Longevity startup Gero AI has a mobile API for quantifying health changes

Sensor data from smartphones and wearables can meaningfully predict an individual’s ‘biological age’ and resilience to stress, according to Gero AI.

The ‘longevity’ startup — which condenses its mission to the pithy goal of “hacking complex diseases and aging with Gero AI” — has developed an AI model to predict morbidity risk using ‘digital biomarkers’ that are based on identifying patterns in step-counter sensor data which tracks mobile users’ physical activity.

A simple measure of ‘steps’ isn’t nuanced enough on its own to predict individual health, is the contention. Gero’s AI has been trained on large amounts of biological data to spots patterns that can be linked to morbidity risk. It also measures how quickly a personal recovers from a biological stress — another biomarker that’s been linked to lifespan; i.e. the faster the body recovers from stress, the better the individual’s overall health prognosis.

A research paper Gero has had published in the peer-reviewed biomedical journal Aging explains how it trained deep neural networks to predict morbidity risk from mobile device sensor data — and was able to demonstrate that its biological age acceleration model was comparable to models based on blood test results.

Another paper, due to be published in the journal Nature Communications later this month, will go into detail on its device-derived measurement of biological resilience.

The Singapore-based startup, which has research roots in Russia — founded back in 2015 by a Russian scientist with a background in theoretical physics — has raised a total of $5 million in seed funding to date (in two tranches).

Backers come from both the biotech and the AI fields, per co-founder Peter Fedichev. Its investors include Belarus-based AI-focused early stage fund, Bulba Ventures (Yury Melnichek). On the pharma side, it has backing from some (unnamed) private individuals with links to Russian drug development firm, Valenta. (The pharma company itself is not an investor).

Fedichev is a theoretical physicist by training who, after his PhD and some ten years in academia, moved into biotech to work on molecular modelling and machine learning for drug discovery — where he got interested in the problem of ageing and decided to start the company.

As well as conducting its own biological research into longevity (studying mice and nematodes), it’s focused on developing an AI model for predicting the biological age and resilience to stress of humans — via sensor data captured by mobile devices.

“Health of course is much more than one number,” emphasizes Fedichev. “We should not have illusions about that. But if you are going to condense human health to one number then, for a lot of people, the biological age is the best number. It tells you — essentially — how toxic is your lifestyle… The more biological age you have relative to your chronological age years — that’s called biological acceleration — the more are your chances to get chronic disease, to get seasonal infectious diseases or also develop complications from those seasonal diseases.”

Gero has recently launched a (paid, for now) API, called GeroSense, that’s aimed at health and fitness apps so they can tap up its AI modelling to offer their users an individual assessment of biological age and resilience (aka recovery rate from stress back to that individual’s baseline).

Early partners are other longevity-focused companies, AgelessRx and Humanity Inc. But the idea is to get the model widely embedded into fitness apps where it will be able to send a steady stream of longitudinal activity data back to Gero, to further feed its AI’s predictive capabilities and support the wider research mission — where it hopes to progress anti-ageing drug discovery, working in partnerships with pharmaceutical companies.

The carrot for the fitness providers to embed the API is to offer their users a fun and potentially valuable feature: A personalized health measurement so they can track positive (or negative) biological changes — helping them quantify the value of whatever fitness service they’re using.

“Every health and wellness provider — maybe even a gym — can put into their app for example… and this thing can rank all their classes in the gym, all their systems in the gym, for their value for different kinds of users,” explains Fedichev.

“We developed these capabilities because we need to understand how ageing works in humans, not in mice. Once we developed it we’re using it in our sophisticated genetic research in order to find genes — we are testing them in the laboratory — but, this technology, the measurement of ageing from continuous signals like wearable devices, is a good trick on its own. So that’s why we announced this GeroSense project,” he goes on.

“Ageing is this gradual decline of your functional abilities which is bad but you can go to the gym and potentially improve them. But the problem is you’re losing this resilience. Which means that when you’re [biologically] stressed you cannot get back to the norm as quickly as possible. So we report this resilience. So when people start losing this resilience it means that they’re not robust anymore and the same level of stress as in their 20s would get them [knocked off] the rails.

“We believe this loss of resilience is one of the key ageing phenotypes because it tells you that you’re vulnerable for future diseases even before those diseases set in.”

“In-house everything is ageing. We are totally committed to ageing: Measurement and intervention,” adds Fedichev. “We want to building something like an operating system for longevity and wellness.”

Gero is also generating some revenue from two pilots with “top range” insurance companies — which Fedichev says it’s essentially running as a proof of business model at this stage. He also mentions an early pilot with Pepsi Co.

He sketches a link between how it hopes to work with insurance companies in the area of health outcomes with how Elon Musk is offering insurance products to owners of its sensor-laden Teslas, based on what it knows about how they drive — because both are putting sensor data in the driving seat, if you’ll pardon the pun. (“Essentially we are trying to do to humans what Elon Musk is trying to do to cars,” is how he puts it.)

But the nearer term plan is to raise more funding — and potentially switch to offering the API for free to really scale up the data capture potential.

Zooming out for a little context, it’s been almost a decade since Google-backed Calico launched with the moonshot mission of ‘fixing death’. Since then a small but growing field of ‘longevity’ startups has sprung up, conducting research into extending (in the first instance) human lifespan. (Ending death is, clearly, the moonshot atop the moonshot.) 

Death is still with us, of course, but the business of identifying possible drugs and therapeutics to stave off the grim reaper’s knock continues picking up pace — attracting a growing volume of investor dollars.

The trend is being fuelled by health and biological data becoming ever more plentiful and accessible, thanks to open research data initiatives and the proliferation of digital devices and services for tracking health, set alongside promising developments in the fast-evolving field of machine learning in areas like predictive healthcare and drug discovery.

Longevity has also seen a bit of an upsurge in interest in recent times as the coronavirus pandemic has concentrated minds on health and wellness, generally — and, well, mortality specifically.

