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Today — January 19th 2021Your RSS feeds

ETH spin-off LatticeFlow raises $2.8M to help build trustworthy AI systems

By Frederic Lardinois

LatticeFlow, an AI startup that was spun out of ETH Zurich in 2020, today announced that it has raised a $2.8 million seed funding round led by Swiss deep-tech fund btov and Global Founders Capital, which previously backed the likes of Revolut, Slack and Zalando.

The general idea behind LatticeFlow is to build tools that help AI teams build and deploy AI models that are safe, reliable and trustworthy. The problem today, the team argues, is that models get very good at finding the right statistical patterns to hit a given benchmark. That makes them inflexible, though, since these models were optimized for accuracy in a lab setting, not for robustness in the real world.

“One of the most commonly used paradigms for evaluating machine learning models is just aggregate metrics, like accuracy. And, of course, this is a super coarse representation of how good a model really is,” Pavol Bielik, the company’s CTO explained. “What we want to do is, we provide systematic ways of monitoring models, assessing their reliability across different relevant data slices and then also provide tools for improving these models.”

Image Credits: LatticeFlow

Building these kinds of models that are more flexible yet still provide robust results will take a new arsenal of tools, though, as well as the right team with deep expertise in these areas. Clearly, though, this is a founding team with the right background. In addition to CTO Bielik, the founding team includes Petar Tsankov, the company’s CEO and former senior researcher and lecturer at ETH Zurich, as well as ETH professors Martin Vechev, who leads the Secure, Reliable and Intelligence Systems lab at ETH, and Andreas Krause, who leads ETH’s Learning & Adaptive Systems lab. Tsankov’s last startup, DeepCode, was acquired by cybersecurity firm Snyk in 2020.

It’s also worth noting that Vechev, who previously co-founded ETH spin-off ChainSecurity, and his group at ETH previously developed ERAN, a verifier for large deep learning models with millions of parameters, that last year won the first competition for certifying deep neural networks. While the team was already looking at creating a company before winning this competition, Vechev noted that gave the team the confirmation that it was on the right path.

Image Credits: LatticeFlow

“We want to solve the main AI problem, which is making AI usable. This is the overarching goal,” Vechev told me. “[…] I don’t think you can actually found the company just purely based on the certification work. I think the kinds of skills that people have in the company, my group, Andreas [Krause]’s group, they all complement each other and cover a huge space, which I think is very, very unique. I don’t know of other companies who have covered this range of skills in these pressing points and have done groundbreaking work before.”

LatticeWorks already has a set of pilot customers who are trialing its tools. These include Swiss railways (SBB), which is using it to build a tool for automatic rail inspections, Germany’s Federal Cyber Security Bureau and the U.S. Army. The team is also working with other large enterprises that are using its tools to improve their computer vision models.

“Machine Learning (ML) is one of the core topics at SBB, as we see a huge potential in its application for an improved, intelligent and automated monitoring of our railway infrastructure,” said Dr. Ilir Fetai and Andre Roger, the leads of SBB’s AI team. “The project on robust and reliable AI with LatticeFlow, ETH, and Siemens has a crucial role in enabling us to fully exploit the advantages of using ML.”

For now, LatticeFlow remains in early access. The team plans to use the funding to accelerate its product development and bring on new customers. The team also plans to build out a presence in the U.S. in the near future.

Qualcomm-backed chipmaker Kneron nails Foxconn funding, deal

By Rita Liao

A startup based out of San Diego and Taipei is quietly nailing fundings and deals from some of the biggest names in electronics. Kneron, which specializes in energy-efficient processors for edge artificial intelligence, just raised a strategic funding round from Taiwan’s manufacturing giant Foxconn and integrated circuit producer Winbond.

The deal came a year after Kneron closed a $40 million round led by Hong Kong tycoon Li Ka-Shing’s Horizons Ventures. Amongst its other prominent investors are Alibaba Entrepreneurship Fund, Sequoia Capital, Qualcomm and SparkLabs Taipei.

