AI
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Investors in AI-first technology companies serving the defense industry, such as Palantir, Primer and Anduril, are doing well. Anduril, for one, reached a valuation of over $4 billion in less than four years. Many other companies that build general-purpose, AI-first technologies — such as image labeling — receive large (undisclosed) portions of their revenue from the defense industry.
Investors in AI-first technology companies that aren’t even intended to serve the defense industry often find that these firms eventually (and sometimes inadvertently) help other powerful institutions, such as police forces, municipal agencies and media companies, prosecute their duties.
Most do a lot of good work, such as DataRobot helping agencies understand the spread of COVID, HASH running simulations of vaccine distribution or Lilt making school communications available to immigrant parents in a U.S. school district.
The first step in taking responsibility is knowing what on earth is going on. It’s easy for startup investors to shrug off the need to know what’s going on inside AI-based models.
However, there are also some less positive examples — technology made by Israeli cyber-intelligence firm NSO was used to hack 37 smartphones belonging to journalists, human-rights activists, business executives and the fiancée of murdered Saudi journalist Jamal Khashoggi, according to a report by The Washington Post and 16 media partners. The report claims the phones were on a list of over 50,000 numbers based in countries that surveil their citizens and are known to have hired the services of the Israeli firm.
Investors in these companies may now be asked challenging questions by other founders, limited partners and governments about whether the technology is too powerful, enables too much or is applied too broadly. These are questions of degree, but are sometimes not even asked upon making an investment.
I’ve had the privilege of talking to a lot of people with lots of perspectives — CEOs of big companies, founders of (currently!) small companies and politicians — since publishing “The AI-First Company” and investing in such firms for the better part of a decade. I’ve been getting one important question over and over again: How do investors ensure that the startups in which they invest responsibly apply AI?
Let’s be frank: It’s easy for startup investors to hand-wave away such an important question by saying something like, “It’s so hard to tell when we invest.” Startups are nascent forms of something to come. However, AI-first startups are working with something powerful from day one: Tools that allow leverage far beyond our physical, intellectual and temporal reach.
AI not only gives people the ability to put their hands around heavier objects (robots) or get their heads around more data (analytics), it also gives them the ability to bend their minds around time (predictions). When people can make predictions and learn as they play out, they can learn fast. When people can learn fast, they can act fast.
Like any tool, one can use these tools for good or for bad. You can use a rock to build a house or you can throw it at someone. You can use gunpowder for beautiful fireworks or firing bullets.
Substantially similar, AI-based computer vision models can be used to figure out the moves of a dance group or a terrorist group. AI-powered drones can aim a camera at us while going off ski jumps, but they can also aim a gun at us.
This article covers the basics, metrics and politics of responsibly investing in AI-first companies.
Investors in and board members of AI-first companies must take at least partial responsibility for the decisions of the companies in which they invest.
Investors influence founders, whether they intend to or not. Founders constantly ask investors about what products to build, which customers to approach and which deals to execute. They do this to learn and improve their chances of winning. They also do this, in part, to keep investors engaged and informed because they may be a valuable source of capital.
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SmartNews, a Tokyo-headquartered news aggregation website and app that’s grown in popularity despite hefty competition from built-in aggregators like Apple News, today announced it has closed on $230 million in Series F funding. The round brings SmartNews’ total raise to date to over $400 million and values the business at $2 billion — or as the company touts in its press release, a “double unicorn.” (Ha!)
The funding included new U.S. investors Princeville Capital and Woodline Partners, as well as JIC Venture Growth Investments, Green Co-Invest Investment, and Yamauchi-No.10 Family Office in Japan. Existing investors participating in this round included ACA Investments and SMBC Venture Capital.
Founded in 2012 in Japan, the company launched to the U.S. in 2014 and expanded its local news footprint early last year. While the app’s content team includes former journalists, machine learning is used to pick which articles are shown to readers to personalize their experience. However, one of the app’s key differentiators is how it works to pop users’ “filter bubbles” through its “News From All Sides” feature, which allows its users to access news from across a range of political perspectives.
It has also developed new products, like its COVID-19 vaccine dashboard and U.S. election dashboard, that provide critical information at a glance. With the additional funds, the company says it plans to develop more features for its U.S. audience — one of its largest, in addition to Japan — that will focus on consumer health and safety. These will roll out in the next few months and will include features for tracking wildfires and crime and safety reports. It also recently launched a hurricane tracker.
The aggregator’s business model is largely focused on advertising, as the company has said before that 85-90% of Americans aren’t paying to subscribe to news. But SmartNews’ belief is that these news consumers still have a right to access quality information.
In total, SmartNews has relationships with more than 3,000 global publishing partners whose content is available through its service on the web and mobile devices.
