player to come out of the UK, and continues to be one of the bigger players in Europe — is that it doesn’t believe that the technology plays as important a role as having a solid relationship with the companies it’s targeting, key given that now the top Marketplace sellers are likely being feted by a number of companies as acquisition targets.
“The tech is very important,” said Alessio in an interview. “It helps us build robust processes that tie all the systems together across multiple brands and marketplaces. But what we have is very different from a SaaS business. We are not building an app, and tech is not the core of what we do. From the acquisitions side, we believe that human interactions ultimately win. We don’t think tech can replace a strong acquisition process.”
Image Credits: Heroes
Heroes’ three founder-brothers (two of them, Riccardo and Alessio, pictured above) have worked across a number of investment, finance and operational roles (the CVs include Merrill Lynch, EQT Ventures, Perella Weinberg Partners, Lazada, Nomura and Liberty Global) and they say there have been strong signs so far of its strategy working: of the brands that it has acquired since launching in November, they claim business (sales) has grown five-fold.
The picture that is emerging across many of these operations is that many of these companies, Heroes included, do not try to make their particular approaches particularly more distinctive than those of their competitors, simply because — with nearly 10 million third-party sellers today on Amazon globally — the opportunity is likely big enough for all of them, and more, not least because of current market dynamics.
“It’s no secret that we were inspired by Thrasio and others,” Riccardo said. “Combined with Covid-19, there has been a massive acceleration of e-commerce across the continent.” It was that, plus the realization that the three brothers had the right e-commerce, fundraising and investment skills between them, that made them see what was a “perfect storm” to tackle the opportunity, he continued. “So that is why we jumped into it.”
In the case of Heroes, while the majority of the funding will be used for acquisitions, it’s also planning to double headcount from its current 70 employees before the end of this year with a focus on operational experts to help run their acquired businesses.