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CMU researchers show potential of privacy-preserving activity tracking using radar – TechCrunch

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Imagine if you could settle/rekindle domestic arguments by asking your smart speaker when the room last got cleaned or whether the bins already got taken out?

Or — for an altogether healthier use-case — what if you could ask your speaker to keep count of reps as you do squats and bench presses? Or switch into full-on ‘personal trainer’ mode — barking orders to peddle faster as you spin cycles on a dusty old exercise bike (who needs a Peloton!).

And what if the speaker was smart enough to just know you’re eating dinner and took care of slipping on a little mood music?

Now imagine if all those activity tracking smarts were on tap without any connected cameras being plugged inside your home.

Another bit of fascinating research from researchers at Carnegie Mellon University’s Future Interfaces Group opens up these sorts of possibilities — demonstrating a novel approach to activity tracking that does not rely on cameras as the sensing tool. 

Installing connected cameras inside your home is of course a horrible privacy risk. Which is why the CMU researchers set about investigating the potential of using millimeter wave (mmWave) doppler radar as a medium for detecting different types of human activity.

The challenge they needed to overcome is that while mmWave offers a “signal richness approaching that of microphones and cameras”, as they put it, data-sets to train AI models to recognize different human activities as RF noise are not readily available (as visual data for training other types of AI models is).

Not to be deterred, they set about sythensizing doppler data to feed a human activity tracking model — devising a software pipeline for training privacy-preserving activity tracking AI models. 

The results can be seen in this video — where the model is shown correctly identifying a number of different activities, including cycling, clapping, waving and squats. Purely from its ability to interpret the mmWave signal the movements generate — and purely having been trained on public video data. 

“We show how this cross-domain translation can be successful through a series of experimental results,” they write. “Overall, we believe our approach is an important stepping stone towards significantly reducing the burden of training such as human sensing systems, and could help bootstrap uses in human-computer interaction.”

Researcher Chris Harrison confirms the mmWave doppler radar-based sensing doesn’t work for “very subtle stuff” (like spotting different facial expressions). But he says it’s sensitive enough to detect less vigorous activity — like eating or reading a book.

The motion detection ability of doppler radar is also limited by a need for line-of-sight between the subject and the sensing hardware. (Aka: “It can’t reach around corners yet.” Which, for those concerned about future robots’ powers of human detection, will surely sound slightly reassuring.)

Detection does require special sensing hardware, of course. But things are already moving on that front: Google has been dipping its toe in already, via project Soli — adding a radar sensor to the Pixel 4, for example.

Google’s Nest Hub also integrates the same radar sense to track sleep quality.

“One of the reasons we haven’t seen more adoption of radar sensors in phones is a lack of compelling use cases (sort of a chicken and egg problem),” Harris tells TechCrunch. “Our research into radar-based activity detection helps to open more applications (e.g., smarter Siris, who know when you are eating, or making dinner, or cleaning, or working out, etc.).”

Asked whether he sees greater potential in mobile or fixed applications, Harris reckons there are interesting use-cases for both.

“I see use cases in both mobile and non mobile,” he says. “Returning to the Nest Hub… the sensor is already in the room, so why not use that to bootstrap more advanced functionality in a Google smart speaker (like rep counting your exercises).

“There are a bunch of radar sensors already used in building to detect occupancy (but now they can detect the last time the room was cleaned, for example).”

“Overall, the cost of these sensors is going to drop to a few dollars very soon (some on eBay are already around $1), so you can include them in everything,” he adds. “And as Google is showing with a product that goes in your bedroom, the threat of a ‘surveillance society’ is much less worry-some than with camera sensors.”

Startups like VergeSense are already using sensor hardware and computer vision technology to power real-time analytics of indoor space and activity for the b2b market (such as measuring office occupancy).

But even with local processing of low-resolution image data, there could still be a perception of privacy risk around the use of vision sensors — certainly in consumer environments.

Radar offers an alternative to such visual surveillance that could be a better fit for privacy-risking consumer connected devices such as ‘smart mirrors‘.

“If it is processed locally, would you put a camera in your bedroom? Bathroom? Maybe I’m prudish but I wouldn’t personally,” says Harris.