Kneron declined to disclose the dollar amount of the investment from Foxconn and Winbond due to investor requests but said it was an “eight figures” deal, founder and CEO Albert Liu told TechCrunch in an interview.

Founded in 2015, Kneron’s latest product is a neural processing unit that can enable sophisticated AI applications without relying on the cloud. The startup is directly taking on the chips of Intel and Google, which it claims are more energy-consuming than its offering. The startup recently got a talent boost after hiring Davis Chen, Qualcomm’s former Taipei head of engineering.

Among Kneron’s customers are Chinese air conditioning giant Gree and German’s autonomous driving software provider Teraki, and the new deal is turning the world’s largest electronics manufacturer into a client. As part of the strategic agreement, Kneron will work with Foxconn on the latter’s smart manufacturing and newly introduced open platform for electric vehicles, while its work with Winbond will focus on microcontroller unit (MCU)-based AI and memory computing.

“Low-power AI chips are pretty easy to put into sensors. We all know that in some operation lines, sensors are quite small, so it’s not easy to use a big GPU [graphics processing unit] or CPU [central processing unit], especially when power consumption is a big concern,” said Liu, who held R&D positions at Qualcomm and Samsung before founding Kneron.

Unlike some of its competitors, Kneron designs chips for a wide range of use cases, from manufacturing, smart home, smartphones, robotics, surveillance, payments, to autonomous driving. It doesn’t just make chips but also the AI software embedded in the chips, a strategy that Liu said differentiates his company from China’s AI darlings like SenseTime and Megvii, which enable AI service through the cloud.

Kneron has also been on a less aggressive funding pace than these companies, which fuel their rapid expansion through outsize financing rounds. Six-year-old SenseTime has raised about $2.6 billion to date, while nine-year-old Megvii has banked about $1.4 billion. Kneron, in comparison, has raised just over $70 million from a Series A round.

Like the Chinese AI upstarts, Kneron is weighing an initial public offering. The company is expected to make a profit in 2023, Liu said, and “that will probably be a good time for us to go IPO.”

Before yesterdayYour RSS feeds

AI-Powered Text From This Program Could Fool the Government

By Will Knight
A Harvard medical student submitted auto-generated comments to Medicaid; volunteers couldn’t distinguish them from those penned by humans.

An Algorithm Is Helping a Community Detect Lead Pipes

By Sidney Fussell
The model had shown promise in Flint before officials rebelled. Now Toledo is using it, while incorporating more public input.

Zocdoc founder returns with Shadow, an app that finds lost dogs

By Sarah Perez

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.

Pat Gelsinger stepping down as VMware CEO to replace Bob Swan at Intel

By Ron Miller

In a move that could have wide ramifications across the tech landscape, Intel announced that VMware CEO Pat Gelsinger would be replacing interim CEO Bob Swan at Intel on February 15th. The question is why would he leave his job to run a struggling chip giant.

The bottom line is he has a long history with Intel, working with some of the biggest names in chip industry lore before he joined VMware in 2009. It has to be a thrill for him to go back to his roots and try to jump start the company.

“I was 18 years old when I joined Intel, fresh out of the Lincoln Technical Institute. Over the next 30 years of my tenure at Intel, I had the honor to be mentored at the feet of Grove, Noyce and Moore,” Gelsinger wrote in a blog post announcing his new position.

Certainly Intel recognized that the history and that Gelsinger’s deep executive experience should help as the company attempts to compete in an increasingly aggressive chip industry landscape. “Pat is a proven technology leader with a distinguished track record of innovation, talent development, and a deep knowledge of Intel. He will continue a values-based cultural leadership approach with a hyper focus on operational execution,” Omar Ishrak, independent chairman of the Intel board, said in a statement.

But Gelsinger is walking into a bit of a mess. As my colleague Danny Crichton wrote in his year-end review of the chip industry last month, Intel is far behind its competitors, and it’s going to be tough to play catch-up:

Intel has made numerous strategic blunders in the past two decades, most notably completely missing out on the smartphone revolution and also the custom silicon market that has come to prominence in recent years. It’s also just generally fallen behind in chip fabrication, an area it once dominated and is now behind Taiwan-based TSMC, Crichton wrote.