To generate revenue, the company sells inline ads and video ads, where revenue is shared with publishers. Over 75% of its publishing partners also take advantage of its “SmartView” feature. This is the app’s quick-reading mode, an alternative to something like Google AMP. Here, users can quickly load an article to read, even if they’re offline. The company promises publishers that these mobile-friendly stories, which are marked with a lightning bolt icon in the app, deliver higher engagement — and its algorithm rewards that type of content, bringing them more readers. Among SmartView partners are well-known brands like USA Today, ABC, HuffPost and others. Currently, over 70% of all SmartNews’ pageviews are coming from SmartView first.
SmartNews’ app has proven to be very sticky, in terms of attracting and keeping users’ attention. The company tells us, citing App Annie July 2021 data, that it sees an average time spent per user per month on U.S. mobile devices that’s higher than Google News or Apple News combined.
Image Credits: App Annie data provided by SmartNews
The company declined to share its monthly active users (MAUs), but had said in 2019 it had grown to 20 million in the U.S. and Japan. Today, it says its U.S. MAUs doubled over the last year.
According to data provided to us by Apptopia, the SmartNews app has seen around 85 million downloads since its October 2014 launch, and 14 million of those took place in the past 365 days. Japan is the largest market for installs, accounting for 59% of lifetime downloads, the firm noted.
“This latest round of funding further affirms the strength of our mission, and fuels our drive to expand our presence and launch features that specifically appeal to users and publishers in the United States,” said SmartNews co-founder and CEO Ken Suzuki. “Our investors both in the U.S. and globally acknowledge the tremendous growth potential and value of SmartNews’s efforts to democratize access to information and create an ecosystem that benefits consumers, publishers and advertisers,” he added.
The company says the new funds will be used to invest in further U.S. growth and expanding the company’s team. Since its last fundraise in 2019, where it became a unicorn, the company more than doubled its headcount to approximately 500 people globally. it now plans to double its headcount of 100 in the U.S., with additions across engineering, product, and leadership roles.
The Wall Street Journal reports SmartNews is exploring an IPO, but the company declined to comment on this.
The SmartNews app is available on iOS and Android across more than 150 countries worldwide.
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As artificial intelligence continues to weave its way into more enterprise applications, a startup that has built a platform to help businesses, especially non-tech organizations, build more customized AI decision-making tools for themselves has picked up some significant growth funding. Peak AI, a startup out of Manchester, England, that has built a “decision intelligence” platform, has raised $75 million, money that it will be using to continue building out its platform, expand into new markets and hire some 200 new people in the coming quarters.
The Series C is bringing a very big name investor on board. It is being led by SoftBank Vision Fund 2, with previous backers Oxx, MMC Ventures, Praetura Ventures and Arete also participating. That group participated in Peak’s Series B of $21 million, which only closed in February of this year. The company has now raised $119 million; it is not disclosing its valuation.
(This latest funding round was rumored last week, although it was not confirmed at the time and the total amount was not accurate.)
Richard Potter, Peak’s CEO, said the rapid follow-on in funding was based on inbound interest, in part because of how the company has been doing.
Peak’s so-called Decision Intelligence platform is used by retailers, brands, manufacturers and others to help monitor stock levels and build personalized customer experiences, as well as other processes that can stand to have some degree of automation to work more efficiently, but also require sophistication to be able to measure different factors against each other to provide more intelligent insights. Its current customer list includes the likes of Nike, Pepsico, KFC, Molson Coors, Marshalls, Asos and Speedy, and in the last 12 months revenues have more than doubled.
The opportunity that Peak is addressing goes a little like this: AI has become a cornerstone of many of the most advanced IT applications and business processes of our time, but if you are an organization — and specifically one not built around technology — your access to AI and how you might use it will come by way of applications built by others, not necessarily tailored to you, and the costs of building more tailored solutions can often be prohibitively high. Peak claims that those using its tools have seen revenues on average rise 5%, return on ad spend double, supply chain costs reduce by 5% and inventory holdings (a big cost for companies) reduce by 12%.
Peak’s platform, I should point out, is not exactly a “no-code” approach to solving that problem — not yet at least: It’s aimed at data scientists and engineers at those organizations so that they can easily identify different processes in their operations where they might benefit from AI tools, and to build those out with relatively little heavy lifting.
There have also been different market factors that have played a role. COVID-19, for example, and the boost that we have seen both in increasing “digital transformation” in businesses and making e-commerce processes more efficient to cater to rising consumer demand and more strained supply chains have all led to businesses being more open and keen to invest in more tools to improve their automation intelligently.
This, combined with Peak AI’s growing revenues, is part of what interested SoftBank. The investor has been long on AI for a while; but it also has been building out a section of its investment portfolio to provide strategic services to the kinds of businesses in which it invests.
Those include e-commerce and other consumer-facing businesses, which make up one of the main segments of Peak’s customer base.
Notably, one of its recent investments specifically in that space was made earlier this year, also in Manchester, when it took a $730 million stake (with potentially $1.6 billion more down the line) in The Hut Group, which builds software for and runs D2C businesses.
