Every year, around 10 million pets go missing in the U.S., and millions of those end up in shelters where they aren’t always reunited with their owners, due to their lack of identification or a microchip. A new mobile app, Shadow, aims to tackle this problem by leveraging a combination of a volunteer network and A.I. technology to help dog owners, in particular.
The startup is working in partnership with animal shelters and rescue organizations around the U.S. to pull in photos of the dogs they’re currently housing, then supplements this with photos pulled from social media platforms, like Twitter and Facebook.
It then uses A.I. technology to match the photograph of the missing dogs to possible matches from nearby shelters or the web.
Image Credits: Shadow
If there’s not a match found, Shadow will then programmatically set a search radius based on where and when the dog went missing, and suggest other actions that the dog’s owner can take as the next steps.
This includes viewing all the photographs from the shelters directly, in the case that the technology matching process missed a possible match, as well as working with other Shadow users to help crowdsource activities like hanging “Lost Dog” flyers around a neighborhood, for example, among other things.
The app also relies on a network of volunteers who help by also reviewing shelter photographs and broadcasting missing posters to social media sites they use to increase the chances of the dog being found. Dog owners can even advertise a reward in the app to encourage people to help search.
Today, Shadow has grown its volunteer user base to over 30,000. And it’s partnered with the ASPCA, Animal Care Centers of New York and L.A., the Dallas shelter system, and others.
Image Credits: Shadow
While Shadow is free to use, it makes money through a virtual tipping mechanism when it makes a successful match and the dog is found. It also offers users the ability to buy an Instagram ad in-app for $10. Here, Shadow provides the visual assets and manages the ad-buying process and placement process on owners’ behalf.
The startup, founded by former Zocdoc founder Cyrus Massoumi, has been in a sort of public stealth mode for a few years as it grew beyond its hometown of New York. It’s now offering dog-finding services in 76 counties across 20 U.S. states.
We should note that Massoumi’s exit from Zocdoc was complicated. He sued his co-founders and CFO for orchestrating a plot to oust him from the company during a Nov. 2015 board meeting, claiming fraud. The lawsuit detailed the internal strife inside Zocdoc at the time. A New York Supreme Court judge recently determined this lawsuit, which is ongoing, needs to be filed in Delaware, instead of New York. So a ruling is yet to be determined.
Ahead of this, Zocdoc had been accused by Business Insider of having developed a stressful, “bro culture,” in which young, male employees would make inappropriate remarks about the women who worked there. This was ahead of the larger rise of the Me Too movement, which has since impacted how businesses address these issues in the workplace.
Massoumi disputes the claims were exactly as described by the article. The company had 300 salespeople at the time, and while he agrees some people may have acted inappropriately, he also believes company’s response to those actions was handled properly.
“The allegations were fully investigated at Zocdoc and found to be without merit,” he told TechCrunch, adding that Zocdoc was repeatedly recognized as a “best place to work” while he was CEO.
Shadow today claims a different makeup. It has a team twelve people, and two-thirds of its product and engineering team are women. Some Zocdoc investors have also returned to back Massoumi again.
The startup is funded by Founders Fund, Humbition (Massoumi and Indiegogo founder Slava Rubin’s fund), Lux Capital, firstminute Capital, and other angels, including a prior Zocdoc
Despite the complicated Zocdoc history, the work Shadow is doing is solving a problem many people do care about. Millions of pet owners lose their pets to euthanization as they end up at shelters that cannot keep animals indefinitely due to lack of space. Meanwhile, the current system of having lost pet messages distributed across social media can mean many of those posts aren’t seen — especially in larger metros where there are numerous “lost pet” groups.
Image Credits: Shadow
As Shadow began its work in 2018, it was local to the New York area. Its first year, it reunited 600 dogs. The next year, it reunited 2,000 dogs. The third year, it reunited 5,000 dogs. Today, it’s nearing 10,000 dogs reunited with owners.
More than half of those were since the pandemic began, which saw many new pet owners and increased time spent outdoors with those pets, when dogs can sometimes get loose.
Massoumi says he was inspired to found Shadow after a friend lost his own dog, the namesake Shadow. It took the friend over a month to find the dog after both following false leads and being connected with people who tried to help him.
“I’m thinking to myself, this is something that happens 100 million times a year, globally…and for people who love pets, this is a lost family member,” Massoumi explains. “It seemed to me to be a similar problem that I’d already been solving in healthcare, where there’s fragmentation — people want to see the doctor and the doctor wants to see the patient, but there’s just not a central way to make it work,” he says.
More broadly, he wants to see technology being put to good use to solve problems that people actually care about.
“I think there needs to be more technology that injects the humanity back in what everyone does. I think that it’s very core that’s what we’re doing,” he says.
Shadow’s app is a free download on iOS and Android.
Researchers have created a machine learning system that they claim can determine a person’s political party, with reasonable accuracy, based only on their face. The study, from a group that also showed that sexual preference can seemingly be inferred this way, candidly addresses and carefully avoids the pitfalls of “modern phrenology,” leading to the uncomfortable conclusion that our appearance may express more personal information that we think.
The study, which appeared this week in the Nature journal Scientific Reports, was conducted by Stanford University’s Michal Kosinski. Kosinski made headlines in 2017 with work that found that a person’s sexual preference could be predicted from facial data.
The study drew criticism not so much for its methods but for the very idea that something that’s notionally non-physical could be detected this way. But Kosinski’s work, as he explained then and afterwards, was done specifically to challenge those assumptions and was as surprising and disturbing to him as it was to others. The idea was not to build a kind of AI gaydar — quite the opposite, in fact. As the team wrote at the time, it was necessary to publish in order to warn others that such a thing may be built by people whose interests went beyond the academic:
We were really disturbed by these results and spent much time considering whether they should be made public at all. We did not want to enable the very risks that we are warning against. The ability to control when and to whom to reveal one’s sexual orientation is crucial not only for one’s well-being, but also for one’s safety.
We felt that there is an urgent need to make policymakers and LGBTQ communities aware of the risks that they are facing. We did not create a privacy-invading tool, but rather showed that basic and widely used methods pose serious privacy threats.
Similar warnings may be sounded here, for while political affiliation at least in the U.S. (and at least at present) is not as sensitive or personal an element as sexual preference, it is still sensitive and personal. A week hardly passes without reading of some political or religious “dissident” or another being arrested or killed. If oppressive regimes could obtain what passes for probable cause by saying “the algorithm flagged you as a possible extremist,” instead of for example intercepting messages, it makes this sort of practice that much easier and more scalable.
The algorithm itself is not some hyper-advanced technology. Kosinski’s paper describes a fairly ordinary process of feeding a machine learning system images of more than a million faces, collected from dating sites in the U.S., Canada and the U.K., as well as American Facebook users. The people whose faces were used identified as politically conservative or liberal as part of the site’s questionnaire.
The algorithm was based on open-source facial recognition software, and after basic processing to crop to just the face (that way no background items creep in as factors), the faces are reduced to 2,048 scores representing various features — as with other face recognition algorithms, these aren’t necessary intuitive things like “eyebrow color” and “nose type” but more computer-native concepts.
The system was given political affiliation data sourced from the people themselves, and with this it diligently began to study the differences between the facial stats of people identifying as conservatives and those identifying as liberal. Because it turns out, there are differences.
Of course it’s not as simple as “conservatives have bushier eyebrows” or “liberals frown more.” Nor does it come down to demographics, which would make things too easy and simple. After all, if political party identification correlates with both age and skin color, that makes for a simple prediction algorithm right there. But although the software mechanisms used by Kosinski are quite standard, he was careful to cover his bases in order that this study, like the last one, can’t be dismissed as pseudoscience.