He also points to earlier research which he says underlines the value of incorporating more types of sensing hardware: “The more sensors, the longer tail of interesting applications you can support. Cameras can’t capture everything, nor do they work in the dark.”

“Cameras are pretty cheap these days, so hard to compete there, even if radar is a bit cheaper. I do believe the strongest advantage is privacy preservation,” he adds.

Of course having any sensing hardware — visual or otherwise — raises potential privacy issues.

A sensor that tells you when a child’s bedroom is occupied may be good or bad depending on who has access to the data, for example. And all sorts of human activity can generate sensitive information, depending on what’s going on. (I mean, do you really want your smart speaker to know when you’re having sex?)

So while radar-based tracking may be less invasive than some other types of sensors it doesn’t mean there are no potential privacy concerns at all.

As ever, it depends on where and how the sensing hardware is being used. Albeit, it’s hard to argue that the data radar generates is likely to be less sensitive than equivalent visual data were it to be exposed via a breach.

“Any sensor should naturally raise the question of privacy — it is a spectrum rather than a yes/no question,” agrees Harris.  “Radar sensors happen to be usually rich in detail, but highly anonymizing, unlike cameras. If your doppler radar data leaked online, it’d be hard to be embarrassed about it. No one would recognize you. If cameras from inside your house leaked online, well… ”

What about the compute costs of synthesizing the training data, given the lack of immediately available doppler signal data?

“It isn’t turnkey, but there are many large video corpuses to pull from (including things like Youtube-8M),” he says. “It is orders of magnitude faster to download video data and create synthetic radar data than having to recruit people to come into your lab to capture motion data.

“One is inherently 1 hour spent for 1 hour of quality data. Whereas you can download hundreds of hours of footage pretty easily from many excellently curated video databases these days. For every hour of video, it takes us about 2 hours to process, but that is just on one desktop machine we have here in the lab. The key is that you can parallelize this, using Amazon AWS or equivalent, and process 100 videos at once, so the throughput can be extremely high.”

And while RF signal does reflect, and do so to different degrees off of different surfaces (aka “multi-path interference”), Harris says the signal reflected by the user “is by far the dominant signal”. Which means they didn’t need to model other reflections in order to get their demo model working. (Though he notes that could be done to further hone capabilities “by extracting big surfaces like walls/ceiling/floor/furniture with computer vision and adding that into the synthesis stage”.)

“The [doppler] signal is actually very high level and abstract, and so it’s not particularly hard to process in real time (much less ‘pixels’ than a camera).” he adds. “Embedded processors in cars use radar data for things like collision breaking and blind spot monitoring, and those are low end CPUs (no deep learning or anything).”

The research is being presented at the ACM CHI conference, alongside another Group project — called Pose-on-the-Go — which uses smartphone sensors to approximate the user’s full-body pose without the need for wearable sensors.

CMU researchers from the Group have also previously demonstrated a method for indoor ‘smart home’ sensing on the cheap (also without the need for cameras), as well as — last year — showing how smartphone cameras could be used to give an on-device AI assistant more contextual savvy.

In recent years they’ve also investigated using laser vibrometry and electromagnetic noise to give smart devices better environmental awareness and contextual functionality. Other interesting research out of the Group includes using conductive spray paint to turn anything into a touchscreen. And various methods to extend the interactive potential of wearables — such as by using lasers to project virtual buttons onto the arm of a device user or incorporating another wearable (a ring) into the mix.

The future of human computer interaction looks certain to be a lot more contextually savvy — even if current-gen ‘smart’ devices can still stumble on the basics and seem more than a little dumb.



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As clinical guidelines shift, heart disease screening startup pulls in $43M Series B – TechCrunch

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Cleerly Coronary, a company that uses A.I powered imaging to analyze heart scans, announced a $43 million Series B funding this week. The funding comes at a moment when it seems that a new way of screening for heart disease is on its way. 

Cleerly was started in 2017 by James K. Min a cardiologist, and the director of the Dalio Institute for Cardiac Imaging at New York Presbyterian Hospital/Weill Cornell Medical College. The company, which uses A.I to analyze detailed CT scans of the heart, has 60 employees, and has raised $54 million in total funding.