“In Peak we have a partner with a shared vision that the future enterprise will run on a centralized AI software platform capable of optimizing entire value chains,” Max Ohrstrand, senior investor for SoftBank Investment Advisers, said in a statement. “To realize this a new breed of platform is needed and we’re hugely impressed with what Richard and the excellent team have built at Peak. We’re delighted to be supporting them on their way to becoming the category-defining, global leader in Decision Intelligence.”
It’s not clear that SoftBank’s two Manchester interests will be working together, but it’s an interesting synergy if they do, and most of all highlights one of the firm’s areas of interest.
Longer term, it will be interesting to see how and if Peak evolves to extend its platform to a wider set of users at the organizations that are already its customers.
Potter said he believes that “those with technical predispositions” will be the most likely users of its products in the near and medium term. You might assume that would cut out, for example, marketing managers, although the general trend in a lot of software tools has precisely been to build versions of the same tools used by data scientists for these less technical people to engage in the process of building what it is that they want to use.
“I do think it’s important to democratize the ability to stream data pipelines, and to be able to optimize those to work in applications,” Potter added.
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Y Combinator-backed Kapacity.io is on a mission to accelerate the decarbonization of buildings by using AI-generated efficiency savings to encourage electrification of commercial real estate — wooing buildings away from reliance on fossil fuels to power their heating and cooling needs.
It does this by providing incentives to building owners/occupiers to shift to clean energy usage through a machine learning-powered software automation layer.
The startup’s cloud software integrates with buildings’ HVAC systems and electricity meters — drawing on local energy consumption data to calculate and deploy real-time adjustments to heating/cooling systems which not only yield energy and (CO2) emissions savings but generate actual revenue for building owners/tenants — paying them to reduce consumption such as at times of peak energy demand on the grid.
“We are controlling electricity consumption in buildings, focusing on heating and cooling devices — using AI machine learning to optimize and find the best ways to consume electricity,” explains CEO and co-founder Jaakko Rauhala, a former consultant in energy technology. “The actual method is known as ‘demand response’. Basically that is a way for electricity consumers to get paid for adjusting their energy consumption, based on a utility company’s demand.
“For example if there is a lot of wind power production and suddenly the wind drops or the weather changes and the utility company is running power grids they need to balance that reduction — and the way to do that is either you can fire up natural gas turbines or you can reduce power consumption… Our product estimates how much can we reduce electricity consumption at any given minute. We are [targeting] heating and cooling devices because they consume a lot of electricity.”
“The way we see this is this is a way we can help our customers electrify their building stocks faster because it makes their investments more lucrative and in addition we can then help them use more renewable electricity because we can shift the use from fossil fuels to other areas. And in that we hope to help push for a more greener power grid,” he adds.
Kapcity’s approach is applicable in deregulated energy markets where third parties are able to play a role offering energy saving services and fluctuations in energy demand are managed by an auction process involving the trading of surplus energy — typically overseen by a transmission system operator — to ensure energy producers have the right power balance to meet customer needs.
Demand for energy can fluctuate regardless of the type of energy production feeding the grid but renewable energy sources tend to increase the volatility of energy markets as production can be less predictable versus legacy energy generation (like nuclear or burning fossil fuels) — wind power, for example, depends on when and how strongly the wind is blowing (which both varies and isn’t perfectly predictable). So as economies around the world dial up efforts to tackle climate change and hit critical carbon emissions reduction targets there’s growing pressure to shift away from fossil fuel-based power generation toward cleaner, renewable alternatives. And the real estate sector specifically remains a major generator of CO2, so is squarely in the frame for “greening”.
Simultaneously, decarbonization and the green shift looks likely to drive demand for smart solutions to help energy grids manage increasing complexity and volatility in the energy supply mix.
“Basically more wind power — and solar, to some extent — correlates with demand for balancing power grids and this is why there is a lot of talk usually about electricity storage when it comes to renewables,” says Rauhala. “Demand response, in the way that we do it, is an alternative for electricity storage units. Basically we’re saying that we already have a lot of electricity consuming devices — and we will have more and more with electrification. We need to adjust their consumption before we invest billions of dollars into other systems.”
“We will need a lot of electricity storage units — but we try to push the overall system efficiency to the maximum by utilising what we already have in the grid,” he adds.
There are of course limits to how much “adjustment” (read: switching off) can be done to a heating or cooling system by even the cleverest AI without building occupants becoming uncomfortable.
But Kapacity’s premise is that small adjustments — say turning off the boilers/coolers for five, 15 or 30 minutes — can go essentially unnoticed by building occupants if done right, allowing the startup to tout a range of efficiency services for its customers; such as a peak-shaving offering, which automatically reduces energy usage to avoid peaks in consumption and generate significant energy cost savings.