The most obvious way of addressing this is by having the system make guesses as to the political party of people of the same age, gender and ethnicity. The test involved being presented with two faces, one of each party, and guessing which was which. Obviously chance accuracy is 50%. Humans aren’t very good at this task, performing only slightly above chance, about 55% accurate.
The algorithm managed to reach as high as 71% accurate when predicting political party between two like individuals, and 73% presented with two individuals of any age, ethnicity or gender (but still guaranteed to be one conservative, one liberal).
Getting three out of four may not seem like a triumph for modern AI, but considering people can barely do better than a coin flip, there seems to be something worth considering here. Kosinski has been careful to cover other bases as well; this doesn’t appear to be a statistical anomaly or exaggeration of an isolated result.
The idea that your political party may be written on your face is an unnerving one, for while one’s political leanings are far from the most private of info, it’s also something that is very reasonably thought of as being intangible. People may choose to express their political beliefs with a hat, pin or t-shirt, but one generally considers one’s face to be nonpartisan.
If you’re wondering which facial features in particular are revealing, unfortunately the system is unable to report that. In a sort of para-study, Kosinski isolated a couple dozen facial features (facial hair, directness of gaze, various emotions) and tested whether those were good predictors of politics, but none led to more than a small increase in accuracy over chance or human expertise.
“Head orientation and emotional expression stood out: Liberals tended to face the camera more directly, were more likely to express surprise, and less likely to express disgust,” Kosinski wrote in author’s notes for the paper. But what they added left more than 10 percentage points of accuracy not accounted for: “That indicates that the facial recognition algorithm found many other features revealing political orientation.”
The knee-jerk defense of “this can’t be true — phrenology was snake oil” doesn’t hold much water here. It’s scary to think it’s true, but it doesn’t help us to deny what could be a very important truth, since it could be used against people very easily.
As with the sexual orientation research, the point here is not to create a perfect detector for this information, but to show that it can be done in order that people begin to consider the dangers that creates. If for example an oppressive theocratic regime wanted to crack down on either non-straight people or those with a certain political leaning, this sort of technology gives them a plausible technological method to do so “objectively.” And what’s more, it can be done with very little work or contact with the target, unlike digging through their social media history or analyzing their purchases (also very revealing).
We have already heard of China deploying facial recognition software to find members of the embattled Uyghur religious minority. And in our own country, this sort of AI is trusted by authorities as well — it’s not hard to imagine police using the “latest technology” to, for instance, classify faces at a protest, saying “these 10 were determined by the system as being the most liberal,” or what have you.
The idea that a couple researchers using open-source software and a medium-sized database of faces (for a government, this is trivial to assemble in the unlikely possibility they do not have one already) could do so anywhere in the world, for any purpose, is chilling.
“Don’t shoot the messenger,” said Kosinski. “In my work, I am warning against widely used facial recognition algorithms. Worryingly, those AI physiognomists are now being used to judge people’s intimate traits – scholars, policymakers, and citizens should take notice.”
The last we heard from Luther.ai, the startup was participating in the TechCrunch Disrupt Battlefield in September. The company got a lot of attention from that appearance, which culminated in a $3.2 million seed round it announced today. While they were at it, the founders decided to change the company name to Human AI, which they believe better reflects their mission.
Differential VC led the round joined by Village Global VC, Good Friends VC, Beni VC and Keshif Ventures. David Magerman from Differential will join the startup’s Board.
The investors were attracted to Human AI’s personalized kind of artificial intelligence, and co-founder and CEO Suman Kanuganti says that the Battlefield appearance led directly to investor interest, which quickly resulted in a deal four weeks later.
“I think overall the messaging of what we delivered at TechCrunch Disrupt regarding an individual personal AI that is secured by blockchain to retain and recall [information] really set the stage for what the company is all about, both from a user standpoint as well as from an investor standpoint,” Kanuganti told me.
As for the name change, he reported that there was some confusion in the market that Luther was an AI assistant like Alexa or a chatbot, and the founders wanted the name to better reflect the personalized nature of the product.
“We are creating AI for the individual and there is so much emphasis on the authenticity and the voice and the thoughts of an individual, and how we also use blockchain to secure ownership of the data. So most of the principle lies in creating this AI for an individual human. So we thought, let’s call it Human AI,” he explained.
As Kanuganti described it in September, the tool allows individuals to search for nuggets of information from past events using a variety of AI technologies:
“It’s made possible through a convergence of neuroscience, NLP and blockchain to deliver seamless in-the-moment recall. GPT-3 is built on the memories of the public internet, while Luther is built on the memories of your private self.”
The company is still in the process of refining the product and finding its audience, but reports that so far they have found interest from creative people such as writers, professionals such as therapists, high tech workers interested in AI, students looking to track school work and seniors looking for a way to track their memories for memoir purposes. All of these groups have the common theme of having to find nuggets of information from a ton of signals and that’s where Human AI’s strength lies.
The company’s diverse founding team includes two women, CTO Sharon Zhang and designer Kristie Kaiser, along with Kanuganti, who is himself an immigrant. The founders want to continue building a diverse organization as they add employees. “I think in general we just want to attract a diverse kind of talent, especially because we are also Human AI and we believe that everyone should have the same opportunity,” Zhang told me.
The company currently has 7 full time employees and a dozen consultants, but with the new funding is looking to hire engineers and AI talent and a head of marketing to push the notion of consumer AI. While the company is remote today and has employees around the world, it will look to build a headquarters at some point post-COVID where some percentage of the employees can work in the same space together.
Megvii, one of China’s largest facial recognition startups, is gearing up for an initial public offering in Shanghai. The company is working with CITIC Securities to prepare for its planned listing, according to an announcement posted by the China Securities Regulatory Commission on Tuesday.
The move came more than a year after Megvii, known for its computer vision platform Face++, filed to go public in Hong Kong in August 2019. At the time, Reuters reported that the company could raise between $500 million and $1 billion. However, the firm’s IPO application in Hong Kong has lapsed for undisclosed reasons and its focus is now on Shanghai’s STAR board, a person with knowledge of the matter told TechCrunch.
In 2019, China established the STAR board to attract high-growth, unprofitable Chinese tech startups after losing them to the U.S. for years. In the meantime, a domestic flotation is increasingly appealing to Chinese tech firms, especially those that count on government contracts and are caught in the U.S.-China tech competition.
Megvii and its rivals SenseTime, Yitu, and CloudWalk are collectively recognized as the “Four AI Dragons” of China for their market dominance and fundings from highflying investors. Megvii’s technology can be found powering smart city infrastructure across China as well as many smartphones and mobile apps. Alibaba, Ant Group and the Bank of China are among the group of investors who have pumped about $1.4 billion into the ten-year-old company since its inception.
The AI Dragons are less celebrated outside their home market. Last year, Megvii, Yitu and SenseTime were added to the U.S. Entity List for their alleged roles in enabling mass surveillance of the Muslim minority groups in western China. CloudWalk was subsequently added to the blacklist in 2020 and cut off from its U.S. suppliers.
According to the notice posted by China’s securities authority, Megvii plans to issue Chinese depositary receipts (CDRs), which are similar to American depositary receipts and allow domestic investors to hold overseas shares. That suggests the Beijing-based AI unicorn has not ruled out listing outside mainland China.
Currently seeking guidance in the pre-application stage, Megvii’s planned listing still needs approval from Chinese regulators.
The popularity of podcasting and online video shows no signs of slowing down, and so we continue to see a wave of creators publishing a profusion of audio and video content to fill out the airwaves. Today, a company building a platform to make that work easier and more interesting to execute is announcing a round of growth funding to double down on the opportunity.