The Series B round was led by Vensana Capital, but also included LVR Health, New Leaf Venture Partners, DigiTx Partners, and Cigna Ventures. 

The startup’s aim is to provide analysis of detailed pictures of the human heart that have been examined by artificial intelligence. This analysis is based on images taken via Cardiac Computer Tomography Angiogram (CTA), a new, but rapidly growing manner of scanning for plaques. 

“We focus on the entire heart, so every artery, and its branches, and then atherosclerosis characterization and quantification,” says Min. “We look at all of the plaque buildup in the artery, [and] the walls of the artery, which historical and traditional methods that we’ve used in cardiology have never been able to do.”

Cleerly is a web application, and it requires that a CTA image specifically, which the A.I. is trained to analyze, is actually taken when patients go in for a checkup. 

When a patient goes in for a heart exam after experiencing a symptom like chest pain, there are a few ways they can be screened. They might undergo a stress test, an echocardiogram (ECG), or a coronary angiogram – a catheter and x-ray-based test. CTA is a newer form of imaging in which a scanner takes detailed images of the heart, which is illuminated with an injected dye. 

Cleerly’s platform is designed to analyze those CTA images in detail, but they’ve only recently become a first-line test (a go-to, in essence) when patients come in with suspected heart problems. The European Society of Cardiology updated guidelines to make CTA a first-line test in evaluating patients with chronic coronary disease. In the UK, it became a first-line test in the evaluation of patients with chest pain in 2016.

CTA is already used in the US, but guidelines may expand how often it’s actually used. A review on CTA published on the American College of Cardiology website notes that it shows “extraordinary potential.” 

There’s movement on the insurance side, too. In 2020, United Healthcare announced the company will now reimburse for CTA scans when they’re ordered to examine low-to medium risk patients with chest pain. Reimbursement qualification is obviously a huge boon to broader adoption.

CTA imaging might not be great for people who already have stents in their hearts, or, says Min, those who are just in for a routine checkup (there is low-dose radiation associated with a CTA scan). Rather, Cleerly will focus on patients who have shown symptoms or are already at high risk for heart disease. 

The CDC estimates that currently 18.2 million adults currently have coronary artery heart disease (the most common kind), and that 47 percent of Americans have one of the three most prominent risk factors for the disease: high blood pressure, high cholesterol, or a smoking habit. 

These shifts (and anticipated shifts) in guidelines suggest that a lot more of these high-risk patients may be getting CTA scans in the future, and Cleerly has been working on mining additional information from them in several large-scale clinical trials.

There are plenty of different risk factors that contribute to heart disease, but the most basic understanding is that heart attacks happen when plaques build up in the arteries, which narrows the arteries and constricts the flow of blood. Clinical trials have suggested that the types of plaques inside the body may contain information about how risky certain blockages are compared to others beyond just much of the artery they block. 

A trial on 25,251 patients found that, indeed, the percentage of construction in the arteries increases the risk of heart attack. But the type of plaque in those arteries identified high-risk patients better than other measures. Patients who went on to have sudden heart attacks, for example, tended to have higher levels of fibrofatty or necrotic core plaque in their hearts. 

These results do suggest that it’s worth knowing a bit more detail about plaque in the heart. Note that Min is an author of this study, but it was also conducted at 13 different medical centers. 

As with all A.I based diagnostic tools the big question is: How well does it actually recognize features within a scan? 

At the moment FDA documents emphasize that it is not meant to supplant a trained medical professional who can interpret the results of a scan. But tests have suggested it fares pretty well. 

A June 2021 study compared Cleerly’s A.I analysis of CTA scans to that of three expert readers, and found that the A.I had a diagnostic accuracy of about 99.7 percent when evaluating patients who had severe narrowing in their arteries. Three of nine study authors hold equity in Cleerly. 

With this most recent round of funding, Min says he aims to pursue more commercial partnerships and scale up to meet the existing demand. “We have sort of stayed under the radar, but we came above the radar because now I think we’re prepared to fulfill demand,” he says. 

Still, the product itself will continue to be tested and refined. Cleerly is in the midst of seven performance indication studies that will evaluate just how well the software can spot the litany of plaques that can build up in the heart.