“Our goal — which is a very ambitious goal — is that the customers and occupants in the buildings wouldn’t notice the adjustments. And that they would fall into the normal range of temperature fluctuations in a building,” says Rauhala.
Kapacity’s algorithms are designed to understand how to make dynamic adjustments to buildings’ heating/cooling without compromising “thermal comfort”, as Rauhala puts it — noting that co-founder (and COO) Sonja Salo, has both a PhD in demand response and researched thermal comfort during a stint as a visiting researcher at UC Berkley — making the area a specialist focus for the engineer-led founding team.
At the same time, the carrots it’s dangling at the commercial real estate to sign up for a little algorithmic HVAC tweaking look substantial: Kapacity says its system has been able to achieve a 25% reduction in electricity costs and a 10% reduction in CO2-emissions in early pilots. Although early tests have been limited to its home market for now.
Its other co-founder, Rami El Geneidy, researched smart algorithms for demand response involving heat pumps for his PhD dissertation — and heat pumps are another key focus for the team’s tech, per Rauhala.
Heat pumps are a low-carbon technology that’s fairly commonly used in the Nordics for heating buildings, but whose use is starting to spread as countries around the world look for greener alternatives to heat buildings.
In the U.K., for example, the government announced a plan last year to install hundreds of thousands of heat pumps per year by 2028 as it seeks to move the country away from widespread use of gas boilers to heat homes. And Rauhala names the U.K. as one of the startup’s early target markets — along with the European Union and the U.S., where they also envisage plenty of demand for their services.
While the initial focus is the commercial real estate sector, he says they are also interested in residential buildings — noting that from a “tech core point of view we can do any type of building”.
“We have been focusing on larger buildings — multifamily buildings, larger office buildings, certain types of industrial or commercial buildings so we don’t do single-family detached homes at the moment,” he goes on, adding: “We have been looking at that and it’s an interesting avenue but our current pilots are in larger buildings.”
The Finnish startup was only founded last year — taking in a pre-seed round of funding from Nordic Makers prior to getting backing from YC — where it will be presenting at the accelerator’s demo day next week. (But Rauhala won’t comment on any additional fund raising plans at this stage.)
He says it’s spun up five pilot projects over the last seven months involving commercial landlords, utilities, real estate developers and engineering companies (all in Finland for now), although — again — full customer details are not yet being disclosed. But Rauhala tells us they expect to move to their first full commercial deals with pilot customers this year.
“The reason why our customers are interested in using our products is that this is a way to make electrification cheaper because they are being paid for adjusting their consumption and that makes their operating cost lower and it makes investments more lucrative if — for example — you need to switch from natural gas boilers to heat pumps so that you can decarbonize your building,” he also tells us. “If you connect the new heat pump running on electricity — if you connect that to our service we can reduce the operating cost and that will make it more lucrative for everybody to electrify their buildings and run their systems.
“We can also then make their electricity consumed more sustainable because we are shifting consumption away from hours with most CO2 emissions on the grid. So we try to avoid the hours when there’s a lot of fossil fuel-based production in the grid and try to divert that into times when we have more renewable electricity.
“So basically the big question we are asking is how do we increase the use of renewables and the way to achieve that is asking when should we consume? Well we should consume electricity when we have more renewable in the grid. And that is the emission reduction method that we are applying here.”
In terms of limitations, Kapacity’s software-focused approach can’t work in every type of building — requiring that real estate customers have some ability to gather energy consumption (and potentially temperature) data from their buildings remotely, such as via IoT devices.
“The typical data that we need is basic information on the heating system — is it running at 100% or 50% or what’s the situation? That gets us pretty far,” says Rauhala. “Then we would like to know indoor temperatures. But that is not mandatory in the sense that we can still do some basic adjustments without that.”
It also of course can’t offer much in the way of savings to buildings that are running 100% on natural gas (or oil) — i.e. with electricity only used for lighting (turning lights off when people are inside buildings obviously wouldn’t fly); there must be some kind of air conditioning, cooling or heat pump systems already installed (or the use of electric hot water boilers).
“An old building that runs on oil or natural gas — that’s a target for decarbonization,” he continues. “That’s a target where you could consider installing heat pumps and that is where we could help some of our customers or potential customers to say OK we need to estimate how much would it cost to install a heat pump system here and that’s where our product can come in and we can say you can reduce the operating cost with demand response. So maybe we should do something together here.”
Rauhala also confirms that Kapacity’s approach does not require invasive levels of building occupant surveillance, telling TechCrunch: “We don’t collect information that is under GDPR [General Data Protection Regulation], I’ll put it that way. We don’t take personal data for this demand response.”
So any guestimates its algorithms are making about building occupants’ tolerance for temperature changes are, therefore, not going to be based on specific individuals — but may, presumably, factor in aggregated information related to specific industry/commercial profiles.