Nonetheless, it remains a complex, multi-disciplinary business. Some of these biotech moonshots are focused on bioengineering and gene-editing — pushing for disease diagnosis and/or drug discovery.

Plenty are also — like Gero —  trying to use AI and big data analysis to better understand and counteract biological ageing, bringing together experts in physics, maths and biological science to hunt for biomarkers to further research aimed at combating age-related disease and deterioration.

Another recent example is AI startup Deep Longevity, which came out of stealth last summer — as a spinout from AI drug discovery startup Insilico Medicine — touting an AI ‘longevity as a service’ system which it claims can predict an individual’s biological age “significantly more accurately than conventional methods” (and which it also hopes will help scientists to unpick which “biological culprits drive aging-related diseases”, as it put it).

Gero AI is taking a different tack toward the same overarching goal — by honing in on data generated by activity sensors embedded into the everyday mobile devices people carry with them (or wear) as a proxy signal for studying their biology.

The advantage being that it doesn’t require a person to undergo regular (invasive) blood tests to get an ongoing measure of their own health. Instead our personal device can generate proxy signals for biological study passively — at vast scale and low cost. So the promise of Gero’s ‘digital biomarkers’ is they could democratize access to individual health prediction.

And while billionaires like Peter Thiel can afford to shell out for bespoke medical monitoring and interventions to try to stay one step ahead of death, such high end services simply won’t scale to the rest of us.

If its digital biomarkers live up to Gero’s claims, its approach could, at the least, help steer millions towards healthier lifestyles, while also generating rich data for longevity R&D — and to support the development of drugs that could extend human lifespan (albeit what such life-extending pills might cost is a whole other matter).

The insurance industry is naturally interested — with the potential for such tools to be used to nudge individuals towards healthier lifestyles and thereby reduce payout costs.

For individuals who are motivated to improve their health themselves, Fedichev says the issue now is it’s extremely hard for people to know exactly which lifestyle changes or interventions are best suited to their particular biology.

For example fasting has been shown in some studies to help combat biological ageing. But he notes that the approach may not be effective for everyone. The same may be true of other activities that are accepted to be generally beneficial for health (like exercise or eating or avoiding certain foods).

Again those rules of thumb may have a lot of nuance, depending on an individual’s particular biology. And scientific research is, inevitably, limited by access to funding. (Research can thus tend to focus on certain groups to the exclusion of others — e.g. men rather than women; or the young rather than middle aged.)

This is why Fedichev believes there’s a lot of value in creating a measure than can address health-related knowledge gaps at essentially no individual cost.

Gero has used longitudinal data from the UK’s biobank, one of its research partners, to verify its model’s measurements of biological age and resilience. But of course it hopes to go further — as it ingests more data. 

“Technically it’s not properly different what we are doing — it just happens that we can do it now because there are such efforts like UK biobank. Government money and also some industry sponsors money, maybe for the first time in the history of humanity, we have this situation where we have electronic medical records, genetics, wearable devices from hundreds of thousands of people, so it just became possible. It’s the convergence of several developments — technological but also what I would call ‘social technologies’ [like the UK biobank],” he tells TechCrunch.

“Imagine that for every diet, for every training routine, meditation… in order to make sure that we can actually optimize lifestyles — understand which things work, which do not [for each person] or maybe some experimental drugs which are already proved [to] extend lifespan in animals are working, maybe we can do something different.”

“When we will have 1M tracks [half a year’s worth of data on 1M individuals] we will combine that with genetics and solve ageing,” he adds, with entrepreneurial flourish. “The ambitious version of this plan is we’ll get this million tracks by the end of the year.”

Fitness and health apps are an obvious target partner for data-loving longevity researchers — but you can imagine it’ll be a mutual attraction. One side can bring the users, the other a halo of credibility comprised of deep tech and hard science.

“We expect that these [apps] will get lots of people and we will be able to analyze those people for them as a fun feature first, for their users. But in the background we will build the best model of human ageing,” Fedichev continues, predicting that scoring the effect of different fitness and wellness treatments will be “the next frontier” for wellness and health (Or, more pithily: “Wellness and health has to become digital and quantitive.”)

“What we are doing is we are bringing physicists into the analysis of human data. Since recently we have lots of biobanks, we have lots of signals — including from available devices which produce something like a few years’ long windows on the human ageing process. So it’s a dynamical system — like weather prediction or financial market predictions,” he also tells us.

“We cannot own the treatments because we cannot patent them but maybe we can own the personalization — the AI that personalized those treatments for you.”

From a startup perspective, one thing looks crystal clear: Personalization is here for the long haul.

 

SoftBank leads $15M round for China’s industrial robot maker Youibot

SoftBank has picked its bet in China’s flourishing industrial robotics space. Youibot, a four-year-old startup that makes autonomous mobile robots for a range of scenarios, said it has notched close to 100 million yuan ($15.47 million) in its latest funding round led by SoftBank Ventures Asia, the Seoul-based early-stage arm of the global investment behemoth.

In December, SoftBank Ventures Asia led the financing round for another Chinese robotics startup called KeenOn, which focuses on delivery and service robots.

Youibot’s previous investors BlueRun Ventures and SIG also participated in the round. The startup, based in Shenzhen where it went through SOSV’s HAX hardware accelerator program, secured three financing rounds during 2020 as businesses and investors embrace industrial automation to minimize human contact. Youibot has raised over 200 million yuan to date.

Founded by a group of PhDs from China’s prestigious Xi’an Jiaotong University, Youibot develops solutions for factory automation, logistics management, as well as inspection and maintenance for various industries. For example, its robots can navigate around a yard of buses, inspect every tire of the vehicles and provide a detailed report for maintenance, a feature that helped it rack up Michelin’s contract.

Youibot’s “strongest suits” are in electronics manufacturing and electric power patrol, the company’s spokesperson told TechCrunch.

The startup is also seeing high growth in its semiconductor business, with customers coming from several prominent front-end wafer fabs, which use the firm’s robots for chip packaging, testing, and wafer production. Youibot declined to disclose their names due to confidentiality.