Descript, which builds tools that let creators edit audio and video files by using, for example, natural language processing to link the content to the editing of text files, has picked up $30 million in a Series B round of funding.
Andrew Mason, the CEO and founder of the company, said in an interview that the plan will be to use the money to continue building out tools not just for mass-market and individual professional and amateur creators, but also, increasingly, organizations that might be using the tools for their own in-house video and audio needs, a use case that has definitely grown during the last year of global remote working.
“We see ourselves… as an all encompassing platform for all media needs,” Mason said.
The company had early wins by signing on customers like NPR, Pushkin Industries, VICE, The Washington Post and The New York Times, as well as smaller and more modest media outfits. Mason said that it’s also now seeing startups and bigger businesses using video for communication also adopting Descript tools, especially in cases where it makes more sense to visualise the answers, but the content could still use the ability to be edited.
“Whether it’s externally or internally, for things like bug reporting or personalized introductions or helpdesk videos, we’re seeing people using Descript for internal video,” he added, “sometimes in place of something like an email.”
Spark Capital, and specifically Nabeel Hyatt (who in a past life co-founded a music games specialist, Conduit Labs, acquired by Zynga), led the round, with Andreessen Horowitz and Redpoint Ventures also participating (both backed Descript in its $15 million Series A in 2019).
A number of individuals — some investors, and some investors also famous for their own video, podcasting and publishing work — also participated this Series B, among them Devdatta Akhawe, Alex Blumberg, Jack Conte, Justine Ezarik, Todd Goldberg, Jean-Denis Greze, John Lilly, Tobi Lutke, Bharat Mediratta, Shishir Mehrotra, Casey Neistat, Brian Pokorny, Raghavendra Prabhu, Lenny Rachitsky, Naval Ravikant, Jay Simons, Jake Shapiro, Rahul Vohra, and Ev Williams.
The news comes on the heels of an eventful several months for the company. In October, Descript released its first major update to its editing suite by expanding from audio editing tools to cover video as well. Mason said that the feedback so far has been “excellent” for the tech, although he is still declining to say how many users or usage Descript has for this or its older audio technology.
Descript’s move expanding into the newer medium, in any case, makes a lot of sense, when you consider how closely aligned a lot of audio-based podcasting content has been with corresponding videos, with many of the most popular podcasters often posting videos of their recordings on YouTube and other platforms, for those who prefer to watch as well as listen to recordings.
(It helps, too, that video is highly monetisable. Podcasting is on track to make more than $1 billion in ad revenues in the U.S. in 2021, according to the Internet Advertising Bureau. Meanwhile, even in a year that was considered a downturn, digital video pulled in more than $22 billion.)
But that double-platform approach has largely been executed on auto pilot up to now, as Mason points out, describing a lot of the video as “window dressing.”
“We watch a lot of video and podcasts and think about how we can create a tool that makes it fun and easy to craft great content,” Mason said. “One thing we’ve observed is that a remarkable amount of video is just audio with window dressing. You don’t notice it until you start looking through that lens. A ton of video is about what is happening with the audio, and so a lot of that video is just filler.” A lot of the editing is no more than a series of jump cuts, he said, and notwithstanding other challenges like bad equipment, it’s just not a very exciting experience.
That lays the groundwork for Descript not just to create tools to make it easier to edit but to conceive of how to do so in a way that creates a better and potentially more original product at the end of the process, too.
Mason’s turn to audio-based services for his two past startups — prior to Descript, he founded and eventually sold (to Bose) and audio-based guide service called Detour — have been something of a left turn for a man probably still better known as the quirky co-founder of the once wildly popular sales platform Groupon.
However, Mason studied music at university and it is more than obvious that audio and sound-based experiences — not just music but the impact that aural experiences can have — are really where his passion lies.
Mason is also a bit of a wag. He is quick to quip that his ability to raise money for completely different concepts that are a world away from e-commerce are in no smart part due to his having already won the “startup lottery”.
And yes, it’s a telling and often true term, in my experience and observation, but in this case, I’d say it undersells some of the really interesting innovations that Descript has built and is building — technology that has been proven to be in demand both with customers, and (as it happens) larger companies like Amazon, Spotify, Apple, Google and Facebook, which are picking up a lot of smaller audio technology startups in their own efforts to build out their bigger media business.
And this at the heart of why Descript has attracted this latest round of investment.
The maker of a defunct cloud photo storage app that pivoted to selling facial recognition services has been ordered to delete user data and any algorithms trained on it, under the terms of an FTC settlement.
The regulator investigated complaints the Ever app — which gained earlier notoriety for using dark patterns to spam users’ contacts — had applied facial recognition to users’ photographs without properly informing them what it was doing with their selfies.
Under the proposed settlement, Ever must delete photos and videos of users who deactivated their accounts and also delete all face embeddings (i.e. data related to facial features which can be used for facial recognition purposes) that it derived from photos of users who did not give express consent to such a use.
Moreover, it must delete any facial recognition models or algorithms developed with users’ photos or videos.
This full suite of deletion requirements — not just data but anything derived from it and trained off of it — is causing great excitement in legal and tech policy circles, with experts suggesting it could have implications for other facial recognition software trained on data that wasn’t lawfully processed.
Or, to put it another way, tech giants that surreptitiously harvest data to train AIs could find their algorithms in hot water with the US regulator.
This is revolutionary – and fascinating to see the US beats the EU in drawing this consequence https://t.co/20evtGaZM5
— Mireille Hildebrandt (@mireillemoret) January 12, 2021
That could require deleting the core ML models underlying Facebook Newsfeed or Google Search
— ashkan soltani (@ashk4n) January 12, 2021
The quick background here is that the Ever app shut down last August, claiming it had been squeezed out of the market by increased competition from tech giants like Apple and Google.
However the move followed an investigation by NBC News — which in 2019 reported that app maker Everalbum had pivoted to selling facial recognition services to private companies, law enforcement and the military (using the brand name Paravision) — apparently repurposing people’s family snaps to train face reading AIs.
One commissioner, Rohit Chopra, issued a standalone statement in which he warns that current gen facial recognition technology is “fundamentally flawed and reinforces harmful biases”, saying he supports “efforts to enact moratoria or otherwise severely restrict its use”.
“Until such time, it is critical that the FTC meaningfully enforce existing law to deprive wrongdoers of technologies they build through unlawful collection of Americans’ facial images and likenesses,” he adds.
Chopra’s statement highlights the fact that commissioners have previously voted to allow data protection law violators to retain algorithms and technologies that “derive much of their value from ill-gotten data”, as he puts it — flagging an earlier settlement with Google and YouTube under which the tech giant was allowed to retain algorithms and other technologies “enhanced by illegally obtained data on children”.
And he dubs the Ever decision “an important course correction”.
Ever has not been fined under the settlement — something Chopra describes as “unfortunate” (saying it’s related to commissioners “not having restated this precedent into a rule under Section 18 of the FTC Act”).
He also highlights the fact that Ever avoided processing the facial data of a subset of users in States which have laws against facial recognition and the processing of biometric data — citing that as an example of “why it’s important to maintain States’ authority to protect personal data”. (NB: Ever also avoided processing EU users’ biometric data; another region with data protection laws.)
“With the tsunami of data being collected on individuals, we need all hands on deck to keep these companies in check,” he goes on. “State and local governments have rightfully taken steps to enact bans, moratoria, and other restrictions on the use of these technologies. While special interests are actively lobbying for federal legislation to delete state data protection laws, it will be important for Congress to resist these efforts. Broad federal preemption would severely undercut this multifront approach and leave more consumers less protected.