The Helsinki-based startup is not the only one looking at applying AI to drive energy cost and emissions savings in the commercial buildings sector — another we spoke to recently is Düsseldorf-based Dabbel, for example. And plenty more are likely to take an interest in the space as governments start to pump more money into accelerating decarbonization.
Asked about competitive differentiation, Rauhala points to a focus on real-time adjustments and heat pump technologies.
“One of our key things is we’re developing a system so that we can do close to real-time control — very, very short-term control. That is a valuable service to the power grid so we can then quickly adjust,” he says. “And the other one is we are focusing on heat pump technologies to get started — heat pumps here in the Nordics are a very common and extremely good way to decarbonize and understanding how we can combine these to demand response with new heat pumps that is where we see a lot of advantages to our approach.”
“Heat pumps are a bit more technically complex than your basic natural gas boiler so there are certain things that have to be taken it account and that is where we have been focusing our efforts,” he goes on, adding: “We see heat pumps as an excellent way to decarbonize the global building stock and we want to be there and help make that happen.”
Per capita, the Nordics has the most heat pump installations, according to Rauhala — including a lot of ground source heat pump installations which can replace fossil fuel consumption entirely.
“You can run your building with a ground source heat pump system entirely — you don’t need any supporting systems for it. And that is the area where we here in Europe are more far ahead than in the U.S.,” he says on that.
“The U.K. government is pushing for a lot of heat pump installations and there are incentives in place for people to replace their existing natural gas systems or whatever they have. So that is very interesting from our point of view. The U.K. also has a lot of wind power coming online and there have been days when the U.K. has been running 100% with renewable electricity which is great. So that actually is a really good thing for us. But then in the longer term in the U.S. — Seattle, for example, has banned the use of fossil fuels in new buildings so I’m very confident that the market in the U.S. will open up more and quickly. There’s a lot of opportunities in that space as well.
“And of course from a cooling perspective air conditioning in general in the U.S. is very widespread — especially in commercial buildings so that is already an existing opportunity for us.”
“My estimate on how valuable electricity use for heating and cooling is it’s tens of billions of dollars annually in the U.S. and EU,” he adds. “There’s a lot of electricity being used already for this and we expect the market to grow significantly.”
On the business model front, the startup’s cloud software looks set to follow a SaaS model but the plan is also to take a commission of the savings and/or generated income from customers. “We also have the option to provide the service with a fixed fee, which might be easier for some customers, but we expect the majority to be under a commission,” adds Rauhala.
Looking ahead, were the sought-for global shift away from fossil fuels to be wildly successful — and all commercial buildings’ gas/oil boilers got replaced with 100% renewable power systems in short order — there would still be a role for Kapacity’s control software to play, generating energy cost savings for its customers, even though our (current) parallel pressing need to shrink carbon emissions would evaporate in this theoretical future.
“We’d be very happy,” says Rauhala. “The way we see emission reductions with demand response now is it’s based on the fact that we do still have fossil fuels power system — so if we were to have a 100% renewable power system then the electricity does nothing to reduce emissions from the electricity consumption because it’s all renewable. So, ironically, in the future we see this as a way to push for a renewable energy system and makes that transition happen even faster. But if we have a 100% renewable system then there’s nothing [in terms of CO2 emissions] we can reduce but that is a great goal to achieve.”
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Poland-based health tech AI startup Cardiomatics has announced a $3.2 million seed raise to expand use of its electrocardiogram (ECG) reading automation technology.
The round is led by Central and Eastern European VC Kaya, with Nina Capital, Nova Capital and Innovation Nest also participating.
The seed raise also includes a $1 million non-equity grant from the Polish National Centre of Research and Development.
The 2017-founded startup sells a cloud tool to speed up diagnosis and drive efficiency for cardiologists, clinicians and other healthcare professionals to interpret ECGs — automating the detection and analysis of some 20 heart abnormalities and disorders with the software generating reports on scans in minutes, faster than a trained human specialist would be able to work.
Cardiomatics touts its tech as helping to democratize access to healthcare — saying the tool enables cardiologists to optimise their workflow so they can see and treat more patients. It also says it allows GPs and smaller practices to offer ECG analysis to patients without needing to refer them to specialist hospitals.
The AI tool has analyzed more than 3 million hours of ECG signals commercially to date, per the startup, which says its software is being used by more than 700 customers in 10+ countries, including Switzerland, Denmark, Germany and Poland.
The software is able to integrate with more than 25 ECG monitoring devices at this stage, and it touts offering a modern cloud software interface as a differentiator versus legacy medical software.
Asked how the accuracy of its AI’s ECG readings has been validated, the startup told us: “The data set that we use to develop algorithms contains more than 10 billion heartbeats from approximately 100,000 patients and is systematically growing. The majority of the data-sets we have built ourselves, the rest are publicly available databases.