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Iceland’s Frumtak Ventures raises its third, $57M, fund focusing on post-seed and Series A – TechCrunch

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Frumtak Ventures, one of the few VCs in Iceland, has raised its third fund, Frumtak III. The $57 million (ISK 7b, €48m) fund will focus on post-seed and Series A startups. The firm says its typical ticket size will range from $1-5 million (€850k-4.2m).

Frumtak was a somewhat lesser-known European VC until it popped up on our radar as the backers behind the Controlant real-time supply chain monitoring startup, the technology from which was pictured beside Andrew Cuomo, governor of New York, when he held up a box containing the first-ever shipment of the COVID-19 vaccine to the city. Controlant has been a key player in the global distribution cold chain associated with vaccines.

However, the fund has also backed digital banking solutions provider Meniga, digital therapeutics scaleup Sidekick Health, travel CRM and travel booking system provider Kaptio, live event and fan engagement data analytics company Activity Stream, and Data Market, which was acquired by Qlik in 2014.

Svana Gunnarsdottir, managing partner of Frumtak Ventures said: “We are proud of the accomplishments of our portfolio companies and their teams, as well as the investment decisions we made through our first two funds. We look forward to continuing our support of high-potential startups and brilliant founders with Frumtak III. We are also grateful for the confidence shown to us by our LP’s, many of whom have been with us since our first fund in 2009.”

Concurrently, Asthildur Otharsdottir has joined the firm as partner and Frumtak III’s lead investment manager. Otharsdottir was previously Frumtak’s Chairman for 6 years and has been on the board of Marel and Icelandair Group.



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Aircall raises $120 million for its cloud-based phone system – TechCrunch

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Aircall has raised a $120 million Series D round led by Goldman Sachs Asset Management. Following today’s funding round, the company has reached unicorn status, which means it has a valuation above $1 billion — this is the 16th French unicorn.

The startup has been building a cloud-based phone system for call centers, support lines and sales teams. It integrates with Salesforce, HubSpot, Zendesk, Slack, Intercom and other popular CRM, support and communication systems.

Aircall customers can create local numbers and set up an interactive voice response directory. The service manages the call queue for you and your agents can start answering inbound calls. Agents can transfer calls and put customers on hold. Admins can see analytics, monitor calls and see how everyone is doing.

In addition to Goldman Sachs Asset Management, existing investors DTCP, eFounders, Draper Esprit, Adam Street Partners, NextWorldCap and Gaia are also participating once again in today’s funding round.

As a cloud-based software product, Aircall works well with remote or hybrid teams. For the past year, many companies have been looking for a new phone system with various lockdowns taking place around the world. And Aircall has capitalized on this influx of customers.

When it comes to metrics, it means that signups increased by 65% in 2020. New customers include Caudalie, OpenClassrooms and Too Good To Go. Overall, Aircall has 8,500 customers. 15% of them are based in France, 35% in the U.S. and 50% in other countries.

With the new funding round, the company plans to iterate on its product with new integrations with third-party tools, and in particular industry-specific integrations. There will be new offices in London and Berlin as well as new hires in the company’s existing offices based in New York, Paris, Sydney and Madrid.

The company also plans to control a bigger chunk of its tech stack. It means that it’ll collaborate with big telecommunications companies to leverage their networks. You can also expect more product features with better transcription and better sentiment analysis.



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These Forge cofounders just raised $5 million to work on a new, still-stealth investing startup – TechCrunch

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Sohail Prasad and Samvit Ramadurgam are cofounders who met during Y Combinator’s 2012 summer batch and went on to cofound Forge, which helps accredited investors and institutions buy and sell private company shares and which most recently raised $150 million in new funding in May.

Forge — originally known as Equidate —  has taken off as demand for private company shares has ballooned. The company, launched in 2014, has now raised $250 million altogether, including from, Deutsche Börse, Temasek, Wells Fargo, BNP Paribas, and Munich Re. It acquired rival SharesPost last year for $160 million in cash and stock. According to the company, it now has more than $14 billion in assets under custody.

Prasad and Ramadurgam — who helped hire Forge CEO Kelly Rodriques back in 2018 — say they’re excited about that success. They still own a stake in the company; they remain non-voting board members.