“It will be critical for the Commission, the states, and regulators around the globe to pursue additional enforcement actions to hold accountable providers of facial recognition technology who make false accuracy claims and engage in unfair, discriminatory conduct.”
Paravision has been contacted for comment on the FTC settlement.
This has been quite a week.
Instead of walking backward through the last few days of chaos and uncertainty, here are three good things that happened:
Despite many distractions in our first full week of the new year, we published a full slate of stories exploring different aspects of entrepreneurship, fundraising and investing.
We’ve already gotten feedback on this overview of subscription pricing models, and a look back at 2020 funding rounds and exits among Israel’s security startups was aimed at our new members who live and work there, along with international investors who are seeking new opportunities.
Plus, don’t miss our first investor surveys of 2021: one by Lucas Matney on social gaming, and another by Mike Butcher that gathered responses from Portugal-based investors on a wide variety of topics.
Thanks very much for reading Extra Crunch this week. I hope we can all look forward to a nice, boring weekend with no breaking news alerts.
Senior Editor, TechCrunch
Full Extra Crunch articles are only available to members
Use discount code ECFriday to save 20% off a one- or two-year subscription
Image Credits: Nigel Sussman (opens in a new window)
In February 2020, gaming platform Roblox was valued at $4 billion, but after announcing a $520 million Series H this week, it’s now worth $29.5 billion.
“Sure, you could argue that Roblox enjoyed an epic 2020, thanks in part to COVID-19,” writes Alex Wilhelm this morning. “That helped its valuation. But there’s a lot of space between $4 billion and $29.5 billion.”
Alex suggests that Roblox’s decision to delay its IPO and raise an enormous Series H was a grandmaster move that could influence how other unicorns will take themselves to market. “A big thanks to the gaming company for running this experiment for us.”
I asked him what inspired the headline; like most good ideas, it came to him while he was trying to get to sleep.
“I think that I had ‘The Queen’s Gambit’ somewhere in my head, so that formed the root of a little joke with myself. Roblox is making a strategic wager on method of going public. So, ‘gambit’ seems to fit!”
Image Credits: Erik Von Weber (opens in a new window) / Getty Images
For our first investor survey of the year, Lucas Matney interviewed eight VCs who invest in massively multiplayer online games to discuss 2021 trends and opportunities:
Having moved far beyond shooters and sims, platforms like Twitch, Discord and Fortnite are “where culture is created,” said Daniel Li of Madrona.
Rep. Alexandria Ocasio-Cortez uses Twitch to explain policy positions, major musicians regularly perform in-game concerts on Fortnite and in-game purchases generated tens of billions last year.
“Gaming is a unique combination of science and art, left and right brain,” said Gigi Levy-Weiss of NFX. “It’s never just science (i.e., software and data), which is why many investors find it hard.”
Image Credits: C.J. Burton (opens in a new window) / Getty Images
Startups that lack insight into their sales funnel have high churn, low conversion rates and an inability to adapt or leverage changes in customer behavior.
If you’re hoping to convert and retain customers, “reinforcing your value proposition should play a big part in every level of your customer funnel,” says Joe Procopio, founder of Teaching Startup.
Image Credits: Bloomberg (opens in a new window) / Getty Images
Alex Wilhelm followed up his regular Friday column with another story that tries to find a well-grounded rationale for Tesla’s sky-high valuation of approximately $822 billion.
Meanwhile, GM just unveiled a new logo and tagline.
As ever, I learned something new while editing: A “melt up” occurs when investors start clamoring for a particular company because of acute FOMO (the fear of missing out).
Delivering 500,000 cars in 2020 was “impressive,” says Alex, who also acknowledged the company’s ability to turn GAAP profits, but “pride cometh before the fall, as does a melt up, I think.”
Note: This story has Alex’s original headline, but I told him I would replace the featured image with a photo of someone who had very “richest man in the world” face.
Image Credits: piranka / Getty Images
On Tuesday, enterprise reporter Ron Miller covered a major engineering project at customer data platform Segment called “Centrifuge.”
“Its purpose was to move data through Segment’s data pipes to wherever customers needed it quickly and efficiently at the lowest operating cost,” but as Ron reports, it was also meant to solve “an existential crisis for the young business,” which needed a more resilient platform.
Image Credits: Sophie Alcorn
Now that the U.S. has a new president coming in whose policies are more welcoming to immigrants, I am considering coming to the U.S. to expand my company after COVID-19. However, I’m struggling with the morass of information online that has bits and pieces of visa types and processes.
Can you please share an overview of the U.S. immigration system and how it works so I can get the big picture and understand what I’m navigating?
— Resilient in Romania
The first “Dear Sophie” column of each month is available on TechCrunch without a paywall.
Image Credits: Hiraman (opens in a new window) / Getty Images
For founders who aren’t interested in angel investment or seeking validation from a VC, revenue-based investing is growing in popularity.
To gain a deeper understanding of the U.S. RBI landscape, we published an industry report on Wednesday that studied data from 134 companies, 57 funds and 32 investment firms before breaking out “specific verticals and business models … and the typical profile of companies that access this form of capital.”
Image Credits: Westend61 (opens in a new window)/ Getty Images
Mike Butcher continues his series of European investor surveys with his latest dispatch from Lisbon, where a nascent startup ecosystem may get a Brexit boost.
Here are the Portugal-based VCs he interviewed:
Image Credits: John Lund (opens in a new window)/ Getty Images
How do you scale online tutoring, particularly when demand exceeds the supply of human instructors?
This month, Chegg is replacing its seven-year-old marketplace that paired students with tutors with a live chatbot.
A spokesperson said the move will “dramatically differentiate our offerings from our competitors and better service students,” but Natasha Mascarenhas identified two challenges to edtech automation.
“A chatbot won’t work for a student with special needs or someone who needs to be handheld a bit more,” she says. “Second, speed tutoring can only work for a specific set of subjects.”
Image Credits: Treedeo (opens in a new window) / Getty Images
While I watched insurrectionists invade and vandalize the U.S. Capitol on live TV, I noticed that staffers evacuated so quickly, some hadn’t had time to shut down their computers.
Looters even made off with a laptop from Senator Jeff Merkley’s office, but according to security reporter Zack Whittaker, the damages to infosec wasn’t as bad as it looked.
Even so, “the breach will likely present a major task for Congress’ IT departments, which will have to figure out what’s been stolen and what security risks could still pose a threat to the Capitol’s network.”
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On New Year’s Eve, I made a list of the 10 “best” Extra Crunch stories from the previous 12 months.
My methodology was personal: From hundreds of posts, these were the 10 I found most useful, which is my key metric for business journalism.
Some readers are skeptical about paywalls, but without being boastful, Extra Crunch is a premium product, just like Netflix or Disney+. I know, we’re not as entertaining as a historical drama about the reign of Queen Elizabeth II or a space western about a bounty hunter. But, speaking as someone who’s worked at several startups, Extra Crunch stories contain actionable information you can use to build a company and/or look smart in meetings — and that’s worth something.
Zack Parisa and Max Nova, the co-founders of the carbon offset company SilivaTerra, have spent the last decade working on a way to democratize access to revenue generating carbon offsets.
As forestry credits become a big, booming business on the back of multi-billion dollar commitments from some of the world’s biggest companies to decarbonize their businesses, the kinds of technologies that the two founders have dedicated ten years of their lives to building are only going to become more valuable.
That’s why their company, already a profitable business, has raised $4.4 million in outside funding led by Union Square Ventures and Version One Ventures, along with Salesforce founder and the driving force between the 1 trillion trees initiative, Marc Benioff .