Patrick Moorhead, founder and principal analyst at Moor Insights & Strategy, agrees with this assertion, saying that Swan was dealt a bad hand, walking in to clean up a mess that has years long timelines. While Gelsinger faces similar issues, Moorhead thinks he can refocus the company. “I am not foreseeing any major strategic changes with Gelsinger, but I do expect him to focus on the company’s engineering culture and get it back to an execution culture,” Moorhead told me.

The announcement comes against the backdrop of massive chip industry consolidation last year with over $100 billion changing hands in four deals, with Nvidia nabbing ARM for $40 billion, the $35 billion AMD-Xilink deal, Analog snagging Maxim for $21 billion and Marvell grabbing Inphi for a mere $10 billion, not to mention Intel dumping its memory unit to SK Hynix for $9 billion.

As for VMware, it has to find a new CEO now. As Moorhead says, the obvious choice would be current COO Sanjay Poonen, but for the time being, it will be CFO Zane Rowe serving as interim CEO, rather than Poonen. In fact, it appears that the company will be casting a wider net than internal options. The official announcement states, “VMware’s Board of Directors is initiating a global executive search process to name a permanent CEO…”

Holger Mueller, an analyst at Constellation Research, says it will be up to Michael Dell to decide who to hand the reins to, but he believes Gelsinger was stuck at Dell and would not get a broader role, so he left.

“VMware has a deep bench, but it will be up to Michael Dell to get a CEO who can innovate on the software side and keep the unique DNA of VMware inside the Dell portfolio going strong, Dell needs the deeper profits of this business for its turnaround,” he said.

The stock market seems to like the move for Intel, with the company stock up 7.26%, but not so much for VMware, whose stock was down close to the same amount at 7.72% as we went to publication.

Facial recognition reveals political party in troubling new research

By Devin Coldewey

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.

Chart showing how faces are cropped and reduced to neural network representations.

Image Credits: Michal Kosinski / Nature Scientific Reports

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).

Image Credits: Michal Kosinski / Nature Scientific Reports

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.”

Human AI nabs $3.2M seed to build personal intelligence platform

By Ron Miller

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.

Chinese facial recognition unicorn Megvii prepares China IPO

By Rita Liao

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.

Job Screening Service Halts Facial Analysis of Applicants

By Will Knight
But it’s still using intonation and behavior to assist with hiring decisions.

A Startup Will Nix Algorithms Built on Ill-Gotten Facial Data

By Tom Simonite
The FTC applies a novel remedy, going a step further than simply deleting the source photos.

Descript raises $30M to build the next generation of video and audio editing tools

By Ingrid Lunden

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.

“We’ve been convinced of machine learning’s power to be used as a creative tool for some time,” Hyatt at Spark noted to me. “Descript is perhaps the best example of that in a startup today. The company takes some very complicated technology, but presents it in a way that’s actually easier to use than the status quo products. It’s very rare that you come across a company that uses technology to both empower a creative professional to work ten times faster, and simultaneously makes the creative process ten times easier for an amateur, growing the addressable market. Anyone editing audio or video, which is most of us nowadays, can see the benefits.”

FTC settlement with Ever orders data and AIs deleted after facial recognition pivot

By Natasha Lomas

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

Imagine requiring a firm like @Facebook or @Google to delete “models and algorithms” that relied on deceptively collected information.

That could require deleting the core ML models underlying Facebook Newsfeed or Google Search

Kinda major…

/cc @rcalo @hartzog

— 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.

NBC reported Ever had only added a “brief reference” to the new use in its privacy policy after journalists contacted it to ask questions about the pivot in April of that year.

In a press release yesterday, reported earlier by The Verge, the FTC announced the proposed settlement with Ever received unanimous backing from commissioners.

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.

New York City Proposes Regulating Algorithms Used in Hiring

By Tom Simonite
A bill would require firms to disclose when they use software to assess candidates, and vendors would have to ensure that their tech doesn’t discriminate.