“Ninety percent of the data is used as a training set, and 10% for algorithm validation and testing. According to the data-centric AI we attach great importance to the test sets to be sure that they contain the best possible representation of signals from our clients. We check the accuracy of the algorithms in experimental work during the continuous development of both algorithms and data with a frequency of once a month. Our clients check it everyday in clinical practice.”
Cardiomatics said it will use the seed funding to invest in product development, expand its business activities in existing markets and gear up to launch into new markets.
“Proceeds from the round will be used to support fast-paced expansion plans across Europe, including scaling up our market-leading AI technology and ensuring physicians have the best experience. We prepare the product to launch into new markets too. Our future plans include obtaining FDA certification and entering the US market,” it added.
The AI tool received European medical device certification in 2018 — although it’s worth noting that the European Union’s regulatory regime for medical devices and AI is continuing to evolve, with an update to the bloc’s Medical Devices Directive (now known as the EU Medical Device Regulation) coming into application earlier this year (May).
A new risk-based framework for applications of AI — aka the Artificial Intelligence Act — is also incoming and will likely expand compliance demands on AI health tech tools like Cardiomatics, introducing requirements such as demonstrating safety, reliability and a lack of bias in automated results.
Asked about the regulatory landscape it said: “When we launched in 2018 we were one of the first AI-based solutions approved as medical device in Europe. To stay in front of the pace we carefully observe the situation in Europe and the process of legislating a risk-based framework for regulating applications of AI. We also monitor draft regulations and requirements that may be introduced soon. In case of introducing new standards and requirements for artificial intelligence, we will immediately undertake their implementation in the company’s and product operations, as well as extending the documentation and algorithms validation with the necessary evidence for the reliability and safety of our product.”
However it also conceded that objectively measuring efficacy of ECG reading algorithms is a challenge.
“An objective assessment of the effectiveness of algorithms can be very challenging,” it told TechCrunch. “Most often it is performed on a narrow set of data from a specific group of patients, registered with only one device. We receive signals from various groups of patients, coming from different recorders. We are working on this method of assessing effectiveness. Our algorithms, which would allow them to reliably evaluate their performance regardless of various factors accompanying the study, including the recording device or the social group on which it would be tested.”
“When analysis is performed by a physician, ECG interpretation is a function of experience, rules and art. When a human interprets an ECG, they see a curve. It works on a visual layer. An algorithm sees a stream of numbers instead of a picture, so the task becomes a mathematical problem. But, ultimately, you cannot build effective algorithms without knowledge of the domain,” it added. “This knowledge and the experience of our medical team are a piece of art in Cardiomatics. We shouldn’t forget that algorithms are also trained on the data generated by cardiologists. There is a strong correlation between the experience of medical professionals and machine learning.”
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Today, Tractable is worth $1 billion. Our AI is used by millions of people across the world to recover faster from road accidents, and it also helps recycle as many cars as Tesla puts on the road.
And yet six years ago, Tractable was just me and Raz (Razvan Ranca, CTO), two college grads coding in a basement. Here’s how we did it, and what we learned along the way.
In 2013, I was fortunate to get into artificial intelligence (more specifically, deep learning) six months before it blew up internationally. It started when I took a course on Coursera called “Machine learning with neural networks” by Geoffrey Hinton. It was like being love struck. Back then, to me AI was science fiction, like “The Terminator.”
Narrowly focusing on a branch of applied science that was undergoing a paradigm shift which hadn’t yet reached the business world changed everything.
But an article in the tech press said the academic field was amid a resurgence. As a result of 100x larger training data sets and 100x higher compute power becoming available by reprogramming GPUs (graphics cards), a huge leap in predictive performance had been attained in image classification a year earlier. This meant computers were starting to be able to understand what’s in an image — like humans do.
The next step was getting this technology into the real world. While at university — Imperial College London — teaming up with much more skilled people, we built a plant recognition app with deep learning. We walked our professor through Hyde Park, watching him take photos of flowers with the app and laughing from joy as the AI recognized the right plant species. This had previously been impossible.
I started spending every spare moment on image classification with deep learning. Still, no one was talking about it in the news — even Imperial’s computer vision lab wasn’t yet on it! I felt like I was in on a revolutionary secret.
Looking back, narrowly focusing on a branch of applied science undergoing a breakthrough paradigm shift that hadn’t yet reached the business world changed everything.
I’d previously been rejected from Entrepreneur First (EF), one of the world’s best incubators, for not knowing anything about tech. Having changed that, I applied again.
The last interview was a hackathon, where I met Raz. He was doing machine learning research at Cambridge, had topped EF’s technical test, and published papers on reconstructing shredded documents and on poker bots that could detect bluffs. His bare-bones webpage read: “I seek data-driven solutions to currently intractable problems.” Now that had a ring to it (and where we’d get the name for Tractable).
That hackathon, we coded all night. The morning after, he and I knew something special was happening between us. We moved in together and would spend years side by side, 24/7, from waking up to Pantera in the morning to coding marathons at night.