But after spending 18 months as co-president of Forge at the outset of Rodrigues’s tenure, they left early last year to begin tinkering on a new idea, one that Prasad says is centered around giving a much wider pool of people access to private company shares. Called D/XYZ (pronounced “Destiny”), the idea is to enable any investor — not just the 1% —  to invest in startups whose services they use and love.

Unfortunately, the two aren’t offering much more of a curtain raiser than that right now, though Prasad suggests D/XYZ is neither a new fund nor a crowdfunding vehicle. It’s also not selling any tokens, we gather. Instead, Prasad hints at an entirely new product, saying the company is being cautious in how much it shares publicly because it first wants to “get the go-ahead from regulators, as well as to ensure we have a clear path to market,” he says.

In the meantime, the two have raised $5 million in seed funding from numerous founders who like the idea of making private company shares easier for their parents, friends, customers, partners, and everyone else who likes what they’re building. Among the round’s participants is Coinbase cofounder Fred Ehrsam; Plaid cofounder and CEO Zach Perret; Quora and Expo cofounder Charlie Cheever; Superhuman founder and CEO Rahul Vohra; and serial entrepreneur Siqi Chen, who most recently founded a finance software company called Runway.

As for some of the nascent startup’s most obvious competition, Prasad doesn’t sound concerned. Asked, for example, about Carta, a well-funded company that helps private companies and their employees manage and sell their stock and options and that has long talked about democratizing access to private company shares, Prasad says it remains very much a direct competitor instead to Forge given that both cater first and foremost to companies, not individuals.

And what of SPACs, the special purpose acquisition companies that are moving private companies onto the public market faster, allowing (at least in theory) more people to access high-growth companies at earlier stages? It’s a partial solution, says Prasad. But the way he sees it, “SPACs are more a reflection that people want late-stage access to private tech and their best option right now is giving money to a SPAC manager who will hopefully find a promising company to merge with in two years or less.” He calls them a “layer of abstraction.”

Of course, there’s also the question of whether Forge will be a friend of foe if whatever Prasad and Ramadurgam are building succeeds. Could their tech be sold back to their first company? Could Forge come to see them as a rival to its business?

“What we’re doing now is not competitive,” insists Prasad. “It’s more picking up the mantle where we left off. Forge is focused on trading, custody, company solutions and data. It has built what some call boring plumbing.” Now that the plumbing has been erected, it has “enabled a lot of other interesting things to be built, too.”

So is D/XYZ working with Forge in some capacity? Prasad demurs. “Potentially,” he says.

In other words, stay tuned.

Pictured above, left to right: Sohail Prasad and Samvit Ramadurgam.



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Viva Republica, developer of Korean financial super app Toss, raises $410M at a $7.4B valuation – TechCrunch

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Viva Republica, the Seoul-based fintech company behind Toss, a super app with more than 40 financial services, announced today it has raised $410 million at a post-money valuation of $7.4 billion. The new funding was led by Alkeon Capital, an American investment firm, and included participation from new investors like Korea Development Bank, and returning backers Altos Ventures and Greyhound Capital.

The company plans to launch Toss Bank, a neobank, in September 2021, which it describes as “the final key component” of its super app strategy. It will also use the funding to continue its expansion in overseas markets, including Vietnam, where Toss launched last year.

Viva Republica, which hit unicorn status in 2018, has now raised more than $940 million in equity funding.

Founder and chief executive officer SG Lee told TechCrunch that Toss Bank will focus on lending, and also offer savings accounts with competitive interest rates.

“A lot of challenger banks and neobanks are focusing on the banking experience, such as cards, so their main revenue source is interchange fees,” he said. “Toss is quite different because we already cover all that. We cover P2P payment, money transfer, cards and all sorts of services. So we are focusing on loans, unsecured loans, mortgages, all sorts of loans. We are going to use this vehicle to give the most competitive interest rates to users, and Toss Bank will not have a separate app, since we have super app strategy.”

Toss founder SG Lee

One of the reasons Toss Bank is focusing on loans is because if someone has a middling credit score, many South Korean banks will only offer them loans at subprime interest rates, Lee said. Toss Bank will be able to offer better rates because its risk-scoring model leverages data from its millions of users.