“Key to addressing the climate crisis is changing the balance in the so-called carbon cycle. At present, every year we are adding roughly 5 gigatons of carbon to the atmosphere*. Since atmospheric carbon acts as a greenhouse gas this increases the energy that’s retained rather than radiated back into space which causes the earth to heat up,” writes Union Square Ventures managing partner Albert Wenger in a blog post. “There will be many ways such drawdown occurs and we will write about different approaches in the coming weeks (such as direct air capture and growing kelp in the oceans). One way that we understand well today and can act upon immediately are forests. The world’s forests today absorb a bit more than one gigatons of CO2 per year out of the atmosphere and turn it into biomass. We need to stop cutting and burning down existing forests (including preventing large scale forest fires) and we have to start planting more new trees. If we do that, the total potential for forests is around 4 to 5 gigatons per year (with some estimates as high as 9 gigatons).”
For the two founders, the new funding is the latest step in a long journey that began in the woods of Northern Alabama, where Parisa grew up.
After attending Mississippi State for forestry, Parisa went to graduate school at Yale, where he met Louisville, Kentucky native Max Nova, a computer science student who joined with Parisa to set up the company that would become SiliviaTerra.
SilviaTerra co-founders Max Nova and Zack Parisa. Image Credit: SilivaTerra
The two men developed a way to combine satellite imagery with field measurements to determine the size and species of trees in every acre of forest.
While the first step was to create a map of every forest in the U.S. the ultimate goal for both men was to find a way to put a carbon market on equal footing with the timber industry. Instead of cutting trees for cash, potentially landowners could find out how much it would be worth to maintain their forestland. As the company notes, forest management had previously been driven by the economics of timber harvesting, with over $10 billion spent in the US each year.
The founders at SilviaTerra thought that the carbon market could be equally as large, but it’s hard for moset landowners to access. Carbon offset projects can cost as much as $200,000 to put together, which is more than the value of the smaller offset projects for landowners like Parisa’s own family and the 40 acres they own in the Alabama forests.
There had to be a better way for smaller landowners to benefit from carbon markets too, Parisa and Nova thought.
To create this carbon economy, there needed to be a single source of record for every tree in the U.S. and while SiliviaTerra had the technology to make that map, they lacked the compute power, machine learning capabilities and resources to build the map.
That’s where Microsoft’s AI for Earth program came in.
Working with AI for Earth, TierraSilva created their first product, Basemap, to process terabytes ofsatellite imagery to determine the sizes and species of trees on every acre of America’s forestland. The company also worked with the US Forestry Service to access their data, which was used in creating this holistic view of the forest assets in the U.S.
With the data from Basemap in hand, the company has created what it calls the Natural Capital Exchange. This program uses SilviaTerra’s unparalleled access to information about local forests, and the knowledge of how those forests are currently used to supply projects that actually represent land that would have been forested were it not for the offset money coming in.
Currently, many forestry projects are being passed off to offset buyers as legitimate offsets on land that would never have been forested in the first place — rendering the project meaningless and useless in any real way as an offset for carbon dioxide emissions.
“It’s a bloodbath out there,” said Nova of the scale of the problem with fraudulent offsets in the industry. “We’re not repackaging existing forest carbon projects and try to connect the demand side with projects that already exist. Use technology to unlock a new supply of forest carbon offset.”
The first Natural Capital Exchange project was actually launched and funded by Microsoft back in 2019. In it, 20 Western Pennsylvania land owners originated forest carbon credits through the program, showing that the offsets could work for landowners with 40 acres, or, as the company said, 40,000.
Landowners involved in SilivaTerra’s pilot carbon offset program paid for by Microsoft. Image Credit: SilviaTerra
“We’re just trying to get inside every landowners annual economic planning cycle,” said Nova. “There’s a whole field of timber economics… and we’re helping answer the question of given the price of timber, given the price of carbon does it make sense to reduce your planned timber harvests?”
Ultimately, the two founders believe that they’ve found a way to pay for the total land value through the creation of data around the potential carbon offset value of these forests.
It’s more than just carbon markets, as well. The tools that SilviaTerra have created can be used for wildfire mitigation as well. “We’re at the right place at the right time with the right data and the right tools,” said Nova. “It’s about connecting that data to the decision and the economics of all this.”
The launch of the SilviaTerra exchange gives large buyers a vetted source to offset carbon. In some ways its an enterprise corollary to the work being done by startups like Wren, another Union Square Ventures investment, that focuses on offsetting the carbon footprint of everyday consumers. It’s also a competitor to companies like Pachama, which are trying to provide similar forest offsets at scale, or 3Degrees Inc. or South Pole.
Under a Biden administration there’s even more of an opportunity for these offset companies, the founders said, given discussions underway to establish a Carbon Bank. Established through the existing Commodity Credit Corp. run by the Department of Agriculture, the Carbon Bank would pay farmers and landowners across the U.S. for forestry and agricultural carbon offset projects.
“Everybody knows that there’s more value in these systems than just the product that we harvest off of it,” said Parisa. “Until we put those benefits in the same footing as the things we cut off and send to market…. As the value of these things goes up… absolutely it is going to influence these decisions and it is a cash crop… It’s a money pump from coastal America into middle America to create these things that they need.”
If, as I suspect many of you have, you have worked your way through baking every type of cookie, bread and cake under the sun over the last year, Google has a surprise for you: a pair of AI-generated hybrid treats, the “breakie” and the “cakie.”
The origin of these new items seems to have been in a demonstration of the company’s AutoML Tables tool, a codeless model generation system that’s more spreadsheet automation than what you’d really call “artificial intelligence.” But let’s not split hairs, or else we’ll never get to the recipe.
Specifically it was the work of Sara Robinson, who was playing with these tools earlier last spring, as a person interested in machine learning and baking was likely to start doing around that time as cabin fever first took hold.
What happened was she wanted to design a system that would look at a recipe and automatically tell you whether it was bread, cookie or cake, and why — for instance, a higher butter and sugar content might bias it toward cookie, while yeast was usually a dead giveaway for bread.
But of course, not every recipe is so straightforward, and the tool isn’t always 100% sure. Robinson began to wonder, what would a recipe look like that the system couldn’t decide on?
She fiddled around with the ingredients until she found a balance that caused the machine learning system to produce a perfect 50/50 split between cookie and cake. Naturally, she made some — behold the “cakie.”
“It is yummy. And it strangely tastes like what I’d imagine would happen if I told a machine to make a cake cookie hybrid,” she wrote.
The other hybrid she put together was the “breakie,” which as you surely have guessed by now is half bread, half cookie. This one ended up a little closer to “fluffy cookies, almost the consistency of a muffin.” And indeed they look like muffin tops that have lost their bottoms. But breakie sounds better than muffin tops (or “brookie,” apparently the original name).
These ingredients and ratios were probably invented or tried long ago, but it’s certainly an interesting way to arrive at a new recipe using only old ones.
The recipes below are perfectly doable, but to be transparent were not entirely generated by algorithm. It only indicates proportions of ingredients, and didn’t include any flavorings or features like vanilla or chocolate chips, both which Robinson added. The actual baking instructions had to be puzzled out as well (the AI doesn’t know what temperature is, or pans). But if you need something to try making that’s different from the usual weekend treat, you could probably do worse than one of these.
The ongoing push for social distancing to slow the spread of Covid-19 has meant that more people than ever before are using internet-based services to get things done. And that is having a direct impact on digital customer service, which is seeing unprecedented traffic and demands when things are not running smoothly. Today, one of the startups that’s built an interesting, very “hands-on” approach to addressing that problem is announcing a round of funding to expand its business.