But we also wouldn’t have got where we are without Adrien (Cohen, president), who joined as our third co-founder right after our seed round. Adrien had previously co-founded Lazada, an online supermarket in South East Asia like Amazon and Alibaba, which sold to Alibaba for $1.5 billion. Adrien would teach us how to build a business, inspire trust and hire world-class talent.
Tractable started at EF with a head start — a paying customer. Our first use case was … plastic pipe welds.
It was as glamorous as it sounds. Pipes that carry water and natural gas to your home are made of plastic. They’re connected by welds (melt the two plastic ends, connect them, let them cool down and solidify again as one). Image classification AI could visually check people’s weld setups to ensure good quality. Most of all, it was real-world value for breakthrough AI.
And yet in the end, they — our only paying customer — stopped working with us, just as we were raising our first round of funding. That was rough. Luckily, the number of pipe weld inspections was too small a market to interest investors, so we explored other use cases — utilities, geology, dermatology and medical imaging.
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Visualping, a service that can help you monitor websites for changes like price drops or other updates, announced that it has raised a $6 million extension to the $2 million seed round it announced earlier this year. The round was led by Seattle-based FUSE, a relatively new firm with investors who spun out of Ignition Partners last year. Prior investors Mistral Venture Partners and N49P also participated.
The Vancouver-based company is part of the current Google for Startups Accelerator class in Canada. This program focuses on services that leverage AI and machine learning, and, while website monitoring may not seem like an obvious area where machine learning can add a lot of value, if you’ve ever used one of these services, you know that they can often unleash a plethora of false alerts. For the most part, after all, these tools simply look for something in a website’s underlying code to change and then trigger an alert based on that (and maybe some other parameters you’ve set).
Earlier this week, Visualping launched its first machine learning-based tools to avoid just that. The company argues that it can eliminate up to 80% of false alerts by combining feedback from its more than 1.5 million users with its new ML algorithms. Thanks to this, Visualping can now learn the best configuration for how to monitor a site when users set up a new alert.
“Visualping has the hearts of over a million people across the world, as well as the vast majority of the Fortune 500. To be a part of their journey and to lead this round of financing is a dream,” FUSE’s Brendan Wales said.
Visualping founder and CEO Serge Salager tells me that the company plans to use the new funding to focus on building out its product but also to build a commercial team. So far, he said, the company’s growth has been primarily product led.
As a part of these efforts, the company also plans to launch Visualping Business, with support for these new ML tools and additional collaboration features, and Visualping Personal for individual users who want to monitor things like ticket availability for concerts or to track news, price drops or job postings, for example. For now, the personal plan will not include support for ML. “False alerts are not a huge problem for personal use as people are checking two-three websites but a huge problem for enterprise where teams need to process hundreds of alerts per day,” Salager told me.
The current idea is to launch these new plans in November, together with mobile apps for iOS and Android. The company will also relaunch its extensions around this time, too.
It’s also worth noting that while Visualping monetizes its web-based service, you can still use the extension in the browser for free.
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As financial crime has become significantly more sophisticated, so too have the tools that are used to combat it. Now, Quantexa — one of the more interesting startups that has been building AI-based solutions to help detect and stop money laundering, fraud and other illicit activity — has raised a growth round of $153 million, both to continue expanding that business in financial services and to bring its tools into a wider context, so to speak: linking up the dots around all customer and other data.
“We’ve diversified outside of financial services and working with government, healthcare, telcos and insurance,” Vishal Marria, its founder and CEO, said in an interview. “That has been substantial. Given the whole journey that the market’s gone through in contextual decision intelligence as part of bigger digital transformation, was inevitable.”
The Series D values the London-based startup between $800 million and $900 million on the heels of Quantexa growing its subscriptions revenues 108% in the last year.
Warburg Pincus led the round, with existing backers Dawn Capital, AlbionVC, Evolution Equity Partners (a specialist cybersecurity VC), HSBC, ABN AMRO Ventures and British Patient Capital also participating. The valuation is a significant hike up for Quantexa, which was valued between $200 million and $300 million in its Series C last July. It has now raised over $240 million to date.
Quantexa got its start out of a gap in the market that Marria identified when he was working as a director at Ernst & Young tasked with helping its clients with money laundering and other fraudulent activity. As he saw it, there were no truly useful systems in the market that efficiently tapped the world of data available to companies — matching up and parsing both their internal information as well as external, publicly available data — to get more meaningful insights into potential fraud, money laundering and other illegal activities quickly and accurately.
Quantexa’s machine learning system approaches that challenge as a classic big data problem — too much data for a human to parse on their own, but small work for AI algorithms processing huge amounts of that data for specific ends.
Its so-called “Contextual Decision Intelligence” models (the name Quantexa is meant to evoke “quantum” and “context”) were built initially specifically to address this for financial services, with AI tools for assessing risk and compliance and identifying financial criminal activity, leveraging relationships that Quantexa has with partners like Accenture, Deloitte, Microsoft and Google to help fill in more data gaps.