Chinese clients that it named include CRRC Zhuzhou, a state-owned locomotive manufacturer, Huaneng Group, a state-owned electricity generation giant, Huawei, and more. China currently comprises 80% of Youibot’s total revenues while overseas markets are rapidly catching up. The firm’s revenues tripled last year from 2020.

Youibot plans to spend the fresh proceeds on research and development in its mobile robots and propietary software, team building and market expansion.

AI is ready to take on a massive healthcare challenge

Which disease results in the highest total economic burden per annum? If you guessed diabetes, cancer, heart disease or even obesity, you guessed wrong. Reaching a mammoth financial burden of $966 billion in 2019, the cost of rare diseases far outpaced diabetes ($327 billion), cancer ($174 billion), heart disease ($214 billion) and other chronic diseases.

Cognitive intelligence, or cognitive computing solutions, blend artificial intelligence technologies like neural networks, machine learning, and natural language processing, and are able to mimic human intelligence.

It’s not surprising that rare diseases didn’t come to mind. By definition, a rare disease affects fewer than 200,000 people. However, collectively, there are thousands of rare diseases and those affect around 400 million people worldwide. About half of rare disease patients are children, and the typical patient, young or old, weather a diagnostic odyssey lasting five years or more during which they undergo countless tests and see numerous specialists before ultimately receiving a diagnosis.

No longer a moonshot challenge

Shortening that diagnostic odyssey and reducing the associated costs was, until recently, a moonshot challenge, but is now within reach. About 80% of rare diseases are genetic, and technology and AI advances are combining to make genetic testing widely accessible.

Whole-genome sequencing, an advanced genetic test that allows us to examine the entire human DNA, now costs under $1,000, and market leader Illumina is targeting a $100 genome in the near future.

The remaining challenge is interpreting that data in the context of human health, which is not a trivial challenge. The typical human contains 5 million unique genetic variants and of those we need to identify a single disease-causing variant. Recent advances in cognitive AI allow us to interrogate a person’s whole genome sequence and identify disease-causing mechanisms automatically, augmenting human capacity.

A shift from narrow to cognitive AI

The path to a broadly usable AI solution required a paradigm shift from narrow to broader machine learning models. Scientists interpreting genomic data review thousands of data points, collected from different sources, in different formats.

An analysis of a human genome can take as long as eight hours, and there are only a few thousand qualified scientists worldwide. When we reach the $100 genome, analysts are expecting 50 million-60 million people will have their DNA sequenced every year. How will we analyze the data generated in the context of their health? That’s where cognitive intelligence comes in.

Lightmatter’s photonic AI ambitions light up an $80M B round

AI is fundamental to many products and services today, but its hunger for data and computing cycles is bottomless. Lightmatter plans to leapfrog Moore’s law with its ultra-fast photonic chips specialized for AI work, and with a new $80M round the company is poised to take its light-powered computing to market.

We first covered Lightmatter in 2018, when the founders were fresh out of MIT and had raised $11M to prove that their idea of photonic computing was as valuable as they claimed. They spent the next three years and change building and refining the tech — and running into all the hurdles that hardware startups and technical founders tend to find.

For a full breakdown of what the company’s tech does, read that feature — the essentials haven’t changed.

In a nutshell, Lightmatter’s chips perform certain complex calculations fundamental to machine learning in a flash — literally. Instead of using charge, logic gates, and transistors to record and manipulate data, the chips use photonic circuits that perform the calculations by manipulating the path of light. It’s been possible for years, but until recently getting it to work at scale, and for a practical, indeed a highly valuable purpose has not.

Prototype to product

It wasn’t entirely clear in 2018 when Lightmatter was getting off the ground whether this tech would be something they could sell to replace more traditional compute clusters like the thousands of custom units companies like Google and Amazon use to train their AIs.

“We knew in principle the tech should be great, but there were a lot of details we needed to figure out,” CEO and co-founder Nick Harris told TechCrunch in an interview. “Lots of hard theoretical computer science and chip design challenges we needed to overcome… and COVID was a beast.”

With suppliers out of commission and many in the industry pausing partnerships, delaying projects, and other things, the pandemic put Lightmatter months behind schedule, but they came out the other side stronger. Harris said that the challenges of building a chip company from the ground up were substantial, if not unexpected.

A rack of Lightmatter servers.

Image Credits: Lightmatter

“In general what we’re doing is pretty crazy,” he admitted. “We’re building computers from nothing. We design the chip, the chip package, the card the chip package sits on, the system the cards go in, and the software that runs on it…. we’ve had to build a company that straddles all this expertise.”

That company has grown from its handful of founders to more than 70 employees in Mountain View and Boston, and the growth will continue as it brings its new product to market.

Where a few years ago Lightmatter’s product was more of a well-informed twinkle in the eye, now it has taken a more solid form in the Envise, which they call a ‘general purpose photonic AI accelerator.” It’s a server unit designed to fit into normal datacenter racks but equipped with multiple photonic computing units, which can perform neural network inference processes at mind-boggling speeds. (It’s limited to certain types of calculations, namely linear algebra for now, and not complex logic, but this type of math happens to be a major component of machine learning processes.)

Harris was reticent to provide exact numbers on performance improvements, but more because those improvements are increasing than that they’re not impressive enough. The website suggests it’s 5x faster than an NVIDIA A100 unit on a large transformer model like BERT, while using about 15 percent of the energy. That makes the platform doubly attractive to deep-pocketed AI giants like Google and Amazon, which constantly require both more computing power and who pay through the nose for the energy required to use it. Either better performance or lower energy cost would be great — both together is irresistible.

It’s Lightmatter’s initial plan to test these units with its most likely customers by the end of 2021, refining it and bringing it up to production levels so it can be sold widely. But Harris emphasized this was essentially the Model T of their new approach.

“If we’re right, we just invented the next transistor,” he said, and for the purposes of large-scale computing, the claim is not without merit. You’re not going to have a miniature photonic computer in your hand any time soon, but in datacenters, where as much as 10 percent of the world’s power is predicted to go by 2030, “they really have unlimited appetite.”