Toss now claims a total of 20 million users (or more than a third of South Korea’s 51.7 population) and of that amount, 11 million are monthly active users.

The app launched as a Venmo-like peer-to-peer money transfer platform in 2015, before adding more services. Now its users can turn to the app for almost all of their financial needs.

For example, they can check their balances at different banks and credit cards on a dashboard. Merchants can use Toss Payments to send and receive online payments and manage their business finances. Other features include budgeting tools, bill payments, a credit score tracker and insurance plans. Lee said more than 20% of bank accounts and credit cards in South Korea are already registered on Toss.

As a financial super app, Toss Bank will be able to supplement information from South Korea’s main credit rating agencies with its own data about user transactions: for example, where do they spend money, how often do they spend, their cash flow and balances.

Lee added that one of South Korea’s leading credit bureaus, KCB (Korea Credit Bureau), backtested Toss’ engine with data from over two million users, and it turned out to be 150% better in terms of differential power analysis and 30% lower in delinquency rates. “This is the first engine that counts this asset-related data, and no machine-learning technologies have been used in credit evaluation” in South Korea, he said. “I think Toss Bank is really well-positioned to disrupt the whole loan market.”

In March, Toss also launched an investment service called Toss Securities, designed to make stock trading accessible to new investors who shy away from traditional brokerages. Over the past three months, it has signed up more than 3.5 million users.

Viva Republica launched Toss in Vietnam, its first international market, in 2020, and the app now has services like no-fee money transfers, debit cards and a financial dashboard through a partnership with CIMB bank. Toss currently claims more than three million monthly active users in Vietnam and says it adds more than 500,000 active users every month. Toss is planning to enter other Southeast Asian markets, too.

Toss hasn’t finalized a timeline, but it is targeting Malaysia for its next market by the end of this year. “The product that we built for Vietnam is actually quite scalable across all Southeast Asia markets, so it’s a matter of time,” Lee said. “But we want to focus on the Vietnam market because it’s scaling increasingly fast and we have to cover the growth.”

As for the possibility of holding an initial public offering or finding another exit opportunity, Lee said the company is still finalizing its plans. “As an Asian company, reaching a $7.4 billion valuation is pretty high, and I think at some point we will face not being able to do more fundraising in the private market. So we’re targeting to raise once more by the end of this year or early next year for over $300 million. That will be our last private fundraising, and then we’re thinking a timeline of three years, and we are reviewing not only for a Korean listing but also a U.S. listing.”



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Oyster, an HR platform for distributed workforces, snaps up $50M on a $475M valuation – TechCrunch

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The future of work is long on long-distance, and today a startup that’s built a platform to help organizations hire global talent and build out those remote workforces is announcing a round of funding on the heels of strong growth.

Oyster — which provides tools to help with hiring, onboarding, payroll, benefits and salary management services for both contractors and full-time employees working outside of an organization’s home country — has closed a Series B of $50 million.

We understand that the funding is coming in at a $475 million valuation, six times the company’s valuation when it last raised money — a $20 million round just four months ago. The company itself has seen business grow “exponentially” since then, said Tony Jamous, London-based Oyster’s CEO who co-founded the company with Jack Mardack. The company now works with 80 large businesses, he said, helping them fill knowledge worker roles.

Stripes is leading the Series B, with previous backers Emergence Capital and The Slack Fund, as well as new investor Avid Ventures, also participating.

Jamous told me back in February that the idea for building Oyster was first planted when he was working at his first startup, Nexmo (which eventually he sold to Vonage), after being faced with the challenges of hiring talent internationally, and specifically the millions the company invested to build out the infrastructure to do so itself, since every country has very specific procedures for employing people and handling all of the contractural, tax, and regulatory details related to that.

Oyster’s mission has been to  make it possible for any company to hire wherever they want, without going through that pain themselves, making the “world their oyster,” so to speak.

While that in itself is a great idea that definitely fills a need for businesses, it has also been compounded by recent changing tides. Not only are more people wanting to work further afield, but at “home”, many companies — especially those who need to fill knowledge worker roles — are facing talent shortages. All of this is driving even more demand for sourcing and hiring candidates from further afield, and a culture in the workplace that it’s possible to work well even if you are not in the same physical space.