The US Needs More Foreign Artificial Intelligence Know-How

By Will Knight
Jason Furman, a top economic adviser to President Obama, says good ideas come from everywhere—but Trump has dissuaded tech workers from coming to the US.

Extra Crunch roundup: 2 VC surveys, Tesla’s melt up, The Roblox Gambit, more

By Walter Thompson

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:

  • Google employee Sara Robinson combined her interest in machine learning and baking to create AI-generated hybrid treats.
  • A breakthrough could make water desalination 30%-40% more effective.
  • Bianca Smith will become the first Black woman to coach a professional baseball team.

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.

Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist


Full Extra Crunch articles are only available to members
Use discount code ECFriday to save 20% off a one- or two-year subscription


The Roblox Gambit

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!”

8 investors discuss social gaming’s biggest opportunities

girl playing games on desktop computer

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:

  • Hope Cochran, Madrona Venture Group
  • Daniel Li, Madrona Venture Group
  • Niko Bonatsos, General Catalyst
  • Ethan Kurzweil, Bessemer Venture Partners
  • Sakib Dadi, Bessemer Venture Partners
  • Jacob Mullins, Shasta Ventures
  • Alice Lloyd George, Rogue
  • Gigi Levy-Weiss, NFX

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.”

How to convert customers with subscription pricing

Giant hand and magnet picking up office and workers

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.

What is up with Tesla’s value?

Elon Musk, founder of SpaceX and chief executive officer of Tesla Inc., arrives at the Axel Springer Award ceremony in Berlin, Germany, on Tuesday, Dec. 1, 2020. Tesla Inc. will be added to the S&P 500 Index in one shot on Dec. 21, a move that will ripple through the entire market as money managers adjust their portfolios to make room for shares of the $538 billion company. Photographer: Liesa Johannssen-Koppitz/Bloomberg via Getty Images

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.

How Segment redesigned its core systems to solve an existential scaling crisis

Abstract glowing grid and particles

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.

Dear Sophie: Banging my head against the wall understanding the US immigration system

Image Credits: Sophie Alcorn

Dear Sophie:

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.

Revenue-based financing: The next step for private equity and early-stage investment

Shot of a group of people holding plants growing out of soil

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.”

Lisbon’s startup scene rises as Portugal gears up to be a European tech tiger

Man using laptop at 25th of April Bridge in Lisbon, Portugal

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:

  • Cristina Fonseca, partner, Indico Capital Partners
  • Pedro Ribeiro Santos, partner, Armilar Venture Partners
  • Tocha, partner, Olisipo Way
  • Adão Oliveira, investment manager, Portugal Ventures
  • Alexandre Barbosa, partner, Faber
  • António Miguel, partner, Mustard Seed MAZE
  • Jaime Parodi Bardón, partner, impACT NOW Capital
  • Stephan Morais, partner, Indico Capital Partners
  • Gavin Goldblatt, managing partner, Portugal Gateway

How late-stage edtech companies are thinking about tutoring marketplaces

Life Rings flying out beneath storm clouds are a metaphor for rescue, help and aid.

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.”

Decrypted: How bad was the US Capitol breach for cybersecurity?

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.”

Extra Crunch’s top 10 stories of 2020

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.

SilviaTerra wants to bring the benefits of carbon offsets to every landowner everywhere

By Jonathan Shieber

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.” 

Google AI concocts ‘breakie’ and ‘cakie’ hybrid baked goods

By Devin Coldewey

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.

Image Credits: Sara Robinson

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.”

A cakie, left, and breakies, right, with Robinson.

A cakie, left, and breakies, right, with Robinson. Image Credits: Sara Robinson / Google

“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.

 Image Credits: Sara Robinson / Google

 Image Credits: Sara Robinson / Google

Glia raises $78M for its integrated, hands-on, AI-based customer service platform

By Ingrid Lunden

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.”

Veo raises $25M for AI-based cameras that record and analyze football and other team sports

By Ingrid Lunden

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.

Veo Måløv

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.”

‘We’re a Shopify, not an Amazon’

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.

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