The color of math

A Lightmatter chip with its logo on the side.

Image Credits: Lightmatter

There are two main ways by which Lightmatter plans to improve the capabilities of its photonic computers. The first, and most insane sounding, is processing in different colors.

It’s not so wild when you think about how these computers actually work. Transistors, which have been at the heart of computing for decades, use electricity to perform logic operations, opening and closing gates and so on. At a macro scale you can have different frequencies of electricity that can be manipulated like waveforms, but at this smaller scale it doesn’t work like that. You just have one form of currency, electrons, and gates are either open or closed.

In Lightmatter’s devices, however, light passes through waveguides that perform the calculations as it goes, simplifying (in some ways) and speeding up the process. And light, as we all learned in science class, comes in a variety of wavelengths — all of which can be used independently and simultaneously on the same hardware.

The same optical magic that lets a signal sent from a blue laser be processed at the speed of light works for a red or a green laser with minimal modification. And if the light waves don’t interfere with one another, they can travel through the same optical components at the same time without losing any coherence.

That means that if a Lightmatter chip can do, say, a million calculations a second using a red laser source, adding another color doubles that to two million, adding another makes three — with very little in the way of modification needed. The chief obstacle is getting lasers that are up to the task, Harris said. Being able to take roughly the same hardware and near-instantly double, triple, or 20x the performance makes for a nice roadmap.

It also leads to the second challenge the company is working on clearing away, namely interconnect. Any supercomputer is composed of many small individual computers, thousands and thousands of them, working in perfect synchrony. In order for them to do so, they need to communicate constantly to make sure each core knows what other cores are doing, and otherwise coordinate the immensely complex computing problems supercomputing is designed to take on. (Intel talks about this “concurrency” problem building an exa-scale supercomputer here.)

“One of the things we’ve learned along the way is, how do you get these chips to talk to each other when they get to the point where they’re so fast that they’re just sitting there waiting most of the time?” said Harris. The Lightmatter chips are doing work so quickly that they can’t rely on traditional computing cores to coordinate between them.

A photonic problem, it seems, requires a photonic solution: a wafer-scale interconnect board that uses waveguides instead of fiber optics to transfer data between the different cores. Fiber connections aren’t exactly slow, of course, but they aren’t infinitely fast, and the fibers themselves are actually fairly bulky at the scales chips are designed, limiting the number of channels you can have between cores.

“We built the optics, the waveguides, into the chip itself; we can fit 40 waveguides into the space of a single optical fiber,” said Harris. “That means you have way more lanes operating in parallel — it gets you to absurdly high interconnect speeds.” (Chip and server fiends can find that specs here.)

The optical interconnect board is called Passage, and will be part of a future generation of its Envise products — but as with the color calculation, it’s for a future generation. 5-10x performance at a fraction of the power will have to satisfy their potential customers for the present.

Putting that $80M to work

Those customers, initially the “hyper-scale” data handlers that already own datacenters and supercomputers that they’re maxing out, will be getting the first test chips later this year. That’s where the B round is primarily going, Harris said: “We’re funding our early access program.”

That means both building hardware to ship (very expensive per unit before economies of scale kick in, not to mention the present difficulties with suppliers) and building the go-to-market team. Servicing, support, and the immense amount of software that goes along with something like this — there’s a lot of hiring going on.

The round itself was led by Viking Global Investors, with participation from HP Enterprise, Lockheed Martin, SIP Global Partners, and previous investors GV, Matrix Partners and Spark Capital. It brings their total raised to about $113 million; There was the initial $11M A round, then GV hopping on with a $22M A-1, then this $80M.

Although there are other companies pursuing photonic computing and its potential applications in neural networks especially, Harris didn’t seem to feel that they were nipping at Lightmatter’s heels. Few if any seem close to shipping a product, and at any rate this is a market that is in the middle of its hockey stick moment. He pointed to an OpenAI study indicating that the demand for AI-related computing is increasing far faster than existing technology can provide it, except with ever larger datacenters.

The next decade will bring economic and political pressure to rein in that power consumption, just as we’ve seen with the cryptocurrency world, and Lightmatter is poised and ready to provide an efficient, powerful alternative to the usual GPU-based fare.

As Harris suggested hopefully earlier, what his company has made is potentially transformative in the industry and if so there’s no hurry — if there’s a gold rush, they’ve already staked their claim.

Figure raises $7.5M to help startup employees better understand their compensation

The topic of compensation has historically been a delicate one that has left many people — especially startup employees — wondering just what drives what can feel like random decisions around pay and equity.

Last June, software engineers (and housemates) Miles Hobby and Geoffrey Tisserand set about trying to solve the problem for companies by developing a data-driven platform that aims to help companies structure their compensation plans and transparently communicate them to candidates.

Now today, the startup behind that platform, Figure, announced it has raised $7.5 million in seed funding led by CRV. Bling Capital, Better Tomorrow Ventures and Garage Capital also participated in the financing, along with angel investors such as AngelList co-founder Naval Ravikant, Jason Calacanis, Reddit CEO Steve Huffman and other executives based in Silicon Valley.

The startup has amassed a client list that includes other startups such as fintechs Brex and NerdWallet and AI-powered fitness company Tempo. 

Put simply, Hobby and Tisserand’s mission is to improve workflows and transparency around pay, particularly equity. The pair had both worked at startups themselves (Uber and Instacart, respectively) and ended up leaving money on the table when they left those companies because no one had properly explained to them what their equity, which changed at every valuation, meant.  

Image Credits: Figure co-founders and co-CEOs Miles Hobby and Geoffrey Tisserand. Image Credits: Figure

So, one of their goals was to create a solution that would provide a user-friendly explanation of what a person’s equity stake really means, from tax implications to whether or not they have to buy the stock and/or hold onto it.

“I’ve gone through the job search process many times before and there’s all these complex legal documents to understand why you’re getting 10,000 stock options, but obviously we knew the vast majority of people have no idea how that works,” Tisserand told TechCrunch. “We saw an opportunity there to help companies actually convey the value to their candidates while also making them aware of the potential risks of owning something that’s so illiquid.”