“What’s happening in the world is that there’s a talent shortage, and also there’s no need to be in the office anymore,” he said. “When it comes to tapping into the global talent pool, if you think about it, if you’re a London-based company, then the chances that your best talent is in London is less than 1%. So by tapping into the global talent pool, suddenly you’re dramatically increasing your chances, especially if you depend on talent as as a key source of your success.”

The number of startups in the market today targeting the remote working opportunity — helping companies source and hire people wherever they happen to be located — and Oyster is not the only one of them raising big money to scale. Others include Deel, which is now valued at $1.25 billion; Turing; Papaya Global (now also valued at over $1 billion); Remote, and many more.

Oyster is not — yet? — in the business of helping to source or vet potential hires, but once someone is identified and an organization wants to make an offer, Oyster provides a seamless way to handle the rest, including giving advice on whether it’s best to hire the person as a contractor or full time employee (the trend here, he said, is full-time), how to handle benefits based on the country in which the talent is based; and other aspects of remuneration, again particular to each local market. Pricing ranges from $29 per person, per month for contractors, to $399 for working with full employees, to other packages for larger deployments.

The company also has a public service mission in all this. Jamous himself originally hails from Lebanon and has a particular mission to help people from less high-profile parts of the world, and emerging countries, also get on the career ladder. In this day and age, since relocation and migration are no longer a must-do, it opens up a lot of opportunities for people that didn’t exist before. Oyster applied for, and now has B-Corp certification, which it’s using to fill out that global employment and talent mandate.

This is not just for greater good, though. There are actual talent shortages, and a recent study from Korn Ferry, cited by Oyster, found that 1.5 billion knowledge workers will be entering the workforce in the next decade from emerging economies. Building tools to help hire and manage that talent makes business sense.

“We’re thrilled to partner with Stripes for the next chapter of growth and positive impact for Oyster,” said Jack Mardack, co-Founder of Oyster, in a statement. “Investors like Stripes, Emergence, Slack Fund, Avid, and PeopleTech Partners among others, who share in our passion for the Oyster mission and vision for the future of work, give us the rocket fuel we need to change the world by unblocking access to job opportunities for everyone.”

“The transition to remote work is one of the most fundamental macro trends in business today and COVID-19 accelerated that transition by 10 years,” said Saagar Kulkarni, partner at Stripes, in a statement. “Oyster makes it seamless for any company to hire the best person for each job, removing location as a barrier. Tony and the team have built the best software product in the market and are poised to build a market-defining company. We are thrilled to join the entire Oyster team on their mission to level the playing field for the global workforce.”



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A.I. drug discovery platform Insilico Medicine announces $255 million in Series C funding – TechCrunch

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Insilico Medicine, an A.I-based platform for drug development and discovery announced $255 million in Series C financing on Tuesday. The massive round is reflective of a recent breakthrough for the company: proof that it’s A.I based platform can create a new target for a disease, develop a bespoke molecule to address it, and begin the clinical trial process. 

It’s also yet another indicator that A.I and drug discovery continues to be especially attractive for investors. 

Insilico Medicine is a Hong Kong-based company founded in 2014 around one central premise: that A.I assisted systems can identify novel drug targets for untreated diseases, assist in the development of new treatments, and eventually predict how well those treatments may perform in clinical trials. Previously, the company had raised $51.3 million in funding, according to Crunchbase

Insilico Medicine’s aim to use A.I to drive drug development isn’t particularly new, but there is some data to suggest that the company might actually accomplish that gauntlet of discovery all the way through trial prediction. In 2020, the company identified a novel drug target for idiopathic pulmonary fibrosis, a disease in which tiny air sacs in the lungs become scarred, which makes breathing laborious. 

Two A.I-based platforms first identified 20 potential targets, narrowed it down to one, and then designed a small molecule treatment that showed promise in animal studies. The company is currently filing an investigational new drug application with the FDA and will begin human dosing this year, with aims to begin a clinical trial late this year or early next year. 

The focus here isn’t on the drug, though, it’s on the process. This project condensed the process of preclinical drug development that typically takes multiple years and hundreds of millions of dollars into just 18 months, for a total cost of about $2.6 million. Still, founder Alex Zhavoronkov doesn’t think that Insilico Medicine’s strengths lie primarily in accelerating preclinical drug development or reducing costs: its main appeal is in eliminating an element of guesswork in drug discovery, he suggests. 