Glia, which has built a platform that not only integrates and helps manage different customer support channels, but also provides tools to help agents proactively get into a customer’s app or web page to help them find things or fix issues, is today announcing that it has picked up $78 million, a Series C. Dan Michaeli, said will be used to continue developing its technology and expanding to address inbound interest for its services after seeing its revenues grow by 150% in 2020.
The company’s original focus was around financial services and it counts a large base of customers in that area, but it is also seeing a lot of activity in adjacent industries like insurance, as well as education, retail and other categories Michaeli said.
“We’ve had overwhelming demand and it’s incredible to see how businesses want to adopt us right now,” he said in an interview. “The plan is to significantly scale up and continue to define and meet that demand for digital customer service.” The company is likely also to use some of the funding for acquisitions in what appears to be a rapidly consolidating market.
The round is being led by Insight Partners, with Don Brown (an entrepreneur in the world of customer service, with his company Interactive Intelligence acquired by Genesys for $1.4 billion) also participating.
Glia isn’t disclosing other investors, but past backers include Tola Capital, Temerity Capital, Grassy Creek and Wildcat Capital, as well as Insight. Prior to this, the company, which has been around since 2012 and was previously known as Salemove, had raised just $28 million and its valuation was a modest $69 million according to PitchBook data (and it’s not disclosing valuation today).
While there are a lot of customer service startups in the market today, and a number of them are seeing huge boosts in their business, and even some consolidation as others snap up tech to make sure they have their own customer service strategies going in the right direction. (Witness Facebook of all companies acquiring omnichannel customer support and CRM leader Kustomer for $1 billion in November.)
Glia is not unlike many of the new guard of these companies, in that its focus is very squarely on providing a platform to be able to manage and interact across whatever digital channel a customer happens to be using. Glia, I should point out, means “glue” in Greek.
What makes Glia quite interesting and different from these are some of the twists it uses to engage with users. One of these involves being able to give agents the ability to actually get on the screen of the user in question, in order to both guide the user around the screen, and to see what the user is doing on that screen.
To be clear, the connection and ability to track what the user is doing is just on the screen in question, and it’s done with the user’s awareness of what is going on. In the demo of the service that I went through, it’s a very smooth service, which reminded me just a little of things like Clippy on Microsoft Word.
Alongside this, Glia provides tools to agents to let coach them on questions to ask, phrasing to use, and links for answers, and Glia also develops virtual customer service assistants, to help with more basic questions. These also have the ability to interact with people’s screens when they make contact with a company. This in effect sees the company combining a number of technologies in one place, from natural language to suggest (and in some cases run) customer service responses, through to computer vision to help detect what is going on on the remote screen, through to more fundamental CRM technology to run those services across multiple platforms.
While screen sharing has been a well-used tool in other areas — for example in workforce collaboration environments, or for presenting online — Glia is seen as one of the pioneers in leveraging that for customer service. For investors, the interest in Glia has been to tap into that.
“We are proud to expand our investment in Glia as the company continues to lead the evolution of Digital Customer Service for businesses across the globe,” said Lonne Jaffe, managing director at Insight Partners, in a statement. “Glia’s platform provides the modern technology necessary for businesses to meet customers in their digital journeys and communicate through the customer’s channel of choice. With this capital, the company will continue to scale and keep up with skyrocketing demand.”
We are in a key moment of digital transformation in customer services. Surprisingly, there are still many who opt for calling in to ask questions, but as Michaeli noted, these days, even when they are still using phones, customers will do so with “their screens in front of them.”
Brown believes that this is the other opportunity to seize. “Many companies are still focused on moving antiquated, on-premises telephony systems to cloud contact centers that essentially offer the same functionality,” he said in a statement. “Instead, businesses can leapfrog this process and move directly to a digital-first cloud approach by partnering with Glia. If I were to build Interactive Intelligence for today’s contact center, I would take Glia’s approach.”
Sports have been among some of the most popular and lucrative media plays in the world, luring broadcasters, advertisers and consumers to fork out huge sums to secure the chance to watch (and sponsor) their favorite teams and athletes.
That content, unsurprisingly, also typically costs a ton of money to produce, narrowing the production and distribution funnel even more. But today, a startup that’s cracked open that model with an autonomous, AI -based camera that lets any team record, edit and distribute their games, is announcing a round of funding to build out its business targeting the long tail of sporting teams and fixtures.
Veo Technologies, a Copenhagen startup that has designed a video camera and cloud-based subscription service to record and then automatically pick out highlights of games, which it then hosts on a platform for its customers to access and share that video content, has picked up €20 million (around $24.5 million) in a Series B round of funding.
The funding is being led by Danish investor Chr. Augustinus Fabrikker, with participation from US-based Courtside VC, France’s Ventech and Denmark’s SEED Capital. Veo’s CEO and co-founder Henrik Teisbæk said in an interview that the startup is not disclosing its valuation, but a source close to funding tells me that it’s well over $100 million.
Teisbæk said that the plan will be to use to the funds to continue expanding the company’s business on two levels. First, Veo will be digging into expanding its US operations, with an office in Miami.
Second, it plans to continue enhancing the scope of its technology: The company started out optimising its computer vision software to record and track the matches for the most popular team sport in the world, football (soccer to US readers), with customers buying the cameras — which retail for $800 — and the corresponding (mandatory) subscriptions — $1,200 annually — both to record games for spectators, as well as to use the footage for all kinds of practical purposes like training and recruitment videos. The key is that the cameras can be set up and left to run on their own. Once they are in place, they can record using wide-angles the majority of a soccer field (or whatever playing space is being used) and then zoom and edit down based on that.
Now, Veo is building the computer vision algorithms to expand that proposition into a plethora of other team-based sports including rugby, basketball and hockey, and it is ramping up the kinds of analytics that it can provide around the clips that it generates as well as the wider match itself.
Even with the slowdown in a lot of sporting activity this year due to COVID — in the U.K. for example, we’re in a lockdown again where team sports below professional leagues, excepting teams for disabled people, have been prohibited — Veo has seen a lot of growth.
The startup currently works with some 5,000 clubs globally ranging from professional sports teams through to amateur clubs for children, and it has recorded and tracked 200,000 games since opening for business in 2018, with a large proportion of that volume in the last year and in the US.
For a point of reference, in 2019, when we covered a $6 million round for Veo, the startup had racked up 1,000 clubs and 25,000 games, pointing to customer growth of 400% in that period.
The Covid-19 pandemic has indeed altered the playing field — literally and figuratively — for sports in the past year. Spectators, athletes, and supporting staff need to be just as mindful as anyone else when it comes to spreading the coronavirus.
That’s not just led to a change in how many games are being played, but also for attendance: witness the huge lengths that the NBA went to last year to create an extensive isolation bubble in Orlando, Florida, to play out the season, with no actual fans in physical seats watching games, but all games and fans virtually streamed into the events as they happened.
That NBA effort, needless to say, came at a huge financial cost, one that any lesser league would never be able to carry, and so that predicament has led to an interesting use case for Veo.
Pre-pandemic, the Danish startup was quietly building its business around catering to the long tail of sporting organizations who — even in the best of times — would be hard pressed to find the funds to buy cameras and/or hire videographers to record games, not just an essential part of how people can enjoy a sporting event, but useful for helping with team development.
“There is a perception that football is already being recorded and broadcast, but in the UK (for example) it’s only the Premier League,” Teisbæk said. “If you go down one or two steps from that, nothing is being recorded.” Before Veo, to record a football game, he added, “you need a guy sitting on a scaffold, and time and money to then cut that down to highlights. It’s just too cumbersome. But video is the best tool there is to develop talent. Kids are visual learners. And it’s a great way to get recruited sending videos to colleges.”