Image Credits: Figure

Another goal of Figure’s is to help create a more fair and balanced process about decisions around pay and equity so that there’s less inequality out there. Pointedly, it aims to remove some of the biases that exist around those decisions by systematizing the process.

“We saw a void in this kind of context around equity…and knew that there had to be a better way for companies to structure, manage and explain their compensation plans,” Hobby said.

To Hobby and Tisserand, Figure is designed to help stop instances of implicit bias.

“Compensation should be based on the work that you’re doing, and not gender or ethnic background,” Tisserand told TechCrunch. “We’re trying to give that context and remove biases. So, we’re trying to help at two different stages –– to surface inequities that already exist and make sure there are no anomalies, and then to help stop them before they can exist.”

Figure also aims to give companies the tools to educate candidates and employees on their total compensation — including equity, salary, benefits and bonuses — in a “straightforward and user-friendly” way. For example, it can create custom offer letters that interactively detail a candidate’s compensation.

“Our goal is for Figure to become an operating system for compensation, where a company can encode their compensation philosophy into our system, and we help them determine their job architecture, compensation bands and offer numbers while monitoring their compensation health to provide adjustment suggestions when needed,” Hobby said.

Post-hire, Figure’s compensation management system “helps keep everything running smoothly.”

Anna Khan, general partner of enterprise software at CRV, is joining Figure’s board as part of the funding. The decision to back the startup was in part personal, she said.

“I’d been investing in software for eight years and was alarmed that no one was building anything around pay equity when it comes to how we’re paid, why we’re paid what we’re paid and on how to build equity long term,” Khan told TechCrunch. “Unfortunately, discussions around compensation and equity still happen behind closed doors and this extends into workflow around compensation — equally broken — with manual leveling, old data and large pay inequities.”

The company plans to use its new capital to expand its product offerings and scale its organization.

Shift Technology raises $220M at a $1B+ valuation to fight insurance fraud with AI

While insurance providers continue to get disrupted by startups like Lemonade, Alan, Clearcover, Pie and many others applying tech to rethink how to build a business around helping people and companies mitigate against risks with some financial security, one issue that has not disappeared is fraud. Today, a startup out of France is announcing some funding for AI technology that it has built for all insurance providers, old and new, to help them detect and prevent it.

Shift Technology, which provides a set of AI-based SaaS tools to insurance companies to scan and automatically flag fraud scenarios across a range of use cases — they include claims fraud, claims automation, underwriting, subrogation detection and financial crime detection — has raised $220 million, money that it will be using both to expand in the property and casualty insurance market, the area where it is already strong, as well as to expand into health, and to double down on growing its business in the U.S. It also provides fraud detection for the travel insurance sector.

This Series D is being led Advent International, via Advent Tech, with participation from Avenir and others. Accel, Bessemer Venture Partners, General Catalyst, and Iris Capital — who were all part of Shift’s Series C led by Bessemer in 2019 — also participated. With this round, Paris and Boston-based Shift Technology has now raised some $320 million and has confirmed that it is now valued at over $1 billion.

The company currently has around 100 customers across 25 different countries — with customers including Generali France and Mitsui Sumitomo — and says that it has already analyzed nearly two billion claims, data that’s feeding its machine learning algorithms to improve how they work.

The challenge (or I suppose, opportunity) that Shift is tackling, however, is much bigger. The Coalition Against Insurance Fraud, a non-profit in the U.S., estimates that at least $80 billion of fraudulent claims are made annually in the U.S. alone, but the figure is likely significantly higher. One problem has, ironically, been the move to more virtualized processes, which open the door to malicious actors exploiting loopholes in claims filing and fudging information.

Shift is also not alone in tackling this issue: the market for insurance fraud detection globally was estimated to be worth $2.5 billion in 2019 and projected to be worth as much as $8 billion by 2024.

In addition to others in claims management tech such as Brightcore and Guidewire, many of the wave of insuretech startups are building in their own in-house AI-based fraud protection, and it’s very likely that we’ll see a rise of other fraud protection services, built out of fintech to guard against financial crime, making their way to insurance, as the mechanics of how the two work and the compliance issues both face are very closely aligned.

“The entire Shift team has worked tirelessly to build this company and provide insurers with the technology solutions they need to empower employees to best be there for their policyholders. We are thrilled to partner with Advent International, given their considerable sector expertise and global reach and are taking another giant step forward with this latest investment,” stated Jeremy Jawish, CEO and co-founder, Shift Technology, in a statement. “We have only just scratched the surface of what is possible when AI-based decision automation and optimization is applied to the critical processes that drive the insurance policy lifecycle.”

For its backers, one key point with Shift is that it’s helping older providers bring on more tools and services that can help them improve their margins as well as better compete against the technology built by newer players.

“Since its founding in 2014, Shift has made a name for itself in the complex world of insurance,” said Thomas Weisman, an Advent director, in a statement. “Shift’s advanced suite of SaaS products is helping insurers to reshape manual and often time-consuming claims processes in a safer and more automated way. We are proud to be part of this exciting company’s next wave of growth.”

Tesla supplier Delta Electronics invests $7M in AI chip startup Kneron

Despite a persistent semiconductor shortage that is disrupting the global automotive industry, investors remain bullish on the chips used to power next-generation vehicles.

Kneron, a startup that develops semiconductors to give devices artificial intelligence capabilities by using edge computing, just got funded by Delta Electronics, a Taiwanese supplier of power components for Apple and Tesla. The $7 million investment boosts the startup’s total financing to over $100 million to date.

As part of the deal, Kneron also agreed to buy Vatics, a part of Delta Electronics’ subsidiary Vivotek, for $10 million in cash. The new assets nicely complement Kneron’s business as the startup extends its footprint to the booming smart car industry.

Vatics, an image signal processing provider, has been selling system-on-a-chip (SoC) and intellectual property to manufacturers of surveillance, consumer, and automotive products for many years across the United States and China.