“Currently we have 16 therapeutic assets, not just IPF,” he says. “It definitely raised some eyebrows.” 

“It’s about the probability of success,” he continues. “So the probability of success of connecting the right target to the right disease with a great molecule is very, very low. The fact that we managed to do it in IPF and other diseases I can’t talk about yet – it increases confidence in A.I in general.” 

Bolstered partially by the proof-of-concept developed by the IPF project and enthusiasm around A.I based drug development, Insilico Medicine attracted a long list of investors in this most recent round. 

The round is led by Warburg Pincus, but also includes investment from Qiming Venture Partners, Pavilion Capital, Eight Roads Ventures, Lilly Asia Ventures, Sinovation Ventures, BOLD Capital Partners, Formic Ventures, Baidu Ventures, and new investors. Those include CPE, OrbiMed, Mirae Asset Capital, B Capital Group, Deerfield Management, Maison Capital, Lake Bleu Capital, President International Development Corporation, Sequoia Capital China and Sage Partners. 

This current round was oversubscribed four-fold, according to Zhavoronkov. 

A 2018 study of 63 drugs approved by the FDA between 2009 and 2018 found that the median capitalized research and development investment needed to bring a drug to market was $985 million, which also includes the cost of failed clinical trials. 

Those costs and the low likelihood of getting a drug approved has initially slowed the process of drug development. R&D returns for biopharmaceuticals hit a low of 1.6 percent in 2019, and bounced back to a measly 2.5 percent in 2020 according to a 2021 Deloitte report

Ideally, Zhavoronkov imagines an A.I-based platform trained on rich data that can cut down on the amount of failed trials. There are two major pieces of that puzzle: PandaOmics, an A.I platform that can identify those targets; and Chemistry 42, a platform that can manufacture a molecule to bind to that target.

“We have a tool, which incorporates more than 60 philosophies for target discovery,” he says. 

“You are betting something that is novel, but at the same time you have some pockets of evidence that strengthen your hypothesis. That’s what our A.I does very well.” 

Although the IPF project has not been fully published in a peer-reviewed journal, a similar project published in Nature Biotechnology was. In that paper, Insilco’s deep learning model was able to identify potential compounds in just 21 days

The IPF project is a scale-up of this idea. Zhavoronkov doesn’t just want to identify molecules for known targets, he wants to find new ones and shepherd them all the way through clinical trials. And, indeed, also to continue to collect data during those clinical trials that might improve future drug discovery projects. 

“So far nobody has challenged us to solve a disease in partnership” he says. “If that happens, I’ll be a very happy man.” 

That said, Insilico Medicine’s approach to novel target discovery has been used piecemeal, too. For instance, Insilico Medicine has collaborated with Pfizer on novel target discovery, and Johnson and Johnson on small molecule design and done both with Taisho Pharmaceuticals. Today, the company also announced a new partnership with Teva Branded Pharmaceutical Products R&D, Inc. Teva will aim to use PandaOmics to identify new drug targets.

That said, it’s not just Insilico Medicine raking in money and partnerships. The whole field of A.I-based novel targets has been experiencing significant hype.

In 2019 Nature noted that at least 20 partnerships between major drug companies and A.I drug discovery tech companies had been reported. In 2020, investment in A.I companies pursuing drug development increased to $13.9 billion, a four-fold increase from 2019, per Stanford University’s Artificial Intelligence Index annual report. R&D cost 

Drug discovery projects received the greatest amount of private A.I investment in 2020, a trend that can partially be attributed to the pandemic’s need for rapid drug development. However, the roots of the hype predate Covid-19. 

Zhavorokov is aware that A.I based drug development is riding a bit of a hype wave right now. “Companies without substantial evidence supporting their A.I powered drug discovery claims manage to raise very quickly,” he notes. 

Insilico Medicine, he says, can distinguish itself based on the quality of its investors. “Our investors don’t gamble,” he says. 

But like so many other A.I-based drug discovery platforms, we’ll have to see whether they make it through the clinical trial churn. 



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