Those use cases then expanded with the pandemic, he said. “Under coronavirus rules, parents cannot go out and watch their kids, and so video becomes a tool to follow those matches.”
The business model for Veo up to now has largely been around what Teisbæk described as “the long tail theory”, which in the case of sports works out, he said, as “There won’t be many viewers for each match, but there are millions of matches out there.” But if you consider how a lot of high school sports will attract locals beyond those currently attached to a school — you have alumni supporters and fans, as well as local businesses and neighborhoods — even that long tail audience might be bigger than one might imagine.
Veo’s long-tail focus has inevitably meant that its target users are in the wide array of amateur or semi-pro clubs and the people associated with them, but interestingly it has also spilled into big names, too.
Veo’s cameras are being used by professional soccer clubs in the Premier League, Spain’s La Liga, Italy’s Serie A and France’s Ligue 1, as well as several clubs in the MLS such as Inter Miami, Austin FC, Atlanta United and FC Cincinnati. Teisbæk noted that while this might never be for primary coverage, it’s there to supplement for training and also be used in the academies attached to those organizations.
The plan longer term, he said, is not to build its own media empire with trove of content that it has amassed, but to be an enabler for creating that content for its customers, who can in turn use it as they wish. It’s a “Shopify, not an Amazon,” said Teisbæk.
“We are not building the next ESPN, but we are helping the clubs unlock these connections that are already in place by way of our technology,” he said. “We want to help help them capture and stream their matches and their play for the audience that is there today.”
That may be how he views the opportunity, but some investors are already eyeing up the bigger picture.
Vasu Kulkarni, a partner at Courtside VC — a firm that has focused (as its name might imply) on backing a lot of different sports-related businesses, with The Athletic, Beam (acquired by Microsoft), and many others in its portfolio — said that he’d been looking to back a company like Veo, building a smart, tech-enabled way to record and parse sports in a more cost-effective way.
“I spent close to four years trying to find a company trying to do that,” he said.
“I’ve always been a believer in sports content captured at the long tail,” he said. Coincidentally, he himself started a company called Krossover in his dorm room to help somewhat with tracking and recording sports training. Krossover eventually was acquired by Hudl, a competitor to Veo.
“You’ll never have the NBA finals recorded on Veo, there is just too much at stake, but when you start to look at all the areas where there isn’t enough mass media value to hire people, to produce and livestream, you get to the point where computer vision and AI are going to be doing the filming to get rid of the cost.”
He said that the economics are important here: the camera needs to be less than $1,000 (which it is) and produce something demonstrably better than “a parent with a Best Buy camcorder that was picked up for $100.”
Kulkarni thinks that longer term there could definitely be an opportunity to consider how to help clubs bring that content to a wider audience, especially using highlights and focusing on the best of the best in amateur games — which of course are the precursors to some of those players one day being world-famous elite athletes. (Think of how exciting it is to see the footage of Michael Jordan playing as a young student for some context here.) “AI will be able to pull out the best 10-15 plays and stitch them together for highlight reels,” he said, something that could feasibly find a market with sports fans wider than just the parents of the actual players.
All of that then feeds a bigger market for what has started to feel like an insatiable appetite for sports, one that, if anything, has found even more audience at a time when many are spending more time at home and watching video overall. “The more video you get from the sport, the better the sport gets, for players and fans,” Teisbæk said.
The Covid-19 pandemic and specifically need for social distancing to slow the spread of the virus have continued to keep many of us away from the office. Now, increasingly, many organizations and people believe that it could usher in a more permanent shift to remote, distributed and virtual work. Today, a startup that has built a set of tools specifically to help salespeople with that change — by way of digital sales — has raised a substantial growth round to meet that demand.
SalesLoft, a sales platform based out of Atlanta, Georgia that provides AI-baseed tools to help salespeople run their sales process virtually — from finding and following up on leads, through to helping them sell with virtual coaching tools, and then assisting in the post-sales process — has closed $100 million in funding.
The company’s co-founder and CEO Kyle Porter confirmed to TechCrunch that the company is now valued at $1.1 billion post-money, a substantial hike on its previous valuation. In April 2019, well before any global health pandemics, the company had raised a Series D of $70 million at around a $600 million valuation (a figure we confirmed at the time with sources close to the company).
This latest round is being led by Owl Rock Capital, with previous investors Insight Partners, HarbourVest, and Emergence Capital — a VC focused specifically on enterprise startups, which notably was an early backer of Zoom and many others — also participating.
SalesLoft has now raised some $245 million, an impressive sum for any startup, but also worth pointing out for the fact that its not based out of the Valley but Atlanta, Georgia (a state in the news for other reasons at the moment, as the focus of a hotly contested US Senate runoff election).
The company has been on a growth tear for several years now, as one of the big players in the area of so-called sales engagement: tools to help salespeople sell better to clients (or would-be clients), which can include real-time monitoring of interactions to provide coaching to improve the process, suggestions for supplementary content to enhance the pitch, and more basic software simply to manage records and communications.
Even before the pandemic hit, this was a key growth area in enterprise software, with both in-person and online/digital salespeople relying on these kinds of products to help them get more of an edge with their work, but a lot of the focus had really been on inside sales (B2B sales focusing on bigger purchases). Porter described the effect of Covid-19 as a “tailwind” propelling that already strong trend.
“The effects of Covid have been a tailwind due to the effects of digital selling,” he said. “All sellers immediately became remote. But now the genie is out of the bottle and not going back in. It’s meant that inside sales are now all sales. Whether the opportunities are mid-funnel or upgrades or renewals, we are establishing ourselves as the engagement platform of record because it’s all becoming digital and all sellers are finding more success.”
He added that SalesLoft’s own sales cycle has improved by 40% since the pandemic, a reflection, he said, of the “urgency and need” for tools like those that the startup develops.
Another shift has been in terms of the kinds of customers SalesLoft works with. The company originally was focused on the mid-market, but that has changed with more larger enterprises also coming on board. Google, LinkedIn (which backs SalesLoft and is in a strategic partnership with it), Cisco, Dell and IBM are all customers, and Porter said that more “mainstream” businesses like Cargil, 3M and Standard & Poor are also increasingly becoming clients.
That is leading the startup to building out bigger solutions, beyond the basic pitch of “sales engagement” that has been SalesLoft’s mainstay up to now. The company competes against a plethora of others including of Clari, Chorus.ai, Gong, Conversica, Afiniti and Outreach, as well as biggies like Salesforce. Outreach, notably, had a big mid-Covid round of its own, raising at a $1.3 billion valuation in June last year, a mark of that wider market demand. Porter notes that SalesLoft’s big selling point is that it offers an increasingly end-to-end sales solution to customers, meaning less shopping around.
“Building high quality pipeline requires a tight partnership between marketing and sales,” said Alison Wagonfeld, CMO of Google Cloud, in a statement. “SalesLoft has helped us align around efficiency in our process and consistency in prospect experience, and we are excited to continue growing with them as a partner.”
While the world continues to await the arrival of safe, reliable and cost-effective self-driving cars, one of the pioneers in the world of autonomous vehicle software has raised some substantial funding to double down on what it sees as a more immediate opportunity: providing technology to industrial companies to build off-road applications.
Oxbotica, the Oxford, England startup that builds what it calls “universal autonomy” — flexible technology that it says can power the navigation, perception, user interfaces, fleet management and other features needed to run self-driving vehicles in multiple environments, regardless of the hardware being used — has picked up $47 million in a Series B round of funding from an interesting mix of strategic and financial investors.