Headquartered in San Diego with a development force in Taipei, Kneron has emerged in recent years as a challenge to AI chip incumbents like Intel and Google. Its chips boast of low-power consumption and enable data processing directly on the chips using the startup’s proprietary software, a departure from solutions that require data to be computed through powerful cloud centers and sent back to devices.

The approach has won Kneron a list of heavyweight backers, including strategic investor Foxconn, Qualcomm, Sequoia Capital, Alibaba, and Li Ka-shing’s Horizons Ventures.

Kneron has designed chips for scenarios ranging from manufacturing, smart homes, smartphones, robotics, surveillance and payments to autonomous driving. In the automotive field, it has struck partnerships with Foxconn and Otus, a supplier for Honda and Toyota.

Following the acquisition, Vatics executives will join Kneron to lead its surveillance and security camera division. The merged teams will jointly develop surveillance and automotive products for Kneron going forward. Image signal processors, coupled with neural processing units, are helpful in detecting objects and ensuring the safety of automated cars.

“This acquisition will allow us to offer full-stack AI solutions, along with our current class-leading NPUs [neural processing units], and will significantly speed up our go-to-market strategy,” said Kneron’s founder and CEO, Albert Liu.

Cymulate nabs $45M to test and improve cybersecurity defenses via attack simulations

With cybercrime on course to be a $6 trillion problem this year, organizations are throwing ever more resources at the issue to avoid being a target. Now, a startup that’s built a platform to help them stress-test the investments that they have made into their security IT is announcing some funding on the back of strong demand from the market for its tools.

Cymulate, which lets organizations and their partners run machine-based attack simulations on their networks to determine vulnerabilities and then automatically receive guidance around how to fix what is not working well enough, has picked up $45 million, funding that the startup — co-headquartered in Israel and New York — will be using to continue investing in its platform and to ramp up its operations after doubling its revenues last year on the back of a customer list that now numbers 300 large enterprises and mid-market companies, including the Euronext stock exchange network as well as service providers such as NTT and Telit.

London-based One Peak Partners is leading this Series C, with previous investors Susquehanna Growth Equity (SGE), Vertex Ventures Israel, Vertex Growth and Dell Technologies Capital also participating.

According to Eyal Wachsman, the CEO and co-founder, Cymulate’s technology has been built not just to improve an organization’s security, but an automated, machine-learning-based system to better understand how to get the most out of the security investments that have already been made.

“Our vision is to be the largest cybersecurity ‘consulting firm’ without consultants,” he joked.

The valuation is not being disclosed but as some measure of what is going on, David Klein, managing partner at One Peak, said in an interview that that he expects Cymulate to hit a $1 billion valuation within two years at the rate it’s growing and bringing in revenue right now. The startup has now raised $71 million, so it’s likely the valuation is in the mid-hundreds of millions. (We’ll continue trying to get a better number to have a more specific data point here.)

Cymulate — pronounced “sigh-mulate”, like the “cy” in “cyber” and a pun of “simulate”) is cloud-based but works across both cloud and on-premises environments and the idea is that it complements work done by (human) security teams both inside and outside of an organization, as well as the security IT investments — in terms of software or hardware) that they have already made.

“We do not replace — we bring back the power of the expert by validating security controls and checking whether everything is working correctly to optimize a company’s security posture,” Wachsman said. “Most of the time, we find our customers are using only 20% of the capabilities that they have. The main idea is that we have become a standard.”

The company’s tools are based in part on the MITRE ATT&CK framework, a knowledge base of threats, tactics and techniques used by a number of other cybersecurity services, including a number of others building continuous validation services that compete with Cymulate. These include the likes of FireEye, Palo Alto Networks, Randori, Khosla-backed AttackIQ and many more.

Although Cymulate is optimized to help customers better use the security tools they already have, it is not meant to replace other security apps, Wachsman noted, even if the by-product might become buying less of those apps in the future.

“I believe my message every day when talking with security experts is to stop buying more security products,” he said in an interview. “They won’t help defend you from the next attack. You can use what you’ve already purchased as long as you configure it well.”

In his words, Cymulate acts as a “black box” on the network, where it integrates with security and other software (it can also work without integrating but integrations allow for a deeper analysis). After running its simulations, it produces a map of the network and its threat profile, an executive summary of the situation that can be presented to management and a more technical rundown, which includes recommendations for mitigations and remediations.

Alongside validating and optimising existing security apps and identifying vulnerabilities in the network, Cymulate also has built special tools to fit different kinds of use cases that are particularly relevant to how businesses are operation today. They include evaluating remote working deployments, the state of a network following an M&A process, the security landscape of an organization that links up with third parties in supply chain arrangements, how well an organization’s security architecture is meeting (or potentially conflicting) with privacy and other kinds of regulatory compliance requirements, and it has built a “purple team” deployment, where in cases where security teams do not have the resources for running separate “red teams” to stress test something, blue teams at the organization can use Cymulate to build a machine learning-based “team” to do this.

The fact that Cymulate has built the infrastructure to run all of these processes speaks to a lot of potential of what more it could build, especially as our threat landscape, and how we do business, both continue to evolve. Even as it is, though, opportunity today is a massive one, with Gartner estimating that some $170 billion will be spent on information security by enterprises in 2022. That’s one reason why investors are here, too.

“The increasing pace of global cyber security attacks has resulted in a crisis of trust in the security posture of enterprises and a realization that security testing needs to be continuous as opposed to periodic, particularly in the context of an ever-changing IT infrastructure and rapidly evolving threats. Companies understand that implementing security solutions is not enough to guarantee protection against cyber threats and need to regain control,” said Klein, in a statement. “We expect Cymulate to grow very fast,” he told me more directly.

Sprout.ai raises $11m Series A led by Octopus Ventures to apply AI to insurance claims

It was way back in 2018 that Omni:us appeared to disrupt the insurance market by applying AI to this most legacy of all industries. It has now gone on to raise $44.1 million. In a similar vein, Shift Technology in France has raised $100 million.