The company says its software — and this, not the data, is what is sold to companies to use over their own data sets — has handled up to 60 billion records in a single engagement. It then presents insights in the form of easily digestible graphs and other formats so that users can better understand the relationships between different entities and so on.
Today, financial services companies still make up about 60% of the company’s business, Marria said, with seven of the top 10 U.K. and Australian banks and six of the top 14 financial institutions in North America among its customers. (The list includes its strategic backer HSBC, as well as Standard Chartered Bank and Danske Bank.)
But alongside those — spurred by a huge shift in the market to rely significantly more on wider data sets, to businesses updating their systems in recent years, and the fact that, in the last year, online activity has in many cases become the “only” activity — Quantexa has expanded more significantly into other sectors.
“The Financial crisis [of 2007] was a tipping point in terms of how financial services companies became more proactive, and I’d say that the pandemic has been a turning point around other sectors like healthcare in how to become more proactive,” Marria said. “To do that you need more data and insights.”
So in the last year in particular, Quantexa has expanded to include other verticals facing financial crime, such as healthcare, insurance, government (for example in tax compliance) and telecoms/communications, but in addition to that, it has continued to diversify what it does to cover more use cases, such as building more complete customer profiles that can be used for KYC (know your customer) compliance or to serve them with more tailored products. Working with government, it’s also seeing its software getting applied to other areas of illicit activity, such as tracking and identifying human trafficking.
In all, Quantexa has “thousands” of customers in 70 markets. Quantexa cites figures from IDC that estimate the market for such services — both financial crime and more general KYC services — is worth about $114 billion annually, so there is still a lot more to play for.
“Quantexa’s proprietary technology enables clients to create single views of individuals and entities, visualized through graph network analytics and scaled with the most advanced AI technology,” said Adarsh Sarma, MD and co-head of Europe at Warburg Pincus, in a statement. “This capability has already revolutionized the way KYC, AML and fraud processes are run by some of the world’s largest financial institutions and governments, addressing a significant gap in an increasingly important part of the industry. The company’s impressive growth to date is a reflection of its invaluable value proposition in a massive total available market, as well as its continued expansion across new sectors and geographies.”
Interestingly, Marria admitted to me that the company has been approached by big tech companies and others that work with them as an acquisition target — no real surprises there — but longer term, he would like Quantexa to consider how it continues to grow on its own, with an independent future very much in his distant sights.
“Sure, an acquisition to the likes of a big tech company absolutely could happen, but I am gearing this up for an IPO,” he said.
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Hello and welcome back to Equity, TechCrunch’s venture-capital-focused podcast where we unpack the numbers behind the headlines.
This is Equity Monday Tuesday, our weekly kickoff that tracks the latest private market news, talks about the coming week, digs into some recent funding rounds and mulls over a larger theme or narrative from the private markets. You can follow the show on Twitter here and myself here.
What a busy weekend we missed while mostly hearing distant explosions and hugging our dogs close. Here’s a sampling of what we tried to recap on the show:
It’s going to be a busy week! Chat tomorrow.
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Ahead of our TechCrunch City Spotlight: Pittsburgh event tomorrow, I spoke to current Mayor Bill Peduto and Dave Mawhinney, the executive director of Carnegie Mellon University’s Swartz Center for Entrepreneurship. Like many in the Steel City startup community, both share a focus on the historically difficult task of keeping startups in town.
For more on investing in Pittsburgh, be sure to tune in to our City Spotlight on Tuesday, June 29, where we will be joined by Peduto, Duolingo director of engineering Karin Tsai and Carnegie Mellon University President Farnam Jahanian. Register for the free event here.
I asked Peduto and Mawhinney what the single biggest obstacle has been in building out Pittsburgh’s startup ecosystem. Both responded the same way: venture capital. Raising funding is, of course, a hurdle regardless of location, but many VCs have been reluctant to invest in startups outside of traditional hubs like San Francisco and New York.
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“But one of the challenges is getting that capital to come into the community,” said Mawhinney, who leads CMU’s startup efforts. “If you look at how much Uber ATG brought in, how much Argo AI and Aurora — collectively, those three companies, which have all licensed CMU technologies, they’ve all got over $7 billion in collective capital. Not all of it will be spent here, but a lot of it will be spent here. But that doesn’t necessarily trickle down to the next AI startup raising their first $3 million.”
Image Credits: Eilis Garvey/Unsplash
Peduto said growing the VC pipeline has been a focus during his time as mayor.
“I think we’ve been able to convince investors from the coast that the companies don’t need to leave Pittsburgh in order to be highly successful and see their investment pay off,” he told TechCrunch. “However, I believe if we had more venture capital arriving here to help take early-stage companies into that critical next stage of expansion, it would build off itself and it would excel growth in all of the industry cluster, significantly.”
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