Led by bp ventures, the investing arm of oil and gas giant bp, the round also includes BGF, safety equipment maker Halma, pension fund HostPlus, IP Group, Tencent, Venture Science, and funds advised by Doxa Partners.
Oxbotica said it plans to use the capital to fuel a raft of upcoming deployments — several that will be coming online this year, according to its CEO — for clients in areas like mining, port logistics and more, with its lead investor bp an indication of the size of its customers and the kinds of projects that are in its sights.
The question, CEO Ozgur Tohumcu said in an interview, is “Where is the autonomy needed today? If you go to mines or ports, you can see vehicles in use already,” he said. “We see a huge transformation happening in the industrial domain.”
The funding, and focus on industry, are interesting turns for Oxbotica. The startup has been around since about 2014, originally as a spinout from Oxford University co-founded by academics Paul Newman and Ingmar Posner — Newman remains at the startup as its CTO, while Posner remains an AI professor at Oxford.
Oxbotica has been associated with a number of high profile projects — early on, it provided sensor technology for Nasa’s Mars Rover, for example.
Over time, it has streamlined what it does to two main platforms that it calls Selenium and Caesium, covering respectively navigation, mapping, perception, machine learning, data export and related technology; and fleet management.
Newman says that what makes Oxbotica stand out from other autonomous software providers is that its systems are lighter and easier to use.
“Where we are good is in edge compute,” he said. “Our radar-based maps are 10 megabytes to cover a kilometer rather than hundreds of megabytes… Our business plan is to build a horizontal software platform like Microsoft’s.” That may underplay the efficiency of what its building, however: Oxbotica also has worked out how to efficiently transfer the enormous data loads associated with autonomous systems, and is working with companies like Cisco to bring these online.
In recent years Oxbotica has been synonymous with some of the more notable on-road self-driving schemes in the UK. But, as you would expect with autonomous car projects, not everything has panned out as expected.
A self-driving pilot Oxbotica kicked off with London-based car service Addison Lee in 2018 projected that it would have its first cars on the road by 2021. That project was quietly shut down, however, when Addison Lee was sold on by Carlyle last year and the company abandoned costly moonshots. Another effort, the publicly backed Project Endeavour to build autonomous car systems across towns in England, appears to still be in progress.
The turn to industrial customers, Newman said, is coming alongside those more ambitous, larger-scale applications. “Industrial autonomy for off-road refineries, ports and airports happens on the way to on-road autonomy,” he said, with the focus firmly remaining on providing software that can be used with different hardware. “We’ve always had this vision of ‘no atoms, just software,” he said. “There is nothing special about the road. Our point is to be agnostic, to make sure it works on any hardware platform.”
It may claim to have always been interested in hardware- and application-agnostic autonomy, but these days its being joined by others that have tried the other route and have decided to follow the Oxbotica strategy instead. They include FiveAI, another hyped autonomous startup out of the UK that originally wanted to build its own fleet of self-driving vehicles but instead last year pivoted to providing its software technology on a B2B basis for other hardware makers.
Oxbotica has now raised about $80 million to date, and it’s not disclosing its valuation but is optimistic that the coming year — with deployments and other new partnerships — will bear out that it’s doing just fine in the current market.
“bp ventures are delighted to invest in Oxbotica – we believe its software could accelerate the market for autonomous vehicles,” said Erin Hallock, bp ventures managing partner, in a statement. “Helping to accelerate the global revolution in mobility is at the heart of bp’s strategy to become an integrated energy company focused on delivering solutions for customers.”
OpenAI’s latest strange yet fascinating creation is DALL-E, which by way of hasty summary might be called “GPT-3 for images.” It creates illustrations, photos, renders or whatever method you prefer, of anything you can intelligibly describe, from “a cat wearing a bow tie” to “a daikon radish in a tutu walking a dog.” But don’t write stock photography and illustration’s obituaries just yet.
As usual, OpenAI’s description of its invention is quite readable and not overly technical. But it bears a bit of contextualizing.
What researchers created with GPT-3 was an AI that, given a prompt, would attempt to generate a plausible version of what it describes. So if you say “a story about a child who finds a witch in the woods,” it will try to write one — and if you hit the button again, it will write it again, differently. And again, and again, and again.
Some of these attempts will be better than others; indeed, some will be barely coherent while others may be nearly indistinguishable from something written by a human. But it doesn’t output garbage or serious grammatical errors, which makes it suitable for a variety of tasks, as startups and researchers are exploring right now.
DALL-E (a combination of Dali and WALL-E) takes this concept one further. Turning text into images has been done for years by AI agents, with varying but steadily increasing success. In this case the agent uses the language understanding and context provided by GPT-3 and its underlying structure to create a plausible image that matches a prompt.
As OpenAI puts it:
GPT-3 showed that language can be used to instruct a large neural network to perform a variety of text generation tasks. Image GPT showed that the same type of neural network can also be used to generate images with high fidelity. We extend these findings to show that manipulating visual concepts through language is now within reach.
What they mean is that an image generator of this type can be manipulated naturally, simply by telling it what to do. Sure, you could dig into its guts and find the token that represents color, and decode its pathways so you can activate and change them, the way you might stimulate the neurons of a real brain. But you wouldn’t do that when asking your staff illustrator to make something blue rather than green. You just say, “a blue car” instead of “a green car” and they get it.
So it is with DALL-E, which understands these prompts and rarely fails in any serious way, although it must be said that even when looking at the best of a hundred or a thousand attempts, many images it generates are more than a little… off. Of which later.
In the OpenAI post, the researchers give copious interactive examples of how the system can be told to do minor variations of the same idea, and the result is plausible and often quite good. The truth is these systems can be very fragile, as they admit DALL-E is in some ways, and saying “a green leather purse shaped like a pentagon” may produce what’s expected but “a blue suede purse shaped like a pentagon” might produce nightmare fuel. Why? It’s hard to say, given the black-box nature of these systems.
But DALL-E is remarkably robust to such changes, and reliably produces pretty much whatever you ask for. A torus of guacamole, a sphere of zebra; a large blue block sitting on a small red block; a front view of a happy capybara, an isometric view of a sad capybara; and so on and so forth. You can play with all the examples at the post.
It also exhibited some unintended but useful behaviors, using intuitive logic to understand requests like asking it to make multiple sketches of the same (non-existent) cat, with the original on top and the sketch on the bottom. No special coding here: “We did not anticipate that this capability would emerge, and made no modifications to the neural network or training procedure to encourage it.” This is fine.
Interestingly, another new system from OpenAI, CLIP, was used in conjunction with DALL-E to understand and rank the images in question, though it’s a little more technical and harder to understand. You can read about CLIP here.
The implications of this capability are many and various, so much so that I won’t attempt to go into them here. Even OpenAI punts:
In the future, we plan to analyze how models like DALL·E relate to societal issues like economic impact on certain work processes and professions, the potential for bias in the model outputs, and the longer term ethical challenges implied by this technology.
Right now, like GPT-3, this technology is amazing and yet difficult to make clear predictions regarding.
Notably, very little of what it produces seems truly “final” — that is to say, I couldn’t tell it to make a lead image for anything I’ve written lately and expect it to put out something I could use without modification. Even a brief inspection reveals all kinds of AI weirdness (Janelle Shane’s specialty), and while these rough edges will certainly be buffed off in time, it’s far from safe, the way GPT-3 text can’t just be sent out unedited in place of human writing.
It helps to generate many and pick the top few, as the following collection shows:
That’s not to detract from OpenAI’s accomplishment here. This is fabulously interesting and powerful work, and like the company’s other projects it will no doubt develop into something even more fabulous and interesting before long.