Now a UK startup aims to do something similar, but this time it will be coming out of the key market of the UK, where the insurance industry is enormous.

Sprout.ai is an insurtech startup that use AI to help instance companies to settle claims within 24 hours. It’s now raised £8m/$11m Series A round led by Octopus Ventures. The round was joined by existing investors, Amadeus Capital Partners, Playfair Capital and Techstars. It was Seed funded buy Amadeus in 2020.

Sprout.ai supplies global insurers, such as Zurich, with a product that applies NLP and OCR to insurance claims (which might involve such as handwritten doctors’ notes for instance) to enable them to be resolved faster, in not a dissimilar fashion to Omni:us and SHift. Sprout.ai says it now has deployments in Europe, South America and APAC.

Niels Thoné, CEO of Sprout.ai, said in a statement: “Sprout.ai’s mission is to revolutionize customer service within global claims automation. Our innovative and industry-leading AI claims engine is poised to solve the current market inefficiencies, allowing insurers to focus on customers in their moments of need.”

Nick Sando, early-stage fintech investor at Octopus Ventures, said: “We are often at our most vulnerable when we submit insurance claims, and it doesn’t help when we then have to wait another month for it to be processed. Sprout.ai empowers insurers to process claims in a fraction of the time, creating much better outcomes for customers when they need it most.”

As we can see, the market is hotting up for this kind of service, so it will be interesting see if these startups end up ‘land-locked’ to their language markets or not. Certainly, I can see M&A opportunities for whoever starts to lead the pack.

StudySmarter books $15M for a global ‘personalized learning’ push

More money for the edtech boom: Munich-based StudySmarter, which makes digital tools to help learners of all ages swat up — styling itself as a ‘lifelong learning platform’ — has closed a $15 million Series A.

The round is led by sector-focused VC fund, Owl Ventures. New York-based Left Lane Capital is co-investing, along with Lars Fjeldsoe-Nielsen (ex WhatsApp, Uber and Dropbox; now GP at Balderton Capital), and existing early stage investor Dieter von Holtzbrinck Ventures (aka DvH Ventures).

The platform, which launched back in 2018 and has amassed a user-base of 1.5M+ learners — with a 50/50 split between higher education students and K12 learners, and with main markets so far in German speaking DACH countries in Europe — uses AI technologies like natural language processing (NLP) to automate the creation of text-based interactive custom courses and track learners’ progress (including by creating a personalized study plan that adjusts as they go along).

StudySmarter claims its data shows that 94% of learners achieve better grades as a result of using its platform.

While NLP is generally most advanced for the English language, the startup says it’s confident its NLP models can be transferred to new languages without requiring new training data — claiming its tech is “scalable in any language”. (Although it concedes its algorithms increase in accuracy for a given language as users upload more content so the software itself is undertaking a learning journey and will necessarily be at a different point on the learning curve depending on the source content.)

Here’s how StudySmarter works: Users input their study goals to get recommendations for relevant revision content that’s been made available to the platform’s community.

They can also contribute content themselves to create custom courses by uploading assets like lecture slides and revisions notes. StudySmarter’s platform can then turn this source material into interactive study aids — like flashcards and revision exercises — and the startup touts the convenience of the approach, saying it enables students to manage all their revision in one place (rather than wrangling multiple learning apps).

In short, it’s both a (revision) content marketplace and a productivity platform for learning — as it helps users create their own study (or lesson) plans, and offers them handy tools like a digital magic marker that automatically turns highlighted text into flashcards, while the resulting “smart” flashcards also apply the principle of spaced repetition learning to help make the studied content stick.

Users can choose to share content they create with other learners in the StudySmarter community (or not). The startup says a quarter (25%) of its users are creators, and that 80% of the content they create is shared. Overall, it says its platform provides access to more than 25 million pieces of shared content currently.

It’s topic agnostic, as you’d expect, so course content covers a diverse range of subjects. We’re told the most popular courses to study are: Economics, Medicine, Law, Computer Science, Engineering and school subjects such as Maths, Physics, Biology and English.

Regardless of how learners use it, the platform uses AI to nudge users towards relevant revision content and topics (and study groups) to keep extending and supporting their learning process — making adaptive, ongoing recommendations for other stuff they should check out.

The ease of creating learning materials on the StudySmarter platform results in a democratization of high-quality educational content, driven by learners themselves,” is the claim.   

As well as user generated content (UGC), StudySmarter’s platform hosts content created by verified educationists and publishers — and there’s an option for users to search only for such verified content, i.e. if they don’t want to dip into the UGC pool.

“In general, there is no single workflow,” says co-founder and CMO Maurice Khudhir. “We created StudySmarter to adapt to different learner types. Some are very active learners and prefer to create content, some only want to search and consume content from other peers/publishers.”

“Our platform focuses on the art of learning itself, rather than being bound by topics, sectors, industries or content types. This means that anyone, regardless of what they’re learning, can use StudySmarter to improve how they learn. We started in higher education as it was the closest, most relevant market to where we were at the time of launch. We more recently expanded to K12, and are currently running our first corporate learning pilot.”

Gamification is a key strategy to encourage engagement and advance learning, with the platform dishing out encouraging words and emoji, plus rewards like badges and achievements based on the individual’s progress. Think of it as akin to Duolingo-style microlearning — but where users get to choose the subject (not just the language) and can feed in source material if they wish.

StudySmarter says it’s taken inspiration from tech darlings like Netflix and Tinder — baking in recommendation algorithms to surface relevant study content for users -(a la Netflix’s ‘watch next’ suggestions), and deploying a Tinder-swipe-style learning UI on mobile so that its “smart flashcards” can to adapt to users’ responses.

“Firstly, we individualise the learning experience by recommending appropriate content to the learner, depending on their demographics, demands and study goals,” explains Khudhir. “For instance, when an economics student uploads a PDF on the topic of marginal cost, StudySmarter will recommend several user-generated courses that cover marginal cost and/or several flashcards on marginal cost as well as e-books on StudySmarter that cover this topic.

“In this way, StudySmarter is similar to Netflix — Netflix will suggest similar TV shows and films depending on what you’ve already watched and StudySmarter will recommend different learning materials depending on the types of content and topics you interact with.

“As well, depending on how the student likes to learn, we also individualise the learning journey through things such as the smart flashcard learning algorithm. This is based on spaced repetition. For example, if a student is testing themselves on microeconomics, the flashcard set will go through different questions and responses and the student can swipe through the flashcards, in a similar way to Tinder. The flashcards’ sequence will adapt after every response.

“The notifications are also personalised — so they will remind the student to learn at particular points in the day, adapted to how the student uses the app.”

There’s also a scan functionality which uses OCR (optical character recognition) technology that lets users upload (paper-based) notes, handouts or books — and a sketch feature lets them carry out further edits, if they want to add more notes and scribbles.

Once ingested into the platform, this scanned (paper-based) content can of course also be used to create digital learning materials — extending the utility of the source material by plugging it into the platform’s creation and tracking capabilities.

“A significant cohort of users access StudySmarter on tablets, and they find this learning flow very useful, especially for our school-age pupils,” he adds.

StudySmarter can also offer educators and publishers detailed learning analytics, per Khudhir — who says its overarching goal is to establish itself as “the leading marketplace for educational content”, i.e. by using the information it gleans on users’ learning goals to directly recommend (relevant) professional content — “making it an extremely effective distribution platform”, as he puts it.

In addition to students, he says the platform is being used by teachers, professors, trainers, and corporate members — ie. to create content to share with their own students, team members, course participants etc, or just to publish publicly. And he notes a bit of a usage spike from teachers in March last year as the pandemic shut down schools in Europe. 

StudySmarter co-founders, back from left to right: Christian Felgenhauer (co-founder & CEO), Till Söhlemann (co-founder); front: Maurice Khudir (co-founder & CMO), Simon Hohentanner (COO & co-founder). Image credits: StudySmarter

What about copyright? Khudir says they follow a three-layered system to minimize infringement risks — firstly by not letting users share or export any professional content hosted on the platform.

Uploaded documents like lecture notes and users’ own comments can be shared within one university course/class in a private learning group. But only UGC (like flashcards, summaries and exercises) can be shared freely with the entire StudySmarter community, if the user wants to.

“It’s important to note that no content is shared without the author’s permission,” he notes. “We also have a contact email for people to raise potential copyright infringements. Thanks to this system, we can say that we never had a single copyright issue with universities, professors or publishers.”

Another potential pitfall around UGC is quality. And, clearly, no student wants to waste their time revising from poor (or just plain wrong) revision notes.

StudySmarter says it’s limiting that risk by tracking how learners engage with shared content on the platform — in order to create quality scores for UGC — monitoring factors like how often such stuff is used for learning; how often the students who study from it answer questions correctly; and by looking the average learning time for a particular flashcard or summary, etc.

“We combine this with an active feedback system from the students to assign each piece of content a dynamic quality score. The higher the score is, the more often it is shown to new users. If the score falls below a certain threshold, the content is removed and is only visible to the original creator,” he goes on, adding: “We track the quality of shared content on the creator level so users who consistently share low-quality content can be banned from sharing more content on the platform.”

There are unlikely to be quality issues with verified educator/publisher content. But since it’s professional content, StudySmarter can’t expect to get it purely for free — so it says it “mostly” follows revenue-sharing agreements with these types of contributors.

It is also sharing data on learning trends and to help publishers reach relevant learners, as mentioned above. So the information it can provide education publishers about potential customers is probably the bigger carrot for pulling them in.

“We are very happy to say that the vast majority of our content is not created or shared on StudySmarter for any financial incentive but rather because our platform and technology simply make the creation significantly easier,” says Khudir, adding: “We have not paid a single Euro to any user on StudySmarter to create content and do not intend to do so going forward.” 

It’s still early days for monetization, which he says isn’t front of mind yet — with the team focused on building out the platform’s global reach — but he notes that the model allows for a number of b2b revenue streams, adding that they’ve been doing some early b2b monetization by working with employers and businesses to promote their graduate programs or to support recruitment drives. 

The new funding will be put towards product development and supporting the platform’s global expansion, per Khudir.

“We’ve run successful pilots in the U.K. and U.S. so they’re our primary focus to expand to by Q3 this year. In fact, following a test pilot in the U.K. in December, we became the number one education app within 24 hours (ahead of the likes of Duolingo, Quizlet, Kahoot, and Photomath), which bodes well!” he goes on. 

“Brazil, India and Indonesia are key targets for us due to a wider need for digital education. We’re also looking to launch in France, Nordics, Spain, Russia and many more countries. Due to the fact our platform is content-agnostic, and the technology that underpins it is universal, we’re able to scale effectively in multiple countries and languages. Within the next 12 months, we will be expanding to more than 12 countries and support millions of learners globally.”

StudySmarter’s subject-agnostic, feature-packed, one-stop-shop platform approach sets it apart from what Khudir refers to as “single-feature apps”, i.e. which just help you learn one thing — be that Duolingo (only languages), or apps that focus on teaching a particular skill-set (like Photomath for maths equations, or dedicated learn-to-code apps/courses (and toys)). 

But where the process of learning is concerned, there are lots of ways of going about it, and no one that suits everyone (or every subject), so there’s undoubtedly room for (and value in) a variety of approaches (which may happily operate in parallel). So it seems a safe bet that broad-brush learning platforms aren’t going to replace specialized tools — or (indeed) vice versa.

StudySmarter names the likes of Course Hero, StuDocu, Quizlet and Anki as taking a similar broad approach — while simultaneously claiming they’re not doing it in “quite the same, holistic, end-to-end, all-in-one bespoke platform for learners” way.  

Albeit, some of those edtech rivals are doing it with a lot more capital already raised. So StudySmarter is going to need to work smart and hard to localize and grab students’ attention as it guns for growth far beyond its European base.